An adaptive hybrid variable-large neighborhood search algorithm for profit maximization problem in designing production lines

Abstract

Designing production lines is an important research issue for both academy as well as industry since it should consider both system efficiency and production costs. In this study, the buffer allocation problem (BAP) is solved to maximize the profit of unreliable lines. The profit of a production line is a function of the throughput and various costs, such as WIP, holding and buffer space costs. In this respect, a crucial decision-making problem, i.e., BAP, is how to allocate the finite buffers to deal with the trade-off between maximizing the throughput of the line and production costs. In this study, profit maximization problem is formulated considering two different objective functions and solved under different constraint sets. An adaptive hybrid variable neighborhood search algorithm that incorporates large neighborhood search as a part of the diversification strategy is proposed to solve the problem. Moreover, a new initialization procedure based on the buffer location providing more profit is proposed to reduce the search effort. The efficiency of the proposed algorithm is tested on existing benchmark problems as well as the newly introduced large-sized data sets. In addition to these experiments, a comprehensive experimental design is conducted to determine the influencing factors on the problem at hand. The experimental study reveals that the proposed solution algorithm is capable to solve the profit maximization problem for large production lines, and the number of machines and the reliability parameters are the most influential factors in solving the BAP for profit maximization. Moreover, it has been observed that the proposed initialization procedure significantly reduces the search effort.