Facilities Design

Facilities Design S.S. Heragu Industrial Engineering Department University of Louisville

Chapter 9Basic Modelsfor theLocation Problem

Outline • 9.1 Introduction • 9.2 Important Factors in Location Decisions • 9.3 Techniques for Discrete Space Location Problems • 9.3.1 Qualitative Analysis • 9.3.2 Quantitative Analysis • 9.3.3 Hybrid Analysis

Outline Cont... • 9.4 Techniques for Continuous Space Location Problems • 9.4.1 Median Method • 9.4.2 Contour Line Method • 9.4.3 Gravity Method • 9.4.4 Weiszfeld Method • 9.5 Facility Location Case Study • 9.6 Summary • 9.7 Review Questions and Exercises • 9.8 References

McDonald’s • QSCV Philosophy • 11,000 restaurants (7,000 in USA, remaining in 50 countries) • 700 seat McDonald’s in Pushkin Square, Moscow • $60 million food plant combining a bakery, lettuce plant, meat plant, chicken plant, fish plant and a distribution center, each owned and operated independently at same location

McDonald’s cont... • Food taste must be the same at any McDonald, yet food must be secured locally • Strong logistical chain, with no weak links between • Close monitoring for logistical performance • 300 in Australia • Central distribution since 1974 with the help of F.J. Walker Foods in Sydney • Then distribution centers opened in several cities

McDonald’s cont... • 2000 ingredients, from 48 food plants, shipment of 200 finished products from suppliers to DC’s, 6 million cases of food and paper products plus 500 operating items to restaurants across Australia • Delivery of frozen, dry and chilled foods twice a week to each of the 300 restaurants 98% of the time within 15 minutes of promised delivery time, 99.8% within 2 days of order placement • No stockouts, but less inventory

Supplier Supplier Raw Material(s) Manufacturing Plant Manufacturing Plant Assembly Plant Central Distribution Center(s) Retail Outlets Retail Outlets Regional DistributionCenter(s) Regional Distribution Center(s) Entities in a Supply Chain

Introduction • Design and Operation of a Supply chain • Warehousing • Distribution Channels • Freight Transportation • Freight Consolidation • Transportation Modes

Introduction • Logistics management can be defined as the management of transportation and distribution of goods. • facility location • transportation • goods handling and storage.

Introduction Cont... Some of the objectives in facility location decisions: (1) It must first be close as possible to raw material sources and customers; (2) Skilled labor must be readily available in the vicinity of a facility’s location; (3) Taxes, property insurance, construction and land prices must not be too “high;” (4) Utilities must be readily available at a “reasonable” price;

Introduction Cont... (5) Local , state and other government regulations must be conducive to business; and (6) Business climate must be favorable and the community must have adequate support services and facilities such as schools, hospitals and libraries, which are important to employees and their families.

Introduction Cont... Logistics management problems can be classified as: (1) location problems; (2) allocation problems; and (3) location-allocation problems.

List of Factors AffectingLocation Decisions • Proximity to raw materials sources • Cost and availability of energy/utilities • Cost, availability, skill and productivity of labor • Government regulations at the federal, state, country and local levels • Taxes at the federal, state, county and local levels • Insurance • Construction costs, land price

List of Factors AffectingLocation Decisions Cont... • Government and political stability • Exchange rate fluctuation • Export, import regulations, duties, and tariffs • Transportation system • Technical expertise • Environmental regulations at the federal, state, county and local levels • Support services

List of Factors AffectingLocation Decisions Cont... • Community services, i.e. schools, hospitals, recreation, etc. • Weather • Proximity to customers • Business climate • Competition-related factors

9.2Important Factors in Location Decisions • International • National • State-wide • Community-wide

9.3.1Qualitative Analysis Step 1: List all the factors that are important, i.e. have an impact on the location decision. Step 2: Assign appropriate weights (typically between 0 and 1) to each factor based on the relative importance of each. Step 3: Assign a score (typically between 0 and 100) for each location with respect to each factor identified in Step 1.

9.3.1Qualitative Analysis Step 4: Compute the weighted score for each factor for each location by multiplying its weight with the corresponding score (which were assigned Steps 2 and 3, respectively) Step 5: Compute the sum of the weighted scores for each location and choose a location based on these scores.

