In this paper, we propose 260 scheduling problems whose size is greater than that of the rare examples published. Such sizes correspond to real dimensions of industrial problems.

In this paper we present a new technique that can be used to improve performance of job scheduling with a constraint programming language. We show how, by focusing on some special sets of tasks, one can bring CLP in the same range of efficiency as traditional OR algorithms on a classical benchmark (MT10 [MT63]), thus making CLP both a flexible and an efficient technique for such combinatorial problems. We then present our programming methodology which we have successfully used on many problems, and draw conclusions on what features constraint programming languages should offer to allow its use. 1. Introduction Real-life scheduling problems are often the composition of various well-identified hard problems. In the previous years, we have worked on applications such as task-technician assignments [CK92] or staff timetable scheduling [CGL93] and developed a methodology for solving such problems with an extensible constraint logic programming language. In both cases we have applied the s...

. From a computational point of view, the job-shop scheduling problem is one of the most notoriously intractable NP-hard optimization problems. In spite of a great deal of substantive research, there are instances of even quite modest size for which it is beyond our current understanding to solve to optimality. We propose several new lower bounding procedures for this problem, and show how to incorporate them into a branch-and-bound procedure. Unlike almost all of the work done on this problem in the past thirty years, our enumerative procedure is not based on the disjunctive graph formulation, but is rather a timeoriented branching scheme. We show that our approach can solve most of the standard benchmark instances, and obtains the best known lower bounds on each. 1 Introduction In the job-shop scheduling problem we are given a set of n jobs, J , a set of m machines, M, and a set of operations, O. Each job consists of a chain of operations, let O j be the chain of operati...

:- Due to the stubborn nature of the deterministic job-shop scheduling problem many solutions proposed are of hybrid construction cutting across the traditional disciplines. The problem has been investigated from a variety of perspectives resulting in several analytical techniques combining generic as well as problem specific strategies. We seek to assess a subclass of this problem in which the objective is minimising makespan, by providing an overview of the history, the techniques used and the researchers involved. The sense and direction of their work is evaluated by assessing the reported results of their techniques on the available benchmark problems. From these results the current situation and pointers for future work are provided. KEYWORDS:- Scheduling Theory; Job-Shop; Review; Computational Study; 1. INTRODUCTION Current market trends such as consumer demand for variety, shorter product life cycles and competitive pressure to reduce costs have resulted in the need for zero i...

Introduction The resource constrained project scheduling problem (RCPSP) can be given as follows. A single project consists of a set J = f0; 1; : : : ; n; n+1g of activities which have to be processed. Fictitious activities 0 and n + 1 correspond to the "project start" and to the "project end", respectively. The activities are interrelated by two kinds of constraints. First, precedence constraints force activity j not to be started before all its immediate predecessor activities comprised in the set P j have been finished. Second, performing the activities requires resources with limited capacities. We have K resource types, given by the set K = f1; : : : ; Kg. While being processed, activity j requires r j;k units of resource type<F

We survey solution methods for the job shop scheduling problem with an emphasis on local search. Both deterministic and randomized local search methods as well as the proposed neighborhoods are discussed. We compare the computational performance of the various methods in terms of their effectiveness and efficiency on a standard set of problem instances.

Many practical problems in Artificial Intelligence have search trees that are too large to search exhaustively in the amount of time allowed. Systematic techniques such as chronological backtracking can be applied to these problems, but the order in which they examine nodes makes them unlikely to find a solution in the explored fraction of the space. Nonsystematic techniques have been proposed to alleviate the problem by searching nodes in a random order. A technique known as iterative sampling follows random paths from the root of the tree to the fringe, stopping if a path ends at a goal node. Although the nonsystematic techniques do not suffer from the problem of exploring nodes in a bad order, they do reconsider nodes they have already ruled out, a problem that is serious when the density of solutions in the tree is low. Unfortunately, for many practical problems the order of examing nodes matters and the density of solutions is low. Consequently, neither chronological backtracking...

Weintroduce the multiple capacitated job shop scheduling problem as a generalization of the job shop scheduling problem. In this problem machines may process several operations simultaneously.We presentan algorithm based on constraint satisfaction techniques to handle the problem e#ectively. The most importantnovel feature of our algorithm is the consistency checking. An empirical performance analysis is performed using a well-known set of instances of the job shop scheduling problem and a newly constructed set of instances of the multiple capacitated job shop scheduling problem. We show that our algorithm performs well for both sets of instances.

Abstract. In this paper, we present a major improvement in the search procedures in constraint programming. First, we integrate various search procedures from AI and OR. Second, we parallelize the search on shared-memory computers. Third, we add an object-oriented extensible control language to implement complex complete and incomplete search procedures. The result is a powerful set of tools which offers both brute force search using simple search procedures and parallelism, and finely tuned search procedures using that expressive control language. With this, we were able both to solve difficult and open problems using complete search procedures, and to quickly produce good results using incomplete search procedures. 1

Recent scheduling work has challenged the need for sophisticated heuristics such as those based on texture measurements. This paper examines these claims in the light of advances in scheduling technology. We compare a number of current heuristic commitment techniques against a texture-based heuristic. Our results demonstrate that texture-based heuristics can outperform these widely-used heuristic commitment techniques. Introduction Our research goal is to be able to model and quickly solve scheduling problems as they exist in the real world. We are less interested in optimal solutions than in fast approximate solutions: a quickly found solution that takes into account all the constraints in the real problem is of significantly more use than an optimal solution that either takes too long to find or does not accurately represent the problem. We are applying and extending constraint-directed scheduling techniques toward this end. Our search philosophy is to spend significant but ...