A bi-objective identical parallel machine scheduling problem with controllable processing times: a just-in-time approach

Zarandi, M. H. Fazel; Kayvanfar, Vahid

doi:10.1007/s00170-014-6461-8

Cited by 17 publications

(16 citation statements)

References 45 publications

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“…Mohri, Masuda, and Ishii (1999) consider release times, due dates, set-up times in a bicriteria problem, in which the objectives are the minimisation of the makespan and the maximum lateness. The works considering only two criteria for 30 identical parallel machines follow basically two lines of research: ones deal with an objective for production and another for maintenance (Berrichi and Yalaoui 2013;Moradi and Zandieh 2010); the others take into account the combination of the total completion time or makespan and the tardiness (Zarandi and Kayvanfar 2015) …”

Section: Aq7mentioning

confidence: 99%

A bi-objective parallel machine problem with eligibility, release dates and delivery times of the jobs

Mateo

Teghem

Tuyttens

2017

International Journal of Production Research

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The scheduling of parallel machines is a well-known problem in many companies. Nevertheless, not always all the jobs can be manufactured in any machine and the eligibility appears. Based on a real-life problem, we present a model which has m parallel machines with different level of quality from the highest level for the first machine till the lowest level for the last 10 machine. The set of jobs to be scheduled on these m parallel machines are also distributed among these m levels: one job from a level can be manufactured in a machine of the same or higher level but a penalty, depending on the level, appears when a job is manufactured in a machine different from the highest level i.e. different from the first machine. Besides, there are release dates and delivery times associated to each job. The tackled problem is bi-objective with the criteria: minimisation of the final date -i.e. the maximum for all the jobs of their completion time plus the delivery time -and the minimisation 15 of the total penalty generated by the jobs. In a first step, we analyse the sub-problem of minimisation of the final date on a single machine for jobs with release dates and delivery times. Four heuristics and an improvement algorithm are proposed and compared on didactic examples and on a large set of instances. In a second step an algorithm is proposed to approximate the set of efficient solutions and the Pareto front of the bi-objective problem. This algorithm contains two phases: the first is a depth search phase and the second is a backtracking phase. The procedure is illustrated in detail on an instance with 20 20 jobs and 3 machines. Then extensive numerical experiments are realised on two different sets of instances, with 20, 30 and 50 jobs, 3 or 4 machines and various values of penalties. Except for the case of 50 jobs, the results are compared with the exact Pareto front.

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Section: Aq7mentioning

confidence: 99%

A bi-objective parallel machine problem with eligibility, release dates and delivery times of the jobs

Mateo

Teghem

Tuyttens

2017

International Journal of Production Research

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“…The makespan is related to the maximum completion time of the jobs (i.e.,

C_{m a x}

), while the flow time in this problem represents the summation of the completion time of each job (i.e.,

\sum C_{i}

). In general, the PMSP looks for a schedule of a set of jobs on a set of parallel machines, attaining a given objective (e.g., minimize the makespan, minimize the flow time, minimize the earliness, minimize the tardiness, minimize the cost of controlling jobs, or minimize a fuzzy makespan) (Mokotoff, ; Zarandi and Kayvanfar, ; Cheng and Huang, ).…”

Section: Introductionmentioning

confidence: 99%

“…Although there is a vast body of research on the PMSP because this problem has received much attention recently, many of the studies rely on heuristics to solve it and its variants. Some of its variants consider, for example, identical machines, which have the same speed regardless of the job (Gupta and Ruiz‐Torres, ; Mokotoff, ); nonidentical machines, such that the processing time varies according to the machine (e.g., uniform and unrelated machines; Cheng and Huang, ; Santos et al., ); controllable processing times in which the processing time can be compressed or expanded (Zarandi and Kayvanfar, ); fuzzy processing time in order to model uncertainties of the environment (Yeh et al., ); job splitting, where jobs can be split into subjobs (Eroglu and Ozmutlu, ; Eroglu et al., ); and sequence‐dependent setup time in which the setup time depends on the job being scheduled and its immediate predecessor job (Behnamian et al., ; Hamzadayi and Yildiz, ). In this work, the PMSP assumes identical machines and sequence‐dependent setup times.…”

Section: Introductionmentioning

confidence: 99%

“…() minimized the total weighted flow time, total weighted tardiness, and total machine load variation, where setup times are sequence‐dependent and there are fuzzy resource constraints for jobs; Bathrinath et al. () considered the minimization of both makespan and the number of tardy jobs; and Zarandi and Kayvanfar () minimized the total tardiness and earliness, and the makespan, in which the processing times can be controlled.…”

Section: Introductionmentioning

confidence: 99%

“…Other heuristics can be found in Torabi et al. () who proposed a multiple objective particle swarm optimization, and Zarandi and Kayvanfar () who adapted the nondominated sorting genetic algorithm (NSGA) and the nondominated ranking genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Multiobjective pseudo‐variable neighborhood descent for a bicriteria parallel machine scheduling problem with setup time

Queiroz

Mundim

2019

Int Trans Operational Res

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A multiobjective variable neighborhood descent (VND) based heuristic is developed to solve a bicriteria parallel machine scheduling problem. The problem considers two objectives, one related to the makespan and the other to the flow time, where the setup time depends on the sequence, and the machines are identical. The heuristic has a set of neighborhood structures based on swap, remove, and insertion moves. We propose changing the local search inside the VND to a sequential search through the neighborhoods to obtain nondominated points for the Pareto‐front quickly. In the numerical tests, we consider a single‐objective version of the heuristic, comparing the results on 510 benchmark instances to show that it is quite effective. Moreover, new instances are generated in accordance with the literature for the bicriteria problem, showing the ability of the proposed heuristic to return an efficient set of nondominate solutions compared with the well‐known nondominated sorting genetic algorithm II.

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A hybrid genetic algorithm for operating room scheduling

Lin

Chou

2019

Health Care Manag Sci

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A bi-objective identical parallel machine scheduling problem with controllable processing times: a just-in-time approach

Cited by 17 publications

References 45 publications

A bi-objective parallel machine problem with eligibility, release dates and delivery times of the jobs

A bi-objective parallel machine problem with eligibility, release dates and delivery times of the jobs

Multiobjective pseudo‐variable neighborhood descent for a bicriteria parallel machine scheduling problem with setup time

A hybrid genetic algorithm for operating room scheduling

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