A genetic algorithm for job shop scheduling problems with alternate routing

Hussain, Mushahid; Joshi, Sanjay

doi:10.1109/icsmc.1998.724986

Cited by 11 publications

(9 citation statements)

References 13 publications

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“…Two of the studies dealing with quadratic JIT costs were carried out by Hussain and Joshi [22] and Zambrano Rey et al [25], using GA. Other studies were designed for less complex problems such as the flow-shop-like problem [39] or the single machine problem [4,5]. In fact, in a recent study by Demir and Kürşat [10], out of the 23 papers reviewed, none targeted the FJSSP with quadratic earliness and tardiness costs.…”

Section: Relevant Literaturementioning

confidence: 99%

Solving the flexible job-shop just-in-time scheduling problem with quadratic earliness and tardiness costs

Rey

Bekrar

Trentesaux

et al. 2015

Int J Adv Manuf Technol

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The flexible job-shop scheduling problem is known to be an (Non-deterministic Polynomial-time hard) combinatorial problem and has become a challenge in optimization and manufacturing control. Although flexibility is important in order to respond effectively to higher product variety, shorter lead times, and smaller batch sizes, industrialists also require just-in-time scheduling strategies to increase customer satisfaction. The aim of this paper is to find adequate job release times to meet production demands in relation to specific due dates. Since large deviations in job completion times are undesirable, the scheduling objective for just-in-time production is translated into the minimization of the mean-square due date deviation (MSD), quadratically penalizing inventory (earliness) costs and backlogging (tardiness) costs. Given the computational complexity of the problem, two meta-heuristics are proposed: a genetic algorithm (GA) and particle swarm optimization (PSO), as well as two different approaches to handle job release times. In the GA, job release times are treated as dependent variables, whereas the PSO enables the integration of job release times as independent variables within the particle encoding. These meta-heuristic approaches were compared using three benchmarks, two adapted from the literature and one inspired from a real manufacturing cell. The simulation results show that the GA and PSO attained similar performances, each one with advantages and disadvantages for constrained and unconstrained MSD problems.

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Section: Relevant Literaturementioning

confidence: 99%

Solving the flexible job-shop just-in-time scheduling problem with quadratic earliness and tardiness costs

Rey

Bekrar

Trentesaux

et al. 2015

Int J Adv Manuf Technol

View full text Add to dashboard Cite

show abstract

“…One of the few studies dealing with quadratic JIT costs was carried out by Hussain and Joshi (Hussain and Joshi, 1998). They addressed the job-shop problem with alternative routing and minimization of the sum of the mean square due date deviation.…”

Section: F-ii a Genetic Algorithm For Solving The Flexible Job-shop mentioning

confidence: 99%

“…The main advantage of this encoding technique is that it constraints the chromosome size to its minimum length. For the machine-sequence chromosome, an indirect encoding technique (inspired from Hussain and Joshi, (1998)) was used to explore the machinesequence alternatives available on the FMS.…”

Section: B Ga Encodingmentioning

confidence: 99%

Reducing myopic behavior in FMS control: A semi-heterarchical simulation–optimization approach

Rey

Bonte

Prabhu

et al. 2014

Simulation Modelling Practice and Theory

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“…Due to the complexity of decoding the representation, their algorithm incurs significant computational cost. Hussain and Joshi (1998) proposed a two pass GA to solve job shop problem with alternative routing with the objective of minimizing the sum of squared weighted due date deviation for every job. The first pass picks the alternatives using a genetic algorithm and the second pass provides the order and start time of jobs on the selected alternatives by solving a non-linear program.…”

Section: Review Of Solution Methodologiesmentioning

confidence: 99%