A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility

Gong, Guiliang; Chiong, Raymond; Deng, Qianwang; Gong, Xuran

doi:10.1080/00207543.2019.1653504

Cited by 84 publications

(28 citation statements)

References 45 publications

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“…In contrast, AMSABC EL only achieves a significantly better value on problem 16. In addition to the refreshed solutions, the advantage of AMSABC is mainly concentrated after problem 16, indicating that the problem-specific local search method has obvious competence for solving large-scale (problems [16][17][18][19][20][21][22][23][24] problems. As for the metric , AMSABC is significantly better than these 8 problems, whereas AMSABC EL obtained only one optimal value.…”

Section: Effectiveness Of Local Search Strategymentioning

confidence: 99%

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An Adaptive Multi-Strategy Artificial Bee Colony Algorithm for Integrated Process Planning and Scheduling

Cao

Shi

2021

IEEE Access

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Traditionally, process planning and scheduling were performed sequentially, where scheduling depended on the result of process planning. Considering their complementarity, the two functions are more tightly integrated to improve the performance of job shop flexible manufacturing environment. This study proposes an adaptive multi-strategy artificial bee colony (AMSABC) algorithm to solve integrated process planning and scheduling (IPPS) problem. In AMSABC, two search strategies with different characteristics are introduced into employed bees and onlooker bees to take on the responsibility of both exploration and exploitation. The selection probability of each search strategy is dynamically adjusted according to previous experiences. To further improve the exploitation performance of the approach, a problem-specific multi-objective local search has been embedded in the proposed algorithm. Furthermore, AMSABC algorithm presents a unique solution representation where a food source is represented by three discrete vectors, and a well-designed decoding scheme is developed. Next, the corresponding neighborhood structure is adopted that it can directly generate feasible solutions in the search space. The proposed algorithm is tested on the well-known benchmark instances and compared with the state-of-the-art algorithms. Through detail analysis of experimental results, AMSABC algorithm is more beneficial in the quality and efficiency of solution.INDEX TERMS integrated process planning and scheduling, artificial bee colony algorithm, multiobjective optimization, multi-strategy collaboration, strategy adaptation This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.

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Section: Effectiveness Of Local Search Strategymentioning

confidence: 99%

“…The artificial bee colony (ABC) algorithm is proposed by Karaboga [14] to optimize multivariable and multimodal continuous functions in 2005. It has been applied successful to solve the multi-objective scheduling problem [15][16][17]. The ABC algorithm distributes information on food location throughout the population of bees.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Multi-Strategy Artificial Bee Colony Algorithm for Integrated Process Planning and Scheduling

Cao

Shi

2021

IEEE Access

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“…RF operates based on the regression or classification trees [70], [71]. This study applies the former approach, RF analysis based on the regression tree that comprises training and prediction steps.…”

Section: A: Phase I Rf Analysis For Setup Time Estimationmentioning

confidence: 99%

Learning-Based Metaheuristic for Scheduling Unrelated Parallel Machines With Uncertain Setup Times

et al. 2020

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Setup time consists of all the activities that need to be completed before the production process takes place. The extant scheduling predominantly relies on simplistic methods, like the average value obtained from historical data, to estimate setup times. However, such methods are incapable of representing the real industry situation, especially when the setup time is subject to significant uncertainties. In this situation, the estimation error increases proportionally to the problem size. This study proposes a Random-Forest-based metaheuristic to minimize the makespan in an Unrelated Parallel Machines Scheduling Problem (UPMSP) with uncertain machine-dependent and job sequence-dependent setup times (MDJSDSTs). Taking the forging industry as an example, the numerical experiments show that the error percentage for the setup time estimation substantially decreases when the proposed approach is applied. This improvement is particularly significant when large-scale problems are sought. Overall, this study highlights the role of advanced analytics in bridging the gap between scheduling theory and practice. INDEX TERMS Scheduling, unrelated parallel machines, setup times, random-forest, metaheuristic.

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“…Then Akbari et al [17] have verified its superiority by comparing it with other traditional algorithms. Nowadays, the ABC algorithm has been used in a variety of fields such as shop scheduling [18][19][20][21][22][23], network planning [24][25][26][27], nurse rostering [28][29][30] and economic scheduling [31][32][33]. Since MOABC has been successfully applied in above fields, this paper propose a discrete MOABC algorithm to solve EDTMAP.…”

Section: Introductionmentioning

confidence: 99%

A Discrete Multi Objective Artificial Bee Colony Algorithm for a Real World Electronic Device Testing Machine Allocation Problem

Xie¹,

Li²,

Gao³

2021

Preprint

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This paper studies the Electronic Device Testing Machine Allocation Problem (EDTMAP), aiming to improve the production of electronic devices and reduce the scheduling distance of testing machines through reasonable machine allocation. Firstly, a mathematical model is formulated for the EDTMAP to maximize both production and the opposite of the scheduling distance of testing machines. Secondly, we develop a Discrete Multi Objective Artificial Bee Colony (DMOABC) algorithm to solve the EDTMAP. A crossover operator and a local search operator are designed to improve the exploration and exploitation of the algorithm, respectively. Some numerical experiments are designed to evaluate the performance of the proposed algorithm. The experimental results demonstrate the superiority of the proposed algorithm compared with NSGA --Ⅱ and SPEA2. Finally, the mathematical model and the DMOABC algorithm areapplied to a real world factory that tests radio frequency modules. The result also verifies our method can significantly improve production and reduce the scheduling distance of testing machines.

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A hybrid artificial bee colony algorithm for flexible job shop scheduling with worker flexibility

Cited by 84 publications

References 45 publications

An Adaptive Multi-Strategy Artificial Bee Colony Algorithm for Integrated Process Planning and Scheduling

An Adaptive Multi-Strategy Artificial Bee Colony Algorithm for Integrated Process Planning and Scheduling

Learning-Based Metaheuristic for Scheduling Unrelated Parallel Machines With Uncertain Setup Times

A Discrete Multi Objective Artificial Bee Colony Algorithm for a Real World Electronic Device Testing Machine Allocation Problem

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