View in EDS HTML Full Text PDF Full Text

Teaching Location Planning with the Center-Of-Gravity Method Using Real Cities and Distances

Saved in:
Bibliographic Details
Title: Teaching Location Planning with the Center-Of-Gravity Method Using Real Cities and Distances
Language: English
Authors: Jason M. Riley (ORCID 0000-0003-4554-6376), Kevin Sweeney
Source: Decision Sciences Journal of Innovative Education. 2024 22(2):106-116.
Availability: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
Peer Reviewed: Y
Page Count: 11
Publication Date: 2024
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Locational Skills (Social Studies), Teaching Methods, Facility Planning, Site Selection, Site Analysis, Undergraduate Students, Operations Research, Information Management, Supply and Demand, Business Education, Authentic Learning, Experiential Learning, Learning Activities, Course Content, Curriculum Enrichment, Municipalities, Geographic Location
DOI: 10.1111/dsji.12311
ISSN: 1540-4595
1540-4609
Abstract: Facility placement is of strategic importance to most organizations as a well-placed distribution center minimizes delivery costs and reduces fulfillment lead times, thus improving customer service levels. Because organizations value the location planning process, this teaching brief offers an exercise that analyzes the planning process using the center-of-gravity algorithm, a service area map, and real-world constraints. The objective of the exercise is to identify two locations within a service area that minimize total network distribution costs. Our exercise is intended to complement standard course content and support instructors developing curricula for undergraduate operations management and supply chain management courses. Student-based survey results indicate that the assignment enhanced classroom engagement and helped students better understand the complexities of location planning.
Abstractor: As Provided
Entry Date: 2024
Accession Number: EJ1420555
Database: ERIC
Full text is not displayed to guests.
FullText Links:
  – Type: pdflink
    Url: https://content.ebscohost.com/cds/retrieve?content=AQICAHj0k_4E0hTGH8RJwT4gCJyBsGNe_WN95AvKlDbXJGqwxwGolB6dMPvWLI0A9NUV4fcaAAAA4zCB4AYJKoZIhvcNAQcGoIHSMIHPAgEAMIHJBgkqhkiG9w0BBwEwHgYJYIZIAWUDBAEuMBEEDOGd_3q1tHdsnX-TeQIBEICBm2Ify21EwbB5vSx09B9nnOQt8qEaowEN_Qzv-6JgGTxaDm-7trJ-Ljft9KxeeMhy8lv5Q7ejXDeMb_C8CVLgr1QBIjynISkjoEhm9Z9t_R3dy9_9YLqdDKkoigf6gwkoGYWLX_VfeN-A4lGTSMzzs-gVbbB-MDnqLLGQ3JyanosrTv_ulEw0gHbyg5qH5wQt1CQw1uLdcdAC6BE9
Text:
  Availability: 1
  Value: <anid>AN0176537428;q1n01apr.24;2024Apr15.05:52;v2.2.500</anid> <title id="AN0176537428-1">Teaching location planning with the center‐of‐gravity method using real cities and distances </title> <p>Facility placement is of strategic importance to most organizations as a well‐placed distribution center minimizes delivery costs and reduces fulfillment lead times, thus improving customer service levels. Because organizations value the location planning process, this teaching brief offers an exercise that analyzes the planning process using the center‐of‐gravity algorithm, a service area map, and real‐world constraints. The objective of the exercise is to identify two locations within a service area that minimize total network distribution costs. Our exercise is intended to complement standard course content and support instructors developing curricula for undergraduate operations management and supply chain management courses. Student‐based survey results indicate that the assignment enhanced classroom engagement and helped students better understand the complexities of location planning.</p> <p>Keywords: experiential learning; facility location analysis; games and simulations; operations management; supply chain management</p> <hd id="AN0176537428-2">INTRODUCTION</hd> <p>Active learning pedagogies continue to be popular with supply chain management (SCM) instructors since they help bridge the gap between theoretical education and real‐world business practices (Angolia & Pagliari, [<reflink idref="bib4" id="ref1">4</reflink>]). In this teaching brief, we introduce a location planning assignment designed to be used within an undergraduate supply chain or operations management curriculum. Students are given information on customer demand, operating costs, and system constraints, and are asked to plan a distribution network with two supply points by minimizing round‐trip transportation costs while considering both fixed and variable costs for multiple customer locations within a finite service area. The overall learning objective of this assignment is for students to gain fundamental knowledge of the center‐of‐gravity method for location and network planning while developing the mathematical skills necessary to analyze the consequences of their plan. This exercise is best used when discussing how location planning and transportation networks can influence supply chain network design.</p> <p>Facility location theory is used to determine where to locate facilities such as an office, production facility, or distribution center within a given space (Ahmadi‐Javid et al., [<reflink idref="bib1" id="ref2">1</reflink>]). The conceptual tenets have been used for more than a century to frame a variety of location planning questions (Kuo & White, [<reflink idref="bib22" id="ref3">22</reflink>]; Love et al., [<reflink idref="bib23" id="ref4">23</reflink>]). Early research sought to mathematically determine the lowest cost solution of moving product from source to destination in an efficient manner (Hitchcock, [<reflink idref="bib16" id="ref5">16</reflink>]; Weber & Friedrich, [<reflink idref="bib35" id="ref6">35</reflink>]). However, as the discipline evolved, much of the literature began focusing on heuristics where specification flexibility allowed users to study complex location problems (Cooper, [<reflink idref="bib9" id="ref7">9</reflink>]; Kuehn & Hamburger, [<reflink idref="bib21" id="ref8">21</reflink>]).</p> <p>Throughout the 1970s and 1980s, many of the facility location models were computationally complex and provided both the formulation and solution (Francis et al., [<reflink idref="bib12" id="ref9">12</reflink>]). Aikens ([<reflink idref="bib2" id="ref10">2</reflink>]) reviews the different models and identifies them as dynamic (Khumawala & Whybark, [<reflink idref="bib18" id="ref11">18</reflink>]; Warszawski, [<reflink idref="bib34" id="ref12">34</reflink>]), multiechelon (Tcha & Lee, [<reflink idref="bib32" id="ref13">32</reflink>]), multicommodity (Khumawala & Neebe, [<reflink idref="bib17" id="ref14">17</reflink>]; Neebe & Khumawala, [<reflink idref="bib26" id="ref15">26</reflink>]), capacitated (Akinc & Khumawala, [<reflink idref="bib3" id="ref16">3</reflink>]; Dearing & Newruck, [<reflink idref="bib11" id="ref17">11</reflink>]), uncapacitated (Roodman & Schwarz, [<reflink idref="bib30" id="ref18">30</reflink>]; Wesolowsky & Truscott, [<reflink idref="bib36" id="ref19">36</reflink>]), multiperiod (Van Roy & Erlenkotter, [<reflink idref="bib33" id="ref20">33</reflink>]), and models with side constraints (Geoffrion & Bride, [<reflink idref="bib13" id="ref21">13</reflink>]). In most cases, the objective of the various models is to "determine the spatial distribution of the facilities at each time period so as to minimize the total cost for meeting the customer demand over time" (Correia & Melo, [<reflink idref="bib10" id="ref22">10</reflink>], p. 2).</p> <p>Today, scholars and business organizations use these planning techniques to determine the location for all types of facilities/services including vehicle charging stations (Kizhakkan et al., [<reflink idref="bib20" id="ref23">20</reflink>]; Mao et al., [<reflink idref="bib24" id="ref24">24</reflink>]), solar energy panels (Mostafaeipour et al., [<reflink idref="bib25" id="ref25">25</reflink>]), and park and ride facilities. Business organizations favor these algorithms since they help identify where a facility can be placed knowing market dynamics will change (Owen & Daskin, [<reflink idref="bib27" id="ref26">27</reflink>]). Typically, they consider variables including where existing facilities are located, expected customer demand, and transportation costs as they seek to optimize one or more factors. In this context, these planning models enable practitioners to identify locations where organizations can efficiently service demand (Chen et al., [<reflink idref="bib8" id="ref27">8</reflink>]). Making efficient decisions about location placement is a necessary capability as organizations work to improve supply chain agility (Ritchie et al., [<reflink idref="bib29" id="ref28">29</reflink>]).