Simulated annealing and genetic algorithm based method for a bi-level seru loading problem with worker assignment in seru production systems

Abstract

<p style='text-indent:20px;'><i>Seru</i> production is one of the latest manufacturing modes arising from Japanese production practice. <i>Seru</i> can achieve efficiency, flexibility, and responsiveness simultaneously. To accommodate the current business environment with volatile demands and fierce competitions, <i>seru</i> has attracted more and more attention both from researchers and practitioners. A new planning management system, just-in-time organization system (JIT-OS), is used to manage and control a <i>seru</i> production system. The JIT-OS contains two decisions: <i>seru</i> formation and <i>seru</i> loading. By <i>seru</i> formation, a <i>seru</i> system with one or multiple appropriate <i>serus</i> is configured; by <i>seru</i> loading, customer ordered products are allocated to <i>serus</i> to implement production plans. In the process of <i>seru</i> formation, workers have to be assigned to <i>serus</i>. In this paper, a <i>seru</i> loading problem with worker assignment is constructed as a bi-level programming model, and the worker assignment on the upper level is to minimize total idle time while the lower level is to minimize the makespan by finding out optimal product allocation. A product lot can be splitted and allocated to different <i>serus</i>. The problem of this paper is shown to be NP-hard. Therefore, a simulated annealing and genetic algorithm (SA-GA) is developed. The SA is for the upper level programming and the GA is for the lower level programming. The practicality and effectiveness of the model and algorithm are verified by two numerical examples, and the results show that the SA-GA algorithm has good scalability.