Abstract
This study investigates a two-stage assembly-type flow shop with limited waiting time constraints for minimizing the makespan. The first stage consists of m machines fabricating m types of components, whereas the second stage has a single machine to assemble the components into the final product. In the flow shop, the assembly operations in the second stage should start within the limited waiting times after those components complete in the first stage. For this problem, a mixed-integer programming formulation is provided, and this formulation is used to find an optimal solution using a commercial optimization solver CPLEX. As this problem is proved to be NP-hard, various heuristic algorithms (priority rule-based list scheduling, constructive heuristic, and metaheuristic) are proposed to solve a large-scale problem within a short computation time. To evaluate the proposed algorithms, a series of computational experiments, including the calibration of the metaheuristics, were performed on randomly generated problem instances, and the results showed outperformance of the proposed iterated greedy algorithm and simulated annealing algorithm in small- and large-sized problems, respectively.
Highlights
Competition in manufacturing industries is intensifying around the world, and these industries are facing changes, such as the diversification of customer requirements, shorter product life cycles, and the transition to a multiproduct small-volume production system
This study aims to minimize the makespan in a two-stage assembly flow shop with limited waiting time constraints
After calibrating the proposed metaheuristics, we examined the solutions obtained from the heuristics by comparing them with those from CPLEX
Summary
Competition in manufacturing industries is intensifying around the world, and these industries are facing changes, such as the diversification of customer requirements, shorter product life cycles, and the transition to a multiproduct small-volume production system. To enhance manufacturing competitiveness and improve productivity, several manufacturers are focusing on reducing the production lead time and minimizing inventory. To achieve these goals, production management and scheduling techniques have emerged as core competencies [1]. Numerous scheduling problems have been studied for various types of products that require assembly operations, considering the production of components and subsequent assembly processes together [2]. There are scheduling problems with assembly operations arising from various manufacturing systems (refrigerators [5], clothing [6], food [7], and semiconductors [8,9,10,11])
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