An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem

Li, Yibing; Huang, Wenxin; Wu, Rui; Guo, Kai

doi:10.1016/j.asoc.2020.106544

Cited by 113 publications

(37 citation statements)

References 30 publications

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“…Very little research has been done on saving energy and reducing pollution emissions. Li et al [33] designed an improved ABC to solve a multi-objective low-carbon job shop scheduling problem with variable machining speed constraints. Zhang et al [34] studied HFGSP with variable machine processing speed to minimize MS and TEC and proposed a multi-objective DABC (MDABC) to solve HFGSP.…”

Section: Related Workmentioning

confidence: 99%

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Wang

2021

Mathematics

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In this era of unprecedented economic and social prosperity, problems such as energy shortages and environmental pollution are gradually coming to the fore, which seriously restrict economic and social development. In order to solve these problems, green shop scheduling, which is a key aspect of the manufacturing industry, has attracted the attention of researchers, and the widely used flow shop scheduling problem (HFSP) has become a hot topic of research. In this paper, we study the fuzzy hybrid green shop scheduling problem (FHFGSP) with fuzzy processing time, with the objective of minimizing makespan and total energy consumption. This is more in line with real-life situations. The non-linear integer programming model of FHFGSP is built by expressing job processing times as triangular fuzzy numbers (TFN) and considering the machine setup times when processing different jobs. To address the FHFGSP, a discrete artificial bee colony (DABC) algorithm based on similarity and non-dominated solution ordering is proposed, which allows individuals to explore their neighbors to different degrees in the employed bee phase according to a sequence of positions, increasing the diversity of the algorithm. During the onlooker bee phase, individuals at the front of the sequence have a higher chance of being tracked, increasing the convergence rate of the colony. In addition, a mutation strategy is proposed to prevent the population from falling into a local optimum. To verify the effectiveness of the algorithm, 400 test cases were generated, comparing the proposed strategy and the overall algorithm with each other and evaluating them using three different metrics. The experimental results show that the proposed algorithm outperforms other algorithms in terms of quantity, quality, convergence and diversity.

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Section: Related Workmentioning

confidence: 99%

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Wang

2021

Mathematics

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“…BRdata set contains 10 instances, ranging from 10 parts and 6 machines to 20 parts and 15 machines. Use the HABC algorithm to compare the simulation results of the BRdata problem with the classic GA algorithm and the HABC algorithm proposed by Li et al [10]. The population size of the three algorithms is the same as the number of iterations, namely: the number of iterations is 60; the population size is 80.…”

Section: ) Brdata Instancesmentioning

confidence: 99%

“…Li et al [10] designed an improved artificial bee colony (IABC) algorithm to solve the multi-objective low-carbon job-shop scheduling problem(MLFJSP) with variable processing speed constraint. The optimization objectives of MLFJSP include minimizing the makespan, total carbon emission and machine loading.…”

Section: Introductionmentioning

confidence: 99%

Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm

Gu¹

2021

IEEE Access

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This paper proposes a hybrid artificial bee colony (HABC) to solve the multiobjective lowcarbon flexible job-shop scheduling problem (MLFJSP). HABC algorithm uses a two-layer coding method to establish the initial population as the nectar source for the employed bees. In the optimization process, the employed bee phase and the onlooker bee phase adopt improved crossover mutation strategies and adaptive neighborhood search strategies to generate new nectar sources, and the greedy method is used to retain better solutions. The scout bee update mechanism prevents the algorithm from falling into a local optimum and enhances the convergence of the algorithm. In order to prevent the loss of the optimal solution, the optimization results of each phase are saved in the Pareto archive (PA). Finally, two sets of international standard instances are used to carry out simulation experiments. After analyzing the simulation results, it is concluded that HABC is an effective algorithm to solve the multiobjective low-carbon flexible job shop scheduling problem.INDEX TERMS artificial bee colony, multiobjective flexible job-shop problem, low-carbon, adaptive variable neighborhood search

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“…The multi-objective ABC algorithm is a widely used algorithm in the literature for varied multi-objective problems. Some of these problems are scheduling problem [12][13][14][15][16], short-term scheduling of hydrothermal system [17], software requirement optimization [18] ,optimizing network topology design [19], manufacturing [5,20], smart grid communication [21], resource leveling problem [22], vehicle routing problem [23], band selection problem [24], and feature selection problem [25][26][27].…”

Section: Related Workmentioning

confidence: 99%

Solving Multi-Objective Resource Allocation Problem Using Multi-Objective Binary Artificial Bee Colony Algorithm

Acar

Başçiftçi

2021

Arab J Sci Eng

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Resource allocation is the optimal distribution in a limited number of resources available for certain activities. The allocation of the resources for a large number of activities requires exponentially multiplying a computation cost. Therefore, the resource allocation problem is known as NP-Hard problem in the literature. In this study, a multi-objective binary artificial bee colony algorithm has been proposed for solving the multi-objective resource allocation problems. The proposed algorithm has benefited from the robust structure and easy implementation properties of the artificial bee colony algorithm. The contribution is to introduce the multi-objective version of the artificial bee colony algorithm with advanced local search and binary format using transfer functions. The multi-objective binary artificial bee colony algorithm has been improved as two versions using sigmoid and hyperbolic tangent transfer functions to be able to search in the binary search space. With the proposed algorithms, the multi-objective resource allocation problems in the literature are solved, and the algorithms are compared with other algorithms that develop for the same problems. The results obtained show that the proposed algorithms give effective results on the problem. Especially, in large-scale problems, higher accuracy values are reached with a smaller number of evaluations.

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An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem

Cited by 113 publications

References 30 publications

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Sorting-Based Discrete Artificial Bee Colony Algorithm for Solving Fuzzy Hybrid Flow Shop Green Scheduling Problem

Application Research for Multiobjective Low-Carbon Flexible Job-Shop Scheduling Problem Based on Hybrid Artificial Bee Colony Algorithm

Solving Multi-Objective Resource Allocation Problem Using Multi-Objective Binary Artificial Bee Colony Algorithm

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