A Bi-Population Evolutionary Algorithm With Feedback for Energy-Efficient Fuzzy Flexible Job Shop Scheduling

Pan, Zixiao; Lei, Deming

doi:10.1109/tsmc.2021.3120702

Cited by 63 publications

(14 citation statements)

References 57 publications

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“…Fuzzy arithmetic is a powerful tool to introduce uncertainty into mathematical models. Some of the reviewed papers, though not many, use triangular fuzzy numbers to model uncertainty regarding processing times [104,125,126] and setup times and defective product rate [37]. On the other hand, in [127], the uncertainty associated with delivery times is modeled through trapezoidal fuzzy numbers.…”

Section: Heuristic Methodsmentioning

confidence: 99%

“…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%

“…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%

See 2 more Smart Citations

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: Heuristic Methodsmentioning

confidence: 99%

Section: Other Objective Functionsmentioning

confidence: 99%

Section: Multi-objective Algorithmsmentioning

confidence: 99%

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

2022

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“…In [63] the authors propose an evolutionary algorithm to reduce the nonprocessing energy and the total weighted tardiness in a fuzzy job shop, while two memetic algorithms are proposed in [64] and [41], the former to minimize the non-processing energy and the makespan in a fuzzy job shop and the latter to minimize total energy consumption in a flexible job shop. A multiobjective fuzzy flexible job shop problem is tackled in [65], with the goal of minimising the fuzzy makespan together with the fuzzy total energy consumption as well as maximising flexible due-date satisfaction. The proposed solving method is a bi-population evolutionary algorithm with a feedback mechanism and enhanced local search, thus falling in the category of memetic algorithms.…”

Section: Introductionmentioning

confidence: 99%

Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops

Gómez

González-Rodríguez

Vela

2023

ICA

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The flexible job shop is a well-known scheduling problem that has historically attracted much research attention both because of its computational complexity and its importance in manufacturing and engineering processes. Here we consider a variant of the problem where uncertainty in operation processing times is modeled using triangular fuzzy numbers. Our objective is to minimize the total energy consumption, which combines the energy required by resources when they are actively processing an operation and the energy consumed by these resources simply for being switched on. To solve this NP-Hard problem, we propose a memetic algorithm, a hybrid metaheuristic method that combines global search with local search. Our focus has been on obtaining an efficient method, capable of obtaining similar solutions quality-wise to the state of the art using a reduced amount of time. To assess the performance of our algorithm, we present an extensive experimental analysis that compares it with previous proposals and evaluates the effect on the search of its different components.

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“…For the energy-efficient scheduling problem, He et al 17 studied the energy-efficient job shop scheduling problem with SDST to minimize makespan, TD and TEC. Pan et al 18 provided a bi-population evolutionary algorithm to solve the energy-efficient fuzzy flexible job shop scheduling problem (FJSP). Wang et al 19 designed a whale swarm algorithm for the distributed welding flow shop scheduling problem aiming at minimizing the TEC and makespan.…”

Section: Introductionmentioning

confidence: 99%

Energy-efficient distributed heterogeneous re-entrant hybrid flow shop scheduling problem with sequence dependent setup times considering factory eligibility constraints

Geng

Liu

2022

Sci Rep

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In the face of energy crisis, manufacturers pay more and more attention to energy-saving scheduling. In the paper, we consider the distributed heterogeneous re-entrant hybrid flow shop scheduling problem (DHRHFSP) with sequence dependent setup times (DHRHFSP-SDST) considering factory eligibility constraints under time of use (TOU) price, which means that each job can only be assigned to its available set of factories and all factories have different number of machines and processing capacity, and so on. To deal with DHRHFSP-SDST, a multi-objective Artificial Bee Colony Algorithm (MOABC) is proposed to optimize both the makespan and total energy consumption. For the MOABC, firstly, a hybrid initialization method is presented to initialize the population; then, due to the electricity price shows significant differences vary from periods under TOU price, the energy saving operator based on right-shift strategy is proposed to avoid processing jobs with the high electricity price without affecting the productivity; thirdly, based on the full consideration of distributed heterogeneous and factory eligibility, crossover and mutation operators, three neighborhood search operators and new food sources generation strategy are designed; lastly, extensive experiments demonstrate the effectiveness of the proposed algorithm on solving the DHRHFSP-SDST.

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A Bi-Population Evolutionary Algorithm With Feedback for Energy-Efficient Fuzzy Flexible Job Shop Scheduling

Cited by 63 publications

References 57 publications

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

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

Enhanced memetic search for reducing energy consumption in fuzzy flexible job shops

Energy-efficient distributed heterogeneous re-entrant hybrid flow shop scheduling problem with sequence dependent setup times considering factory eligibility constraints

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