</p> <hd id="AN0176537428-3">RELATED LITERATURE</hd> <p>Within the extant literature, several teaching briefs leverage location planning techniques and/or the center‐of‐gravity method as part of an active learning assignment. Brusco ([<reflink idref="bib7" id="ref29">7</reflink>]) uses spreadsheets to teach multisource continuous facility location problems, while Strakos and Brazhkin ([<reflink idref="bib31" id="ref30">31</reflink>]) develop a system‐integrated spreadsheet exercise where students model facility locations within a network. Both exercises use hypothetical locations and service areas. Similarly, Grasas and Ramalhinho ([<reflink idref="bib14" id="ref31">14</reflink>]) offer an exercise where students learn about vehicle routing. While routing is a key distribution planning concept, the work uses a decision support system, rather than the center‐of‐gravity method, to plan the network. Ritchie et al. ([<reflink idref="bib29" id="ref32">29</reflink>]) and King and Arnette ([<reflink idref="bib19" id="ref33">19</reflink>]) created classroom exercises that leverage geographic information systems as part of a location planning process. While these exercises do not use the center‐of‐gravity methodology, they teach students key location planning concepts. Finally, Kuo and White ([<reflink idref="bib22" id="ref34">22</reflink>]) discuss how the center‐of‐gravity method has been poorly treated within operations management textbooks.</p> <p>While our experiential learning activity has some similarity to the above teaching briefs, we set out to create an assignment that explicitly uses the center‐of‐gravity method and real‐world parameters. A novel aspect of the assignment is the fact the planning parameters can be easily modified each academic term and/or for different groups of students. Instructors can simply change the "optimal" solution for each class. Potential changes include modifications to customer locations, demand estimates, planning constraints, and/or the figures used to calculate round‐trip transportation costs. Furthermore, instructors can customize the assignment by switching the service area map used during the planning process. Rather than defining the service area as one state within the United States, a new map could include customer demand points across several states or a country such as Scotland. We advise modifying the service area map so it is familiar to students. Existing research shows that customized examples and assignments can help students better understand concepts being taught (Phan et al., [<reflink idref="bib28" id="ref35">28</reflink>]).</p> <hd id="AN0176537428-4">Center‐of‐gravity methodology</hd> <p>Within the existing literature, there are a variety of quantitative location planning techniques. The most common are factor rating, location break‐even analysis, load‐distance, transportation modeling, and the center‐of‐gravity method (Kuo & White, [<reflink idref="bib22" id="ref36">22</reflink>]). Our work focuses on the center‐of‐gravity methodology since the algorithm provides users with a reasonably accurate way to estimate where a new location should be placed.</p> <p>As it pertains to this teaching brief, the center‐of‐gravity methodology is used to minimize the expected costs of operating two distribution centers. No matter how many supply points are used, the purpose of the center‐of‐gravity method is to minimize the following general 2‐dimensional cost function: <ephtml> <math display="block" altimg="urn:x-wiley:15404595:media:dsji12311:dsji12311-math-0001" xmlns="http://www.w3.org/1998/Math/MathML"><semantics><mrow><mi>Z</mi><mspace width="0.28em" /><mo linebreak="badbreak">=</mo><mspace width="0.28em" /><munderover><mo>∑</mo><mrow><mi>j</mi><mspace width="0.28em" /><mo>=</mo><mspace width="0.28em" /><mn>1</mn></mrow><mi>n</mi></munderover>min1≤k≤K<mfenced separators="" open="[" close=""><msub><mi>c</mi><mi>j</mi></msub></mfenced><mfenced separators="" open="{" close="}"><mrow><msup><mfenced separators="" open="|" close="|"><mrow><msub><mi>x</mi><mi>j</mi></msub><mo>−</mo><msub><mi>u</mi><mi>k</mi></msub></mrow></mfenced><mn>2</mn></msup><mo>+</mo><msup><mfenced separators="" open="|" close="|"><mrow><msub><mi>y</mi><mi>j</mi></msub><mo>−</mo><msub><mi>v</mi><mi>k</mi></msub></mrow></mfenced><mn>2</mn></msup></mrow></mfenced><mo>,</mo></mrow><annotation encoding="application/x-tex">$$\begin{equation*}Z{\mathrm{\;}} = {\mathrm{\;}}\mathop \sum \limits_{j{\mathrm{\;}} = {\mathrm{\;}}1}^n \def\eqcellsep{&}\begin{array}{@{}*{1}{c}@{}} {min}\\ {\left({1 \le k \le K} \right)} \end{array} \left[ {{c_j}} \right.\left\{ {{{\left| {{x_j} - {u_k}} \right|}^2} + {{\left| {{y_j} - {v_k}} \right|}^2}} \right\},\end{equation*}$$</annotation></semantics></math> </ephtml> where <emph>K</emph> is the number of supply points, <emph>n</emph>vvx is the number of customers served by supply point <emph>k</emph>, <emph>x<subs>j</subs></emph> and <emph>y<subs>j</subs></emph> refer to the <emph>x</emph> and y coordinates for customer <emph>j</emph> supplied by supply point <emph>k</emph>, <emph>u<subs>k</subs></emph> and <emph>v<subs>k</subs></emph> refer to the <emph>x</emph> and <emph>y</emph> coordinates of supply point <emph>k</emph>, and <emph>c<subs>j</subs></emph> refers to the cost per unit of distance between customer <emph>j</emph> and supply point <emph>k</emph> (Kuo & White, [<reflink idref="bib22" id="ref37">22</reflink>]).</p> <p>Organizations typically use the center‐of‐gravity methodology as part of a more strategic location planning process. The technique allows users to identify a mathematically ideal location for a facility within a finite service area. Managers then consider other criteria such as customer coverage, labor availability, transportation infrastructure, and/or freight transportation regulations (Awasthi et al., [<reflink idref="bib5" id="ref38">5</reflink>]; Gutierrez et al., [<reflink idref="bib15" id="ref39">15</reflink>]) as they determine where to place a facility like a distribution center.</p> <p>The mathematical techniques associated with the center‐of‐gravity method are of profound importance to most supply chain and operations management curricula, and the topic is found in textbooks of both disciplines (Brusco, [<reflink idref="bib7" id="ref40">7</reflink>]). However, even though multilocation problems are a valuable part of these curricula, it can be hard to get students interested in the center‐of‐gravity methodology due to its mathematical focus and perceived difficulty.</p> <p>To help students overcome these obstacles, we developed an activity inspired by experiential learning principles to help students understand the theoretical concepts and mathematical principles associated with the algorithm. We have used this activity in multiple undergraduate SCM classes over a one‐year period at a medium‐sized university in the southwest region of the United States. We suggest that it could also be used with operations management (OM) students. Brusco ([<reflink idref="bib7" id="ref41">7</reflink>]) argued that instructors should include multisource location problems in OM curricula since these types of problems are common in practice and could be used to extend the discussion and learning associated with single‐source location problems.</p> <hd id="AN0176537428-5">ASSIGNMENT OVERVIEW</hd> <p>The assignment is designed to give students the opportunity to engage in a simulated location planning process. The objective is to minimize total round‐trip distribution costs for an entire service network, while considering both customer demand and system constraints. In our exercise, we included 16 demand points, several constraints, and a map (drawn to scale) to illustrate where customers reside within the service area.