Integrating scheduling with optimal sublot for parallel machine with job splitting and dependent setup times

Sethanan, Kanchana; Wisittipanich, Warisa; Wisittipanit, Nuttachat; Nitisiri, Krisanarach; Moonsri, Karn

doi:10.1016/j.cie.2019.106095

Cited by 10 publications

(10 citation statements)

References 27 publications

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“…, 2017) on non-identical parallel batch processing machines, and a similar problem is explored in (Hulett and Damodaran, 2018) with unequal ready times. Sethanan et al. (2019) combine PSO and differential evolution algorithm to solve a parallel capacitated machine scheduling problem with consideration of job splitting and dependent set up times.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Compared with other artificial intelligent algorithms, PSO is characterized by simple operations, few parameter settings, low computation complexity, fast convergence rates and high convergence stability. This makes it attract wide recognition from both academic studies and practical applications especially in production scheduling (Sethanan et al. , 2019; Li et al ., 2018, 2019; Ho et al.…”

Section: Algorithm Designmentioning

confidence: 99%

“…Total weighted tardiness is minimized in (Hulett et al, 2017) on non-identical parallel batch processing machines, and a similar problem is explored in (Hulett and Damodaran, 2018) with unequal ready times. Sethanan et al (2019) combine PSO and differential evolution algorithm to solve a parallel capacitated machine scheduling problem with consideration of job splitting and dependent set up times. Lalwani et al (2019) employ discrete PSO for minimizing job makespan on identical parallel machines connected through message passing interface.…”

Section: Literature Reviewmentioning

confidence: 99%

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Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

Zhao

Kong

et al. 2022

IMDS

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PurposeCycle time reduction is important for order fulling process but often subject to resource constraints. This study considers an unrelated parallel machine environment where orders with random demands arrive dynamically. Processing speeds are controlled by resource allocation and subject to diminishing marginal returns. The objective is to minimize long-run expected order cycle time via order schedule and resource allocation decisions.Design/methodology/approachA stochastic optimization algorithm named CAP is proposed based on particle swarm optimization framework. It takes advantage of derived bound information to improve local search efficiency. Parameter impacts including demand variance, product type number, machine speed and resource coefficient are also analyzed through theoretic studies. The algorithm is evaluated and benchmarked with four well-known algorithms via extensive numerical experiments.FindingsFirst, cycle time can be significantly improved when demand randomness is reduced via better forecasting. Second, achieving processing balance should be of top priority when considering resource allocation. Third, given marginal returns on resource consumption, it is advisable to allocate more resources to resource-sensitive machines.Originality/valueA novel PSO-based optimization algorithm is proposed to jointly optimize order schedule and resource allocation decisions in a dynamic environment with random demands and stochastic arrivals. A general quadratic resource consumption function is adopted to better capture diminishing marginal returns.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Algorithm Designmentioning

confidence: 99%

Section: Literature Reviewmentioning

confidence: 99%

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Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

Zhao

Kong

et al. 2022

IMDS

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“…Parallel machine scheduling is defined as sequencing and assigning jobs into machines when similar types of machines are available and jobs can be scheduled in these machines. A variety of sequencing and/or processing restrictions often exist when decision makers try to minimize some related objective functions [14][15][16][17][18][19][20][21][22][23][24]; however, most enterprises still use advanced machines running alongside outdated ones. In contrast to the old ones, modern machines are usually adjusted to work at a high speed and save energy.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, this addressed scheduling is both an objective optimization problem and an NP-hard problem. The differential evolutionary (DE) algorithm is suitable to solve this kind of problem because it can obtain non-dominated solutions in a single run and has been successfully applied to optimization problems [17,20,24,[27][28][29]. In addition, metaheuristics based on local search methods, such as the variable neighborhood strategies adaptive search (VaNSAS), have been successfully applied to solve many combinatorial optimizations problems [30][31][32][33][34][35][36][37][38][39][40], which inspired us to develop a parallel-machine-scheduling model, and to propose VaNSAS and new neighborhood strategies: (1) solution destroy and repair (SDR); (2) track-transition method (TTM); and (3) multiplier factor (MF).…”

Section: Introductionmentioning

confidence: 99%

Variable Neighborhood Strategy Adaptive Search to Solve Parallel-Machine Scheduling to Minimize Energy Consumption While Considering Job Priority and Control Makespan

et al. 2021

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Environmental concerns and rising energy prices put great pressure on the manufacturing industry to reduce pollution and save energy. Electricity is one of the main machinery energy sources in a plant; thus, reducing energy consumption both saves energy costs and protects our planet. This paper proposes the novel method called variable neighborhood strategy adaptive search (VaNSAS) in order to minimize energy consumption while also considering job priority and makespan control for parallel-machine scheduling problems. The newly presented neighborhood strategies of (1) solution destroy and repair (SDR), (2) track-transition method (TTM), and (3) multiplier factor (MF) were proposed and tested against the original differential evaluation (DE), current practice procedure (CU), SDR, TTM, and MF for three groups of test instances, namely small, medium, and large. Experimental results revealed that VaNSAS outperformed DE, CU, SDR, TTM, and MF, as it could find the optimal solution and the mathematical model in the small test instance, while the DE could only find 25%, and the others could not. In the remaining test instances, VaNSAS performed 16.35–19.55% better than the best solution obtained from Lingo, followed by DE, CU, SDR, TTM, and MF, which performed 7.89–14.59% better. Unfortunately, the CU failed to improve the solution and had worse performance than that of Lingo, including all proposed methods.

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Hybrid PSO and ALNS algorithm for software and mobile application for transportation in ice manufacturing industry 3.5

Pitakaso¹,

Sethanan

Jamrus

2020

Computers & Industrial Engineering

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Integrating scheduling with optimal sublot for parallel machine with job splitting and dependent setup times

Cited by 10 publications

References 27 publications

Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

Resource-controlled stochastic customer order scheduling via particle swarm optimization with bound information

Variable Neighborhood Strategy Adaptive Search to Solve Parallel-Machine Scheduling to Minimize Energy Consumption While Considering Job Priority and Control Makespan

Hybrid PSO and ALNS algorithm for software and mobile application for transportation in ice manufacturing industry 3.5

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