Non-identical parallel-machine scheduling using genetic algorithm and fuzzy logic approach

Raja, K.; Arumugam, Chamundeswari; Selladurai, V.

doi:10.1504/ijsom.2008.015941

Cited by 16 publications

(7 citation statements)

References 32 publications

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“…Constraint (2) ensures that only one job can be the first job on each machine. Constraint (3) shows that each job can be the first job or a job after a scheduled job on each machine. Constraint (4) ensures that completion time of first job on each machine must be equal or greater than its processing time.…”

Section: Modelmentioning

confidence: 99%

“…A population of solutions is then created and genetic operators such as mutation and crossover are applied to make new solutions. [3], [12] and [13] used Genetic Algorithm to solve parallel machine scheduling problem.…”

Section: The Proposed Hybrid Algorithmmentioning

confidence: 99%

“…Four heuristic algorithms were compared in [2] for solving an unrelated parallel machine scheduling problem with sequencedependent setup times to minimize total weighted tardiness. A GA-Fuzzy approach was proposed in [3] to deal with a non-identical parallel machine scheduling problem. In their problem, setup times were assumed to be sequence dependent and the goal was to minimize earliness and tardiness penalties.…”

Section: Introductionmentioning

confidence: 99%

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A hybrid metaheuristic method to minimize cost on non-identical parallel machines

Moshref‐Javadi

Tabrizi

2010

2010 2nd International Conference on Software Technology and Engineering

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In this paper the problem of scheduling of n jobs on m non-identical parallel machines is considered. All jobs can be processed on all machines and the processing time and cost of each job depend on the machine on which the job is performed. Jobs cannot be split or divided and all jobs are available at time zero. The goal is to minimize cost which is composed of two parts: earliness-tardiness cost and production cost. The problem is formulated as a MILP model. A hybrid method based on simulated annealing and genetic algorithm is proposed to solve this problem. After parameter tuning of the algorithm, the proposed algorithm was tested on different combinations of jobs and machines and the results were compared with those obtained by genetic algorithm.

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Section: Modelmentioning

confidence: 99%

Section: The Proposed Hybrid Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A hybrid metaheuristic method to minimize cost on non-identical parallel machines

Moshref‐Javadi

Tabrizi

2010

2010 2nd International Conference on Software Technology and Engineering

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“…Raja et al [25] utilized fuzzy logic to combine two goals in reducing E&T penalties. Gharehgozli et al [26] formulated a goal programming model for PMS problem with release dates and SD setup times considering fuzzy goals and fuzzy processing time.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Scheduling Problem on Unrelated Parallel Machine in JIT Production System

Rezaeian¹,

Mohammad-Hosseini²,

Zabihzadeh³

et al. 2020

Artificial Intelligence Evolution

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This paper deals with unrelated parallel machines scheduling problem with sequence dependent setup times under fully fuzzy environment to minimize total weighted fuzzy earliness and tardiness penalties, which belongs to NP-hard class. Due to inherent uncertainty in Processing times, setup times and due dates of jobs, they are considered here with triangular and trapezoidal fuzzy numbers in order to take into account the unpredictability of parameters in practical settings. Although this study is not the first one to study on fuzzy parallel machines scheduling problem, it advances this area of research in three fields: (1) it selects a fuzzy environment to cover the whole area of the considered problem not just part of it, and also, it chooses an appropriate fuzzy method based on an in-depth investigation of the effect of spread of fuzziness on the variables; (2) It introduces a mathematical programming model for the addressed problem as an exact method; and (3) due to NP-hardness of the problem, it develops an existing algorithm in the literature for the considered problem through extensive simulated experiments and statistical tests on the same benchmark problem test by proposing a genetic algorithm (GA) and a modified simulated annealing (SA) methods to solve this hard combinatorial optimization problem. The result shows the superiority of our modified SA.

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“…The scheduling and planning of production order have an important role in the manufacturing system. The diversity of products, increased number of orders, the increased number and size of workshops and expansion of factories have made the issue of scheduling production orders more complicated, hence the traditional methods of optimization are unable to solve them [6] [7].…”

mentioning

confidence: 99%

Agent-Based Modeling: Methods and Techniques for Scheduling Industrial Machine Operation

Hyacinth¹,

Patrick²,

Chiagunye³

2017

IJTSRD

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Owing to its impact on the industrial economy, job shop scheduler and controller are vital algorithms for modern manufacturing processes. Hence a production scheduling and control that performs reactive (not deterministic) scheduling and can make decision which job to process next based solely on its partial (not central) view of the plant becomes necessary. This requirement puts the problem in the class of agent based model (ABM). Hence this work adopt an alternative view on job-shop scheduling probl where each resource is equipped with adaptive agent that, independent of other agents makes job dispatching decision based on its local view of the plant. A combination of Markov Chain instruments and agent oriented analysis is used in the analysis of the proposed agent based model (ABM) for the job shop scheduling problem. The Markov Chain approach allows a rigorous analysis of the ABM. It provides a general framework of aggregation in agent based and related computational models by use of Markov Chain aggregation and lumpability theory in order to link the micro and the macro level of observation. Simulated annealing technique is used for carrying out the optimization modeling for the ABM.

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Non-identical parallel-machine scheduling using genetic algorithm and fuzzy logic approach

Cited by 16 publications

References 32 publications

A hybrid metaheuristic method to minimize cost on non-identical parallel machines

A hybrid metaheuristic method to minimize cost on non-identical parallel machines

Fuzzy Scheduling Problem on Unrelated Parallel Machine in JIT Production System

Agent-Based Modeling: Methods and Techniques for Scheduling Industrial Machine Operation

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