Single machine scheduling problems under position-dependent fuzzy learning effect with fuzzy processing times

Toksarı, Murat; Arık, Oğuzhan Ahmet

doi:10.1016/j.jmsy.2017.08.006

Cited by 26 publications

(18 citation statements)

References 44 publications

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“…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: 97%

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: 97%

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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“…Therefore, three well-known metaheuristic methods and a hybrid method that is called population-based Tabu search algorithm (TS POP ) with evolutionary strategies are proposed. Taillard's (1993) problem sets of 20, 50, and 100 jobs with 5, 10, and 20 machines are chosen to test performances of proposed methods.…”

Section: Introductionmentioning

confidence: 99%

Population-based Tabu search with evolutionary strategies for permutation flow shop scheduling problems under effects of position-dependent learning and linear deterioration

Arık

2020

Soft Comput

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This paper investigates permutation flow shop scheduling (PFSS) problems under the effects of position-dependent learning and linear deterioration. In a PFSS problem, there are n jobs and m machines in series. Jobs are separated into operations on m different machines in series, and jobs have to follow the same machine order with the same sequence. The PFSS problem under the effects of learning and deterioration is introduced with a mixed-integer nonlinear programming model. The time requirement for solving large-scale problems type of PFSS problem is exceedingly high. Therefore, wellknown metaheuristic methods for the PFSS problem without learning and deterioration effects such as iterated greedy algorithms and discrete differential evolution algorithm are adapted for the problem with learning and deterioration effects in order to find a faster and near-optimal or optimal solution for the problem. Furthermore, this paper proposes a hybrid solution algorithm that is called population-based Tabu search algorithm (TS POP) with evolutionary strategies such as crossover and mutation. The search algorithm is built on the basic structure of Tabu search and it searches for the best candidate from a solution population instead of improving the current best candidate at each iteration. Furthermore, the performances of these methods in view of solution quality are discussed in this paper by using test problems for 20, 50, and 100 jobs with 5, 10, 20 machines. Experimental results show that the proposed TS POP algorithm outperforms the other existing algorithms in view of solution quality.

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“…This paper investigates fuzzy scheduling problems with fuzzy chance-constrained programming approaches. For the readers, some scheduling papers (Toksarı and Arık [43], Arık and Toksarı [44], [45]) investigating fuzzy chance-constrained programming approach can be a good guidance for applicability and validity of the proposed method.…”

Section: Introductionmentioning

confidence: 99%

Credibility based chance constrained programming for project scheduling with fuzzy activity durations

Arık

2019

Int. J. Optim. Control, Theor. Appl. (IJOCTA)

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This paper proposes a credibility based chance constrained programming approach for project scheduling problems with fuzzy activity durations where the objective is to minimize the fuzzy project completion time. This paper expresses the fuzzy events such as a project activity’s duration or project completion time with fuzzy chance constraints and the chance of a fuzzy event is illustrated with fuzzy credibility distribution. Due to uncertainty in durations of a project, fuzzy sets and fuzzy numbers can be used in order to illustrate the uncertainty and find a solution space for the problem. Therefore, fuzzy credibility based chance constraint technique is investigated for project scheduling problems with fuzzy activity durations considering the uncertainty or chance of a fuzzy event within a closed interval. In this paper, a fuzzy mathematical model and its crisp equivalent by using credibility measure and chance-constrained programming are given for project scheduling problems with fuzzy activity durations.

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Single machine scheduling problems under position-dependent fuzzy learning effect with fuzzy processing times

Cited by 26 publications

References 44 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

Population-based Tabu search with evolutionary strategies for permutation flow shop scheduling problems under effects of position-dependent learning and linear deterioration

Credibility based chance constrained programming for project scheduling with fuzzy activity durations

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