Pareto archived simulated annealing for job shop scheduling with multiple objectives

Suresh, R.; Sundaram, K. Mohana

doi:10.1007/s00170-004-2492-x

Cited by 85 publications

(28 citation statements)

References 26 publications

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“…Local search algorithms such as Genetic Algorithms (GA) [22][23][24][25][26][27][28][29][30][31][32][33][34][35], Tabu Search (TS) [17,19,25,31,36,37], ant optimization and genetic local search (GLS) [39,41,42,43], scatter search and path relinking (SS and PR) and Simulated Annealing (SA). The majority of the GA methods gave a poor result due to the difficulty in crossover operation and schedule representation.…”

Section: Literature Reviewmentioning

confidence: 99%

Integrating Genetic Algorithm, Tabu Search and Simulated Annealing For Job Shop Scheduling Proble

Thamilselvan¹,

Balasubramanie²

2012

IJCA

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Job Shop Scheduling Problem (JSSP) is an optimization problem in which ideal jobs are assigned to resources at particular times. In recent years many attempts have been made at the solution of JSSP using a various range of tools and techniques such as Branch and Bound and Heuristics algorithms. This paper proposed a new algorithm based on Genetic Algorithm (GA), Tabu Search (TS) and Simulated Annealing (SA) algorithms to solve JSSP. The proposed algorithm is mainly based on the genetic algorithm. The reproduction phase of the genetic algorithm uses the tabu search to generate new population. Simulated annealing algorithm is used to speed up the genetic algorithm to get the solution by applying the simulated annealing test for all the population members. The proposed algorithm used many small but important features such as chromosome representation, effective genetic operators, and restricted neighbourhood strategies. The above features are used in the hybrid algorithm to solve several bench mark problems. Keywords

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Section: Literature Reviewmentioning

confidence: 99%

Integrating Genetic Algorithm, Tabu Search and Simulated Annealing For Job Shop Scheduling Proble

Thamilselvan¹,

Balasubramanie²

2012

IJCA

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“…In the remaining part of this section, a heuristic algorithm based on the simulating annealing (SA) algorithm is proposed. SA is known to be one of the most successful algorithms amongst the other applied meta-heuristic ones and its acceptable performance is demonstrated by experience in the conducted researches relevant to scheduling problems, for example (Krishna, 1995), (Steinhofel, 1999), (Suresh, & Mohanasundaram, 2006), (Ponnambalam, 1999), (Bozejko, 2009 In this part, s is a schedule, s best is the best available solution, f(s) is the objective function value for schedule s, k is the counter, K is the maximum iteration number which specifies the termination criteria, T k is the temperature in k-th iteration, α is the cooling factor which belongs to the interval (0, 1), and…”

Section: Proposed Heuristic Algorithmmentioning

confidence: 99%

A new mathematical model for the job shop scheduling problem with uncertain processing times

Shafia¹,

Aghaee²,

Jamili³

2011

IJIEC

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“…SA has been applied to a vast number of single objective optimization problems over the last two decades. It has also been applied as a tool for multiobjective optimization problems in some applications, for instance by Suresh and Mohanasundaram [27] and Loukil et al [15]. Readers for review of approaches to multiobjective optimization by means of SA may refer to Suman and Kumar [26], Nam and Park [19], and Czyzak and Jaszkiewicz [5].…”

Section: The Mosa Approachmentioning

confidence: 99%

Bicriteria scheduling of a two-machine flowshop with sequence-dependent setup times

Mansouri

Hendizadeh

Salmasi

2008

Int J Adv Manuf Technol

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A two-machine flowshop scheduling problem is addressed to minimize setups and makespan where each job is characterized by a pair of attributes that entail setups on each machine. The setup times are sequence dependent on both machines. It is shown that these objectives are conflicting so Pareto optimization approach is considered. The scheduling problems considering either of these objectives are N P-hard, so exact optimization techniques are impractical for large size problems. We propose two multi-objective metaheurisctics based on genetic algorithms (MOGA) and simulated annealing (MOSA) to find approximations of Paretooptimal sets. The performances of these approaches are compared with lower bounds for small problems. In larger problems, performance of the proposed algorithms are compared with each other. Experimentations revealed that both algorithms perform very similar on small problems. Moreover, it was observed that MOGA outperforms MOSA in terms of the quality of solutions on larger problems.

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Pareto archived simulated annealing for job shop scheduling with multiple objectives

Cited by 85 publications

References 26 publications

Integrating Genetic Algorithm, Tabu Search and Simulated Annealing For Job Shop Scheduling Proble

Integrating Genetic Algorithm, Tabu Search and Simulated Annealing For Job Shop Scheduling Proble

A new mathematical model for the job shop scheduling problem with uncertain processing times

Bicriteria scheduling of a two-machine flowshop with sequence-dependent setup times

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