A scatter search based hyper-heuristic for sequencing a mixed-model assembly line

被引:29
|
作者
Cano-Belman, Jaime [1 ]
Rios-Mercado, Roger Z.
Bautista, Joaquin [2 ]
机构
[1] Univ Autonoma Nuevo Leon, Grad Program Syst Engn, San Nicolas De Los Garza 66450, NL, Mexico
[2] Univ Politecn Cataluna, UPC Nissan Chair, E-08028 Barcelona, Spain
关键词
Just-in-time scheduling; Assembly line; Priority rules; Work overload; Scatter search; Hyper-heuristic; MINIMUM JOB SETS; WORK OVERLOAD; ALGORITHM;
D O I
10.1007/s10732-009-9118-2
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
We address a mixed-model assembly-line sequencing problem with work overload minimization criteria. We consider time windows in work stations of the assembly line (closed stations) and different versions of a product to be assembled in the line, which require different processing time according to the work required in each work station. In a paced assembly line, products are feeded in the line at a predetermined constant rate (cycle time). Then, if many products with processing time greater than cycle time are feeded consecutively, work overload can be produced when the worker has insufficient time to finish his/her job. We propose a scatter search based hyper-heuristic for this NP-hard problem. In the low-level, the procedure makes use of priority rules through a constructive procedure. Computational experiments over a wide range of instances from the literature show the effectiveness of the proposed hyper-heuristics when compared to existing heuristics. The relevance of the priority rules was evaluated as well.
引用
收藏
页码:749 / 770
页数:22
相关论文
共 50 条
  • [21] Hyper-heuristic Based Local Search for Combinatorial Optimisation Problems
    Turky, Ayad
    Sabar, Nasser R.
    Dunstall, Simon
    Song, Andy
    AI 2018: ADVANCES IN ARTIFICIAL INTELLIGENCE, 2018, 11320 : 312 - 317
  • [22] A Hyper-Heuristic Method for UAV Search Planning
    Wang, Yue
    Zhang, Min-Xia
    Zheng, Yu-Jun
    ADVANCES IN SWARM INTELLIGENCE, ICSI 2017, PT II, 2017, 10386 : 454 - 464
  • [23] Minimizing Lost-Work Costs in a Mixed-Model Assembly Line
    Bautista, Joaquin
    Alfaro-Pozo, Rocio
    Batalla-Garcia, Cristina
    CLOSING THE GAP BETWEEN PRACTICE AND RESEARCH IN INDUSTRIAL ENGINEERING, 2018, : 213 - 221
  • [24] Stochastic mixed-model assembly line sequencing problem: Mathematical modeling and Q-learning based simulated annealing hyper-heuristics
    Mosadegh, H.
    Ghomi, S. M. T. Fatemi
    Suer, G. A.
    EUROPEAN JOURNAL OF OPERATIONAL RESEARCH, 2020, 282 (02) : 530 - 544
  • [25] A tensor-based selection hyper-heuristic for cross-domain heuristic search
    Asta, Shahriar
    Oezcan, Ender
    INFORMATION SCIENCES, 2015, 299 : 412 - 432
  • [26] A backtracking search hyper-heuristic for the distributed assembly flow-shop scheduling problem
    Lin, Jian
    Wang, Zhou-Jing
    Li, Xiaodong
    SWARM AND EVOLUTIONARY COMPUTATION, 2017, 36 : 124 - 135
  • [27] Optimal production sequencing problem to minimise line stoppage time in a mixed-model assembly line
    Tamura, Takayoshi
    Okumura, Taiji
    Dhakar, Tej Singh
    Ohno, Katsuhisa
    INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2011, 49 (14) : 4299 - 4315
  • [28] Hyper-Heuristic Based on ACO and Local Search for Dynamic Optimization Problems
    Muller, Felipe Martins
    Bonilha, Iae Santos
    ALGORITHMS, 2022, 15 (01)
  • [29] Sequencing mixed-model assembly lines to level parts usage with consideration of line balancing
    Song, HM
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON MECHANICAL ENGINEERING AND MECHANICS 2005, VOLS 1 AND 2, 2005, : 1655 - 1659
  • [30] A hyper-heuristic approach for stochastic parallel assembly line balancing problems with equipment costs
    Ozbakir, Lale
    Secme, Gokhan
    OPERATIONAL RESEARCH, 2022, 22 (01) : 577 - 614