Concurrent scheduling of manufacturing cells considering sequence-dependent family setup times and intercellular transportation times

Halat, Kourosh; Bashirzadeh, Reza

doi:10.1007/s00170-014-6511-2

Cited by 28 publications

(19 citation statements)

References 27 publications

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“…() analyzed the intercell scheduling approach for job shop cells in the context of multiple single‐processing machines and one batch‐processing machine, and accordingly developed a combinational ant colony optimization approach to solve the addressed complex scheduling problem. Halat and Bashirzadeh () proposed an integer linear programming model for JCS to minimize the makespan, while a heuristic approach based on the genetic algorithm is developed in order to efficiently solve real‐size problems in a reasonable amount of time. Zeng and Tang () and Zeng et al.…”

Section: Introductionmentioning

confidence: 99%

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Deliktaş

Torkul

Üstün

2017

Int Trans Operational Res

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In this paper, we propose novel mathematical models involving both single‐ and biobjective functions that deal with a flexible job shop scheduling problem in cellular manufacturing environment by taking into consideration exceptional parts, intercellular moves, intercellular transportation times, sequence‐dependent family setup times, and recirculation. The problem has been known as NP‐hard. The proposed models have been tested and solved using Lingo 11.0 with minimization of makespan for the problems involving about 4 cells, 4 part families, 15 parts, and 12 machines. The most suitable model among the proposed single‐objective models is determined using the test results. Then, another objective function as total tardiness is added to this model. The obtained biobjective model is solved using the scalarization methods, the weighted sum method, ɛ‐constraint method, and conic scalarization method (CSM), in order to convert the mathematical model's objectives into a single‐objective function. By utilizing these scalarization methods, the Pareto effective solutions are generated for a specific test problem. The advantages of the CSM are demonstrated by considering the Pareto effective solutions.

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Section: Introductionmentioning

confidence: 99%

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Deliktaş

Torkul

Üstün

2017

Int Trans Operational Res

View full text Add to dashboard Cite

show abstract

“…Moreover, to determine whether the range of T pkmw and β may have any impact on the performance of the HSA, four different distributions of T pkmw are used, including T pkmw ∼ DU [5,10], T pkmw ∼ DU [5,20], T pkmw ∼ DU [5,30] and T pkmw ∼ DU [5,40], and four different distributions of β are used, including β ∼ DU [1,5], β ∼ DU [1,6], β ∼ DU [1,7] and β ∼ DU [1,8], where DU[a, b] represents a discrete uniform distribution with an integer range from a to b.…”

Section: Computational Experimentsmentioning

confidence: 99%

“…In the first set, P is allowed to vary, given M = 20, W = 5, B u = 2, T pkmw ∼ DU [5,20] and β ∼ DU [1,4]. In the second set, M is allowed to vary, given P = 20, W = 5, B u = 2, T pkmw ∼ DU [5,20] and β ∼ DU [1,4].…”

Section: Computational Experimentsmentioning

confidence: 99%

“…In the second set, M is allowed to vary, given P = 20, W = 5, B u = 2, T pkmw ∼ DU [5,20] and β ∼ DU [1,4]. In the third set, W is allowed to vary, given M = 20, P = 20, B u = 2, T pkmw ∼ DU [5,20] and β ∼ DU [1,4]. In the fourth set, B u is allowed to vary, given M = 30, P = 30, W = 5, T pkmw ∼ DU [5,20] and β ∼ DU [1,4].…”

Section: Computational Experimentsmentioning

confidence: 99%

“…In the sixth set, the distribution of β is allowed to vary, given M = 20, P = 30, W = 5, B u = 8 and T pkmw ∼ DU [5,40]. Given K p ∼ DU [20,40], The experiments have been performed on a Pentium-based Lenovo-compatible personal computer with 2.3 GHz clock-pulse and 4 GB RAM. The HSA and SA have been coded in C++, compiled with the Microsoft Visual C++ 9.0 compiler, and tested under Microsoft Windows 7 operating system.…”

Section: Computational Experimentsmentioning

confidence: 99%

See 2 more Smart Citations

Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing

Liu¹,

Wang²

2016

IJCIS

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This paper designs a non-linear integer mathematical model for the cellular manufacturing system (CMS) with dual-resource constrained setting. The multi-functional machines and the multi-skilled workers need to be grouped and assigned to the cells. Moreover, each operation of the parts has different processing times if processed by different machines or workers. Each part with operation sequence is allowed to move from one machine to another for processing subsequent operation, which might reduce processing time although it will incur additional movement time. In order to solve the simultaneous and intertwined optimization problem, a hybrid simulated annealing (HSA) which embedding priority rule based heuristic algorithm is proposed to minimize the makespan. Computational experiments are conducted to show that the proposed HSA performs well with respect to accuracy and efficiency of solution than the traditional simulated annealing algorithm.

show abstract

Developing a bi-objective schedule for an online cellular manufacturing system in an MTO environment

et al. 2021

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Concurrent scheduling of manufacturing cells considering sequence-dependent family setup times and intercellular transportation times

Cited by 28 publications

References 27 publications

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

A flexible job shop cell scheduling with sequence‐dependent family setup times and intercellular transportation times using conic scalarization method

Cell formation and task scheduling considering multi-functional resource and part movement using hybrid simulated annealing

Developing a bi-objective schedule for an online cellular manufacturing system in an MTO environment

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