Study Of Coordinated Scheduling And Transportation Based On Serial-batching

With the continuous development of network technology and global economic integration, the competition in supply chains becomes more and more fierce. There is increasing awareness of the supply chain participants that they have to reinfore the cooperation between each other to improve the competitiveness of supply chain so as to decrease each operation cost. The development of Internet of Things technology provides a information basis of the cooperation between the participants of supply chain. It can not only return the production and transportation information to the management center, but also share the information to other participants. The Internet of Things technology pushes the cooperation between supply cahin participants to a new level that by using the information effectively can decrease the production cost, increase the profit, improve the satisfaction of customers, and in the end enhance the competitiveness of the whole supply chain. Besides, introducing the technology of the Internet of Things also broadens the theoretical area of the research on production and transportation collaboration scheduling problems. Therefore, how to transform the information value into economic and social value, and use the information acquired by the Internet of Things to obtain efficient production and transpiration collaboration plans becomes the key issues. This thesis is conducted on the background of Alumi-num production manufacturing chain, and focuses on the production and transporta-tion collaborative scheduling problem in supply chain environment from the perspec-tive of scheduling.This thesis systematically analyzes the multi-period production and transporta-tion collaborative scheduling problems under several circumstances, which are ab-stracted from the processing procedures on the serial-batching machine of extrusion factory. These different circumstances are including the cases of limited vehicles, dy-namically jobs arrival, deteriorating jobs processing time, machine breakdown, and collaborative scheduling of multiple manufacturers distributed in different locations. Since that all these problems are NP-Hard, this thesis is devoted to analyzing the properties of optimal scheduling plans, based on which efficient heuristics and intel-ligent algorithms can be developed. On the other hand, the lower bounds of these problems are also derived, which can be used to assess the accuracy of the developed algorithms. The research results of the production and transporation collaboration scheduling problems based on serial-batching can be conclude as follows: (1) The production and transportation collaborative scheduling problem is stud-ied when the vehicle is limited, and the scheduling objective is to minimize the makespan. Based on the constraint condition of limited vehicle, the mathematical model is established. The problem can be divided into two cases according to the rela-tionship between the batch transportation time from the supplier to the manufacturer and the job processing time on the manufacturer’s machine. Two heuristics and lower bounds are designed respectively for these two cases. Based on the lower bounds, the worst case performance ratios of the heuristics are proved. Then, a large amount of experiments on small-scale and large-scale random data sets are carried out. The re-sults show that when the number of jobs is1000, the average relative gaps of these two heuristics are respectively converges to0.48%and0.8%, which performs signifi-cantly better than the existing FOE and SPT algorithm and LOE and LPT algorithm.(2) The production and transportation collaborative scheduling problem is stud-ied when the jobs arrive dynamically, and the objective is to minimize the makespan. Based on the constraint conditions of jobs arriving dynamically, the mathematical model is established. Besides, the relative properties of the jobs arriving dynamically in optimal scheduling plans are analyzed, and the relationship between the optimal scheduling plan and the number of batches are discussed under the special circum-stance when the jobs arrive at the same time. Based on the above properties, a two-stage TP-H algorithm is constructed, and then the worst case performance ratio is proved to be7/2. Two lower bounds are derived through relaxing job arriving time and assuming no additional free time on the manufacturer’s machine. Then, the simu-lation experiments are conducted based on different capabilities of machine. The re-sults of a large amount experiments validate the effectiveness of the TP-H algorithm. When the number of jobs is1000, the average relative gap of the developed TP-H algorithm converges to0.21%, which outperforms the existing MBF and MFF algo-rithms.(3) The production and transportation collaborative scheduling problem is stud-ied when the jobs processing time is deteriorating. Several single machine scheduling problems under the circumstances of deteriorating jobs processing time are analyzed, of which the objectives are respectively minimizing the makespan, minimizing the number of tardy jobs, minimizing the total jobs completion time, and the accordingly optimization algorithms are designed. Based on the research results of the single ma-chine scheduling problem, the production and transportation collaborative scheduling problem with buffer area is considered, and corresponding mathematical model is es-tablished, of which the objective function is to minimize the makespan. Besides, the production and transportation collaborative scheduling problem without buffer area is also studied, and the mathematical model is established based on the constraint condi-tion of no buffer area, of which the objective function is to minimize the makespan. The properties of optimal solution are analyzed, new lower bound is derived, and a heuristic for the problem is constructed. The results of simulation experiments show that when the number of jobs are larger than260, the average and maximum relative gaps of the heuristic are both less than0.01%.(4) The production and transportation collaborative scheduling problem is stud-ied when the machines breakdown is considered. The mathematical model is estab-lished for the possible situation of two parallel manufacturing machines breakdown, of which the objective function is to minimize the makespan. The properties of the optimal scheduling plan are analyzed, and the lower bound of the problem is derived. The scheduling rules under the two difference circumstances are respectively con-structed, based on which a new heuristic algorithm is designed. A large amount of simulation experiments illustrate that this heuristic algorithm can efficiently resolve a variety of problems of different scales.(5) The production and transportation collaborative scheduling problem is stud-ied when multiple manufacturers are distributed in different locations. According to the features of distributed multiple manufacturers, the corresponding mathematical model is established, of which the objective function is to minimize the makespan. The properties of the optimal solution are analyzed. A novel MGSA algorithm is pro-posed, in which the coding modification strategy, initial population strategy, and op-timal population reserve strategy are designed, and the insert operation, exchange op-eration, and mutation operation are proposed. For the problem of job batching, the DP-H mixed algorithm is proposed, which combines dynamic programming and heu-ristic rules. The results of a large amount of simulation experiments show that the MGSA outperforms the existing GA and PSO algorithms.