Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling

Rifai, Achmad Pratama; Thọ, Nguyễn Hữu; Dawal, Siti Zawiah Md

doi:10.1016/j.asoc.2015.11.034

Cited by 125 publications

(42 citation statements)

References 54 publications

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“…For solving the DPFSP, the neighborhood structures commonly used in the literature are insertion, such as insertion, swap, pairwise exchange, and variable neighborhood search algorithm. [61][62][63] Considering the characteristics of the problem in this article and the development and exploration ability of the proposed algorithm, we propose four kinds of neighborhood structures, namely two kinds of insertion and two kinds of swap neighborhood structure, as given in Algorithm 4. In the neighborhood structures, we use two kinds of search operators, namely insertion operator and exchange operator.…”

Section: Problem-specific Neighboring Structuresmentioning

confidence: 99%

Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

Gao

et al. 2019

International Journal of Advanced Robotic Systems

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In this study, we propose an improved iterated greedy algorithm for solving the distributed permutation flowshop problem, where there is a single robot in each factory and the makespan needs to be minimized. In the problem considered, the robot is used to transfer each job from the predecessor machine to the successor machine. A blocking constraint between machines is considered, thus jobs should remain on the completed machine while waiting for the robot. The loading and unloading times are considered and different for all of the jobs conducted by the robot, and the deteriorating time is also considered. In the proposed algorithm, first, four types of neighborhood structures are developed. Then, the simulated annealing algorithm is embedded in the proposed algorithm to enhance the exploration abilities. Furthermore, a problem-specific destruction and construction strategies are investigated. Finally, several realistic instances were generated to test the proposed algorithm, and its competitive performance was verified based on detailed experimental comparisons.

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Section: Problem-specific Neighboring Structuresmentioning

confidence: 99%

Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

Gao

et al. 2019

International Journal of Advanced Robotic Systems

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“…Metaheuristics has been used to solve many combinatorial optimization challenges, which include the job shop scheduling problem [22], fuzzy job-shop scheduling problems [23], the capacitated vehicle routing problem with soft time windows [24], the fleet size and mix vehicle routing problem [25,26], a combination of assignment and transportation problems [27], bicriterion transportation problems [28], transportation for handicapped persons [29], the robust capacitated vehicle routing problem [30], distributed reentrant permutation flow shop scheduling [31], cumulative capacitated vehicles [32], multi-stage logistic chain networks [33], supply chain management [34], the dynamic technician routing and scheduling problem [35], a combination of vehicle routing and scheduling problems with time window constraints [36,37], cyclic scheduling of a hoist with time window constraints [38], and generalized assignment problems [10]. Metaheuristics involves the methods of finding a good solution within a reasonable computational time.…”

Section: Literature Reviewmentioning

confidence: 99%

Adaptive Large Neighborhood Search to Solve Multi-Level Scheduling and Assignment Problems in Broiler Farms

Praseeratasang

Pitakaso

Sethanan

et al. 2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research aimed to present a solution to the problem of production scheduling and assignment in broiler farms, which thus enabled the farms to achieve maximum profit. In the operation of farms, there are many factors that affect profits, such as the number of broilers being consistent with the demand of production plants, including profits from the sales and transportation costs. Therefore, we formulated a mathematical model and tested it while using three problem groups through the Lingo v.11 program. The results indicated that this mathematical model could find a suitable solution. However, finding the best solution had time constraints, which resulted in various other problems that prevented a search for an optimal solution due to time consumption exceeding 72 h. We developed an algorithm using the Adaptive Large Neighborhood Search (ALNS) method in order to find another possible solution using a shorter time period, which consisted of ALNS1, ALNS2, and ALNS3. These algorithms are based on a combination of the method of destruction solutions and methods accepting different solutions. We aimed to effectively solve the problems and ensure that they are appropriate for the case study, a broiler farm in Buriram. When comparing the algorithm efficiency with the Lingo v.11 program, it was found that the ALNS1 algorithm was the most suitable for finding the optimal solution in the shortest time, which resulted in a 5.74% increase in operating profits.

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“…Zheng and Wang [16] developed a Pareto-based estimation of distribution algorithm to solve a production scheduling problem. Rifai et al [17] proposed a novel multi-objective adaptive large neighborhood search algorithm to address a distributed permutation flow shop scheduling problem. A recent review on the multiobjective permutation flow shop scheduling problem is provided by Yenisey and Yagmahan [18] .…”

Section: Introductionmentioning

confidence: 99%

An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production

Xiao

et al. 2016

Advances in Engineering Software

108

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a b s t r a c tThis paper aims to provide a solution method for a real-world scheduling case from a welding process, which is one of the important processes in modern industry. The unique characteristic of the welding scheduling problem (WSP) is that multiple machines can process one operation at a time. Thus, WSP is a new scheduling problem. We first formulate a new multi-objective mixed integer programming model for this WSP based on a comprehensive investigation. This model involves some realistic constraints, controllable processing times (CPT), sequence dependent setup times (SDST) and job dependent transportation times (JDTT). Then we propose a multi-objective discrete grey wolf optimizer (MODGWO) considering not only production efficiency but also machine load on this real-world scheduling case. The solution is encoded as a two-part representation including a permutation vector and a machine assignment matrix. A reduction machine load strategy is used to adjust the number of machines aiming to minimize the machine load. To evaluate the effectiveness of the proposed MODGWO, we compare it with other well-known multi-objective evolutionary algorithms including NSGA-II and SPEA2 on a set of instances. Experimental results demonstrate that the proposed MODGWO is superior to the compared algorithms in terms of convergence, spread and coverage on most instances. Finally, MODGWO is successfully applied to this real-world WSP. This implies that the proposed model is feasible and the proposed algorithm can solve this real-world scheduling problem very well.

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Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling

Cited by 125 publications

References 54 publications

Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

Solving robotic distributed flowshop problem using an improved iterated greedy algorithm

Adaptive Large Neighborhood Search to Solve Multi-Level Scheduling and Assignment Problems in Broiler Farms

An effective multi-objective discrete grey wolf optimizer for a real-world scheduling problem in welding production

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