Research on the Multi-objective Optimized Scheduling of the Flexible Job-Shop Considering Multi-Resource Allocation

Abstract

Targeting at the problems existing in the multi-objective scheduling of traditional flexible job shop and the complexity of multi-resource allocation, this paper establishes an improved calculation model considering the optimization of such four targets as completion time, labour distribution, equipment compliance and production cost. The multi-objective integrated constraint optimization algorithm is designed and the Pareto solution set following different rules based on the NSGA-П algorithm is finally obtained. The research results show that the centralized selection of processing equipment and low efficiency of the job sequencing in the scheduling of traditional flexible job shop get improved. The personnel scheduling in the flexible working resources is highlighted, and multi-rule dynamic programming is introduced to get the optimal completion time and personnel allocation program. The optimal scheduling program can be quickly searched out using the NSGA-П algorithm, which effectively improves the search efficiency. The batch production within certain range can reduce the product processing time, but at the same time, it will increase the manufacturing costs. The use of smooth movement can reduce the overall processing time, but a too small movement volume will cause the increase in the number of movements. The exact match between the operators, numerical control equipment and the product processing procedures contributes to the feasibility of the preproduction operation plan.

A Novel Job-Shop Scheduling Strategy Based on Particle Swarm Optimization and Neural Network

“…Since the middle of the 20 th century, job-shop scheduling problem (JSP) has been a thorny issue among manufacturing enterprises [1,2]. This non-deterministic polynomial-time (NP) hard problem [3] is extremely difficult to model or optimize.…”

Three-Machine Job Shop Scheduling with Intermediate Transfer

“…To obtain the minimization of the makespan, the strongly NP-hard problems were simulated by employing a mixed integer programming model and computing experiments were performed, from which a heuristic algorithm and a worst-case error constraint were derived. The makespan minimization of the two-machine job shop scheduling for a single transporter with a limited capacity was reported in the work of Gong and Tang, where a heuristic algorithm was proposed with a worst-case ratio of 2 and 7/3 respectively for two scenarios: each job occupies the same space and different spaces of the transporter, respectively [9][10][11][12][13][14][15][16][17][18][19]. For the latter case, the asymptotic worst-case ratio of 20/9 was obtained.…”