Algorithms for single-machine scheduling to minimize the total tardiness with learning effects and two competing agents

Lee, Wen‐Chiung; Wang, Jen-Ya; Su, Hsin-Wei

doi:10.1177/1063293x14559750

Cited by 15 publications

(10 citation statements)

References 34 publications

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“…In the mutation stage (Steps 12-14), we perform the extract-shift-insert mutation operation [22,26]. For each generation, about R mutate N chromosomes are randomly chosen to mutate.…”

Section: Algorithm Greedymentioning

confidence: 99%

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A three-agent scheduling problem for minimizing the flow time on two machines

Lee

Wang

2020

RAIRO-Oper. Res.

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This study introduces a two-machine three-agent scheduling problem. We aim to minimize the total tardiness of jobs from agent 1 subject to that the maximum completion time of jobs from agent 2 cannot exceed a given limit and that two maintenance activities from agent 3 must be conducted within two maintenance windows. Due to the NP-hardness of this problem, a genetic algorithm (named GA+) is proposed to obtain approximate solutions. On the other hand, a branch-and-bound algorithm (named B&B) is developed to generate the optimal solutions. When the problem size is small, we use B&B to verify the solution quality of GA+. When the number of jobs is large, a relative deviation is proposed to show the gap between GA+ and another ordinary genetic algorithm. Experimental results show that the proposed genetic algorithm can generate approximate solutions by consuming reasonable execution time.

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“…In the mutation stage (Steps 12-14), we perform the extract-shift-insert mutation operation [22,26]. For each generation, about R mutate N chromosomes are randomly chosen to mutate.…”

Section: Algorithm Greedymentioning

confidence: 99%

“…Consequently, they considered three agents and made their models more realistic but more complicated. For more recent information about multi-agent scheduling, we can refer to [14,26,34,39,42,48,50].…”

Section: Introductionmentioning

confidence: 99%

A three-agent scheduling problem for minimizing the flow time on two machines

Lee

Wang

2020

RAIRO-Oper. Res.

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“…In the literature, this phenomenon, which was first entitled by Mosheiov [8], is known as a "learning effect". Recently, researchers [9][10][11][12] have been working on job scheduling problems under learning effect. This paper will present the temperature effect in the working environment for two scheduling problems.…”

Section: Temperature-performancementioning

confidence: 99%

Total Completion Time Scheduling Problem with Temperature Considerations

Toksarı¹

2016

Nevşehir Bilim Ve Teknoloji Dergisi

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The focus of this work is to analyze working environment temperature in single-machine scheduling problems. In classical scheduling problems, the processing times are considered usually without the effect of working environment temperature. However, in many real scheduling environments, working environment temperature may affect processing time due to the nature of work. In this paper, we present job processing times based on the working environment temperature for total completion time (square) problem. We derive solutions with polynomial-time for both objectives.

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“…In the remanufacturing process, reasonable scheduling can further enhance resource utilization, reduce energy consumption, and improve customer satisfaction. Currently, most studies have focused on optimizing the makespan (Peng et al, 2019), cost (Junior and Godinho Filho, 2017), energy consumption (Zhao et al, 2019), customer satisfaction measured based on a predetermined due date (Kim et al, 2017; Lee et al, 2015), or a combination of these factors (Gong et al, 2020). However, the relationship between remanufacturers and customers is disregarded.…”

Section: Introductionmentioning

confidence: 99%

Game-relationship-based remanufacturing scheduling model with sequence-dependent setup times using improved discrete particle swarm optimization algorithm

Zhang

et al. 2022

Concurrent Engineering

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Remanufacturing has become a Frontier technology in sustainable manufacturing and enables end-of-life products to be restored to their new conditions. Although remanufacturing scheduling has been widely investigated, the relationship between remanufacturers and customers is rarely examined. Therefore, a new game-relationship-based remanufacturing scheduling model with sequence-dependent setup times is proposed herein. In the model, the relationship between the remanufacturer and customers is constructed as a non-cooperative game, and the interval due dates are set based on the uncertain product quality to achieve effective remanufacturing and improve customer satisfaction. Multiple remanufacturing lines differentiated based on the quality grade of products are integrated into the proposed model. In addition, sequence-dependent setup times are considered in the model, which depend on the similarity between two adjacent tasks processed on a reprocessing unit. An improved discrete particle swarm optimization algorithm is proposed to obtain Nash equilibrium solutions via an efficient global search structure and a local search strategy. The algorithm is embedded with the Nash equilibrium solution evaluation method and integrated with multiple genetic operators to update the particles. The performance of the proposed algorithm in solving the proposed model is verified via a comparison with three baseline algorithms for managing different problem instances.

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Algorithms for single-machine scheduling to minimize the total tardiness with learning effects and two competing agents

Cited by 15 publications

References 34 publications

A three-agent scheduling problem for minimizing the flow time on two machines

A three-agent scheduling problem for minimizing the flow time on two machines

Total Completion Time Scheduling Problem with Temperature Considerations

Game-relationship-based remanufacturing scheduling model with sequence-dependent setup times using improved discrete particle swarm optimization algorithm

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