No abstract

The booming of Internet economics brings new opportunities for small-and medium-sized product and service providers in e-platforms. Usually, the Internet platform (thereafter "platform") and Internet providers (thereafter "providers") operate under a consignment revenue sharing production model. Another production model is profit sharing, under which the platform undertakes part of the providers' operational costs. Intuitively, common sense conjectures that the platform prefers the revenue sharing model while the providers may prefer the profit sharing scheme. However, this is not the case. In this paper, we compare these two forms of emerging production models with a theoretical framework and investigate both market participants' performance under different schemes. Starting from the single provider case, we find that the provider has less incentive to operate under the revenue sharing contract when compared with the profit sharing contract. Counter intuitively, we identify the threshold of the cutting ratio above which it is more beneficial for the platform to choose the profit sharing mode. Our results are proved to be robust when the number of the providers increases. A numerical study is provided to illustrate this effect.

Production coordination is a common phenomenon in supply chains. Unlike the existing literature, we examine the production coordination problem from the perspective of asymmetric information: how a manufacturer (leading firm) coordinates the relationships with its subsidiary firm(s) and, subsequently, how market returns influence the leading firm's expected utilities, agency cost and the subsidiary firm's expected incomes. We develop an incentive contract model with asymmetric information based on principal-agent theory. Comparative analysis and simulations are conducted to test the model. Results show that the leading firm's expected utilities and agency cost and the subsidiary firm's expected incomes are significantly affected by the subsidiary firm's capability, cost coefficient, absolute risk aversion factor and output variance (common factors); sharp differences among the leading firm's expected utilities and agency cost and the subsidiary firm's expected incomes were found due to different market returns. Thus, the proposed approach (incentive contract model) can help leading firms apply incentives to optimize production modes to obtain production coordination while considering common factors; market returns differences are included in the new model, in contrast to previous approaches.

Sharing charging stations are an effective solution for daily usage of electric vehicles charging, however, the area with high demand cannot provide enough stations while there are plenty of stations left idle in remote areas with less demand. The core of the problem is the imbalance of demand and supply. In other word, we need to allocate the charging station to the appropriate locations to balance demand and supply. This study aims to solve the problem of locating charging stations for public electric vehicles (PUEVs), to improve the sharing charging level. We take into consideration the factors affecting charging station locations including mileage, PUEV distribution and passenger distribution. A Non-deterministic Polynomial (NP) model aiming to minimize the total vehicle service distance is developed. We use an agent-based model to simulate the optimized charging station location based on Anylogic. Through a case study of Beijing, we test the model in five situations. This paper concludes that priority, mileage, PUEV distribution and passenger distribution are the key factors affecting the location of PUEV charging stations, with exogenous variables such as the type of circuit and the voltage drawn as constants. The results of one situation show that the existing layout of the charging stations is unreasonable when charging frequency is sharply variant; this paper optimizes the existing location by improving the constraint for the smallest number of charging stations; the proposed model can be used for EV charging stations' location in densely populated metropolis.INDEX TERMS Agent, charging frequency, sharing charging, electric vehicles, location.

Electric vehicles support low-carbon emissions to revitalize sustainable transportation, and more charging stations are being built to meet the daily charging demand. Charging piles and service workers are the most important resources for electric vehicle charging stations, and the scheduling of these resources is an important factor affecting the charging stations' profits and sustainable industrial development. In this paper, we simulate the charging piles and service workers in charging station resource scheduling and analyze the impacts of the number of service workers, the charging pile replacement policy and the charging pile maintenance times on an electric vehicle charging station's profits. An orthogonal test can achieve the following optimal resource scheduling results when their range is known: (1) In the lifetime of the charging pile, seven maintenance times are needed; (2) Even if the charging pile is still in normal condition, it needs to be replaced in order to achieve the maximum profits for the charging station; (3) a comprehensive analysis of service efficiency and service costs indicates that 8 service workers are needed to achieve the optimal profits for the charging station. Therefore, the scientific contribution of this research is to establish one resource scheduling simulation model that can assess the effects of the number of service workers, the charging pile replacement policy and the charging pile maintenance times on charging station revenues and to obtain the optimal results. In addition, if the model parameters change, we can still obtain the optimal results.

In this study, we aim to find the key factors affecting the location of electric vehicle charging stations. We first developed a Non-deterministic Polynomial (NP) model that aims to minimize the total travel distance of cars. Second, we applied an agent-based simulation algorithm to determine the optimized location for charging stations. Finally, we conducted multi-simulation and statistical analysis of passenger priority, car mileage, electric vehicle distribution and passenger distribution using a one-way analysis of variance (ANOVA). The results of this study show that priority is not a factor affecting the location of electric vehicle (EV) charging stations and that mileage, the EV distribution and the passenger distribution are factors affecting the location of EV charging stations, with exogenous variables such as the type of circuit and the voltage drawn as constants. The proposed model can help provide a reference for the location of charging stations in urban areas.

This study investigates electric vehicles battery recycling problem. In this study, based on Agent theory and Anylogic platform, Agent model of battery recycling is built. We have done simulation for electric vehicle batteries recycling: this paper analyses the influence that factors (battery renovation rate, quantities of electric vehicles, electric vehicle lifetime, battery lifetime, battery renovation time) have on recycling (quantities of wasted batteries, quantities of reused batteries, optimal quantities of batteries). Through simulation, this study shows that factors' influence on recycling depends on the relative life RL greatly. When renovation rate changes in the interval [0.7, 0.8], the results fluctuate greatly, such as optimal quantities of batteries will decrease about 10 %, quantities of reused batteries can increase about 30 %, and quantities of wasted batteries will have a sharp decline by about 40 %; the model is optimal until battery renovation times are increased to three.

This study investigates environment sensitive and perishable products (ESPPs) logistics problem, which is called cold chain logistics problem (CCLs). Based on a comprehensive literature review, we found that there is much room to improve regarding of the risks management in cold chain logistics, that is, the development of a comprehensive cold chain logistics design methodology should considered uncertainty sources and risk exposures. In this study, we propose a neural network model to illustrate the problems. Firstly, the paper develops input indicators at different points in cold chain logistics to examine the effects of environment fluctuations including temperature control, humidity monitoring, the temperature interruption time and electric vehicle mapping, etc; secondly, the improved neural network algorithm can achieve model convergence, including the increase of momentum term, the adjustment of learning rate and the change of error function. At last, through simulation, this study shows that comprehensive risk prediction of cold chain logistics will be calculated based on the input indicators using the improved neural network algorithm, and the predictive value is accurate. So not only the analyzing of kinds of cold chain logistics indicators can be realized through the Neural Network model, but we can take priorities resorting to the predictive results accordingly.