Example 1: • A payroll processing company has recently won several major contracts in the midwest region of the U.S. and central Canada and wants to open a new, large facility to serve these areas. Since customer service is of utmost importance, the company wants to be as near it’s “customers” as possible. Preliminary investigation has shown that Minneapolis, Winnipeg, and Springfield, Ill., would be the three most desirable locations and the payroll company has to select one of these three.

Example 1: Cont... A subsequent thorough investigation of each location with respect to eight important factors has generated the raw scores and weights listed in table 2. Using the location scoring method, determine the best location for the new payroll processing facility.

Solution: Steps 1, 2, and 3 have already been completed for us. We now need to compute the weighted score for each location-factor pair (Step 4), and these weighted scores and determine the location based on these scores (Step 5).

Table 9.2. Factors and Weights for Three Locations Wt. Factors Location Minn.Winn.Spring. .25 Proximity to customers 95 90 65 .15 Land/construction prices 60 60 90 .15 Wage rates 70 45 60 .10 Property taxes 70 90 70 .10 Business taxes 80 90 85 .10 Commercial travel 80 65 75

Table 9.2. Cont... Wt. Factors Location Minn. Winn. Spring. .08 Insurance costs 70 95 60 .07 Office services 90 90 80 Click here

Solution: Cont... From the analysis in Table 3, it is clear that Minneapolis would be the best location based on the subjective information.

Table 9.3. Weighted Scores for the Three Locations in Table 9.2 Weighted Score Location Minn. Winn. Spring. Proximity to customers 23.75 22.5 16.25 Land/construction prices 9 9 13.5 Wage rates 10.5 6.75 9 Property taxes 7 9 8.5 Business taxes 8 9 8.5

Table 9.3. Cont... Weighted Score Location Minn. Winn. Spring. Commercial travel 8 6.5 7.5 Insurance costs 5.6 7.6 4.8 Office services 6.3 6.3 5.6

Solution: Cont... Of course, as mentioned before, objective measures must be brought into consideration especially because the weighted scores for Minneapolis and Winnipeg are close.

9.3.2Quantitative Analysis

General Transportation Model

General Transportation Model Parameters cij: cost of transporting one unit from warehouse i to customer j ai: supply capacity at warehouse i bi: demand at customer j Decision Variables xij: number of units transported from warehouse i to customer j

General Transportation Model

Transportation Simplex Algorithm Step 1: Check whether the transportation problem is balanced or unbalanced. If balanced, go to step 2. Otherwise, transform the unbalanced transportation problem into a balanced one by adding a dummy plant (if the total demand exceeds the total supply) or a dummy warehouse (if the total supply exceeds the total demand) with a capacity or demand equal to the excess demand or excess supply, respectively. Transform all the > and < constraints to equalities. Step 2: Set up a transportation tableau by creating a row corresponding to each plant including the dummy plant and a column corresponding to each warehouse including the dummy warehouse. Enter the cost of transporting a unit from each plant to each warehouse (cij) in the corresponding cell (i,j). Enter 0 cost for all the cells in the dummy row or column. Enter the supply capacity of each plant at the end of the corresponding row and the demand at each warehouse at the bottom of the corresponding column. Set m and n equal to the number of rows and columns, respectively and all xij=0, i=1,2,...,m; and j=1,2,...,n. Step 3: Construct a basic feasible solution using the Northwest corner method.

Transportation Simplex Algorithm Step 4: Set u1=0 and find vj, j=1,2,...,n and ui, i=1,2,...,n using the formula ui + vj = cij for all basic variables. Step 5: If ui + vj - cij< 0 for all nonbasic variables, then the current basic feasible solution is optimal; stop. Otherwise, go to step 6. Step 6: Select the variable xi*j* with the most positive value ui* + vj*-cij*. Construct a closed loop consisting of horizontal and vertical segments connecting the corresponding cell in row i* and column j* to other basic variables. Adjust the values of the basic variables in this closed loop so that the supply and demand constraints of each row and column are satisfied and the maximum possible value is added to the cell in row i* and column j*. The variable xi*j* is now a basic variable and the basic variable in the closed loop which now takes on a value of 0 is a nonbasic variable. Go to step 4.

