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

Abstract

Seru production is one of the latest manufacturing modes arising from Japanese production practice. Seru can achieve efficiency, flexibility, and responsiveness simultaneously. To accommodate the current business environment with volatile demands and fierce competitions, seru 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 seru production system. The JIT-OS contains two decisions: seru formation and seru loading. By seru formation, a seru system with one or multiple appropriate serus is configured; by seru loading, customer ordered products are allocated to serus to implement production plans. In the process of seru formation, workers have to be assigned to serus. In this paper, a seru 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 serus. 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.

Scheduling of Software Test to Minimize the Total Completion Time

“…Kunhare, Tiwari, and Dhar [16] further used a genetic algorithm to compose a hybrid approach to intrusion detection. For solving a worker assignment bi-level programming problem, Luo, Zhang, and Yin [17] designed a two-level algorithm, which simulated annealing as the upper level to minimize the worker idle time and the genetic algorithm as the lower level to minimize the production time. For more general coverage, the reader is referred to Ansari and Daxini [18] and Rachih, Mhada, and Chiheb [19].…”

DMD maskless lithography optimization based on an improved genetic algorithm

“…Concurrently, the realm of optimization has witnessed the ascendancy of the genetic algorithm (GA). [26][27][28] As a search heuristic inspired by natural evolution, the GA has demonstrated remarkable prowess in tackling intricate optimization conundrums, a prime example being mask optimization in lithography. [29][30][31] The intrinsic capability of the GA to traverse expansive solution domains underscores its potential.…”

Assembly line-Seru conversion in the C2M enterprise: an empirical study in China

“…Ayough et al (2020) proposed an efficient invasive weed optimization algorithm to solve the job rotation scheduling and line-Seru conversion. In addition, a SA genetic algorithm (Luo, 2021), an improved genetic SA algorithm Zhang et al (2021b) and an NSGA II-based modal algorithm (Liu et al , 2021b) are used for the Seru loading problem. A generalized exact solution method and an automatic heuristic design method (Zhan et al , 2021) are used for the Seru scheduling problem.…”