A smart algorithm for multi-criteria optimization of model sequencing problem in assembly lines. (February 2020) - Rauf, Mudassar; Guan, Zailin; Sarfraz, Shoaib; Mumtaz, Jabir; Shehab, Essam; Jahanzaib, Mirza; Hanif, Muhammad
Highlights: Multi-criteria model sequence problem is considered. A smart heuristic algorithm for multi-criteria optimization by extended NEH algorithm is proposed. Trade-off between three conflicting criteria (flow time, makespan and idle time) is provided. Taillard's benchmark instances (120 instances with 12 different problem sizes) is solved. ARPD and MCDM analysis are performed to evaluate the effectiveness of the proposed SMC-NEH algorithm. Abstract: Assembly Lines (ALs) are used for mass production as they offer lots of advantages over other production systems in terms of lead time and cost. The advent of mass customization has forced the manufacturing industries to update to Mixed-Model Assembly Lines (MMALs) but at the cost of increased complexity. In the real world, industries need to determine the sequence of models based on various conflicting performance measures/criteria. This paper investigates the Multi-Criteria Model Sequencing Problem (MC-MSP) using a modified simulation integrated Smart Multi-Criteria Nawaz, Enscore, and Ham (SMC-NEH) algorithm. To address the multiple criteria, a modified simulation integrated Smart Multi-Criteria Nawaz, Enscore, and Ham (SMC-NEH) algorithm was developed by integrating a priori approach with NEH algorithm. Discrete Event Simulation (DES) was used to evaluate each solution. A mathematical model was developed for three criteria: flow time, makespan and idle time. Further, to validate the effectiveness of the proposed SMC-NEH a case study and Taillard's benchmark instances were solved and a Multi-Criteria Decision-Making (MCDM) analysis was performed to compare the performance of the proposed SMC-NEH algorithm with the traditional NEH algorithm and its variants. The results showed that the proposed SMC-NEH algorithm outperformed the others in optimizing the conflicting multi-criteria problem.