Chapter 7 Transportation, Assignment, and Transshipment Problems Transportation Problem Assignment Problem The Transshipment Problem

Transportation, Assignment, and Transshipment Problems A network model is one which can be represented by a set of nodes, a set of arcs, and functions (e.g. costs, supplies, demands, etc.) associated with the arcs and/or nodes.

Transportation, Assignment, and Transshipment Problems Each of the three models of this chapter (transportation, assignment, and transshipment models) can be formulated as linear programs and solved by general purpose linear programming codes. For each of the three models, if the right-hand side of the linear programming formulations are all integers, the optimal solution will be in terms of integer values for the decision variables. However, there are many computer packages (including The Management Scientist) which contain separate computer codes for these models which take advantage of their network structure.

Transportation Problem The transportation problem seeks to minimize the total shipping costs of transporting goods from m origins (each with a supply si) to n destinations (each with a demand dj), when the unit shipping cost from an origin, i, to a destination, j, is cij. The network representation for a transportation problem with two sources and three destinations is given on the next slide.

Transportation Problem Network Representation 1 d1 c11 1 c12 s1 c13 2 d2 c21 c22 2 s2 c23 3 d3 SOURCES DESTINATIONS

Transportation Problem LP Formulation The LP formulation in terms of the amounts shipped from the origins to the destinations, xij , can be written as: Min cijxij i j s.t. xij < si for each origin i j xij = dj for each destination j i xij > 0 for all i and j

Transportation Problem LP Formulation Special Cases The following special-case modifications to the linear programming formulation can be made: Minimum shipping guarantee from i to j: xij > Lij Maximum route capacity from i to j: xij < Lij Unacceptable route: Remove the corresponding decision variable.

Example: BBC Building Brick Company (BBC) has orders for 80 tons of bricks at three suburban locations as follows: Northwood -- 25 tons, Westwood -- 45 tons, and Eastwood -- 10 tons. BBC has two plants, each of which can produce 50 tons per week. Delivery cost per ton from each plant to each suburban location is shown on the next slide. How should end of week shipments be made to fill the above orders?

Example: BBC Delivery Cost Per Ton Northwood Westwood Eastwood Plant 1 24 30 40 Plant 2 30 40 42

Example: BBC Partial Spreadsheet Showing Problem Data

Example: BBC Partial Spreadsheet Showing Optimal Solution

Example: BBC Optimal Solution From To Amount Cost Plant 1 Northwood 5 120 Plant 1 Westwood 45 1,350 Plant 2 Northwood 20 600 Plant 2 Eastwood 10 420 Total Cost = $2,490

Transportation Simplex Method The transportation simplex method requires that the sum of the supplies at the origins equal the sum of the demands at the destinations. If the total supply is greater than the total demand, a dummy destination is added with demand equal to the excess supply, and shipping costs from all origins are zero. (If total supply is less than total demand, a dummy origin is added.) When solving a transportation problem by its special purpose algorithm, unacceptable shipping routes are given a cost of +M (a large number).

Assignment Problem An assignment problem seeks to minimize the total cost assignment of m workers to m jobs, given that the cost of worker i performing job j is cij. It assumes all workers are assigned and each job is performed. An assignment problem is a special case of a transportation problem in which all supplies and all demands are equal to 1; hence assignment problems may be solved as linear programs. The network representation of an assignment problem with three workers and three jobs is shown on the next slide.

Assignment Problem Network Representation c11 c12 c13 c21 c22 c23 c31 AGENTS TASKS c21 c22 2 2 c23 c31 c32 3 3 c33

Assignment Problem LP Formulation Min cijxij i j s.t. xij = 1 for each agent i j xij = 1 for each task j i xij = 0 or 1 for all i and j Note: A modification to the right-hand side of the first constraint set can be made if a worker is permitted to work more than 1 job.

Assignment Problem LP Formulation Special Cases Number of agents exceeds the number of tasks: xij < 1 for each agent i j Number of tasks exceeds the number of agents: Add enough dummy agents to equalize the number of agents and the number of tasks. The objective function coefficients for these new variable would be zero.

Assignment Problem LP Formulation Special Cases (continued) The assignment alternatives are evaluated in terms of revenue or profit: Solve as a maximization problem. An assignment is unacceptable: Remove the corresponding decision variable. An agent is permitted to work a tasks: xij < a for each agent i j

Example: Hungry Owner A contractor pays his subcontractors a fixed fee plus mileage for work performed. On a given day the contractor is faced with three electrical jobs associated with various projects. Given below are the distances between the subcontractors and the projects. Projects Subcontractor A B C Westside 50 36 16 Federated 28 30 18 Goliath 35 32 20 Universal 25 25 14 How should the contractors be assigned to minimize total costs?