</p> <p>Students are instructed to use online mapping applications to determine distances between potential supply points and customer demand locations. They also are provided with information to calculate the round‐trip transportation costs of each route (i.e., fixed cost and variable costs per mile traveled). Once potential distribution points are selected, students use Excel to compute the total costs for each route, which are then used to compute the total costs for their entire distribution network.</p> <hd id="AN0176537428-6">Assignment components</hd> <p>Information about the different assignment components is presented to students via the handout and PowerPoint presentation provided in the supplemental files for this article. Instructors should review this information and answer clarifying questions at the beginning of the assignment.</p> <p></p> <ulist> <item> <emph>Current business situation (Slide 1)</emph>: Students are presented with background information about the hypothetical business and service area. Instructors describe how an existing distribution center is closing and that the company is looking to identify two new distribution centers within an existing service network.</item> <p></p> <item> <emph>Assignment objective (Slide 2)</emph>: Information in the objective section explains how the student, acting as a supply chain planner, needs to identify two new distribution centers within the service area, meet all customer demand, and adhere to the various system constraints, all while minimizing total network distribution costs. The instructor should describe how students will use the center‐of‐gravity algorithm to identify and select two distribution points. Further, the instructor should emphasize that students need to minimize total network distribution costs.</item> <p></p> <item> <emph>Customer demand and service area map (Slide 3)</emph>: Students must be provided data about expected demand over the next 12 months for all customer locations they are to consider in the activity (Figure 1). The data are aggregated by city (i.e., Austin, total demand = 1200 units). Students should assume all demand in a specific city is for a single customer to remove issues that could arise with multiple addresses within the same city.</item> </ulist> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/Q1N/01apr24/dsji12311-fig-0001.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="dsji12311-fig-0001.jpg" title="1 Illustration of customer demand and service area." /> </p> <p></p> <p>In addition, an accurate map should be presented to students to illustrate where customers reside within a service area. Using Figure 1 as an example, we identify cities such as Houston and Dallas, since these cities have corresponding customer demand. We did not include cities with no customer demand.</p> <p></p> <ulist> <item> 4. <emph>Transportation costs (Slide 4)</emph>: Information about fixed and variable transportation costs is presented to the students. These figures should be used to determine the total cost for each transportation lane. For this brief, we used a fixed cost of $100 per truck (applied only once for each round‐trip shipment) and a variable cost of $0.50 per mile.</item> <p></p> <item> 5. <emph>Constraints (Slide 5–6)</emph>: Information about various constraints should be presented to the students. In our example of the activity, we describe the following constraints.</item> <p></p> <item> a. <emph>Truck capacity</emph>: Instructors need to establish how much product can fit onto a full truckload shipment (i.e., 100 units, 250 units, or 300 units per truck). In our version of the assignment, we limit each truck to a maximum of 200 units.</item> <p></p> <item> b. <emph>Customer mixing guidelines</emph>: Instructors need to state that product from two or more customers cannot be shipped on the same vehicle, for example, a planner cannot combine 150 units from one customer with 50 units from another. While trucking companies frequently ship one truck to multiple customer destinations, it significantly complicates the solution and grading process for this assignment.</item> <p></p> <item> c. <emph>Delivery rounding methodology</emph>: Instructors must explain how students need to round up when planning shipments. For instance, for the lane between a distribution center and Amarillo, demand is 3700 units (Figure 1). Assuming 200 units per truck, a student would initially determine a need of 17.5 trucks (i.e., 3700/200) and then need to round up to 18 shipments. This parameter is necessary to ensure that students properly calculate total round‐trip transportation costs. Students frequently multiply the unrounded truckload number by the fixed cost figure (i.e., 17.5 truckloads multiplied by a fixed cost of $100 per shipment), when the correct calculation should be 18 truckloads at $100 per shipment.</item> <p></p> <item> d. <emph>Maximum number of customers assigned to a proposed supply point</emph>: The instructor should provide information regarding the maximum percentage of customers that can be assigned to a distribution center. We limit each proposed distribution center to a maximum of 66% of the locations found in Table 1. This constraint is designed to ensure that students select two distribution centers.</item> <p></p> <item> e. <emph>Methodology to determine mileage</emph>: The instructor should indicate how to use a mapping program/application, such as Google Maps, to estimate mileage for each shipping lane. Students should use the center of the city/town for the distance computation. Most online mapping programs/applications use the center of the city/town by default unless a specific address is used.</item> </ulist> <p>1 TABLE Demand data and X and Y Map coordinates.</p> <p> <ephtml> <table><thead><tr><th>City</th><th align="left">Demand</th><th align="left"><italic>X</italic></th><th align="left"><italic>Y</italic></th></tr></thead><tbody><tr><td>Amarillo</td><td>3700</td><td>3.3</td><td>6.7</td></tr><tr><td>Austin</td><td>1200</td><td>5.4</td><td>3.4</td></tr><tr><td>Brownsville</td><td>7800</td><td>5.9</td><td>0.3</td></tr><tr><td>Lubbock</td><td>3400</td><td>3.3</td><td>6.0</td></tr><tr><td>Dallas</td><td>9600</td><td>6.6</td><td>5.2</td></tr><tr><td>Fort Worth</td><td>700</td><td>6.0</td><td>5.1</td></tr><tr><td>Waco</td><td>800</td><td>6.3</td><td>4.3</td></tr><tr><td>Houston</td><td>2100</td><td>7.5</td><td>2.9</td></tr><tr><td>Galveston</td><td>1400</td><td>7.7</td><td>2.5</td></tr><tr><td>Corpus Christi</td><td>7100</td><td>6.1</td><td>1.6</td></tr><tr><td>S. Padre Island</td><td>7700</td><td>6.2</td><td>1.9</td></tr><tr><td>Laredo</td><td>3400</td><td>4.8</td><td>1.3</td></tr><tr><td>San Antonio</td><td>3000</td><td>5.0</td><td>2.6</td></tr><tr><td>El Paso</td><td>8500</td><td>0.3</td><td>4.2</td></tr><tr><td>Odessa</td><td>4200</td><td>2.5</td><td>4.2</td></tr><tr><td>Midland</td><td>1200</td><td>2.9</td><td>4.5</td></tr></tbody></table> </ephtml> </p> <p>We standardize the assignment and instruct all students to use Google Maps to determine mileage between a proposed distribution center and a customer's location.</p> <p>In situations where there are multiple potential routes between a proposed supply point and a customer location, students should be instructed on which route to choose. When we assign this activity, students are instructed to use the value with the fewest miles, not the shortest time duration (see Figure 2). In many cases, the shortest transportation lane distance may not be the quickest drive time. Instructors need to clarify which methodology should be used so results can be examined and compared.</p> <p></p> <ulist> <item> f. <emph>Minimum population count</emph>: The instructor can establish a minimum population count (i.e., 5000, 10,000, 20,000) for the location of the proposed distribution centers. While this constraint is optional, its inclusion can help students understand the need for sufficient population to ensure basic manpower needs are met for the proposed facilities. We use 10,000 as a population minimum for any location that can host the distribution center.