A memetic algorithm for the cost-oriented robotic assembly line balancing problem. (November 2018)

• Record Type: Journal Article
• Authors: Pereira, Jordi; Ritt, Marcus; Vásquez, Óscar C.
• Is Part Of: Computers & operations research. Volume 99(2018)
• Page Start: 249
• Page End: 261
• Publication Date: 2018-11
• Subjects: Line balancing -- Cost-oriented line balancing -- Robotic assembly line -- Hybrid algorithms; Operations research -- Periodicals; Electronic digital computers -- Periodicals; 004.05
• DOI: 10.1016/j.cor.2018.07.001
• Languages: English
• ISSNs: 0305-0548

Abstract

Highlights: We put forward a novel cost-oriented line balancing problem. Several properties and special cases of the problem are studied. We design a memetic algorithm that hybridises these properties with a genetic algorithm. Computational experiments highlight the contributions of each component to the quality of the proposed method. Abstract: In order to minimize costs, manufacturing companies have been relying on assembly lines for the mass production of commodity goods. Among other issues, the successful operation of an assembly line requires balancing work among the stations of the line in order to maximize its efficiency, a problem known in the literature as the assembly line balancing problem, ALBP. In this work, we consider an ALBP in which task assignment and equipment decisions are jointly considered, a problem that has been denoted as the robotic ALBP. Moreover, we focus on the case in which equipment has different costs, leading to a cost-oriented formulation. In order to solve the problem, which we denote as the cost-oriented robotic assembly line balancing problem, cRALBP, a hybrid metaheuristic is proposed. The metaheuristic embeds results obtained for two special cases of the problem within a genetic algorithm in order to obtain a memetic algorithm, applicable to the general problem. An extensive computational experiment shows the advantages of the hybrid approach and how each of the components of the algorithm contributes to the overall ability of the method to obtain good solutions.