Machine scheduling with deteriorating jobs and DeJong’s learning effect

Ji, Min; Tang, Xiaoying; Zhang, Xin; Cheng, T.C.E.

doi:10.1016/j.cie.2015.10.015

Cited by 42 publications

(10 citation statements)

References 49 publications

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“…where α ≥ 0, β ≥ 0, and 0 < a ≤ 1 are the parameters obtained empirically and α + β � 1. Other studies have validated DeJong's learning model, e.g., Okolowski and Gawiejnowicz [6] and Ji et al [7,8]. More recent papers which have considered scheduling problems with learning effect include studies of Wang and Wang and Wang [9,10]; Wang et al [11]; Xu et al [12]; Chen et al [13]; Toksari and Arik [14]; Bai et al [15]; Mustu and Eren [16]; Pei et al [17]; Zhang et al [18]; Wang and Wang [19,20]; Wang et al [21], and Przybylski [22].…”

Section: Review Of Existing Modelsmentioning

confidence: 91%

Group Scheduling Problems with Time-Dependent and Position-Dependent DeJong’s Learning Effect

Sun

Lei

Huo

2020

Mathematical Problems in Engineering

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In this paper, we introduce a group scheduling model with time-dependent and position-dependent DeJong's learning effect. e objectives of scheduling problems are to minimize makespan, the total completion time, and the total weighted completion time, respectively. We show that the problems remain solvable in polynomial time under the proposed model.Example 2. As seen in Example 1, we change the objective to the total completion time minimization. By Algorithm 2, we solve the problem as follows:Step 1: In group G 1 , the optimal job sequence is J 11 ⟶ J 12 . In group G 2 , the optimal job sequence is J 23 ⟶ J 22 ⟶ J 21 .Step 2: To groups G 1 and G 2 , calculate

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Section: Review Of Existing Modelsmentioning

confidence: 91%

Group Scheduling Problems with Time-Dependent and Position-Dependent DeJong’s Learning Effect

Sun

Lei

Huo

2020

Mathematical Problems in Engineering

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“…We present two main properties of Partition that we will use in the development of our FPTAS A ε . Kovalyov and Kubiak [24,25] first provided the properties, which Ji and Cheng [18,19], and Ji et al [20,21] extended subsequently. We provide a formal description of the FPTAS A ε for problem P m |M j , SM | C max + S as follows:…”

Section: Procedures Partition(a E δ)mentioning

confidence: 99%

Parallel-machine scheduling in shared manufacturing

Ji¹,

Ye²,

Qian³

et al. 2022

JIMO

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<p style='text-indent:20px;'>We consider parallel-machine scheduling in the context of shared manufacturing where each job has a machine set to which it can be assigned for processing. Such a set is called the processing set. In the shared manufacturing setting, a job can be assigned not only to certain machines for processing, but can also be processed on the remaining machines at a certain cost. Compared with traditional scheduling with job rejection, the scheduling model under study embraces the notion of sustainable manufacturing. Showing that the problem is <i>NP</i>-hard, we develop a fully polynomial-time approximation scheme to solve the problem when the number of machines is fixed.</p>

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“…For the latter objectives, the problem is shown to have a polynomial solution. The parallel machine scheduling problem with DeJong's learning effect is addressed in [12]. A polynomial approximation heuristic is proposed for this problem in order to minimize the makespan.…”

Section: Literature Reviewmentioning

confidence: 99%

Near-optimal solutions and tight lower bounds for the parallel machines scheduling problem with learning effect

Hidri

Jemmali

2020

RAIRO-Oper. Res.

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In this paper, the parallel machines scheduling problem with Dejong’s learning effect is addressed. The considered problem has a practical interest since it models real-world situations. In addition, this problem is a challenging one because of its NP-Hardness. In this work, a set of heuristics are proposed. The developed heuristics are categorized into two types. The first category is based on the dispatching methods, with new enhancement variants. The second type is more sophisticated and requires solving NP-Hard problems. Furthermore, several lower bounds are developed in order to assess the performance of the proposed heuristics. These lower bounds are based on solving the problem of the determination of the minimum average load under taking into account some observations. Among these observations, the existence of a limit position that the jobs are not allowed to exceed in any optimal schedule. Finally, an extensive experimental study is conducted over benchmark test problems, with up to 1500 jobs and 5280 instances. The obtained results are outperforming those proposed in the literature.

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Machine scheduling with deteriorating jobs and DeJong’s learning effect

Cited by 42 publications

References 49 publications

Group Scheduling Problems with Time-Dependent and Position-Dependent DeJong’s Learning Effect

Group Scheduling Problems with Time-Dependent and Position-Dependent DeJong’s Learning Effect

Parallel-machine scheduling in shared manufacturing

Near-optimal solutions and tight lower bounds for the parallel machines scheduling problem with learning effect

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