An improved preference-based variable neighborhood search algorithm with ar-dominance for assembly line balancing considering preventive maintenance scenarios. (March 2022)

• Record Type: Journal Article
• Authors: Zhao, Lianpeng; Tang, Qiuhua; Zhang, Zikai
• Is Part Of: Engineering applications of artificial intelligence. Volume 109(2022)
• Publication Date: 2022-03
• Subjects: Preference-based multi-objective optimization -- Variable neighborhood search -- Assembly line balancing -- Preventive maintenance; 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.2021.104593
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: For assembly line balancing problem considering preventive maintenance scenarios (ALBP-PM), the production managers' preferences should be concerned and hence the derived Pareto-optimal solution set ( POS ) should be advanced towards the corresponding region-of-interest. Otherwise, production managers might select a final solution far from their interests, and then obviously reducing the quality and efficiency of decision-making. Thus, a preference-based multi-objective optimization problem is formulated to simultaneously minimize the cycle times under different scenarios and total task adjustments among scenarios. An improved variable neighborhood search algorithm (IVNS) with novel ar -dominance and three modifications is proposed to obtain a preferred POS . Specifically, ar -dominance is integrated into it to guide the advancement direction of the preferred POS . Enhanced neighborhood structures based on critical stations are proposed to generate better-quality neighbor solutions whose cycle times are mathematically proven smaller. On this basis, a local search strategy is designed to randomly select a neighbor solution to guarantee quality and decrease the computational cost. A problem-specific adaptive restart mechanism is developed to escape from the local optimum. Computational results suggest that the IVNS obtains a better preferred POS than other eight state-of-the-art algorithms in terms of convergence, distribution and closeness to the preferences, and furthermore, incorporating preferences into the ALBP-PM helps production managers make final decisions in a more precise and faster way. Highlights: A novel ALBP-PM concerning production managers' preferences is tackled. The ar -dominance advances Pareto-optimal solutions towards the region-of-interest. Enhanced neighborhood structures generate neighbor solutions with smaller cycle time. Problem-specific restart mechanism sets threshold adaptively to escape local optimum. Statistical results indicate IVNS outperforms all other comparison algorithms.