Scheduling hybrid flow shop problem with non-fixed availability constraints

Besbes, Walid; Teghem, Jacques; Loukil, Taïcir

doi:10.1504/ejie.2010.035652

Cited by 27 publications

(12 citation statements)

References 18 publications

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“…Another problem that can be addressed is the problem with a weighted number of tardy jobs since all the jobs may not have the same importance, e.g., Soroush [35]. Yet, another problem that can be addressed is in the area of scheduling multipurpose tasks, e.g., Manaa and Chu [36] or in the area of hybrid flowshop, e.g., Besbes et al [37]. Another extension is to apply the proposed heuristics through actual experimentation on selected industries, where the proposed heuristics are compared with the current performance for the objective of maximizing timeliness (i.e., percentage of on or before due time shipment).…”

Section: Resultsmentioning

confidence: 99%

No-wait flowshop scheduling problem to minimize the number of tardy jobs

Aldowaisan

Allahverdi

2011

Int J Adv Manuf Technol

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This research addresses the m-machine no-wait flowshop scheduling problem with the objective of minimizing the number of tardy jobs. The problem is known to be NP-hard even for the two machines, and literature reveals that no heuristics have been developed for this problem. In this paper, we propose an efficient heuristic based on simulated annealing, where we first adapt the single-machine optimal algorithm to our problem to develop two new heuristics, NOTA and NOTM. An improved simulated annealing heuristic, called SA-GI, is then developed by feeding the best performing heuristic among NOTA, NOTM, and EDD into the simulated annealing algorithm. A second proposed heuristic, called SA-IP, further improves the SA-GI solution by using insertion and pair-wise exchange techniques. Based on the computational experiments, the overall relative percentage errors of the heuristic SA, SA-GI, and SA-IP are 8. 848, 8.428, and 0.207, respectively. The computational times of the three heuristics are close to each other, and the largest average time is less than one second, and hence, the computational time is not an issue. Therefore, the heuristic SA-IP is the best one.

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Section: Resultsmentioning

confidence: 99%

No-wait flowshop scheduling problem to minimize the number of tardy jobs

Aldowaisan

Allahverdi

2011

Int J Adv Manuf Technol

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“…They proposed a solution approach based on a variable neighborhood search. Besbes, Teghem, and Loukil [9] focused on the FFS scheduling problem by considering the availability constraints for machines. They proposed a GA-based approximation algorithm to minimize makespan.…”

Section: Literature Reviews 21 Metaheuristic Algorithms For Flexiblementioning

confidence: 99%

Flexible flow shop scheduling problem to minimize makespan with renewable resources

2019

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This paper deals with a flexible flow shop (FFS) scheduling problem with unrelated parallel machines and renewable resource shared among the stages. The FFS scheduling problem is one of the most common manufacturing environment in which there is more than a machine in at least one production stage. In such a system, to decrease the processing times, additional renewable resources are assigned to the jobs or machines, and it can lead to decrease the total completion time. For this purpose, a mixed integer linear programming (MILP) model is proposed to minimize the maximum completion time (makespan) in an FFS environment. The proposed model is computationally intractable. Therefore, a particle swarm optimization (PSO) algorithm as well as a hybrid PSO and simulated annealing (SA) algorithm named SA-PSO, are developed to solve the model. Through numerical experiments on randomly generated test problems, the authors demonstrate that the hybrid SA-PSO algorithm outperforms the PSO, especially for large size test problems.

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“…Thus, many approximation approaches, based on the characteristics of the problem, have been proposed. These approaches such as simulated annealing (Aarts et al 1986;Satoh & Nara, 1991), tabu search (Glover 1986, Gopalakrishnan et al, 2001, and genetic algorithm (Goldberg, 1989;Besbes et al, 2010) are generally known as heuristics or meta-heuristics techniques, generate acceptable schedules with less computational time than the exact approaches. Practically, for greater acceptance, approximation approaches must take care of dominant uncertainty of changes such as system failures.…”

Section: Introductionmentioning

confidence: 99%

Scheduling a maintenance activity under skills constraints to minimize total weighted tardiness and late tasks

Hedjazi

2015

10.5267/j.ijiec

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Skill management is a key factor in improving effectiveness of industrial companies, notably their maintenance services. The problem considered in this paper concerns scheduling of maintenance tasks under resource (maintenance teams) constraints. This problem is generally known as unrelated parallel machine scheduling. We consider the problem with a both objectives of minimizing total weighted tardiness (TWT) and number of tardiness tasks. Our interest is focused particularly on solving this problem under skill constraints, which each resource has a skill level. So, we propose a new efficient heuristic to obtain an approximate solution for this NP-hard problem and demonstrate his effectiveness through computational experiments. This heuristic is designed for implementation in a static maintenance scheduling problem (with unequal release dates, processing times and resource skills), while minimizing objective functions aforementioned.

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Scheduling hybrid flow shop problem with non-fixed availability constraints

Cited by 27 publications

References 18 publications

No-wait flowshop scheduling problem to minimize the number of tardy jobs

No-wait flowshop scheduling problem to minimize the number of tardy jobs

Flexible flow shop scheduling problem to minimize makespan with renewable resources

Scheduling a maintenance activity under skills constraints to minimize total weighted tardiness and late tasks

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