</item> <p></p> <item> g. <emph>Proximity to interstate highway</emph>: Instructors could require that potential distribution center locations must be within a certain distance (i.e., 5, 10, or 25 miles) of an interstate highway or major transportation motorway. While this constraint is optional, most distribution centers are purposefully placed near high‐velocity transportation corridors due to the time sensitive nature of day‐to‐day shipment activities (Bowen, [<reflink idref="bib6" id="ref42">6</reflink>]). Every instructor associated with this study required distribution centers to be located within 5 miles of an interstate highway.</item> </ulist> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/Q1N/01apr24/dsji12311-fig-0002.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="dsji12311-fig-0002.jpg" title="2 Driving estimates from Abilene, TX to El Paso, TX." /> </p> <p></p> <hd id="AN0176537428-9">DIRECTIONS FOR INSTRUCTORS</hd> <p>Instructors should present the assignment using the PowerPoint slides and handout (available in the supplemental files) illustrating the different customer demand locations within the service area (Figure 1). The presentation needs to outline the current business situation, assignment objective, customers demand estimates, transportation costs, networks constraints, and methodology to estimate mileage and cost. We recommend illustrating the service area with a map (drawn to scale) that shows cities with existing demand. Public‐domain maps can be downloaded for free from a variety of government websites.</p> <p>To solve the assignment, most students use the following process to evaluate demand, determine the optimal locations, and calculate total network distribution costs.</p> <p></p> <ulist> <item> <emph>Analyze demand data and service area map</emph> : To determine the location for two new distribution centers, students begin by analyzing the demand data found in Figure 1. In most cases, they group demand in different ways to see if there is a pattern that might help them solve the assignment. Students then contemplate using the center‐of‐gravity or their own computational technique that closely approximates it.</item> </ulist> <p>After the initial evaluation, most students consider grouping demand in different sections of the service area. Methods include splitting demand into a northern and southern section or an eastern and western section. Moreover, some students adjust the size of the service areas by reassigning customers from one group to another. By creating two smaller groups within the larger service area map, students can use the center‐of‐gravity calculation to approximate where a supply point should be located.</p> <p></p> <ulist> <item> <emph>Center‐of‐gravity method</emph> : To use the center‐of‐gravity methodology, students must assign coordinate values to all the demand points found within the service area map (see Table 1 as an example). This process can be time consuming and inaccurate because students may not fully understand that the center‐of‐gravity method requires an actual map with a grid‐coordinate system. Moreover, some students will use longitude and latitude figures when calculating the optimal locations. This is typically more accurate than just grid coordinates.</item> </ulist> <p>After using the center‐of‐gravity method to determine the mathematically optimal locations for the two smaller services areas, students need to choose a city or town that meets the population and transportation constraints. These factors typically force students to select different distribution center locations, which further differentiates the total cost estimates submitted by each participant.</p> <p></p> <ulist> <item> <emph>Calculate round‐trip transportation expenses</emph> : Once the two distribution center locations are chosen, students must then calculate round‐trip transportation costs for the network. Table 2 illustrates these calculations, and they are implemented in an Excel file provided in the supplemental materials.</item> <p></p> <item> <emph>Solution</emph> : Based on the demand data and service area outlined in Figure 1 , the optimal location for the two distribution centers would be Corpus Christi and Big Spring, Texas. Seven customers would be assigned to the Corpus Christi location, while Big Spring would cover the remaining nine locations.</item> <p></p> <item> 1. <emph>Corpus Christi, Texas</emph> : Using the center‐of‐gravity calculation, the ideal location for the first supply point would be 27°14'58.5" North and −97°18'30.7" West, which is about 20 miles south‐southeast of Corpus Christi. These coordinates indicate the optimal location is actually in the ocean a few miles off the coast. Since the Corpus Christi has a population of greater than 10,000 people and is located on Interstate 37, it meets the network constraints outlined in the assignment.</item> <p></p> <item> 2. <emph>Big Spring, Texas</emph> : Using the center‐of‐gravity calculation, the ideal location for the second supply point would be 32°26'37.1" North and −101°18'03.0" West, which is about nine miles northeast from the city of Big Spring. Since the city has a population of greater than 10,000 people and is located on Interstate 20, it meets the network constraints outlined in the assignment.</item> <item>otal round‐trip transportation costs for entire service network.</item> </ulist> <p> <ephtml> <table><thead><tr><th>DC 1</th><th align="left">Customer location</th><th align="left">Total demand</th><th align="left">Max Qty/truck</th><th align="left">Number of trucks</th><th align="left">Rounded shipments</th><th align="left">Fixed cost ($100)</th><th align="left">One‐way mileage</th><th align="left">Round‐trip mileage</th><th align="left">Variable cost ($0.50/mile)</th><th align="left">Total cost</th></tr></thead><tbody><tr><td>Corpus Christi</td><td>Austin</td><td>1200</td><td>200</td><td>6.0</td><td>6.0</td><td>$600</td><td>199</td><td>398</td><td>$1194.00</td><td>$1794.00</td></tr><tr><td>Corpus Christi</td><td>Brownsville</td><td>7800</td><td>200</td><td>39.0</td><td>39.0</td><td>$3900</td><td>161</td><td>322</td><td>$6279.00</td><td>$10,179.00</td></tr><tr><td>Corpus Christi</td><td>Corpus Christi</td><td>7100</td><td>200</td><td>35.5</td><td>36.0</td><td>$3600</td><td>0</td><td>0</td><td>$—</td><td>$3600.00</td></tr><tr><td>Corpus Christi</td><td>Galveston</td><td>1400</td><td>200</td><td>7.0</td><td>7.0</td><td>$700</td><td>218</td><td>436</td><td>$1526.00</td><td>$2226.00</td></tr><tr><td>Corpus Christi</td><td>Houston</td><td>2100</td><td>200</td><td>10.5</td><td>11.0</td><td>$1100</td><td>208</td><td>416</td><td>$2288.00</td><td>$3388.00</td></tr><tr><td>Corpus Christi</td><td>Laredo</td><td>3400</td><td>200</td><td>17.0</td><td>17.0</td><td>$1700</td><td>145</td><td>290</td><td>$2465.00</td><td>$4165.00</td></tr><tr><td>Corpus Christi</td><td>S. Padre Island</td><td>7700</td><td>200</td><td>38.5</td><td>39.0</td><td>$3900</td><td>181</td><td>362</td><td>$7059.00</td><td>$10,959.00</td></tr><tr><td>Corpus Christi</td><td>San Antonio</td><td>3000</td><td>200</td><td>15.0</td><td>15.0</td><td>$1500</td><td>144</td><td>288</td><td>$2160.00</td><td>$3660.00</td></tr><tr><td>Corpus Christi</td><td>Waco</td><td>800</td><td>200</td><td>4.0</td><td>4.0</td><td>$400</td><td>296</td><td>592</td><td>$1184.00</td><td>$1584.00</td></tr><tr><td /><td /><td /><td /><td /><td /><td /><td /><td>Total for DC 1</td><td>$41,555.00</td></tr></tbody></table> </ephtml> </p> <p></p> <p> <ephtml> <table><thead><tr><th>DC 2</th><th align="left">Customer location</th><th align="left">Total demand</th><th align="left">Max Qty/truck</th><th align="left">Number of trucks</th><th align="left">Rounded shipments</th><th align="left">Fixed cost ($100)</th><th align="left">One‐way mileage</th><th align="left">Round‐trip mileage</th><th align="left">Variable cost ($0.50/mile)</th><th align="left">Total cost</th></tr></thead><tbody><tr><td>Big Spring</td><td>Amarillo</td><td>3700</td><td>200</td><td>18.5</td><td>19.0</td><td>$1900</td><td>226</td><td>452</td><td>$4294.00</td><td>$6194.00</td></tr><tr><td>Big Spring</td><td>Dallas</td><td>9600</td><td>200</td><td>48.0</td><td>48.0</td><td>$4800</td><td>290</td><td>580</td><td>$13,920.00</td><td>$18,720.00</td></tr><tr><td>Big