Modified honey bees mating optimization algorithm for multi-objective uncertain integrated process planning and scheduling problem

Abstract

The new technology of intelligent manufacturing makes the data of process planning and shop scheduling easier to interconnect, and the integration optimization of different manufacturing processes is an important technology to ensure the implementation of intelligent manufacturing. Integrated process planning and scheduling is a significant research focus in recent years, which could improve the performance of manufacturing system. At present, the research on integrated process planning and scheduling is insufficient to consider the multi-objective and uncertain characteristics widely existing in real manufacturing environment. Therefore, multi-objective integrated process planning and scheduling problem with uncertain processing time and due date is addressed in this article. The mathematical model of multi-objective uncertain integrated process planning and scheduling problem with uncertain processing time and fuzzy due date is established based on fuzzy set, in which the calculation method of uncertainty measurement objective is designed. An effective modified honey bees mating optimization algorithm has been designed to solve the proposed model. Queens set is constructed to maintain the non-dominated solutions found in the optimization process. The calculation method of mating probability between drone and queen bee based on Euclidean distance is designed. Fuzzy operators were utilized to evaluate fitness, judge the non-dominated relationship, and decode the scheduling solution. Different instances were designed and carried out to test the performance of the proposed method. The results show that the proposed method is very effective for solving multi-objective uncertain integrated process planning and scheduling.

Multi-layer collaborative optimization method for solving fuzzy multi-objective integrated process planning and scheduling

“…The IPPS problem belongs to the field of shop scheduling problems, so this paper also applies fuzzy set theory to solve the FMOIPPS problem. In mathematical model, triangular fuzzy number and trapezoidal fuzzy number are used to represent fuzzy processing time and fuzzy due date respectively, and the fuzzy numbers operations are presented from Wen et al 17 The FMOIPPS problem studied in this paper can be described as follows: There is a set of jobs J = fJ 1 , J 2 , J 3 , :::, J n g to be processed in a factory equipped with a set of machines M = fM 1 , M 2 , M 3 , :::, M m g. Each job has various machining features which satisfying certain precedence constraints. There are many optional operations for each feature of a job.…”

“…The mathematical model of FMOIPPS and the fuzzy numbers operations can be found in Wen et al 17 In order to better describe the mathematical model, first define the following symbols.…”

“…The greater the average customer satisfaction, the greater the probability that jobs of on-time delivery. The evaluation method of customer satisfaction and the calculation method of average customer satisfaction were proposed by Wen et al 17 The shorter the span of the fuzzy makespan, the smaller the uncertainty of the fuzzy makespan, which is conducive to cost savings, reduced energy consumption, and improved on-time of completion time of jobs.…”

“…Li et al 16 used interval numbers to represent the processing time, proposed an uncertain IPPS model, and designed a corresponding hybrid algorithm combining genetic algorithm and particle swarm algorithm. Wen et al 17 designed a multi-objective IPPS mathematical model with uncertain processing time and fuzzy due date, and designed an improved bee colony algorithm for the problem. Haddadzade et al 2 used random numbers to represent the processing time, and designed a hybrid algorithm combining simulated annealing and tabu search.…”

“…In the production process of the workshop, tool loading and unloading, the proficiency of personnel in the workshop, the operating status of the equipment, the transmission status of the jobs are usually uncertain conditions. [15][16][17] Therefore, considering the uncertain factors in the IPPS problems is more in line with the actual production status. Especially, with the requirements of on-time and the development of intelligent manufacturing technology, it is vital to fully estimate the system uncertainty.…”