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

Nanthapodej, Rujapa; Liu, Cheng-Hsiang; Nitisiri, Krisanarach; Pattanapairoj, Sirorat

doi:10.3390/app11115311

Cited by 10 publications

(3 citation statements)

References 52 publications

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“…In his study the WOA is combined with the FA in a way that each individual (depending on its fitness) is either modified using the operators from WOA or FA. In [260] a weighted sum of the total energy cost, number of tardy jobs and makespan is minimised. The authors consider machine eligibility constraints and the maximum number of tardy jobs that are allowed in the schedule.…”

Section: B Metaheuristicsmentioning

confidence: 99%

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Đurasević,

Jakobović

2021

Preprint

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Today scheduling problems have an immense effect on various areas of human lives, be it from their application in manufacturing and production industry, transportation, or workforce allocation. The unrelated parallel machines scheduling problem (UPMSP), which is only one of the many different problem types that exist, found its application in many areas like production industries or distributed computing. Due to the complexity of the problem, heuristic and metaheuristic methods are gaining more attention for solving it. Although this problem variant did not receive much attention as other models, recent years saw the increase of research dealing with this problem. During that time, many different problem variants, solution methods, or other interesting research directions were considered. However, no study has until now tried to systematise the research in which heuristic methods are applied for the UPMSP. The goal of this study is to provide an extensive literature review on the application of heuristic and metaheuristic methods for solving the UPMSP. The research was systematised and classified into several categories to enable an easy overview of the different problem and solution variants. Additionally, current trends and possible future research directions are also shortly outlined.

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Section: B Metaheuristicsmentioning

confidence: 99%

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Đurasević,

Jakobović

2021

Preprint

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“…Energy-saving scheduling has been viewed as one of the most effective manners by researchers all around the world. A number of research achievements have been yielded for various manufacturing workshops, such as single machine [1][2][3][4][5], parallel machines [6][7][8][9][10][11], flow shop [12][13][14][15][16][17][18], and job shop [19][20][21][22][23][24][25].…”

Section: Introductionmentioning

confidence: 99%

A New Interior Search Algorithm for Energy-Saving Flexible Job Shop Scheduling with Overlapping Operations and Transportation Times

Liu

Jiang

Zhu

et al. 2022

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Energy-saving scheduling has been pointed out as an interesting research issue in the manufacturing field, by which energy consumption can be effectively reduced through production scheduling from the operational management perspective. In recent years, energy-saving scheduling problems in flexible job shops (ESFJSPs) have attracted considerable attention from scholars. However, the majority of existing work on ESFJSPs assumed that the processing of any two consecutive operations in a job cannot be overlapped. In order to be close to real production, the processing overlapping of consecutive operations is allowed in this paper, while the job transportation tasks are also involved between different machines. To formulate the problem, a mathematical model is set up to minimize total energy consumption. Due to the NP-hard nature, a new interior search algorithm (NISA) is elaborately proposed following the feature of the problem. A number of experiments are conducted to verify the effectiveness of the NISA algorithm. The experimental results demonstrate that the NISA provides promising results for the considered problem. In addition, the computational results indicate that the increasing transportation time and sub-lot number will increase the transportation energy consumption, which is largely responsible for the increase in total energy consumption.

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“…To solve the process scheduling problem, Yao et al [21] designed a multi-objective salp swarm algorithm to solve the process scheduling problem of a TFT-LCD panel array that considers energy saving. Nanthapodej et al [22] proposed a variable neighborhood strategy adaptive search method to minimize energy consumption while also considering job priority and makespan control for parallel-machine scheduling problems. In addition, some researchers have also carried out similar studies related to green scheduling (see references [23][24][25], etc.).…”

Section: Introductionmentioning

confidence: 99%

Hybrid Salp Swarm Algorithm for Solving the Green Scheduling Problem in a Double-Flexible Job Shop

2021

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Green scheduling is not only an effective way to achieve green manufacturing but also an effective way for modern manufacturing enterprises to achieve energy conservation and emission reduction. The double-flexible job shop scheduling problem (DFJSP) considers both machine flexibility and worker flexibility, so it is more suitable for practical production. First, a multi-objective mixed-integer programming model for the DFJSP with the objectives of optimizing the makespan, total worker costs, and total influence of the green production indicators is formulated. Considering the characteristics of the problem, three-layer salp individual encoding and decoding methods are designed for the multi-objective hybrid salp swarm algorithm (MHSSA), which is hybridized with the Lévy flight, the random probability crossover operator, and the mutation operator. In addition, the influence of the parameter setting on the MHSSA in solving the DFJSP is investigated by means of the Taguchi method of design of experiments. The simulation results for benchmark instances show that the MHSSA can effectively solve the proposed problem and is significantly better than the MSSA and the MOPSO algorithm in the diversity, convergence, and dominance of the Pareto frontier.

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

Cited by 10 publications

References 52 publications

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

Heuristic and Metaheuristic Methods for the Unrelated Machines Scheduling Problem: A Survey

A New Interior Search Algorithm for Energy-Saving Flexible Job Shop Scheduling with Overlapping Operations and Transportation Times

Hybrid Salp Swarm Algorithm for Solving the Green Scheduling Problem in a Double-Flexible Job Shop

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