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Unformatted text preview: Integer Programming 9 The linearprogramming models that have been discussed thus far all have been continuous , in the sense that decision variables are allowed to be fractional. Often this is a realistic assumption. For instance, we might easily produce 102 3 4 gallons of a divisible good such as wine. It also might be reasonable to accept a solution giving an hourly production of automobiles at 58 1 2 if the model were based upon average hourly production, and the production had the interpretation of production rates . At other times, however, fractional solutions are not realistic, and we must consider the optimization problem: Maximize n X j = 1 c j x j , subject to: n X j = 1 a i j x j = b i ( i = 1 , 2 , . . . , m ), x j ≥ ( j = 1 , 2 , . . . , n ), x j integer ( for some or all j = 1 , 2 , . . . , n ). This problem is called the (linear) integerprogramming problem . It is said to be a mixed integer program when some, but not all, variables are restricted to be integer, and is called a pure integer program when all decision variables must be integers. As we saw in the preceding chapter, if the constraints are of a network nature, then an integer solution can be obtained by ignoring the integrality restrictions and solving the resulting linear program. In general, though, variables will be fractional in the linearprogramming solution, and further measures must be taken to determine the integerprogramming solution. The purpose of this chapter is twofold. First, we will discuss integerprogramming formulations. This should provide insight into the scope of integerprogramming applications and give some indication of why many practitioners feel that the integerprogramming model is one of the most important models in management science. Second, we consider basic approaches that have been developed for solving integer and mixedinteger programming problems. 9.1 SOME INTEGERPROGRAMMING MODELS Integerprogramming models arise in practically every area of application of mathematical programming. To develop a preliminary appreciation for the importance of these models, we introduce, in this section, three areas where integer programming has played an important role in supporting managerial decisions. We do not provide the most intricate available formulations in each case, but rather give basic models and suggest possible extensions. 272 9.1 Some IntegerProgramming Models 273 Capital Budgeting In a typical capitalbudgeting problem, decisions involve the selection of a number of potential investments. The investment decisions might be to choose among possible plant locations, to select a conﬁguration of capital equipment, or to settle upon a set of researchanddevelopment projects. Often it makes no sense to consider partial investments in these activities, and so the problem becomes a go–nogo integer program, where the decision variables are taken to be x j = 0 or 1, indicating that the j th investment is rejected or accepted. Assuming that is rejected or accepted....
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This note was uploaded on 04/15/2010 for the course INDUSTRIAL ie513 taught by Professor Zeynephuygur during the Spring '10 term at Bilkent University.
 Spring '10
 zeynephuygur

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