Example: Hungry Owner Network Representation A Subcontractors Projects 50 West. A 36 16 Subcontractors Projects 28 Fed. 30 B 18 35 32 Gol. C 20 25 25 Univ. 14

Example: Hungry Owner Linear Programming Formulation Min 50x11+36x12+16x13+28x21+30x22+18x23 +35x31+32x32+20x33+25x41+25x42+14x43 s.t. x11+x12+x13 < 1 x21+x22+x23 < 1 x31+x32+x33 < 1 x41+x42+x43 < 1 x11+x21+x31+x41 = 1 x12+x22+x32+x42 = 1 x13+x23+x33+x43 = 1 xij = 0 or 1 for all i and j Agents Tasks

Transshipment Problem Transshipment problems are transportation problems in which a shipment may move through intermediate nodes (transshipment nodes)before reaching a particular destination node. Transshipment problems can be converted to larger transportation problems and solved by a special transportation program. Transshipment problems can also be solved by general purpose linear programming codes. The network representation for a transshipment problem with two sources, three intermediate nodes, and two destinations is shown on the next slide.

Transshipment Problem Network Representation c36 3 c13 1 c37 6 s1 d1 c14 c46 c15 4 c47 Demand Supply c23 c24 c56 7 2 d2 s2 c25 5 c57 SOURCES INTERMEDIATE NODES DESTINATIONS

Transshipment Problem Linear Programming Formulation xij represents the shipment from node i to node j Min cijxij i j s.t. xij < si for each origin i j xik - xkj = 0 for each intermediate i j node k xij = dj for each destination j i xij > 0 for all i and j

Example: Transshipping Thomas Industries and Washburn Corporation supply three firms (Zrox, Hewes, Rockwright) with customized shelving for its offices. They both order shelving from the same two manufacturers, Arnold Manufacturers and Supershelf, Inc. Currently weekly demands by the users are 50 for Zrox, 60 for Hewes, and 40 for Rockwright. Both Arnold and Supershelf can supply at most 75 units to its customers. Additional data is shown on the next slide.

Example: Transshipping Because of long standing contracts based on past orders, unit costs from the manufacturers to the suppliers are: Thomas Washburn Arnold 5 8 Supershelf 7 4 The costs to install the shelving at the various locations are: Zrox Hewes Rockwright Thomas 1 5 8 Washburn 3 4 4

Example: Transshipping Network Representation Zrox ZROX 50 1 Arnold 5 Thomas 75 ARNOLD 5 8 8 Hewes 60 HEWES 3 7 Super Shelf Wash- Burn 4 75 WASH BURN 4 4 Rock- Wright 40

Example: Transshipping Linear Programming Formulation Decision Variables Defined xij = amount shipped from manufacturer i to supplier j xjk = amount shipped from supplier j to customer k where i = 1 (Arnold), 2 (Supershelf) j = 3 (Thomas), 4 (Washburn) k = 5 (Zrox), 6 (Hewes), 7 (Rockwright) Objective Function Defined Minimize Overall Shipping Costs: Min 5x13 + 8x14 + 7x23 + 4x24 + 1x35 + 5x36 + 8x37 + 3x45 + 4x46 + 4x47

Example: Transshipping Constraints Defined Amount Out of Arnold: x13 + x14 < 75 Amount Out of Supershelf: x23 + x24 < 75 Amount Through Thomas: x13 + x23 - x35 - x36 - x37 = 0 Amount Through Washburn: x14 + x24 - x45 - x46 - x47 = 0 Amount Into Zrox: x35 + x45 = 50 Amount Into Hewes: x36 + x46 = 60 Amount Into Rockwright: x37 + x47 = 40 Non-negativity of Variables: xij > 0, for all i and j.

Example: Transshipping Optimal Solution (from The Management Scientist ) Objective Function Value = 1150.000 Variable Value Reduced Costs X13 75.000 0.000 X14 0.000 2.000 X23 0.000 4.000 X24 75.000 0.000 X35 50.000 0.000 X36 25.000 0.000 X37 0.000 3.000 X45 0.000 3.000 X46 35.000 0.000 X47 40.000 0.000

Example: Transshipping Optimal Solution Zrox ZROX 50 50 75 1 Arnold 5 Thomas 75 ARNOLD 5 25 8 8 Hewes 60 35 HEWES 3 7 4 Super Shelf Wash- Burn 40 75 WASH BURN 4 75 4 Rock- Wright 40