Spring</td><td>El Paso</td><td>8500</td><td>200</td><td>42.5</td><td>43.0</td><td>$4300</td><td>336</td><td>672</td><td>$14,448.00</td><td>$18,748.00</td></tr><tr><td>Big Spring</td><td>Fort Worth</td><td>700</td><td>200</td><td>3.5</td><td>4.0</td><td>$400</td><td>258</td><td>516</td><td>$1032.00</td><td>$1432.00</td></tr><tr><td>Big Spring</td><td>Lubbock</td><td>3400</td><td>200</td><td>17.0</td><td>17.0</td><td>$1700</td><td>104</td><td>208</td><td>$1768.00</td><td>$3468.00</td></tr><tr><td>Big Spring</td><td>Midland</td><td>1200</td><td>200</td><td>6.0</td><td>6.0</td><td>$600</td><td>40</td><td>80</td><td>$240.00</td><td>$840.00</td></tr><tr><td>Big Spring</td><td>Odessa</td><td>4200</td><td>200</td><td>21.0</td><td>21.0</td><td>$2100</td><td>62.1</td><td>124.2</td><td>$1304.10</td><td>$3404.10</td></tr><tr><td /><td /><td /><td /><td /><td /><td /><td /><td>Total for DC 2</td><td>$52,806.10</td></tr><tr><td /><td /><td /><td /><td /><td /><td /><td /><td>Network Total</td><td>$94,361.10</td></tr></tbody></table> </ephtml> </p> <hd id="AN0176537428-10">Grading the assignment</hd> <p>When completed, students should submit their calculations and cost figures in an Excel workbook. This allows the instructor to check the formulas to make sure students are accounting for shipment rounding, fixed costs, variable costs, and mileage estimates, as well as properly calculating round‐trip transportation costs.</p> <p>When grading the assignment, we suggest looking for the following errors:</p> <p></p> <ulist> <item> Forgetting to round up to a full truckload. For example, if demand is 500 units and 200 units fit on each truck, the student might calculate 2.5 shipments. However, the proper answer would be 3 shipments after rounding up.</item> <p></p> <item> Improperly calculating one‐way or round‐trip mileage figures between a proposed distribution center and customer demand point.</item> <p></p> <item> Improperly calculating fixed costs. Fixed costs should be based on rounded shipments, not an unrounded number of trucks.</item> <p></p> <item> Improperly calculating round‐trip variable costs. Variable costs should be based on round‐trip mileage, not one‐way mileage figures.</item> <p></p> <item> Failing to calculate an aggregate total cost for the entire network.</item> <p></p> <item> Not assigning enough customer demand points to a distribution center and/or forgetting to assign all customers to a supply point.</item> <p></p> <item> Assigning a supply point to a city/town that is not within 5 miles of an interstate highway.</item> <p></p> <item> Assigning a supply point to a city/town that has a population of fewer than 10,000 people.</item> <p></p> <item> Assigning a supply point to a city/town that is outside the defined service area.</item> </ulist> <p>We reviewed the grades given for this networking planning assignment for 68 students (4 classes) and found the following results: 17 students incorrectly calculated the variable costs figures, 10 students only submitted one‐way transportation costs, 3 students lost points because they did not include all 16 customers in final network plan, 2 students incorrectly calculated some other cost figure, and 1 student did not create a two‐node network.</p> <p>Besides deductions, we regularly offer bonus points for the three submissions with the lowest total costs (15 points for the lowest cost plan, 10 points for the second lowest cost proposal, and 5 points for the third lowest cost submission). This gives students an incentive to iteratively improve upon their initial network plan and look for more creative solutions.</p> <hd id="AN0176537428-11">Modifying the assignment</hd> <p>We designed this location planning exercise so that educators could quickly change design parameters, cost figures, and constraints (core and optional). Specifically, changes can be made to the service area map, customer locations within the service area, number of customers being served, demand estimates, truck capacity, fixed costs associated with shipping a truck, and variable costs for miles driven. Instructors will want to alter the different parameters to better customize the assignment to the student population and to discourage cheating.</p> <p></p> <ulist> <item> <emph>Service area</emph>: One of the easiest parameters to change is the service area or the map used to illustrate where customer demand is located. Typically, we use a map of Texas (Figure 1) as our students are familiar with the cities and interstate highways within this service area. Instructors could easily change the size, shape, and/or location of the service area to cover multiple states. Figure 3 illustrates a service area covering four states in the southern United States. The assignment could use an international service area as well; Figure 4 demonstrates how the service area could be a map of England, Scotland, and Wales.</item> <p></p> <item> <emph>Customer locations</emph>: Instructors could increase, decrease, and/or change the location of customers within the service area. While we identify 16 customer locations in Figure 1, instructors could easily change the number of customers. Further, cities like San Antonio and Austin could be replaced by College Station and Tyler. Both approaches would alter the optimal solutions calculated by the center‐of‐gravity algorithm.</item> <p></p> <item> <emph>Demand estimates</emph>: For each customer location within a service area, we provide demand estimates. Looking at Figure 1, we offer 12‐month demand estimates for every customer location highlighted on the service area map. Educators could change the quantity of each demand estimate; for example, the demand for Amarillo could be changed from 3700 units to 1300 units.</item> <p></p> <item> <emph>Truck capacity</emph>: Instructors could change the truck capacity used to calculate the number of shipments. To determine the number of required shipments between a proposed distribution center and a customer location, the student divides the 12‐month customer demand estimates by the truck's capacity. While we used 200 units per truck, the capacity figure could be changed to 100 or 300 units per truck.</item> <p></p> <item> <emph>Fixed cost figure</emph>: Instructors could change the fixed cost figure, which is used to calculate total transportation cost. We use a fixed cost of $100 per shipment but expect this figure could be changed to $200 or $300 per shipment.</item> <p></p> <item> <emph>Variable cost figure</emph>: Instructors could change the variable cost figure, which is used to calculate total transportation cost. We use a variable cost of $0.50 per mile. However, we envision this figure could be changed to $0.80 or $1.20 per mile.</item> </ulist> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/Q1N/01apr24/dsji12311-fig-0003.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="dsji12311-fig-0003.jpg" title="3 Alternate map illustrating multiple states." /> </p> <p></p> <p> <img src="https://imageserver.ebscohost.com/img/embimages/rdk/Q1N/01apr24/dsji12311-fig-0004.jpg?ephost1=dGJyMNXb4kSepq84yOvqOLCmsE6epq5Srqa4SK6WxWXS" alt="dsji12311-fig-0004.jpg" title="4 Alternate map illustrating England, Scotland, and Wales." /> </p> <p></p> <hd id="AN0176537428-14">PEDAGOGICAL ASSESSMENT</hd> <p>After the assignment was graded, students were surveyed about their experience. An anonymous questionnaire used a seven‐point Likert scale for all questions except the control variables (gender, age, class rank). Both the number of responses and the percentage of students who responded are reported in Table 3. The percentages reported are based on 62 total respondents.</p> <p>3 TABLE Pedagogical assessment: number and percentage of respondents (N = 62).</p> <p> <ephtml> <table><thead><tr><th /><th align="left">1</th><th align="left">2</th><th align="left">3</th><th align="left">4</th><th align="left">5</th><th align="left">6</th><th align="left">7</th><th align="left" /></tr><tr><th /><th align="left">(Strongly disagree)</th><th align="left">(Disagree)</th><th align="left">(Somewhat disagree)</th><th align="left">(Neither agree nor disagree)</th><th align="left">(Somewhat agree)</th><th align="left">(Agree)</th><th align="left">(Strongly agree)</th><th align="left">Median</th></tr></thead><tbody><tr><td>Q1: I understood the objective of the assignment.