Modelling and an improved NSGA-II algorithm for sustainable manufacturing systems with energy conservation under environmental uncertainties: a case study

Ayyoubzadeh, Behnam; Ebrahimnejad, Sadoullah; Bashiri, Mahdi; Baradaran, Vahid; Hosseini, Seyed Mohammad Hassan

doi:10.1080/19397038.2021.1923083

Cited by 5 publications

(4 citation statements)

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“…The second most frequently considered objective is the tardiness, which measures the difference between the job due date and the job completion time. About 20% of the reviewed papers optimize some form of tardiness, namely: total tardiness (T) (e.g., [9,63,[102][103][104]), total weighted tardiness (wT) (e.g., [21,23,25,30,105]), total weighted tardiness and earliness (wTE) [59,69,97], tardiness cost (T cost ) [86,106], mean tardiness (T) [107], and maximum tardiness (T max ) [108,109].…”

Section: Other Objective Functionsmentioning

confidence: 99%

“…The costs accounted for include costs of human resources [13,35,110,111], raw materials [73,112], maintenance [73,113], transportation [88], and processing [97,114,115]. Other economic objectives include penalty costs associated with earliness and/or tardiness [22,97,106], machine under-and overutilization [38], and customer dissatisfaction [116].…”

Section: Other Objective Functionsmentioning

confidence: 99%

“…Several authors introduce and discuss specific case studies on problems that can be cast as energy-efficient (flexible) job shop problems. The problem instances associated with such studies involve the FJSP [10,19,56], the FJSP and tool allocation [7], the FJSP and WoS [13], the FJSP with TrT [11,18,20], the FJSP with LOP and TrS [89], the DS FJSP [106], and the JSP with TrT [116].…”

Section: Problem Instances and Data Setsmentioning

confidence: 99%

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Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

2022

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Energy efficiency has become a major concern for manufacturing companies not only due to environmental concerns and stringent regulations, but also due to large and incremental energy costs. Energy-efficient scheduling can be effective at improving energy efficiency and thus reducing energy consumption and associated costs, as well as pollutant emissions. This work reviews recent literature on energy-efficient scheduling in job shop manufacturing systems, with a particular focus on metaheuristics. We review 172 papers published between 2013 and 2022, by analyzing the shop floor type, the energy efficiency strategy, the objective function(s), the newly added problem feature(s), and the solution approach(es). We also report on the existing data sets and make them available to the research community. The paper is concluded by pointing out potential directions for future research, namely developing integrated scheduling approaches for interconnected problems, fast metaheuristic methods to respond to dynamic scheduling problems, and hybrid metaheuristic and big data methods for cyber-physical production systems.

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Section: Other Objective Functionsmentioning

confidence: 99%

Section: Other Objective Functionsmentioning

confidence: 99%

Section: Problem Instances and Data Setsmentioning

confidence: 99%

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Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

2022

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“…The FJSSP has many applications in manufacturing industries, and hence, has received more attention of researchers in last years [8][9][10]. However, little attention has been paid to address the problem under uncertainty.…”

Section: Literature Reviewmentioning

confidence: 99%

A reactive approach for flexible job shop scheduling problem with tardiness penalty under uncertainty

et al. 2022

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Flexible job shop scheduling under uncertainty plays an important role in real-world manufacturing systems. This paper deals with the flexible job shop scheduling problem to minimize the sum of jobs' tardiness considering machines breakdown and order due date modification as two important disruptions in this production system. To this end, the problem is formulated as a mixed-integer linear programming model. In addition, two strategies are proposed including allocating multiple machines to each job and selecting the best alternative process from other jobs to handle these disruptions. Since the problem is well-known strongly NP-hard, a hybrid metaheuristic algorithm based on the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) is proposed to solve the real-sized instances of these problems. Numerical experiments are used to evaluate the performance and effectiveness of the proposed hybrid algorithm. Obtained results for the small-sized instances show that the proposed algorithm provides proper solutions in terms of optimality and CPU Time. In addition, results for the medium-and large-sized scales validate the efficiency of the proposed algorithm and indicate that the proposed hybrid solution approach outperformed the classic GA in terms of the objective function value and the CPU time.

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Enhancing socioeconomic sustainability in glass wall panel manufacturing: An integrated production planning approach

Saeed Khaled,

Abdelfadeel Shaban,

Hegab

2024

Computers & Industrial Engineering

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Modelling and an improved NSGA-II algorithm for sustainable manufacturing systems with energy conservation under environmental uncertainties: a case study

Cited by 5 publications

References 50 publications

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

Energy-Efficient Scheduling in Job Shop Manufacturing Systems: A Literature Review

A reactive approach for flexible job shop scheduling problem with tardiness penalty under uncertainty

Enhancing socioeconomic sustainability in glass wall panel manufacturing: An integrated production planning approach

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