Abstract
This article considers a two-stage assembly scheduling problem (TSASP) with batch setup times, time-dependent deterioration, and preventive maintenance activities (PMAs). The objective of this problem is to simultaneously determine the optimal component-manufacturing sequence (CMS), product-assembly sequence (PAS), number of setups, and number and position of PMAs (PPMA). First, to determine the optimal solution, a novel mixed integer linear programming model (MILP) for the proposed problem is derived. Then, a standard genetic algorithm (SGA), hybrid genetic algorithm (HGA), standard harmony search (SHS), hybrid harmony search (HHS), and harmony-search-based evolutionary algorithm (HSEA) were proposed owing to the intractability of the optimal solution for large-scale problems. SGA and SHS provide a chromosome to represent a complete solution including three decisions (CMS, PAS, and PPMA). HGA, HHS, and HSEA provide a chromosome to represent a partial solution including PAS. CMS and PPMA are found by an effective local search heuristic based on the partial solution. A computational experiment is then conducted to evaluate the impacts of the factors on the performance of the proposed algorithms.
Highlights
The two-stage assembly scheduling problem (TSASP) is one of the most widely studied scheduling problems in the literature and has many applications in the industry
Similar to hybrid genetic algorithm (HGA), the harmony vector of hybrid harmony search (HHS) consists of a one-dimensional string array that represents a product-assembly sequence (PAS) and the other partial solutions (CMS and position of PMAs (PPMA)) are determined by BSD using the partial solution decoded from the solution structure
Since the complexity of a problem highly depends upon the length of componentmanufacturing sequence (CMS) |L|, several instances of two problem groups of small and largescale problems are randomly generated according to |L|
Summary
The two-stage assembly scheduling problem (TSASP) is one of the most widely studied scheduling problems in the literature and has many applications in the industry. Jung et al [13] proposed two hybrid genetic algorithms (HGAs) for twostage assembly scheduling problem (TSASP) for processing products with dynamic-component sizes and a setup time. They had the same two-stage assembly structure with our study. To the best of our knowledge, no available effective metaheuristic algorithms are capable of generating a nearoptimal solution while the TSASP simultaneously considering batch setup times, time-dependent deterioration, and PMAs. The remainder of this article is organized as follows. A novel MILP model is developed to minimize the makespan for the TSASP with batch setup times, deterioration, and PMAs. The problem statement of this problem has been applied the dynamic-component size.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
