Abstract not found

Search algorithms for solving CSP (Constraint Satisfaction Problems) usually fall into one of two main families: local search algorithms and systematic algorithms. Both families have their advantages. Designing hybrid approaches seems promising since those advantages may be combined into a single approach. In this paper, we present a new hybrid technique. It performs a local search over partial assignments instead of complete assignments, and uses filtering techniques and conflict-based techniques to efficiently guide the search. This new technique benefits from both classical approaches: aprioripruning of the search space from filtering-based search and possible repair of early mistakes from local search. We focus on a specific version of this technique: tabu decision-repair.Experiments done on open-shop scheduling problems show that our approach competes well with the best highly specialized algorithms. 2002 Elsevier Science B.V. All rights reserved.

Over the last decade many metaheuristics have been applied to the flowshop scheduling problem, ranging from Simulated Annealing or Tabu Search to complex hybrid techniques. Some of these methods provide excellent effectiveness and efficiency at the expense of being utterly complicated. In fact, several published methods require substantial implemen-tation efforts, exploit problem specific speed-up techniques that cannot be applied to slight variations of the original problem, and often re-implementations of these methods by other researchers produce results that are quite different from the original ones. In this work we present a new iterated greedy algorithm that applies two phases iteratively, named destruc-tion, were some jobs are eliminated from the incumbent solution, and construction, where the eliminated jobs are reinserted into the sequence using the well known NEH construction £Corresponding author 1 heuristic. Optionally, a local search can be applied after the construction phase. Our iterated greedy algorithm is both very simple to implement and, as shown by experimental results, highly effective when compared to state-of-the-art methods.

In this article we present an ant based approach to Flow Shop Scheduling problems. Ant Colony Optimization is a new algorithmic approach, inspired by the behavior of real ants, that can be used for the solution of combinatorial optimization problems. (Artificial) ants are used to construct solutions for Flow Shop Problems that subsequently are improved by a local search procedure. We compare the results obtained with our procedure to some basic heuristics for Flow Shop Problems, showing that our approach is very promising for the FSP.

this paper, a simple example will be used to illustrate the fact that the landscape structure changes with the operator; indeed, it often depends even on the way the operators are applied. Recent attention has focused on trying to understand better the nature of these `landscapes'. Recent work by Boese et al. [2] has shown that instances of the TSP are often characterised by a `big valley' structure in the case of a 2-opt exchange operator, and a particular distance metric. In this paper their work is developed by investigating the question of how landscapes change under different search operators in the case of the n=m=P=Cmax

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...

In the majority of genetic algorithm implementations, the operator settings are fixed throughout a given run. However, it has been argued that these settings should vary over the course of a genetic algorithm run --- so to account for changes in the ability of the operators to produce children of increased fitness. This paper describes an investigation into this question, in the light of the `No Free Lunch' theorem which suggests that successful adaptation is possible only if certain conditions are satisfied. The effect upon genetic algorithm performance of two adaptation methods upon both well-studied theoretical problems, and a hard problem from Operations Research, the flowshop sequencing problem, are therefore examined. The results obtained indicate that the applicability of operator adaptation is dependent upon three basic assumptions being satisfied by the problem being tackled. 1 Introduction It has long been acknowledged that the choice of operator settings has a significant i...

A general model for job shop scheduling is described which applies to static, dynamic and non-deterministic production environments. Next, a Genetic Algorithm is presented which solves the job shop scheduling problem. This algorithm is tested in a dynamic environment under different workload situations. Thereby, a highly efficient decoding procedure is proposed which strongly improves the quality of schedules. Finally, this technique is tested for scheduling and rescheduling in a non-deterministic environment. It is shown by experiment that conventional methods of production control are clearly outperformed at reasonable runtime costs. Keywords Genetic algorithm, permutation representation, tunable decoding, job shop scheduling problem, dynamic scheduling. 1 Introduction With the spread of automated manufacturing systems the optimization problem of assigning operations to a set of machines receives increasing attention (Parunak, 1992). From the viewpoint of combinatorial op...

. Many scheduling systems assume a static environment within which a schedule will be executed. The real world is not so stable: machines break down, operations take longer to execute than expected, and orders may be added or canceled. One approach to dealing with such disruptions is to generate robust schedules: schedules that are able to absorb some level of unexpected events without rescheduling. In this paper we investigate three techniques for generating robust schedules based on the insertion of temporal slack. Simulation-based results indicate that the two novel techniques out-perform the existing temporal protection technique both in terms of producing schedules with low simulated tardiness and in producing schedules that better predict the level of simulated tardiness. Keywords: Robustness, Uncertainty, Scheduling, Heuristics 1

In a previous paper (Reeves, 1995), a simple genetic algorithm (GA) was developed for finding (approximately) the minimum makespan of the n-job, m-machine permutation flowshop sequencing problem (PFSP). The performance of the algorithm was comparable to that of a naive neighbourhood search technique and a proven Simulated Annealing algorithm. However, recent results (Nowicki & Smutnicki, 1996) have demonstrated the superiority of a tabu search method in solving the PFSP. In this paper, we re-consider the implementation of a GA for this problem, and show that by taking into account the features of the landscape generated by the operators used, we are able to improve its performance significantly. 1 Introduction Finding optimal solutions to large combinatorial problems (COPs) is not in general a realistic endeavour, as has been recognized ever since the implications of the concept of computational complexity (Garey & Johnson, 1979) have been realized. One effect of this recognition has ...