Model and migrating birds optimization algorithm for two-sided assembly line worker assignment and balancing problem

被引:24
|
作者
Janardhanan, Mukund Nilakantan [1 ]
Li, Zixiang [2 ,3 ]
Nielsen, Peter [4 ]
机构
[1] Univ Leicester, Dept Engn, Leicester, Leics, England
[2] Wuhan Univ Sci & Technol, Key Lab Met Equipment & Control Technol, Wuhan, Hubei, Peoples R China
[3] Wuhan Univ Sci & Technol, Hubei Key Lab Mech Transmiss & Mfg Engn, Wuhan, Hubei, Peoples R China
[4] Aalborg Univ, Dept Mat & Prod, Aalborg, Denmark
基金
美国国家科学基金会; 中国博士后科学基金;
关键词
Assembly line balancing; Two-sided assembly line; Worker assignment; Migrating birds optimization; Metaheuristics; AGGREGATION OPERATORS; MATHEMATICAL-MODEL; SEARCH ALGORITHM; BOUND ALGORITHM; INTEGRATION; HEURISTICS; CENTERS;
D O I
10.1007/s00500-018-03684-8
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Worker assignment is a relatively new problem in assembly lines that typically is encountered in situations in which the workforce is heterogeneous. The optimal assignment of a heterogeneous workforce is known as the assembly line worker assignment and balancing problem (ALWABP). This problem is different from the well-known simple assembly line balancing problem concerning the task execution times, and it varies according to the assigned worker. Minimal work has been reported in worker assignment in two-sided assembly lines. This research studies worker assignment and line balancing in two-sided assembly lines with an objective of minimizing the cycle time (TALWABP). A mixed-integer programming model is developed, and CPLEX solver is used to solve the small-size problems. An improved migrating birds optimization algorithm is employed to deal with the large-size problems due to the NP-hard nature of the problem. The proposed algorithm utilizes a restart mechanism to avoid being trapped in the local optima. The solutions obtained using the proposed algorithms are compared with well-known metaheuristic algorithms such as artificial bee colony and simulated annealing. Comparative study and statistical analysis indicate that the proposed algorithm can achieve the optimal solutions for small-size problems, and it shows superior performance over benchmark algorithms for large-size problems.
引用
收藏
页码:11263 / 11276
页数:14
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