A population-based algorithm for the bi-objective assembly line worker assignment and balancing problem. (March 2016)

• Record Type: Journal Article
• Authors: Zacharia, P. Th.; Nearchou, Andreas C.
• Is Part Of: Engineering applications of artificial intelligence. Volume 49(2016:Jan.)
• Page Start: 1
• Page End: 9
• Publication Date: 2016-03
• Subjects: Assembly line worker assignment and balancing -- Genetic algorithms -- Multi-objective optimization -- Smoothness index -- Pareto solutions; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2015.11.007
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: A multi-objective evolutionary algorithm (MOEA) is presented for the solution of the bi-criteria assembly line worker assignment and balancing problem (ALWABP). This problem consists of determining the best assignment of the assembly tasks to workers as well as the workers to workstations in accordance with some desired objectives. Task times differ depending on worker skills. Two optimization criteria are considered to be minimized, the cycle time and the smoothness index of the workload of the line. The efficiency of the proposed MOEA is evaluated over a set of benchmarks test problems taken from the open literature. A suitable performance analysis is deployed concerning the quality of the Pareto solutions. The results demonstrate a very satisfactory performance in terms of solution quality.