Enhancing CLP branch and bound techniques for scheduling problems

✍ Scribed by F. Bosi; M. Milano

• Publisher: John Wiley and Sons
• Year: 2001
• Tongue: English
• Weight: 280 KB
• Volume: 31
• Category: Article
• ISSN: 0038-0644
• DOI: 10.1002/1097-024x(200101)31:1<17::aid-spe355>3.0.co;2-l

✦ Synopsis

In this paper, we propose a constraint logic programming (CLP) approach to the solution of a job shop scheduling problem in the field of production planning in orthopaedic hospital departments. A pure CLP on finite domain (CLP(FD)) approach to the problem has been developed, leading to disappointing results. In fact, although CLP(FD) has been recognized as a suitable tool for solving combinatorial problems, it presents some drawbacks for optimization problems. The main reason concerns the fact that CLP(FD) solvers do not effectively handle the objective function and cost-based reasoning through the simple branch and bound scheme they embed. Therefore, we have proposed an improvement of the standard CLP branch and bound algorithm by exploiting some well-known operations research results. The branch and bound we integrate in a CLP environment is based on the optimal solution of a relaxation of the original problem. In particular, the relaxation used for the job shop scheduling problem considered is the well-known shifted bottleneck procedure considering single machine problems. The idea is to decompose the original problem into subproblems and solve each of them independently. Clearly, the solutions of each subproblem may violate constraints among different subproblems which are not taken into account. However, these solutions can be exploited in order to improve the pruning of the search space and to guide the search by defining cost-based heuristics. The resulting algorithm achieves a significant improvement with respect to the pure CLP(FD) approach that enables the solution of problems which are one order of magnitude greater than those solved by a pure CLP(FD) algorithm. In addition, the resulting code is less dependent on the input data configuration.