:- 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...

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...

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

A great deal of research has been focused on solving the job-shop problem (P J ), over the last forty years, resulting in a wide variety of approaches. Recently, much effort has been concentrated on hybrid methods to solve P J as a single technique cannot solve this stubborn problem. As a result much effort has recently been concentrated on techniques that combine myopic problem specific methods and a meta-strategy which guides the search out of local optima. These approaches currently provide the best results. Such hybrid techniques are known as iterated local search algorithms or meta-heuristics. In this paper we seek to assess the work done in the job-shop domain by providing a review of many of the techniques used. The impact of the major contributions is indicated by applying these techniques to a set of standard benchmark problems. It is established that methods such as Tabu Search, Genetic Algorithms, Simulated Annealing should be considered complementary rather than competitive...

We present constraint-based optimization algorithms and a constraintbased approximation algorithm for the job-shop scheduling problem. An empirical performance analysis shows that both the optimization algorithms and the approximation algorithm perform well. Especially the approximation algorithm is among the best algorithms known to date. We, furthermore, show that we can improve the performance of the optimization algorithms by combining them with the approximation algorithm.

Ilog, the company in which this thesis has been done, develops and markets Ilog Schedule, a C++ library aimed at simplifying the representation and the resolution of scheduling problems. The Schedule library is itself based on Ilog Solver, a generic software tool for object-oriented constraint programming. Two main topics have been studied in the work presented here: ffl A theoretical study of constraint propagation algorithms for preemptive and non-preemptive scheduling: 1. Ilog Schedule is currently limited to activities that execute without interruption and require the same resources from the beginning to the end of their execution. In this context, interruptible activities can be represented as sets of non-interruptible sub-activities. We show how to build a direct representation of interruptible activities and we describe several resource constraint propagation for preemptive scheduling. 2. A theoretical comparison of two constraint propagation algorithms: The edge-finder algo...

Techniques that combine myopic problem specific methods and a meta-strategy, which guides the search out of local optimum, are currently providing the best results. Such hybrid methods are known as iterated local search algorithms or meta-heuristics. Despite the good results achieved by these methods the inability of many current meta-heuristics to provide a wide exploration of the search space in order to overcome strong local minima is highlighted here. Consequently in this work the evolutionary techniques of scatter search (SS) and its generalised form, path relinking (PR) are applied as they can provide the appropriate mixture of intensification (concentrate the search in promising areas) and diversification (search unvisited regions) and they can be integrated at various levels with intelligent search methods such as tabu search. The SS and PR strategies are embedded within a core and shell framework, in order to provide a wide exploration of the solution domain. Initiated from a ...

In the classical job shop scheduling problem (JSSP), n jobs are processed to completion on m unrelated machines. Each job requires processing on each machine exactly once. For each job, technology constraints specify a complete, distinct routing which is fixed and known in advance. Processing times are sequence-independent, fixed, and known in advance. Each machine is continuously available from time zero, and operations are processed without preemption. The objective is to minimize the maximum completion time (makespan). This report describes a dynamic, adaptive tabu search (TS) strategy for JSSP incorporating elements of an earlier investigation by the authors. Improved computational results for a set of reference problems are presented. MS prepared April, 1994 Revised June, 1996New Tabu Search Results for the Job Shop Scheduling Problem

We do a computational study of different local optimization methods to solve the job-shop scheduling problem. In this problem we are given a set of jobs to be scheduled on a set of machines. The objective is to schedule the jobs on the machines so that we minimize the time by which all jobs are completed. This problem has been widely studied and various techniques have been used to solve them. We consider several approximation methods to solve the job-shop scheduling problem. We focus on local optimization methods, reviewing the application of the techniques known as local improvement and simulated annealing to this problem. We propose a variant of the above local optimization methods, known as large-step optimization, to solve the job-shop scheduling problem. We present computational results obtained from the application of all these methods to several instances of the problem. From the computational results we can conclude that the local improvement method is clearly inferior, even w...