</td><td>1 (2%)</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>4 (6%)</td><td>15 (24%</td><td>42 (68%)</td><td>7</td></tr><tr><td>Q2: The assignment helped me understand distribution network planning.</td><td>0 (0%)</td><td>0 (0%)</td><td>1 (2%)</td><td>2 (3%)</td><td>4 (6%)</td><td>16 (26%)</td><td>39 (63%)</td><td>7</td></tr><tr><td>Q3: I see how companies use distribution network planning.</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>1 (2%)</td><td>8 (13%)</td><td>18 (29%)</td><td>35 (56%)</td><td>7</td></tr><tr><td>Q4: I liked the fact that the assignment used real distances.</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>5 (8%)</td><td>6 (10%)</td><td>51 (83%)</td><td>7</td></tr><tr><td>Q5: I liked the fact that the assignment used real locations.</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>4 (6%)</td><td>7 (11%)</td><td>51 (83%)</td><td>7</td></tr><tr><td>Q6: I believe this assignment was appropriate for this class.</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>0 (0%)</td><td>2 (3%)</td><td>7 (11%)</td><td>53 (85%)</td><td>7</td></tr><tr><td>Q7: I enjoyed this assignment.</td><td>1 (2%)</td><td>0 (0%)</td><td>2 (3%)</td><td>4 (6%)</td><td>8 (13%)</td><td>20 (32%)</td><td>27 (44%)</td><td>6</td></tr><tr><td>Gender</td><td align="left">Male = 29, Female = 32, No response = 1</td></tr><tr><td>Age</td><td align="left">Average = 22.9</td></tr><tr><td>School level</td><td align="left">3rd year = 14, 4th year = 40, Graduate = 8</td></tr></tbody></table> </ephtml> </p> <p>Upon review of question 1, we found almost 98% of our students understood the objective of the assignment. To determine this percentage, we included respondents who somewhat agreed, agreed, or strongly agreed to the statement. Understanding how and why an assignment is used within a class is an important factor when teaching concepts like location planning.</p> <p>Beyond the assignment's objective, more than 90% of our students liked that the assignment used real distances (question 4) and real locations (question 5). These traits were included to make the assignment more realistic. By customizing the service area map, instructors can encourage student engagement and ensure that submissions and the optimal answer are not duplicated across groups of students and/or academic terms.</p> <p>Lastly, more than 89% responded positively (somewhat agreed, agreed, or strongly agreed) and said they enjoyed the assignment. We consider this an important measure of student engagement. Moreover, the positive responses to question 6 from Table 3 demonstrate that students believed the assignment was appropriate for a SCM class.</p> <hd id="AN0176537428-15">CONCLUSION</hd> <p>Experiential learning and technology‐mediated assignments are rapidly being adopted by university instructors. This teaching brief provides such an assignment, demonstrating how instructors can teach location planning using real‐world information. It is conceptually and practically beneficial in three distinct ways. First, the assignment helps students explore the center‐of‐gravity algorithm and the mathematical principles underpinning the methodology. It makes content found in the textbook, lectures, and/or in‐class discussions more tangible. Moreover, it encourages students to think critically about an abstract problem, that is, location planning, and develop a functional plan with related cost estimates. Second, we enhance the pedagogical tactics that SCM and OM instructors can use with an exercise that challenges students to develop a distribution plan, thus bridging a knowledge gap between academics and practice. Third, the flexibility of the assignment benefits instructors who can easily modify assignment parameters and constraints in multiple classes over time, without students easily determining and sharing the optimal solution. Finally, the results from our student survey indicate that this active learning assignment engages students and facilitates their understanding of network planning.</p> <hd id="AN0176537428-16">APPENDIX</hd> <p></p> <hd id="AN0176537428-17">LINKS TO ALL FIGURES IN THE STUDY</hd> <p></p> <p> <ephtml> <table><tbody><tr><td>Item</td><td>Link</td></tr><tr><td>Figure 1</td><td> https://onthemap.ces.census.gov/ </td></tr><tr><td>Figure 2</td><td> https://www.google.com/maps/dir/Abilene,+TX/El+Paso,+TX </td></tr><tr><td>Figure 3</td><td> https://onthemap.ces.census.gov/ </td></tr><tr><td>Figure 4</td><td>https://www.cia.gov/the‐world‐factbook/countries/united‐kingdom/map</td></tr></tbody></table> </ephtml> </p> <p>GRAPH: Supporting Information</p> <p>GRAPH: Supporting Information</p> <p>GRAPH: Supporting Information</p> <ref id="AN0176537428-18"> <title> REFERENCES </title> <blist> <bibl id="bib1" idref="ref2" type="bt">1</bibl> <bibtext> Ahmadi‐Javid, A., Seyedi, P., & Syam, S.S. (2017) A survey of healthcare facility location. Computers & Operations Research, 79, 223 – 263.</bibtext> </blist> <blist> <bibl id="bib2" idref="ref10" type="bt">2</bibl> <bibtext> Aikens, C.H. (1985) Facility location models for distribution planning. European Journal of Operational Research, 22 (3), 263 – 279.</bibtext> </blist> <blist> <bibl id="bib3" idref="ref16" type="bt">3</bibl> <bibtext> Akinc, U., & Khumawala, B.M. (1977) An efficient branch and bound algorithm for the capacitated warehouse location problem. Management Science, 23 (6), 585 – 594.</bibtext> </blist> <blist> <bibl id="bib4" idref="ref1" type="bt">4</bibl> <bibtext> Angolia, M.G., & Pagliari, L.R. (2018) Experiential learning for logistics and supply chain management using an SAP ERP software simulation. Decision Sciences Journal of Innovative Education, 16 (2), 104 – 125.</bibtext> </blist> <blist> <bibl id="bib5" idref="ref38" type="bt">5</bibl> <bibtext> Awasthi, A., Chauhan, S.S., & Goyal, S.K. (2011) A multi‐criteria decision making approach for location planning for urban distribution centers under uncertainty. Mathematical and Computer Modelling, 53 (1‐2), 98 – 109.</bibtext> </blist> <blist> <bibl id="bib6" idref="ref42" type="bt">6</bibl> <bibtext> Bowen Jr, J.T. (2008) Moving places: The geography of warehousing in the US. Journal of Transport Geography, 16 (6), 379 – 387.</bibtext> </blist> <blist> <bibl id="bib7" idref="ref29" type="bt">7</bibl> <bibtext> Brusco, M. (2022) Teaching spreadsheet‐based analysis of multisource continuous facility location problems. Decision Sciences Journal of Innovative Education, 20 (2), 102 – 111.</bibtext> </blist> <blist> <bibl id="bib8" idref="ref27" type="bt">8</bibl> <bibtext> Chen, H., Murray, A.T., & Jiang, R. (2021) Open‐source approaches for location cover models: capabilities and efficiency. Journal of Geographical Systems, 23 (3), 361 – 380.</bibtext> </blist> <blist> <bibl id="bib9" idref="ref7" type="bt">9</bibl> <bibtext> Cooper, L. (1964) Heuristic methods for location‐allocation problems. SIAM Review, 6 (1), 37 – 53.</bibtext> </blist> <blist> <bibtext> Correia, I., & Melo, T. (2022) Distribution network redesign under flexible conditions for short‐term location planning. Computers & Industrial Engineering, 174, 108747.</bibtext> </blist> <blist> <bibtext> Dearing, P.M., & Newruck, F.C. (1979) A capacitated bottleneck facility location problem. Management Science, 25 (11), 1093 – 1104.</bibtext> </blist> <blist> <bibtext> Francis, R.L., McGinnis, L.F., & White, J.A. (1983) Locational analysis. European Journal of Operational Research, 12 (3), 220 – 252.</bibtext> </blist> <blist> <bibtext> Geoffrion, A., & Bride, R.M. (1978) Lagrangean relaxation applied to capacitated facility location problems. AIIE Transactions, 10 (1), 40 – 47.</bibtext> </blist> <blist> <bibtext> Grasas, A., & Ramalhinho, H. (2016) Teaching distribution planning: A problem‐based learning approach. The International Journal of Logistics Management, 27 (2), 377 – 394.