Multiobjective Optimization of Energy-Efficient JOB-Shop Scheduling With Dynamic Reference Point-Based Fuzzy Relative Entropy

He, Lijun; Chiong, Raymond; Li, Wenfeng; Dhakal, Sandeep; Cao, Yulian; Zhang, Yu

doi:10.1109/tii.2021.3056425

Cited by 50 publications

(18 citation statements)

References 31 publications

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“…Several authors have resorted to the speed adjusting strategy; however, only machine processing speed (MS) has been considered (see, e.g., [24,30,31,33,[50][51][52][53]). The exception is the work in [54] that considers simultaneously adjusting the machine processing speed and the vehicle traveling speed.…”

Section: Strategies For Energy Efficiencymentioning

confidence: 99%

“…Setup energy is quite often disregarded as, typically, setup times are either disregarded or accounted for in the operation processing times. However, some studies consider setup times explicitly, as well as the associated energy consumption [35,50,62]. Additionally, some authors consider that setup energy consumption (S) includes not only the energy required to exchange tools and machine settings between consecutive operations, but also the energy required to change the state of the machines (e.g., warming up and ramping down) [11,[24][25][26].…”

Section: Pp = Maxmentioning

confidence: 99%

“…To the best of our knowledge, only 13 papers report such evaluations. The metrics used include the generational distance (GD) and inverted generational distance (IGD), which measure the convergence of the solutions to a reference Pareto front [11,104,105,111,148,150]; the hyper volume (HV), which measures the size of the dominated space according to some reference points [50,73,74,105,110]; and the spread, which evaluates the diversity and uniformity of the solutions [50,105,148,150]. One possible reason behind the very small number of papers reporting such metrics may be the lack of reference solutions.…”

Section: Multi-objective Algorithmsmentioning

confidence: 99%

“…Regarding adapted energy-efficient JSP instances, they have been adapted mainly from the JSP instances originally proposed by Adams et al [151] (abz5~abz9), Fisher and Thompson [152] (ft06, ft10, and ft20), Lawrence [153] (la01~la40), and Applagate and Cook [52] (orb01~orb10). Different authors adapted them in different ways and by adding additional data, typically randomly generated, as in the following examples: energy consumption for each operation [70], energy consumption for each operation at different speed levels [30,33,50,80,114], energy consumption for each operation and for turn on/off actions [23,25,26], energy consumption for each operation and each maintenance activity and a due date for each job [105], and energy consumption for each operation at different speed levels and a due date for each job [30].…”

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: Strategies For Energy Efficiencymentioning

confidence: 99%

Section: Pp = Maxmentioning

confidence: 99%

Section: Multi-objective Algorithmsmentioning

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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“…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

Axioms

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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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Multiobjective optimization allocation of multi‐skilled workers considering the skill heterogeneity and time‐varying effects in unit brake production lines

Zeng,

Nie

2023

Engineering Reports

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The optimal allocation of multi‐skilled workers in labor‐intensive industries can improve production capacity and reduce production costs. In actual production, the efficiency of workers will change with time due to their proficiency, fatigue, and other effects. In this article, we attempt to solve the problem of multi‐skilled workers allocation in unit brake production lines considering the heterogeneity of skills and time‐varying effects. A nonlinear mixed‐integer programming model is established, which fully considers the impact of worker efficiency due to proficiency, fatigue, and multi‐task rest recovery. The product production cycle and worker cost are the two objectives of the optimization solution. An enhanced NSGA‐II algorithm that combines the improved NSGA‐II algorithm and the variable neighborhood search (VNS) algorithm is used to solve the multiobjective optimization problem. Finally, the weighted ideal point method is used to obtain the Pareto optimal solution. The application case of a unit brake production is considered to evaluate the proposed model. The results indicate that the time cost and salary cost of workers are reduced by 8.03% and 18.91% compared with the original scheduling. The scheduling model considering learning, fatigue and recovery factors is more suitable for the actual production situation, ensuring the completion time and reducing the labor cost.

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Multiobjective Optimization of Energy-Efficient JOB-Shop Scheduling With Dynamic Reference Point-Based Fuzzy Relative Entropy

Cited by 50 publications

References 31 publications

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

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

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

Multiobjective optimization allocation of multi‐skilled workers considering the skill heterogeneity and time‐varying effects in unit brake production lines

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