| As a derivative of the classical scheduling problem,batch scheduling problem based on parallel batching(p-batch)is extensively applied in semiconductor manufacturing,ceramic firing,and metal smelting.It meets the demand of many manufacturers to improve product throughput.Based on the economic cost and delivery service quality,once the quantity or quality of products specified by the customer exceeds the manufacturer’s production capacity,the manufacturer will naturally choose to outsource partial jobs to the subcontractor and choose the appropriate outsourcing partners from multiple subcontractors.Outsourcing options with multiple subcontractors will increase the difficulty of manufacturers’ job scheduling.The joint scheduling decision-making between outsourcing options and in-house scheduling is crucial for these manufacturers to reduce costs and increase efficiency.Therefore,the research on the joint optimization of job outsourcing and batch scheduling with multiple subcontractors option can not only provide a solution for manufacturers to make job scheduling decision but also help them effectively utilize production capacity resources in the outsourcing market,which has practical guiding significance and application value.Firstly,aiming at the joint optimization problem of subcontractor options and singlemachine batch scheduling with the total outsourcing cost budget,a 0-1 integer programming model was constructed,in which the objective is to minimize the sum of total outsourcing cost and total in-house batch processing cost,while both the total outsourcing cost and the latest leading time for outsourcing jobs were subject to an upper limit.An improved genetic algorithm and greedy method were designed for solving this problem.An instance of the joint decisionmaking scenario of outsourcing and batch scheduling in a ceramic enterprise was given,and the solution performance of the two algorithms was compared by testing it.The improved genetic algorithm shows its comparative advantages in terms of solution efficiency.The sensitivity experimental results on this instance show that the latest leading time for outsourcing jobs has a significant impact on the total operating cost,while the impact of the upper limit of the total outsourcing cost on the total operating cost is of no significance.Secondly,focusing on the joint optimization problem of subcontractor options and singlemachine batch scheduling with an in-house batch processing time window,a mixed integer programming model was constructed,in which the objective is to minimize the sum of total inhouse batch processing cost,total outsourcing cost and total delay penalty cost.while the inhouse batch processing time window was subject to an upper limit.A hybrid genetic algorithm was designed for this problem.This algorithm integrates the job addition heuristic algorithm with polynomial time complexity and uses it to improve the diversity and quality of the initial population.Then,the commercial optimization software CPLEX,genetic algorithm,job additive heuristic algorithm,and hybrid genetic algorithm were compared for the optimization results of the joint scheduling case,and the advantages of the designed hybrid genetic algorithm in solving quality and solving efficiency were confirmed.Then,this algorithm is used to analyze the sensitivity of three controllable factors in the model: common due date,time-sharing electricity price coefficient,and the number of subcontractors.The statistical analysis results show that the total operating cost of the manufacturer is sensitive to the change of the common due date of the job and decreases with the increased number of subcontractors.The manufacturers can reduce the total operating cost by arranging the batch processing time at the off-peak time of electricity consumption.,but with the increased number of subcontractors,the effect of reducing the total operating cost through cross-peak electricity consumption is gradually weakened. |
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