Parallel-machine scheduling with job-dependent cumulative deterioration effect and rejection

Li, Shi‐Sheng; Chen, Ren-Xia; Qi, Feng; Jiao, Chengwen

doi:10.1007/s10878-019-00429-7

Cited by 17 publications

(4 citation statements)

References 22 publications

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“…The literature refers to this phenomenon as the deterioration of the job or resources. The categories in which deterioration effects are grouped in the present review correspond to those mentioned by Li et al [81] and Strusevich and Rustogi [51], such as (i) starting-time deterioration effect, (ii) positional deterioration effect, and (iii) cumulative deterioration effect. The first relates mainly to job deterioration, while the last two refer to resource deterioration.…”

Section: Deterioration Effectmentioning

confidence: 74%

“…The job processing time is an increasing function of the total processing time, load or difficulty related to the completion of previously scheduled works [81]. For instance, Abedi et al [45] focus on a job-shop scheduling problem and define the work processing time, taking into account the cumulative deterioration effect of the speed-scaling machines.…”

Section: Cumulative Deterioration Effect (C-de)mentioning

confidence: 99%

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Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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In traditional scheduling problems, job processing times are considered constant and known in advance. This assumption is, however, a simplification when it comes to hand-intensive real-life production contexts because workers usually induce variability in the job processing times due to several factors such as learning, monotony, fatigue, psychological factors, etc. These effects can decrease or increase the actual processing time when workers execute a job. The academic literature has reported several modeling and resolution approaches to deal with the phenomenon in a variety of configurations. However, there is no comprehensive review of these research outputs to the best of our knowledge. In this paper, we follow a systematic approach to review relevant contributions addressing the scheduling problem with learning and deterioration effects. Modeling approaches for learning and deterioration effects, objective functions, and solution methods employed in the literature are the main topics for the taxonomy proposed in this review. A total of 455 papers from 1999 to 2021 are included and analyzed. Different areas of interest are presented, and some opportunities for future research are identified.

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Section: Deterioration Effectmentioning

confidence: 74%

Section: Cumulative Deterioration Effect (C-de)mentioning

confidence: 99%

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

2022

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“…According to constraints ( 14) and (15), no two jobs processed by the same resource can be executed at the same time because of resource restriction. Constraints (16) guarantee that the processing of each job j has to be started after it is transferred to the machine that processes it. Constraints (17) impose that the completion time of each job will not surpass its deadline.…”

Section: Exact Approachmentioning

confidence: 99%

“…The scheduling problem with job acceptance (also called "job selection" or "job rejection") has been studied by many papers in the literature [2][3][4][5][6][7][8]. The scheduling problem with machine acceptance (called "not-all-machines" or "optional machines") has also been investigated by many researchers [9][10][11][12][13][14][15][16][17][18][19]. The scheduling problem with renewable resource constraints has also been widely studied in the literature due to its wide applicability to real problems [20][21][22][23][24][25][26][27].…”

Section: Introductionmentioning

confidence: 99%

Unrelated Parallel Machine Scheduling with Job and Machine Acceptance and Renewable Resource Allocation

2022

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In this paper, an unrelated parallel machine scheduling problem with job (product) and machine acceptance and renewable resource constraints was considered. The main idea of this research was to establish a production facility without (or with minimum) investment in machinery, equipment, and location. This problem can be applied to many real problems. The objective was to maximize the net profit; that is, the total revenue minus the total cost, including fixed costs of jobs, job transportation costs, renting costs of machines, renting cost of resources, and transportation costs of resources. A mixed-integer linear programming (MILP) model and several heuristics (greedy, GRASP, and simulated annealing) are presented to solve the problem.

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Robust parallel-batching scheduling with fuzzy deteriorating processing time and variable delivery time in smart manufacturing

Pei

Liu

et al. 2020

Fuzzy Optim Decis Making

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Parallel-machine scheduling with job-dependent cumulative deterioration effect and rejection

Cited by 17 publications

References 22 publications

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

Taxonomy of Scheduling Problems with Learning and Deterioration Effects

Unrelated Parallel Machine Scheduling with Job and Machine Acceptance and Renewable Resource Allocation

Robust parallel-batching scheduling with fuzzy deteriorating processing time and variable delivery time in smart manufacturing

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