Equivalent instances of the simple plant location problem

In this paper we deal with a pseudo-Boolean representation of the simple plant location problem. We define instances of this problem that are equivalent, in the sense that each feasible solution has the same goal function value in all such instances. We further define a collection of polytopes whose union describes the set of instances equivalent to a given instance. We use the concept of equivalence to develop a method by which we can extend the set of instances that we can solve using our knowledge of polynomially solvable special cases. We also present a new preprocessing rule that allows us to determine sites in which facilities will not be located in an optimal solution and thereby reduce the size of a problem instance.

Equivalent instances of the simple plant location problem

AbstractIn this paper we deal with a pseudo-Boolean representation of the simple plant location problem. We define instances of this problem that are equivalent, in the sense that each feasible solution has the same goal function value in all such instances. We further define a collection of polytopes whose union describes the set of instances equivalent to a given instance. We use the concept of equivalence to develop a method by which we can extend the set of instances that we can solve using our knowledge of polynomially solvable special cases

Branch and peg algorithms for the simple plant location problem

The simple plant location problem is a well-studied problem in combinatorial optimization. It is one of deciding where to locate a set of plants so that a set of clients can be supplied by them at the minimum cost. This problem of ten appears as a subproblem in other combinatorial problems. Several branch and bound techniques have been developed to solve these problems. In this paper we present a few techniques that enhance the performance of branch and bound algorithms. The new algorithms thus obtained are called branch and peg algorithms, where pegging refers to assigning values to variables outside the branching process. We present exhaustive computational experiments which show that the new algorithms generate less than 60% of the number of subproblems generated by branch and bound algorithms, and in certain cases require less than 10% of the execution times required by branch and bound algorithms.

Solving the simple plant location problem using a data correcting approach

The Data Correcting Algorithm is a branch and bound algorithm in which thedata of a given problem instance is ‘corrected’ at each branching in such a waythat the new instance will be as close as possible to a polynomially solvableinstance and the result satisfies an acceptable accuracy (the difference betweenoptimal and current solution). In this paper the data correcting algorithm isapplied to determining exact and approximate optimal solutions to the simpleplant location problem. Implementations of the algorithm are based on apseudo-Boolean representation of the goal function of the SPLP and a newreduction rule. We study the efficiency of the data correcting approach usingtwo different bounds, the combinatorial bound and the Erlenkotter bound. Wepresent computational results on several benchmark instances of the simpleplant location problem, which confirm the efficiency of the data-correcting approach.

Data Correcting Algorithms in Combinatorial Optimization

This paper describes data correcting algorithms. It provides the theory behind the algorithms and presents the implementation details and computational experience with these algorithms on the asymmetric traveling salesperson problem, the problem of maximizing submodular functions, and the simple plant location problem.