Research On Bi-objective Shop Scheduling With Machine Availability Constraints Based On Improved Genetic Algorithm

Under the policy of “accelerating the construction of a powerful manufacturing country” in the new era,China’s manufacturing enterprises are facing new development requirements.The ever-increasing demand for individualization and the fierce competition environment prompt manufacturers to take a series of effective measures to increase productivity,improve customer service quality and enhance their competitiveness.Production scheduling is the work of organizing and executing production schedules.The availability of the machine determines whether the production operation can be carried out smoothly,which will ultimately affect the on-time completion of the production plan and the customer’s trust in whether the company can deliver on time.Therefore,the availability of machines plays a decisive role in whether the production scheduling objectives can be completed as required.In today’s highly competitive market,in order to ensure the long-term survival and development of enterprises,enterprises should not only consider the maximization of customer demand satisfaction,but also consider the minimization of their own costs.Therefore,it is of practical and theoretical significance for the management and control of production scheduling to study the bi-objective shop scheduling optimization problem with machine availability constraints on the basis of the actual production environment and enterprise requirements.In this paper,an improved genetic algorithm is proposed to solve two kinds of shop scheduling problems with machine availability constraints and bi-objective characteristics.The main research contents are divided into the following three parts:Firstly,an improved genetic algorithm is designed to improve the solving ability and effect of intelligent optimization algorithm in solving shop scheduling optimization problems.The integer coding scheme based on job sequence and the decoding scheme based on machine are designed.In order to improve the quality of the initial population,a two-stage program composed of CDS heuristic algorithm and random rules is used to generate the initial population,and then adaptive crossover and mutation operators are used to obtain the solution of genetic algorithm;In order to expand the search scope and improve the quality of the solution,three neighborhood search mechanisms based on pairwise exchange,single-job insertion and multi-job insertion are proposed to perform local search,so as to obtain the improved near optimal solution of the problem.Secondly,the bi-objective permutation flow shop scheduling optimization problem with machine availability is studied.Taking minimizing the total weighted completion time and total weighted tardiness as the optimization objective,the corresponding mathematical programming model is established and solved by the above improved genetic algorithm.The simulation experiments and comparative analysis of different scale problems show that the proposed improved genetic algorithm can effectively solve the bi-objective permutation flow shop scheduling problem with machine availability.Finally,the bi-objective hybrid flow shop scheduling optimization problem with machine availability is studied to minimize both earliness and tardiness.Considering the constraints of machine availability,job release time and first arrival first processing,an integer programming model is established.The application of the improved genetic algorithm is extended to solve the hybrid flow shop scheduling optimization problem.Simulation experiments on a large number of random data show that the target improvement rates of the above improved genetic algorithm compared with other algorithms are 8.31%,7.39% and 4.28% respectively in an average of 73.79 s.This also shows that the improved genetic algorithm can also show better solution performance when applied to the hybrid flow shop.