Flexible job-shop scheduling problem (FJSP) in the field of production scheduling presents a quite difficult combinatorial optimization problem. Machines are mostly considered to be the only resource in many research projects dealing with FJSP. In actual production, there are many other factors which influence production scheduling, such as transportation, storage and detection. If machines are considered to be the only resource, the problem may not be in accord with the actual production. Thus, in order to make FJSP more in line with the real production situation, machines, warehouses, vehicles and detection equipment are all considered to be the scheduled resources simultaneously due to the shortage of flexible job shop scheduling problem in resources. A new mathematical model for a multiresource flexible job-shop scheduling problem (MRFJSP) is proposed. The constraints of the model are presented. The makespan is the main target which will be minimized. A genetic algorithm which includes elitist strategy is proposed to solve the MRFJSP. Due to the complexity of MRFJSP, each key module of the genetic algorithm is redesigned. Finally, the model and algorithm are proved through an application case.

To survive the fierce market competition, many manufacturing enterprises have applied integration measures at all levels of the production process. Against this backdrop, this paper mainly establishes a job-shop scheduling problem (JSP) that aims to minimize the makespan of products through integrated scheduling of machining and assembly under batch production environment. Then, the established problem was modelled considering the influence of different batch number on the makespan. In view of the complexity and discreteness of the established problem, a genetic algorithm (GA) was designed to obtain the optimal scheduling sequence in different batch production situations. The effectiveness of our problem model and algorithm was verified through the analysis on an example with different batch conditions. The research findings help to design a realistic and feasible scheduling plan for manufacturing enterprises.