Abstract
The two-sided assembly line balancing problem type-II (TALBP-II) is of major importance for the reconfiguration of the two-sided assembly lines which are widely utilized to assemble large-size high-volume products. The TALBP-II is NP-hard, and some assignment restrictions in real applications make this problem much more complex. This paper provides an integer programming model for solving the TALBP-II with assignment restrictions optimally and utilizes a simple and effective iterated greedy (IG) algorithm to address large-size problems. This algorithm utilizes a new local search by considering precedence relationships between tasks in order to reduce the computational time. In particular, a priority-based decoding scheme is developed to handle these assignment restrictions and reduce sequence-dependent idle times by adjusting the priority values. Experimental comparison among the proposed decoding scheme and other published ones demonstrates the efficiency of the priority-based decoding. A comprehensive computational comparison among the IG algorithm and other eight recent algorithms proves effectiveness of the proposed IG algorithm.
Highlights
A two-sided assembly line consists of a set of sequential mated-stations connected by the material handling system
The computational tests are carried out to prove the effectiveness of the priority-based decoding schemes for TALBPII with assignment restrictions and the high performance of the improved iterated greedy (IG) algorithm
A simple and effective iterated greedy (IG) algorithm is developed for the two-sided assembly line balancing problem type-II (TALBP-II) with assignment restrictions
Summary
A two-sided assembly line consists of a set of sequential mated-stations connected by the material handling system. It is widely used to produce large-size high-volume products, such as cars, trucks, and automobiles. Both sides are utilized for performing a set of tasks. A pair of face-to-face stations like station (n, 1) and station (n, 2) is called a matedstation and one of them is the companion of the other. The idle time on each station can be divided into two types: sequence-dependent idle time such as idle time behind task 1 [1] and the remaining idle time existing at the rear of the last task such as idle time behind 3
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