Research For Supply Chain And Production Scheduling Problems With Deteriorating Jobs Under Collaborative Manufacturing

The development of the new information technology has broken the traditional manufacturing model and brought a new round of development opportunities for manufacturing companies.The more complex manufacturing environment also poses a severe challenge for the production optimization of manufacturing enterprises.Today's manufacturing companies are more concerned about how to make systematic decisions in a distributed collaborative manufacturing environment to improve the output of the entire manufacturing system,control the operating costs of the entire manufacturing system and ensure the safety of the production process.Moreover,in the face of increasing global competition,manufacturing companies need to develop real-lifebased scheduling algorithm to provide reliable decision support for the production process.In actual production,as the production process progresses,the machine operating state will decrease due to overload and component loss,that is,the actual processing time of the same job may be extended with the delay of its start-up time.Hence,many traditional scheduling algorithms are difficult to achieve the expected performance in real life production.Studying deteriorating job scheduling problems in collaborative manufacturing and designing efficient intelligent algorithms can help companies further increase production capacity,shorten market response time,and ultimately improve the overall competitiveness of manufacturing companies.This thesis analyzes the impact of the deterioration effect on the optimal nature of the collaborative scheduling problem in complex collaborative manufacturing processes.The production-transportation scheduling algorithm under the deteriorating situation,the manufacturing cell scheduling algorithm under the deteriorating situation,the production-maintenance scheduling algorithm under the deteriorating situation and the production-assembly scheduling algorithm under the deteriorating situation are studied respectively.In order to propose more effective methods,we decompose the studied problem and construct a series of precise algorithms and heuristic algorithms based on mathematical analysis.Due to the complexity of some sub-problems in the studied problems is NP-hard,we have designed a variety of improved meta-heuristic algorithms,and verified the performance of the proposed algorithm through extensive experiments.The main research results of this thesis are as follows:(1)The problem of collaborative scheduling of production and transportation under deteriorating effect is studied.A supply chain scheduling model with linear deteriorating jobs and parallel batch processers is constructed.A single-machine batch scheduling algorithm,a multi-machine job allocation heuristic algorithm,and a hybrid intelligent algorithm are proposed.Comparison experiments and convergence curves verify the performance of the proposed algorithm.The studied problem is divided into two stages.In the production phase,the jobs are processed by the supplier,and each supplier has a parallel batch machine and a transport vehicle.During the transport phase,the supplier delivers jobs to the manufacturer.Since the suppliers are located in different locations,the shipping time between each supplier and manufacturer is different.Some structural properties are proposed for the investigated problem,and an optimal algorithm for minimizing the completion time of a single supplier is designed based on the properties.Then it is proved that the supply chain scheduling problem is NP-hard,so a hybrid VNS-HS algorithm combining variable neighborhood search(VNS)and harmony search(HS)is proposed to find a good solution in a reasonable time.Finally,the computational experiments validate that the proposed VNS-HS has good performance in solving the production and transportation collaborative scheduling problem.(2)The cooperative scheduling problem of manufacturing units(FMCSP)under deteriorating effect is studied.A flow shop scheduling model where there is a dominant relationship between machines is built.A variety of precise algorithms for polynomial time are proposed for different optimization goals,and the complexity of each algorithm is analyzed and calculated.In the proposed collaborative scheduling model,we need to make decisions on the ordering of job types and the jobs For the problem of minimizing the maximum completion time,we derive the structural properties of the optimal solution,based on which the optimization algorithm is developed.For the problem of minimizing the total completion time,we propose a property of the optimal solution,and then develop polynomial time algorithms for the two special cases of the problem,that is,the transfer device time is 0 and the number of jobs in each type of is the same.(3)The problem of collaborative scheduling for production and maintenance under the deteriorating effect is studied.An unrelated parallel machine scheduling model considering mandatory deterioration maintenance is constructed.The collaborative decision-making mechanism for production and maintenance under single machine is discussed.A hybrid metaheuristic algorithm is designed.We verify the effectiveness of the proposed algorithm through the convergence test and variance test.The goal is to develop collaborative decisions on job assignments,maintenance schedules,job batching,and batch sequencing to minimize maximum completion time.First,we developed a mixed integer programming model for this problem.We then analyzed the special case where all jobs have been assigned to machines and developed the polynomial time-optimal algorithm.Since the problem studied is NP-hard,we designed a hybrid ABC-TS algorithm which combines artificial bee colony(ABC)and tabu search(TS)to solve the problem in a reasonable time.The effectiveness and robustness of the proposed algorithm are verified by sufficient simulation experiments.(4)The problem of collaborative scheduling of production and assembly under deteriorating effect is studied.A serial-batching scheduling model under deteriorating and learning effects is constructed.The structured properties of the production and assembly stages are discussed respectively.The corresponding exact algorithms,heuristics and metaheuristics are designed.We verified the performance of the proposed algorithm by calculating the degree of deviation and dispersion of the solution.The problem is divided into two phases.In the production phase,semi-finished manufacturers produce product components and then transport these completed components to the assembly manufacturer.During the assembly phase,the assembly manufacturer will assemble the product components into the final product on multiple assembly machines.Through mathematical derivation,we obtain the structural properties of the scheduling problem in the production stage,and propose an optimal algorithm based on the structural properties.Some important properties are also proposed for the problem of assembly stage,and a heuristic algorithm is designed to improve the quality of the solution.We prove that the problem studied is NP-hard,and then propose an efficient variable neighborhood search algorithm.The computational experiments show that the designed LIMA-VNS is superior to other meta-heuristic algorithms in terms of convergence speed,solution quality and robustness,especially for large-scale problems.The theoretical significance of this thesis is to construct reasonable mathematical models for the problem of deteriorating job scheduling problems in collaborative manufacturing,and derive important structural properties through derivation.This work advances the research on the deteriorating effect of workpieces,and enrichs the optimization methods of deteriorating job scheduling.From a practical viewpoint,this thesis designs complete integrated solution algorithms for the proposed collaborative optimization problem of manufacturing process.These algorithms can be applied to real-life production to provide decision support for manufacturing enterprises or manufacturing alliances,thereby increasing the degree of intelligence and competitiveness for our conuntry.