Production Scheduling Optimization Based On Knowledge Orientation And Due Date

In the past five years,China and other countries have issued a series of policies to promote the industrialization of precast components(PCs)and increase its proportion in all buildings.Compared with the cast-in-place construction,precast components are widely welcomed because of their unique advantages such as durability,aesthetic versatility,energy conservation and environmental protection.Appropriate production scheduling plan can effectively increase the total net revenue of precast components manufacturing enterprises,improve on-time delivery rate and customers’ satisfaction.Thus,this paper studies the single factory order acceptance and scheduling,due date assignment and scheduling integration in the case of manufacturer’s insufficient precast production capacity,and the distributed-multiply-factory scheduling problem under sufficient production capacity:(1)To deal with the problems of tight order due date and limited production capacity in the precast production system,an integrated optimization model for order acceptance and scheduling was established to maximize the total net revenue based on fully considering the characteristics of complex production processes,such as interruptible and uninterruptible processes,serial and parallel situations.In view of the NP hardness of the problem and the high nonlinearity of the model,a hybrid iterated greedy(HIG)framework is proposed by integrating the properties of the schedules,constructive heuristic,local search and mechanism of destruction-reconstruction.In order to improve the solution quality and search efficiency of the algorithm,in the construction steps of the proposed algorithms,two speedup strategies based on knowledge are designed by using order insertion properties.Computational results show that the proposed algorithms perform better than hybrid genetic algorithm and tabu search(GA＿TS),genetic algorithm(GA)and tabu search(TS).It also demonstrated that the speedup strategies can effectively shorten the running time of the algorithms,which could improve the total net revenue and customer’s satisfaction of prefabrication enterprises.(2)In the actual production process,manufacturers need to assign reasonable due date for customers,otherwise,the delivery of prefabricated components will be delayed and customers’ satisfaction will decline due to improper production scheduling.Therefore,in order to solve the difficult problem of due date assignment,order acceptance and scheduling in the actual production management process of precast production,we first analyze the properties of due date assignment under fixed scheduling and gives the optimal due date assignment strategy.Thus,a knowledge-oriented hybrid iterated greedy algorithm(HIG)is proposed to solve the problem.In the objective evaluation stage of the algorithm,in order to improve the efficiency of the algorithm,the optimal target value corresponding to a given schedule is quickly calculated by integrating the optimal due date assignment strategy,which overcomes the difficulty of objective function evaluation caused by enumeration of due dates.At the same time,in order to improve the search accuracy,a fast variable neighborhood search strategy VNS is designed in the local search.The results show that the proposed algorithm has better solution quality in the same running time and the effectiveness of the optimal assignment strategy and VNS search strategy are verified respectively.(3)In this section,we study the order scheduling problem of distributed concrete precast flow shop with the objective of minimizing the total weighted earliness and tardiness.To solve this problem,through analyzing the characteristics of distributed precast production,we first develop a novel mixed integer nonlinear programming model and then transform it into an effective mixed integer linear programming(MILP)model by linearization techniques.Simultaneously,in order to deal with medium and large-scale problems,we propose a hybrid tabu search and iterated greedy(HTS＿IG)in which a hybrid tabu search(HTS)is run at first and then an hybrid iterated greedy(HIG)starts from the best solution obtained by HTS.Aimed at improving search efficiency,some structural properties of the schedules are explored and integrated into the local search steps of HIG and HTS＿IG.We develop a hybrid genetic algorithm and variable neighborhood search(HGA＿VNS)by adding variable neighborhood search operation to the traditional genetic algorithm.At the same time,a two-phase heuristic method(TPHM)is proposed by determining the key factory.Finally,extensive experiments and deep analysis are conducted on instances with different combination of problem parameters.Computational results show the effectiveness of the MILP model and the proposed algorithms.The computational analysis indicates that,on average,HTS＿IG performs best among all the proposed metaheuristics.The effectiveness of local search based on problem specific knowledge(PSK)in HIG and HTS_IG are also verified.