Multi-objective evolutionary algorithms with heuristic decoding for hybrid flow shop scheduling problem with worker constraint

Han, Wenwu; Deng, Qianwang; Gong, Guiliang; Zhang, Like; Luo, Qiang

doi:10.1016/j.eswa.2020.114282

Cited by 41 publications

(10 citation statements)

References 54 publications

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“…Several heuristic decoding methods [41] [42] have been proposed to tackle job shop problems, however, the decoding methods commonly used in TSP lack heuristic. Some algorithms employ mathematical formulas suitable for the continuous problem and need to adopt the decoding method to generate legal TSP paths.…”

Section: ) Algorithms With Decoding Methods For Tspmentioning

confidence: 99%

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

et al. 2022

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The traveling salesman problem (TSP) is one of the most extensively studied problems in the combinatorial optimization area and still presents unsolved challenges due to its NP-hard attribute. Although many real-coded algorithms are available for TSP, they still have some performance challenges in the switch from continuous space to discrete space and perform at low convergence speed. This paper proposes a real-coded carnivorous plant algorithm with a heuristic decoding method (CPA-HDM) to solve the traveling salesman problem (TSP), which exhibits good convergence speed and solution accuracy. In this improved method, a new heuristic decoding method (HDM) is designed, which helps to map continuous variables to discrete ones without losing information, maintain population diversity, and enhance the solution quality after decoding. To balance the algorithm's search capability and enhance the probability of preferable individuals generated, an adaptive attraction probability (AAP), an improved growth model of carnivorous plants (IGMOCP), and a position update method of prey (IPUMOP) are developed. Aiming to reduce the probability of premature and prevent search stagnation, an improved reproduction strategy (IRS) and an adaptive combination perturbation are reconstructed. Finally, a local search algorithm is employed to improve the exploitation capability. To verify its validation, CPA-HDM is compared with six algorithms, for solving 28 TSP instances. The simulation results and statistical analyses demonstrate the superior performance of the proposed algorithm.

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Section: ) Algorithms With Decoding Methods For Tspmentioning

confidence: 99%

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

et al. 2022

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show abstract

“…As shown in the Eqs. (7), our optimization goal is to minimize the production completion time T. Constraint (8) requires that each worker can only be allocated to one stage; constraint (9) ensures that each stage should arrange at least one worker and the number of workers assigned in each stage must be an integer, where…”

Section: Manpower Scheduling Modelmentioning

confidence: 99%

“…The value of T is calculated by Eqs. (7) according to the changed worker schedule X and the number of adjusted workers. The execution time of the algorithm is the total time cost to find the optimal solutions.…”

Section: Test Data and Performance Criteriamentioning

confidence: 99%

“…Moreover, to address the problem on a large scale, they presented a hybrid algorithm based on particle swarm optimization and simulated annealing. Han et al [7] proposed seven multi-objective evolutionary algorithms with heuristic decoding to address this problem. Luo et al [8] proposed a novel Self-encoding Barnacle Mating Optimizer (SBMO) to solve the flow shop manpower scheduling problem.…”

Section: Introductionmentioning

confidence: 99%

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Three-Stage-encoding Barnacle Mating Algorithm for Dynamic Manpower Scheduling in Flow Shop

2022

Proceedings of WCSE 2022 Spring Event: 2022 9th International Conference on Industrial Engineering and Applications

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In actual industrial manufacturing scenarios, the dynamic behavior of workers is widespread.When the production starts for a while, the worker's request for leave and back will break the original production rhythm. This paper concentrates on how to reschedule workers in the flow shop under the consideration of the workers' dynamic behavior to minimize the total time of workers adjustment and production. Inspired by the Self-encoding Barnacle Mating Optimizer (SBMO) to our problem, we design a novel solution mode, offspring generation method, and neighborhood search scheme to propose the Threestage-encoding Barnacle Mating Algorithm (TBMA). Extensive experiments are conducted to test the performance of TBMA, and the results demonstrate that TBMA has competitive performance.

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“…Many real-life applications in manufacturing processes, industries, and parallel computing are modelled throughout the Hybrid flowshop scheduling problems (HFSP). In this context, several processes in the car industry, chemical industry, electronic, and semiconductors are modelled using the HFSP [1][2][3][4]. The HFSP shop is composed of a set of serial stages, where each one contains identical parallel machines.…”

Section: Introductionmentioning

confidence: 99%

Two-Stage Hybrid Flowshop Scheduling Problem with Independent Setup Times

Jemmali¹,

Hidri²,

Alourani³

2022

Int. j. simul. model.

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In this paper, a two-stage hybrid flowshop scheduling problem with independent setup times is examined. The complexity of the studied problem is NP-Hard in the strong sense since it generalized the two-stage hybrid flowshop scheduling problem. Several real-life problems in different areas are modelled using the addressed problem, as in parallel computing and manufacturing processes. Solving the current scheduling problem necessitates the construction of adequate algorithms, providing nearoptimal solutions within a satisfactory computing time. In this study, a genetic algorithm with specific features and three other heuristics are developed. These three heuristics are based on the optimal solution of the parallel machine scheduling problem with release dates and delivery times. In order to assess the performance of the meta-heuristic and the heuristics, a family of three lower bounds is proposed. An exhaustive numerical study is performed over a total of 1920 test problems. The obtained results provide strong evidence of the efficiency and the performance of the proposed procedures.

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Multi-objective evolutionary algorithms with heuristic decoding for hybrid flow shop scheduling problem with worker constraint

Cited by 41 publications

References 54 publications

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

A Carnivorous Plant Algorithm With Heuristic Decoding Method for Traveling Salesman Problem

Three-Stage-encoding Barnacle Mating Algorithm for Dynamic Manpower Scheduling in Flow Shop

Two-Stage Hybrid Flowshop Scheduling Problem with Independent Setup Times

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