Example 2: Seers Inc. has two manufacturing plants at Albany and Little Rock supplying Canmore brand refrigerators to four distribution centers in Boston, Philadelphia, Galveston and Raleigh. Due to an increase in demand of this brand of refrigerators that is expected to last for several years into the future, Seers Inc., has decided to build another plant in Atlanta. The expected demand at the three distribution centers and the maximum capacity at the Albany and Little Rock plants are given in Table 4.

Table 9.4. Costs, Demand and Supply Information Bost. Phil. Galv. Rale. Supply Capacity Albany 10 15 22 20 250 Little Rock 19 15 10 9 300 Atlanta 21 11 13 6 No limit Demand 200 100 300 280

Table 9.5. Transportation Model with Plant at Atlanta Bost. Phil. Galv. Rale. Supply Capacity Albany 10 15 22 20 250 Little Rock 19 15 10 9 300 Atlanta 21 11 13 6 880 Demand 200 100 300 280 880 Click here for Excel formulation Click here for LINGO formulation

Example 3 Consider Example 2. In addition to Atlanta, suppose Seers, Inc., is considering another location – Pittsburgh. Determine which of the two locations, Atlanta or Pittsburgh, is suitable for the new plant. Seers Inc., wishes to utilize all of the capacity available at it’s Albany and Little Rock Locations

Table 9.10. Costs, Demand and Supply Information Bost. Phil. Galv. Rale. Supply Capacity Albany 10 15 22 20 250 Little Rock 19 15 10 9 300 Atlanta 21 11 13 6 330 Pittsburgh 17 8 18 12 330 Demand 200 100 300 280

Table 9.12. Transportation Model with Plant at Pittsburgh Bost. Phil. Galv. Rale. Supply Capacity Albany 10 15 22 20 250 Little Rock 19 15 10 9 300 Pittsburgh 17 8 18 12 880 Demand 200 100 300 280 880 Click here for Excel model Click here for LINDO Model Click here for LINGO Model

Min/Max Location Problem: Location d11 d12 d1n d21 d22 d2n Site dm1 dm2 dmn

9.3.3 Hybrid Analysis • Critical • Objective • Subjective

Hybrid Analysis Cont... CFij = 1 if location i satisfies critical factor j, 0 otherwise OFij = cost of objective factor j at location i SFij = numerical value assigned (on scale of 0-100) to subjective factor j for location i wj = weight assigned to subjective factor (0<w< 1)

Hybrid Analysis Cont...

Hybrid Analysis Cont... The location measure LMi for each location is then calculated as: LMi = CFMi [ OFMi+ (1- ) SFMi ] Where  is the weight assigned to the objective factor. We then choose the location with the highest location measure LMi

Example 4: Mole-Sun Brewing company is evaluating six candidate locations-Montreal, Plattsburgh, Ottawa, Albany, Rochester and Kingston, for constructing a new brewery. There are two critical, three objective and four subjective factors that management wishes to incorporate in its decision-making. These factors are summarized in Table 7. The weights of the subjective factors are also provided in the table. Determine the best location if the subjective factors are to be weighted 50 percent more than the objective factors.

Table 9.13:Critical, Subjective and Objective Factor Ratings for six locations for Mole-Sun Brewing Company, Inc.

Table 9.13 Cont... Location Albany 0 1 Kingston 1 1 Montreal 1 1 Ottawa 1 0 Plattsburgh 1 1 Rochester 1 1 Factors Critical Water Supply Tax Incentives

Table 9.13 Cont... Location Albany 185 80 10 Kingston 150 100 15 Montreal 170 90 13 Ottawa 200 100 15 Plattsburgh 140 75 8 Rochester 150 75 11 Factors Critical Objective Revenue Labor Cost Energy Cost

Table 9.13 Cont... Location 0.3 0.4 Albany 0.5 0.9 Kingston 0.6 0.7 Montreal 0.4 0.8 Ottawa 0.5 0.4 Plattsburgh 0.9 0.9 Rochester 0.7 0.65 Factors Subjective Community Attitude Ease of Transportation

Facilities Design