Two simulated annealing-based heuristics for the job shop scheduling problem

Steinhöfel, Kathleen; Albrecht, Andreas; Wong, Carmen

doi:10.1016/s0377-2217(98)00326-9

Cited by 62 publications

(31 citation statements)

References 35 publications

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“…They theoretically criticise the calculation of longest path of schedules by clarifying the dependency on the number of jobs and machines in an algorithmic parallelism point of view. This is an advanced version of their previous work, which discusses two SA heuristics for job shop scheduling problems (Steinhofel, Albrecht, and Wong, 1999). They presented computational results for very few benchmarks that are worse than ours with respect to the time.…”

Section: Related Work and Conclusionmentioning

confidence: 92%

“…Many works on solving combinatorial optimisation problems by heuristics focused on the effects of initial solutions. When an initial solution has been chosen, there arise limited possible paths to proceed under the certain circumstances since the optimisation process behaves as a Markov chain and each chain offers limited paths to the destination, as widely shown in the literature (Van Laarhoven, Aarts, and Lenstra, 1992;Kolonko, 1999;Steinhofel, Albrecht, and Wong, 1999). Looking at the initial conditions, one can estimate the probability of getting an optimal or useful near optimal solution with a particular initial solution.…”

Section: > ρmentioning

confidence: 99%

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A Distributed Evolutionary Simulated Annealing Algorithm for Combinatorial Optimisation Problems

Aydın

Fogarty

2004

Journal of Heuristics

101

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In this paper, the Evolutionary Simulated Annealing (ESA) algorithm, its distributed implementation (dESA) and its application to two combinatorial problems are presented. ESA consists of a population, a simulated annealing operator, instead of the more usual reproduction operators used in evolutionary algorithms, and a selection operator. The implementation is based on a multi island (agent) system running on the Distributed Resource Machine (DRM), which is a novel, scalable, distributed virtual machine based on Java technology. As WAN/LAN systems are the most common multi-machine systems, dESA implementation is based on them rather than any other parallel machine. The problems tackled are well-known combinatorial optimisation problems, namely, the classical jobshop scheduling problem and the uncapacitated facility location problem. They are difficult benchmarks, widely used to measure the efficiency of metaheuristics with respect to both the quality of the solutions and the central processing unit (CPU) time spent. Both applications show that dESA solves problems finding either the optimum or a very near optimum solution within a reasonable time outperforming the recent reported approaches for each one allowing the faster solution of existing problems and the solution of larger problems.

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Section: Related Work and Conclusionmentioning

confidence: 92%

Section: > ρmentioning

confidence: 99%

A Distributed Evolutionary Simulated Annealing Algorithm for Combinatorial Optimisation Problems

Aydın

Fogarty

2004

Journal of Heuristics

101

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“…A number of heuristic search methods have also been proposed, for example, Adams et al (1988) and Applegate and Cook (1991). A variety of metaheuristic approaches have also been applied to the JSSP, such as Neural Networks (Sabuncuoglu and Gurgun, 1996), Beam Search (Sabuncuoglu and Bayiz, 1999), Simulated Annealing (Steinhofel et al 1999), Tabu Search (Barnes and Chambers, 1995;Nowicki and Smutnicki, 1996;Pezzella and Merelli, 2000;Zhang et al 2008), Genetic Algorithms (Falkenauer and Bouffoix, 1991;Storer et al, 1995;Aarts et al, 1994;Bean, 1994;Croce et al, 1995), Evolutionary Algorithms (Mesghouni and Hammadi, 2004), Variable Neighborhood Search (Sevkli and Aydin, 2007), Global Equilibrium Search technique (Pardalos and Shylo, 2006). Jain and Meeran (1999) provide a good survey of techniques used for the JSSP.…”

Section: Literature Reviewmentioning

confidence: 99%

A NeuroGenetic Approach for Multiprocessor Scheduling

Agarwal¹

2007

Multiprocessor Scheduling, Theory and Applications

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This chapter presents a NeuroGenetic approach for solving a family of multiprocessor scheduling problems. We address primarily the Job-Shop scheduling problem, one of the hardest of the various scheduling problems. We propose a new approach, the NeuroGenetic approach, which is a hybrid metaheuristic that combines augmented-neural-networks (AugNN) and genetic algorithms-based search methods. The AugNN approach is a nondeterministic iterative local-search method which combines the benefits of a heuristic search and iterative neural-network search. Genetic algorithms based search is particularly good at global search. An interleaved approach between AugNN and GA combines the advantages of local search and global search, thus providing improved solutions compared to AugNN or GA search alone. We discuss the encoding and decoding schemes for switching between GA and AugNN approaches to allow interleaving. The purpose of this study is to empirically test the extent of improvement obtained by using the interleaved hybrid approach instead of applied using a single approach on the job-shop scheduling problem. We also describe the AugNN formulation and a Genetic Algorithm approach for the JobShop problem. We present the results of AugNN, GA and the NeuroGentic approach on some benchmark job-shop scheduling problems. IntroductionMultiprocessor scheduling problems occur whenever manufacturing or computing operations are to be scheduled on multiple machines, processors or resources. A variety of such scheduling problems are discussed in the literature. The most general scheduling problem is the resource-constrained project-scheduling problem; this problem has received a lot of attention in the literature Herroelen et al. (1998), Kolisch (1996). The open-shop, flow-shop, job-shop and task scheduling problems can be considered special cases of the resource-constrained project-scheduling problem. While smaller instances of the various types of scheduling problems can be solved to optimality in reasonable computing time using exact solution methods such as branch and bound, most real-world problems are unsolvable in reasonable time using exact methods due to the combinatorial explosion of the feasible solution space. For this reason, heuristics and metaheuristics are frequently employed to obtain satisfactory solutions to these problems in reasonable time. In this paper, we propose a new hybrid metaheuristic approach called the NeuroGenetic approach for solving one family of multiprocessor scheduling problems -the job-shop scheduling problem. The NeuroGenetic approach is a hybrid of the Augmented Neural Networks (AugNN) approach and the Genetic Algorithms (GA) approach. The AugNN approach provides a mechanism for local search, while the GA approach provides a mechanism for global search. An interleaving of the two approaches helps guide the search to better solutions. In this chapter, we focus on the job-shop scheduling problem (JSSP). In JSSP, there are n jobs, each having m operations and each operation requires a diffe...

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“…The paper is a continuation of 16,17,18], where a new neighbourhood function based on the number of longest paths was introduced.…”

Section: Introductionmentioning

confidence: 99%

“…In particular, we h a ve analysed the numberof longest paths in the disjunctive g r a p h representation by a large numberof computational experiments. These experiments were performed by using a cooling schedule with an expected run-time of O(n 4 + " = p m), where n = l m and " represents O(ln ln n= ln n) 18]. …”

Section: Introductionmentioning

confidence: 99%