</bibtext> </blist> <blist> <bibtext> Gutierrez, M.T.E., Naval, J.Q., Paulite, E.M.S., Morco, R.C., Acerado, R.M., & Mutuc, J.E.S. (2018, March). A developed mobile application for optimum facility location using center‐of‐gravity approach. In 7th International Conference on Industrial Technology and Management (ICITM). Oxford, United Kingdom, 427 – 430.</bibtext> </blist> <blist> <bibtext> Hitchcock, F.L. (1941) The distribution of a product from several sources to numerous localities. Journal of Mathematics and Physics, 20 (1), 224 – 230.</bibtext> </blist> <blist> <bibtext> Khumawala, B.M., & Neebe, A.W. (1978) A note on Warszawski's multi‐commodity location problem. Journal of the Operational Research Society, 29 (2), 171 – 172.</bibtext> </blist> <blist> <bibtext> Khumawala, B.M., & Clay Whybark, D. (1976) Solving the dynamic warehouse location problem. International Journal of Physical Distribution, 6 (5), 238 – 251.</bibtext> </blist> <blist> <bibtext> King, M.A., & Arnette, A.N. (2011) Integrating geographic information systems in business school curriculum: An initial example. Decision Sciences Journal of Innovative Education, 9 (3), 325 – 347.</bibtext> </blist> <blist> <bibtext> Kizhakkan, A.R., Rathore, A.K., & Awasthi, A. (2019, December). Review of electric vehicle charging station location planning. In 2019 IEEE Transportation Electrification Conference (ITEC), India, pp. 1 – 5.</bibtext> </blist> <blist> <bibtext> Kuehn, A.A., & Hamburger, M.J. (1963) A heuristic program for locating warehouses. Management Science, 9 (4), 643 – 666.</bibtext> </blist> <blist> <bibtext> Kuo, C.C., & White, R.E. (2004) A note on the treatment of the center‐of‐gravity method in operations management textbooks. Decision Sciences Journal of Innovative Education, 2 (2), 219 – 227.</bibtext> </blist> <blist> <bibtext> Love, R.F., Morris, J.G., & Wesolowsky, G.O. (1988) Facilities location: Models & methods. New York, New York.</bibtext> </blist> <blist> <bibtext> Mao, D., Tan, J., & Wang, J. (2020) Location planning of PEV fast charging station: An integrated approach under traffic and power grid requirements. IEEE Transactions on Intelligent Transportation Systems, 22 (1), 483 – 492.</bibtext> </blist> <blist> <bibtext> Mostafaeipour, A., Qolipour, M., Rezaei, M., Jahangiri, M., Goli, A., & Sedaghat, A. (2021) A novel integrated approach for ranking solar energy location planning: A case study. Journal of Engineering, Design and Technology, 19 (3), 698 – 720.</bibtext> </blist> <blist> <bibtext> Neebe, A.W., & Khumawala, B.M. (1981) An improved algorithm for the multi‐commodity location problem. Journal of the Operational Research Society, 32 (2), 143 – 149.</bibtext> </blist> <blist> <bibtext> Owen, S.H., & Daskin, M.S. (1998) Strategic facility location: A review. European Journal of Operational Research, 111 (3), 423 – 447.</bibtext> </blist> <blist> <bibtext> Phan, L., Ali, I., Labou, S., & Foster, E. (2022) A model for data ethics instruction for non‐experts. IASSIST Quarterly, 46 (4), 1 – 15 DOI https://doi.org/10.29173/iq1028</bibtext> </blist> <blist> <bibtext> Ritchie, W.J., Kerski, J., Novoa, L.J., & Tokman, M. (2023) Bridging the gap between supply chain management practice and curriculum: A location analytics exercise. Decision Sciences Journal of Innovative Education, 21 (2), 83 – 94.</bibtext> </blist> <blist> <bibtext> Roodman, G.M., & Schwarz, L.B. (1975) Optimal and heuristic facility phase‐out strategies. AIIE Transactions, 7 (2), 177 – 184.</bibtext> </blist> <blist> <bibtext> Strakos, J.K., & Brazhkin, V. (2021) Learning management system‐integrated spreadsheet exercises for facility location modeling. Decision Sciences Journal of Innovative Education, 20 (1), 29 – 42.</bibtext> </blist> <blist> <bibtext> Tcha, D.W., & Lee, B.I. (1984) A branch‐and‐bound algorithm for the multi‐level uncapacitated facility location problem. European Journal of Operational Research, 18 (1), 35 – 43.</bibtext> </blist> <blist> <bibtext> Van Roy, T.J., & Erlenkotter, D. (1982) A dual‐based procedure for dynamic facility location. Management Science, 28 (10), 1091 – 1105.</bibtext> </blist> <blist> <bibtext> Warszawski, A. (1973) Multi‐dimensional location problems. Journal of the Operational Research Society, 24 (2), 165 – 179.</bibtext> </blist> <blist> <bibtext> Weber, A., & Friedrich, C.J. (1929) Alfred Weber's theory of the location of industries. University of Chicago Press.</bibtext> </blist> <blist> <bibtext> Wesolowsky, G.O., & Truscott, W.G. (1975) The multiperiod location‐allocation problem with relocation of facilities. Management Science, 22 (1), 57 – 65.</bibtext> </blist> </ref> <aug> <p>By Jason M. Riley and Kevin Sweeney</p> <p>Reported by Author; Author</p> <p></p> <p>Jason Riley (PhD, Clemson University) is an associate professor of supply chain management at Sam Houston State University. His research interests include retail distribution strategies, risk and disaster recovery, and supply chain management. He has published fifteen peer reviewed articles in journals such as International Journal of Physical Distribution and Logistics Management, Young Consumers, and International Review of Retail, Distribution and Consumer Research.</p> <p>Kevin Sweeney (PhD, University of Maryland) is an associate professor of supply chain management in the College of Business Administration at Sam Houston State University. His research interests include inventory management and the influence of customer and supplier choice on supply chain operations. He has published in journals such as the Journal of Business Logistics, International Journal of Production Economics, the International Journal of Logistics Management, and the International Journal of Physical Distribution and Logistics Management.</p> </aug> <nolink nlid="nl1" bibid="bib22" firstref="ref3"></nolink> <nolink nlid="nl2" bibid="bib23" firstref="ref4"></nolink> <nolink nlid="nl3" bibid="bib16" firstref="ref5"></nolink> <nolink nlid="nl4" bibid="bib35" firstref="ref6"></nolink> <nolink nlid="nl5" bibid="bib21" firstref="ref8"></nolink> <nolink nlid="nl6" bibid="bib12" firstref="ref9"></nolink> <nolink nlid="nl7" bibid="bib18" firstref="ref11"></nolink> <nolink nlid="nl8" bibid="bib34" firstref="ref12"></nolink> <nolink nlid="nl9" bibid="bib32" firstref="ref13"></nolink> <nolink nlid="nl10" bibid="bib17" firstref="ref14"></nolink> <nolink nlid="nl11" bibid="bib26" firstref="ref15"></nolink> <nolink nlid="nl12" bibid="bib11" firstref="ref17"></nolink> <nolink nlid="nl13" bibid="bib30" firstref="ref18"></nolink> <nolink nlid="nl14" bibid="bib36" firstref="ref19"></nolink> <nolink nlid="nl15" bibid="bib33" firstref="ref20"></nolink> <nolink nlid="nl16" bibid="bib13" firstref="ref21"></nolink> <nolink nlid="nl17" bibid="bib10" firstref="ref22"></nolink> <nolink nlid="nl18" bibid="bib20" firstref="ref23"></nolink> <nolink nlid="nl19" bibid="bib24" firstref="ref24"></nolink> <nolink nlid="nl20" bibid="bib25" firstref="ref25"></nolink> <nolink nlid="nl21" bibid="bib27" firstref="ref26"></nolink> <nolink nlid="nl22" bibid="bib29" firstref="ref28"></nolink> <nolink nlid="nl23" bibid="bib31" firstref="ref30"></nolink> <nolink nlid="nl24" bibid="bib14" firstref="ref31"></nolink> <nolink nlid="nl25" bibid="bib19" firstref="ref33"></nolink> <nolink nlid="nl26" bibid="bib28" firstref="ref35"></nolink> <nolink nlid="nl27" bibid="bib15" firstref="ref39"></nolink>
Header DbId: eric
DbLabel: ERIC
An: EJ1420555
AccessLevel: 3
PubType: Academic Journal
PubTypeId: academicJournal
PreciseRelevancyScore: 0
IllustrationInfo
Items – Name: Title
  Label: Title
  Group: Ti
  Data: Teaching Location Planning with the Center-Of-Gravity Method Using Real Cities and Distances
– Name: Language
  Label: Language
  Group: Lang
  Data: English
– Name: Author
  Label: Authors
  Group: Au
  Data: <searchLink fieldCode="AR" term="%22Jason+M%2E+Riley%22">Jason M. Riley</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0003-4554-6376">0000-0003-4554-6376</externalLink>)<br /><searchLink fieldCode="AR" term="%22Kevin+Sweeney%22">Kevin Sweeney</searchLink>
– Name: TitleSource
  Label: Source
  Group: Src
  Data: <searchLink fieldCode="SO" term="%22Decision+Sciences+Journal+of+Innovative+Education%22"><i>Decision Sciences Journal of Innovative Education</i></searchLink>. 2024 22(2):106-116.
