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Improving product quality is an effective way for a company to gain goodwill as well as to increase sales. In this paper, the focus is on a two-echelon supply chain consisting of a manufacturer and a retailer. Considering the delayed effect of quality improvement on product quality, the differential equations on the levels of quality and goodwill as the state variables are established to explore the impact of delay time on members’ strategy inputs, supply chain profits and performance under decentralized and centralized decision models. Additionally, in this paper, how different profit maximization scenarios affect supply chain revenue under decentralized models is examined. The equilibrium solutions are obtained by numerical simulation. The results show that both quality improvement efforts and marketing efforts are decreasing functions of delay time, and the delay effect affects the motivation of supply chain members’ strategic inputs. Importantly, supply chain profit under centralized decision is not always higher than that under decentralized decision when the manufacturer’s concern degree and delay time are taken into account. The Rubinstein bargaining model is then used to optimize the profit distribution between the manufacturer and the retailer under centralized decision. In this paper, it is also identified that the longer the delay time, the lower the levels of quality and goodwill. The dual effect of delay effect and concern degree will exacerbate the negative impact on the levels of quality and goodwill.

To further study the impact of passenger personality on customized bus route selection, this paper takes into account passengers’ degree of optimism and studies their psychological expectation of bus arrival time and their tolerance of its untimely arrival. On this foundation, the study obtains the operation schemes of customized buses under different optimism coefficients. The results show that the operation schemes and passengers’ choice will always strike a balance under different optimism coefficients, and with increasing optimism coefficients, the number of buses, operation time, operation cost and travel distance will all decrease, but the untimely rate of the buses will consequently increase. To solve the proposed model, the corresponding particle swarm optimization (PSO) algorithm is designed. In view of the former mode, in which buses can never provide service to the drop-off stations until the boarding stations are completely serviced, the proposed coding rule can provide service to the boarding stations and the drop-off stations alternately. Furthermore, based on the local road network and the actual travel demand of passengers in Lanzhou, China, the paper finds that 0.4 is the optimum value of the optimism coefficient; the operation cost associated with this value is reduced effectively, and the capacities of buses are fully utilized, with acceptable delays.

Emergency vehicle (EV) plays an important role in evacuations or rescues when emergencies occur. To insure that an EV can transfer people in danger to emergency shelters or medical assistance organizations as soon as possible, EV signal preemption (EVSP) strategy is usually adopted. After EV has passed through the intersection, traffic signal has to transfer back to normal signal timing scheme. This paper focuses on the control strategy of EV signal transitioning from EVSP back to normal operation. Considering both efficiency and fairness, the maximum vehicles passing through in per unit time during the transition period and the minimum difference between the maximum and the minimum queue length after transition are selected as objectives, and a multi-objective optimization model is presented. A nondominated sorting genetic algorithm II (NSGA-II) is designed to solve the optimization model and unique encoding and decoding methods are presented. The established model and designed algorithm are verified and the control effect is analyzed. Simulation results indicate that by adopting the control strategy obtained by the presented model, the number of vehicles passing through in per unit time during the transition period is increased and the difference of vehicle length in different directions is reduced significantly, from which we can conclude that the control method proposed in this paper has good performance.

This paper focuses on the signal preemption control of emergency vehicles (EV). A signal preemption control method based on route is proposed to reduce time delay of EV at intersections. According to the time at which EV is detected and the current phase of each intersection on the travelling route of EV, the calculation methods of the earliest start time and the latest start time of green light at each intersection are given. Consequently, the effective time range of green light at each intersection is determined in theory. A multiobjective programming model, whose objectives are the minimal residence time of EV at all intersections and the maximal passing numbers of general society vehicles, is presented. Finally, a simulation calculation is carried out. Calculation results indicate that, by adopting the signal preemption method based on route, the delay of EV is reduced and the number of society vehicles passing through the whole system is increased. The signal preemption control method of EV based on route can reduce the time delay of EV and improve the evacuation efficiency of the system.

This paper focuses on the use of timed colored Petri nets (TCPN) to study emergency vehicle (EV) preemption control problem. TCPN is adopted to establish an urban traffic network model composed of three submodels, namely, traffic flow model, traffic signal display and phase switch model, and traffic signal switch control model. An EV preemption optimization control system, consisting of monitoring subsystem, phase time determination subsystem, and phase switching control subsystem, is designed. The calculation method of the travelling speed of EV on road sections is presented, and the methods of determining the actual green time of current phase and the other phase are given. Some computational comparisons are performed to verify the signal preemption control strategies, and simulation results indicate that the proposed approach can provide efficient and safe running environments for emergency vehicles and minimize EV’s interference to social vehicles simultaneously.

Transportation problem on network needs to determine the freight quantity and the transportation route between supply point and demand point. Therefore, taken the uncertainty of freight supply and demand into account, a collaborative optimization model is formulated with transportation capacity constraint. In addition, a two-stage genetic algorithm (GA) is put forward. Herein, the first stage of this CA is adopted a priority-based encoding method for determining the supply and demand relationship between different points. Then supply and demand relationship which the supply and the demand are both greater than zero is a minimum cost flow (MCF) problem on network in the second stage. Aim at the purpose to solve MCF problem, a GA is employed. Moreover, this algorithm is suitable for balance and unbalance transportation on directed network or undirected network. At last, the model and algorithm are verified to be efficient by a numerical example. [ABSTRACT FROM AUTHOR]