Service routes optimization (SRO) of pallet service center should meet customers' demand firstly and then, through the reasonable method of lines organization, realize the shortest path of vehicle driving. The routes optimization of pallet service center is similar to the distribution problems of vehicle routing problem (VRP) and Chinese postman problem (CPP), but it has its own characteristics. Based on the relevant research results, the conditions of determining the number of vehicles, the one way of the route, the constraints of loading, and time windows are fully considered, and a chance constrained programming model with stochastic constraints is constructed taking the shortest path of all vehicles for a delivering (recycling) operation as an objective. For the characteristics of the model, a hybrid intelligent algorithm including stochastic simulation, neural network, and immune clonal algorithm is designed to solve the model. Finally, the validity and rationality of the optimization model and algorithm are verified by the case.

Rescheduling is often needed when trains stay in segments or stations longer than specified in the timetable due to disturbances. Under crowded situations, it is more challenging to return to normal with heavy passenger flow. Considering making a trade-off between passenger loss and operating costs, we present a train regulation combined with a passenger control model by analyzing the interactive relationship between passenger behaviors and train operation. In this paper, we convert the problem into a Markov decision process and then propose the management strategy of regulating the running time and controlling the number of boarding passengers. Owing to the high dimensions of the large-scale problem, we applied the Approximate Dynamic Programming (ADP) approach, which approximates the value function with state features to improve computational efficiency. Finally, we designed three experimental scenarios to verify the effectiveness of our proposed model and approach. The results show that both the proposed model and the approach have a good performance in the cases with different passenger flows and different disturbances.

To solve the problems in planning and design of automobile mixed-model assembly line, this paper puts forward the improved genetic algorithm-based equilibrium optimization algorithm for the automobile mixed assembly line and establishes corresponding theoretical model. The convergence and feasibility of the model are analysed, and the optimization model presented in this paper is verified by the assembling situation of the actual assembly line of an automobile door. The research conclusions are as follows. The optimized scheduling mathematical model under multiple constrains of the automobile assembly line was established and improvements were made to the traditional genetic algorithm. Self-adaptive genetic operator was added to the original model. The performance verification indicated that the time consumption of CPU in the proposed improved algorithm is much less, and its maximum load is larger, so it has better convergence compared with traditional genetic algorithm. The improved optimal algorithm of automobile mixed ASSEMBLY LINE was verified taking into consideration such constraint conditions as the proportion of a single product put into assembly line, staffing, and balance of the left door and right door. It is found that the overall balance efficiency is about 92 %, reaching the standard for leaving factory. When the proportion of a single product that was put into production gradually rises, the overall time-consumption of the whole assembly line becomes shorter and shorter and the balance efficiency of the mixed assembly line presents a "U-shape" variation trend, first decreasing and then increasing. The growth of workers doesn't have an obvious impact on the assembling time consumption.

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