– Name: Avail
  Label: Availability
  Group: Avail
  Data: Wiley. Available from: John Wiley & Sons, Inc. 111 River Street, Hoboken, NJ 07030. Tel: 800-835-6770; e-mail: cs-journals@wiley.com; Web site: https://www.wiley.com/en-us
– Name: PeerReviewed
  Label: Peer Reviewed
  Group: SrcInfo
  Data: Y
– Name: Pages
  Label: Page Count
  Group: Src
  Data: 11
– Name: DatePubCY
  Label: Publication Date
  Group: Date
  Data: 2024
– Name: TypeDocument
  Label: Document Type
  Group: TypDoc
  Data: Journal Articles<br />Reports - Research
– Name: Audience
  Label: Education Level
  Group: Audnce
  Data: <searchLink fieldCode="EL" term="%22Higher+Education%22">Higher Education</searchLink><br /><searchLink fieldCode="EL" term="%22Postsecondary+Education%22">Postsecondary Education</searchLink>
– Name: Subject
  Label: Descriptors
  Group: Su
  Data: <searchLink fieldCode="DE" term="%22Locational+Skills+%28Social+Studies%29%22">Locational Skills (Social Studies)</searchLink><br /><searchLink fieldCode="DE" term="%22Teaching+Methods%22">Teaching Methods</searchLink><br /><searchLink fieldCode="DE" term="%22Facility+Planning%22">Facility Planning</searchLink><br /><searchLink fieldCode="DE" term="%22Site+Selection%22">Site Selection</searchLink><br /><searchLink fieldCode="DE" term="%22Site+Analysis%22">Site Analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Undergraduate+Students%22">Undergraduate Students</searchLink><br /><searchLink fieldCode="DE" term="%22Operations+Research%22">Operations Research</searchLink><br /><searchLink fieldCode="DE" term="%22Information+Management%22">Information Management</searchLink><br /><searchLink fieldCode="DE" term="%22Supply+and+Demand%22">Supply and Demand</searchLink><br /><searchLink fieldCode="DE" term="%22Business+Education%22">Business Education</searchLink><br /><searchLink fieldCode="DE" term="%22Authentic+Learning%22">Authentic Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Experiential+Learning%22">Experiential Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+Activities%22">Learning Activities</searchLink><br /><searchLink fieldCode="DE" term="%22Course+Content%22">Course Content</searchLink><br /><searchLink fieldCode="DE" term="%22Curriculum+Enrichment%22">Curriculum Enrichment</searchLink><br /><searchLink fieldCode="DE" term="%22Municipalities%22">Municipalities</searchLink><br /><searchLink fieldCode="DE" term="%22Geographic+Location%22">Geographic Location</searchLink>
– Name: DOI
  Label: DOI
  Group: ID
  Data: 10.1111/dsji.12311
– Name: ISSN
  Label: ISSN
  Group: ISSN
  Data: 1540-4595<br />1540-4609
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Facility placement is of strategic importance to most organizations as a well-placed distribution center minimizes delivery costs and reduces fulfillment lead times, thus improving customer service levels. Because organizations value the location planning process, this teaching brief offers an exercise that analyzes the planning process using the center-of-gravity algorithm, a service area map, and real-world constraints. The objective of the exercise is to identify two locations within a service area that minimize total network distribution costs. Our exercise is intended to complement standard course content and support instructors developing curricula for undergraduate operations management and supply chain management courses. Student-based survey results indicate that the assignment enhanced classroom engagement and helped students better understand the complexities of location planning.
– Name: AbstractInfo
  Label: Abstractor
  Group: Ab
  Data: As Provided
– Name: DateEntry
  Label: Entry Date
  Group: Date
  Data: 2024
– Name: AN
  Label: Accession Number
  Group: ID
  Data: EJ1420555
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=eric&AN=EJ1420555
RecordInfo BibRecord:
  BibEntity:
    Identifiers:
      – Type: doi
        Value: 10.1111/dsji.12311
    Languages:
      – Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 11
        StartPage: 106
    Subjects:
      – SubjectFull: Locational Skills (Social Studies)
        Type: general
      – SubjectFull: Teaching Methods
        Type: general
      – SubjectFull: Facility Planning
        Type: general
      – SubjectFull: Site Selection
        Type: general
      – SubjectFull: Site Analysis
        Type: general
      – SubjectFull: Undergraduate Students
        Type: general
      – SubjectFull: Operations Research
        Type: general
      – SubjectFull: Information Management
        Type: general
      – SubjectFull: Supply and Demand
        Type: general
      – SubjectFull: Business Education
        Type: general
      – SubjectFull: Authentic Learning
        Type: general
      – SubjectFull: Experiential Learning
        Type: general
      – SubjectFull: Learning Activities
        Type: general
      – SubjectFull: Course Content
        Type: general
      – SubjectFull: Curriculum Enrichment
        Type: general
      – SubjectFull: Municipalities
        Type: general
      – SubjectFull: Geographic Location
        Type: general
    Titles:
      – TitleFull: Teaching Location Planning with the Center-Of-Gravity Method Using Real Cities and Distances
        Type: main
  BibRelationships:
    HasContributorRelationships:
      – PersonEntity:
          Name:
            NameFull: Jason M. Riley
      – PersonEntity:
          Name:
            NameFull: Kevin Sweeney
    IsPartOfRelationships:
      – BibEntity:
          Dates:
            – D: 01
              M: 04
              Type: published
              Y: 2024
          Identifiers:
            – Type: issn-print
              Value: 1540-4595
            – Type: issn-electronic
              Value: 1540-4609
          Numbering:
            – Type: volume
              Value: 22
            – Type: issue
              Value: 2
          Titles:
            – TitleFull: Decision Sciences Journal of Innovative Education
              Type: main
ResultId 1