An Effective Neighborhood Solution Clipping Method for Large-Scale Job Shop Scheduling Problem

The job shop scheduling problem(JSSP)is a classical combinatorial optimization problem that exists widely in diverse scenarios of manufacturing systems.It is a well-known NP-hard problem,when the number of jobs increases,the difficulty of solving the problem exponentially increases.Therefore,a major challenge is to increase the solving efficiency of current algorithms.Modifying the neighborhood structure of the solutions can effectively improve the local search ability and efficiency.In this paper,a genetic Tabu search algorithm with neighborhood clipping(GTS_NC)is proposed for solving JSSP.A neighborhood solution clipping method is developed and embedded into Tabu search to improve the efficiency of the local search by clipping the search actions of unimproved neighborhood solutions.Moreover,a feasible neighborhood solution determination method is put forward,which can accurately distinguish feasible neighborhood solutions from infeasible ones.Both of the methods are based on the domain knowledge of JSSP.The proposed algorithmis compared with several competitive algorithms on benchmark instances.The experimental results show that the proposed algorithm can achieve superior results compared to other competitive algorithms.According to the numerical results of the experiments,it is verified that the neighborhood solution clippingmethod can accurately identify the unimproved solutions and reduces the computational time by at least 28%.

A Novel MILP Model Based on the Topology of a Network Graph for Process Planning in an Intelligent Manufacturing System

Intelligent process planning(PP)is one of the most important components in an intelligent manufacturing system and acts as a bridge between product designing and practical manufacturing.PP is a nondeterministic polynomial-time(NP)-hard problem and,as existing mathematical models are not formulated in linear forms,they cannot be solved well to achieve exact solutions for PP problems.This paper proposes a novel mixed-integer linear programming(MILP)mathematical model by considering the network topology structure and the OR nodes that represent a type of OR logic inside the network.Precedence relationships between operations are discussed by raising three types of precedence relationship matrices.Furthermore,the proposed model can be programmed in commonly-used mathematical programming solvers,such as CPLEX,Gurobi,and so forth,to search for optimal solutions for most open problems.To verify the effectiveness and generality of the proposed model,five groups of numerical experiments are conducted on well-known benchmarks.The results show that the proposed model can solve PP problems effectively and can obtain better solutions than those obtained by the state-ofthe-art algorithms.

Mathematical Modeling and a Multiswarm Collaborative Optimization Algorithm for Fuzzy Integrated Process Planning and Scheduling Problem

Considering both process planning and shop scheduling in manufacturing can fully utilize their complementarities,resulting in improved rationality of process routes and high-quality and efficient production. Hence,the study of Integrated Process Planning and Scheduling (IPPS) has become a hot topic in the current production field. However,when performing this integrated optimization,the uncertainty of processing time is a realistic key point that cannot be neglected. Thus,this paper investigates a Fuzzy IPPS (FIPPS) problem to minimize the maximum fuzzy completion time. Compared with the conventional IPPS problem,FIPPS considers the fuzzy process time in the uncertain production environment,which is more practical and realistic. However,it is difficult to solve the FIPPS problem due to the complicated fuzzy calculating rules. To solve this problem,this paper formulates a novel fuzzy mathematical model based on the process network graph and proposes a MultiSwarm Collaborative Optimization Algorithm (MSCOA) with an integrated encoding method to improve the optimization. Different swarms evolve in various directions and collaborate in a certain number of iterations. Moreover,the critical path searching method is introduced according to the triangular fuzzy number,allowing for the calculation of rules to enhance the local searching ability of MSCOA. The numerical experiments extended from the well-known Kim benchmark are conducted to test the performance of the proposed MSCOA. Compared with other competitive algorithms,the results obtained by MSCOA show significant advantages,thus proving its effectiveness in solving the FIPPS problem.