The single machine scheduling problem with sequence-dependent setup times and a learning effect on processing times

Muştu, Settar; Eren, Tamer

doi:10.1016/j.asoc.2018.06.051

Cited by 20 publications

(11 citation statements)

References 53 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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“…Since then, many researchers have focused on scheduling with a position-based learning model and various performance measures. The most well-known ones include those of Mosheiov (2001), Lee et al (2004), Zhao et al (2004), and Kuo and Yang (2007), Mustu and Eren (2018). A comprehensive review of different kinds of learning effects is proposed by Azzouz et al (2018).…”

Section: Literature Reviewmentioning

confidence: 99%

Stochastic single machine scheduling problem as a multi-stage dynamic random decision process

et al. 2021

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In this work, we study a stochastic single machine scheduling problem in which the features of learning effect on processing times, sequence-dependent setup times, and machine configuration selection are considered simultaneously. More precisely, the machine works under a set of configurations and requires stochastic sequence-dependent setup times to switch from one configuration to another. Also, the stochastic processing time of a job is a function of its position and the machine configuration. The objective is to find the sequence of jobs and choose a configuration to process each job to minimize the makespan. We first show that the proposed problem can be formulated through two-stage and multi-stage Stochastic Programming models, which are challenging from the computational point of view. Then, by looking at the problem as a multi-stage dynamic random decision process, a new deterministic approximation-based formulation is developed. The method first derives a mixed-integer non-linear model based on the concept of accessibility to all possible and available alternatives at each stage of the decision-making process. Then, to efficiently solve the problem, a new accessibility measure is defined to convert the model into the search of a shortest path throughout the stages. Extensive computational experiments are carried out on various sets of instances. We discuss and compare the results found by the resolution of plain stochastic models with those obtained by the deterministic approximation approach. Our approximation shows excellent performances both in terms of solution accuracy and computational time.

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“…The results showed the efficiency of the Branch and Pound algorithm, as it could solve problems of smaller sizes or equal to 30 in reasonable time. [7]. Perez-Gonzalez and Framinan (2018) discussed the single-machine scheduling problem that aims to minimize the time to complete the final job (Cmax) in their study.…”

Section: Literature Reviewmentioning

confidence: 99%

Sequence Dependent Scheduling Optimization on Plastic Injection Line with Tabu Search Method

Mohamedsalih¹,

Şimşir²

2021

Academic Platform Journal of Engineering and Science

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In today's production conditions, it is very important for companies to produce products that meet the determined quality standards. In addition to the production of semi-finished products produced in plastic injection lines with high quality and efficiency, it is aimed to reduce the lost time and waste materials in production. Since automobile headlights can be in many different colors, it is necessary to keep the product quality at a high level in the transition between colors in the production made by plastic injection and to reduce the number of defective products caused by color errors. In this study, the problem of a single row-dependent machine to reduce the amount of waste that occurs between color transitions in the plastic injection line of an automotive side-industry company producing headlights is discussed. Tabu Search method, which is one of the heuristic optimization algorithms was used to solve the problem. In the solution, production sequences that will reduce the amount of waste of the products in the production program were created by using the annual data kept by the production department of the factory. The algorithm was run using data from two machines out of 18, saving 134 kg of waste materials. It has been clarified that if the developed algorithm is applied to all machines in the department, productivity and product quality will be increased by saving waste materials.

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The single machine scheduling problem with sequence-dependent setup times and a learning effect on processing times

Cited by 20 publications

References 53 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

Stochastic single machine scheduling problem as a multi-stage dynamic random decision process

Sequence Dependent Scheduling Optimization on Plastic Injection Line with Tabu Search Method

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