A multi-objective flow shop scheduling with resource-dependent processing times: trade-off between makespan and cost of resources

Mokhtari, Hadi; Abadi, Isa Nakhai Kamal; Cheraghalikhani, Ali

doi:10.1080/00207543.2010.523724

Cited by 39 publications

(15 citation statements)

References 55 publications

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“…The first algorithm is a GA combined with VNS (GA-VNS) proposed by Behnamian and Fatemi Ghomi (2011). The second algorithm is a discrete DE algorithm hybrid with VNS (DDE-VNS) presented by Mokhtari, Abadi, and Cheraghalikhani (2011). To calculate the objective value f of L P omitting the constraints of the upstream stages, the arrival time of each job is set in the following way.…”

Section: Comparison With Other Methods In the Literaturementioning

confidence: 99%

“…Recently, a greater number of researchers have focused their attention on this field. Mokhtari, Abadi, and Cheraghalikhani (2011) presented hybrid discrete differential evolution (HDDE) and a variable neighbourhood search (VNS) algorithms to solve a multi-objective flow shop scheduling problem with resource-dependent processing times. Mokhtari, Abadi, and Zegordi (2011) devised a two-phase genetic algorithm (GA) for a no-wait job shop scheduling problem with CPT.…”

Section: Scheduling Problems Involving Cptmentioning

confidence: 99%

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A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

Jiang

Liu

Hao

et al. 2015

Computers & Industrial Engineering

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Section: Comparison With Other Methods In the Literaturementioning

confidence: 99%

Section: Scheduling Problems Involving Cptmentioning

confidence: 99%

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

Jiang

Liu

Hao

et al. 2015

Computers & Industrial Engineering

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“…ODE is a discrete di erential evolution algorithm, PGA is a permutation-based genetic algorithm [49], NEH is a well-known constructive heuristic [50], and HDDE is a hybrid discrete di erential evolution proposed by Mokhtari et al [51]. For comparison, the four mentioned ow shop benchmarks are solved with improved ICA, and makespan is compared with other four algorithms solutions.…”

Section: Comparison Methodsmentioning

confidence: 99%

A Joint Scheduling of Production and Distribution Operations in a Flow Shop Manufacturing System

2017

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Abstract. In traditional scheduling problems and many real-world applications, the production operations are scheduled regardless of distribution decisions. Indeed, the completion time of a job in such problems is de ned traditionally as the time when the production sequences of a job are nished. However, in many practical environments, completed orders are delivered to customers immediately after production stages without any further inventory storage. Therefore, in this paper, we investigate an integrated scheduling model of production and distribution problems simultaneously. It is assumed that products proceed through a permutation ow shop scheduling manufacturing system and are delivered to customers via available vehicles. The objective of our integrated model is to minimize the Maximum Returning Time (MRT), which is the time it takes for the last vehicle to deliver the last order to a relevant customer and return to production center. The problem is formulated mathematically, and then an Improved Imperialist Competitive Algorithm (I-ICA) is proposed for solving it. Furthermore, a su cient number of test problems are generated for computational study. Various parameters of the algorithm are analyzed to calibrate the algorithm by means of the Taguchi method. At the end, the e ectiveness of the proposed model and suggested algorithm is evaluated through a computational study where the obtained results show the appropriate performance of the integrated model and solving approach in comparison to those of the other algorithms.

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“…Furthermore, Xu and Zhou [32] provided a flow shop problem in uncertain environment to minimize the completion time and earliness. Mokhtari et al [19,20] considered a multi-objective shop floor scheduling problem with resource-dependent processing (controllable) times of operations.…”

Section: Related Work On Fsspmentioning

confidence: 99%

Research on group search optimizers for a reconfigurable flow shop sequencing problem

Mokhtari

2015

Neural Comput & Applic

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A reconfigurable manufacturing system is usually designed for quick re-adjusting of production capacity in response to market changes. In this paper, we study a flow shop sequencing problem (FSSP) with controllable processing times as a special case of reconfigurable manufacturing system. It is possible to speed up the processing times through assigning additional resources or control of machine speed. After formulating this problem mathematically, a novel evolutionary procedure, entitled group search optimizer (GSO), is devised as solution method. The adapted GSO is a population-based search tool which is devised based on the producer and scrounger behavior. GSO emphasizes on imitating searching model of real-world animals. The basic GSO with four promising improvements is elaborated and discussed for addressing the FSSP with controllable processing times. A set of computational experiments is also conducted to demonstrate the applicability of proposed FSSP and performance of improved GSOs.

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A multi-objective flow shop scheduling with resource-dependent processing times: trade-off between makespan and cost of resources

Cited by 39 publications

References 55 publications

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A bi-layer optimization approach for a hybrid flow shop scheduling problem involving controllable processing times in the steelmaking industry

A Joint Scheduling of Production and Distribution Operations in a Flow Shop Manufacturing System

Research on group search optimizers for a reconfigurable flow shop sequencing problem

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