Volume 2, Issue 4

Vehicle routing problem with hard time window (VRPHTW) is extremely strict in travel time. However, the travel time is usually uncertain due to some stochastic factors such as weather and other road conditions. It is an important issue to take travel time uncertainty into consideration in VRPHTW. This paper develops a robust VRPHTW model to cope with time uncertainty. We use robustness method of Bertismas to consider the maximum change of uncertain travel time in the degree of robustness set by decision maker. The probability that the optimal solution violates constraints is derived. The violated probability shows that the robustness of VRPHTW model can reach a satisfactory level. Finally, one modified max-min ant system algorithm is proposed to solve this problem and one numerical example is conducted to illustrate the model and the algorithm. Both theory analysis and numerical example show the effectiveness of the proposed robust model.

The game among the corporate controlling shareholder, the shareholder in power balance and the manager can lead to severe agency problems. This paper regards the shareholder in power balance as another principal and applies the latest results about double-principal agent theory in the research of manager tunneling, supervision cost and corporate performance, trying to solve the inconsistency of the above corporate governance issue researched by domestic and foreign scholars. The main conclusions are as the followings. The correlation among them depends on which one has a dominant position, the free rider effect of supervision or the positive externality effect on cash flow right. Therefore, the key to excite the positive effect of the corporate governance mechanism such as the check-and-balance of stock ownership is the degree of cooperation between shareholders.

In this paper, a new approach is developed to estimate the value of defaultable securities under the actual probability measure. This model gives the price framework by means of the method of backward stochastic differential equation. Such a method solves some problems in most of existing literatures with respect to pricing the credit risk and relaxes certain market limitations. We provide the price of defaultable securities in discrete time and in continuous time respectively, which is favorable to practice to manage real credit risk for finance institutes.

For time series forecasting, the problem that we often encounter is how to increase the prediction accuracy as much as possible with the irregular and noise data. This study proposes a novel multilayer feedforward neural network based on the improved particle swarm optimization with adaptive genetic operator (IPSO- MLFN). In the proposed IPSO, inertia weight is dynamically adjusted according to the feedback from particles’ best memories, and acceleration coefficients are controlled by a declining arccosine and an increasing arccosine function. Further, a crossover rate which only depends on generation and does not associate with the individual fitness is designed. Finally, the parameters of MLFN are optimized by IPSO. The empirical results on the container throughput forecast of Shenzhen Port show that forecasts with IPSO-MLFN model are more conservative and credible.

The purpose of this paper is to study sales pricing and discount strategy for sellers. Here, the suppliers require fixed order quantities to the sellers. Therefore, to incorporate the concept of sellers’ maximum profit and sales discount into the model, the author classifies the multi-category goods into backlog, flat and profitable one. Then the model to determine the optimal sales price strategy for a sales discount decision system with fixed order quantities is analyzed. As most of the non-linear model can be linearization, a mathematical model for sales discount decision system is developed when demand rate is a liner function of the sales price. By analyzing the total profit function, the authors developed some useful results to characterize the optimal solution and provided a method to find the sales discount for holdovers. By developing a solution algorithm, the optimal retail price and sales discount for holdovers are provided. Case scenarios are presented to validate the proposed model. Through case scenarios analyses, it is observed that optimal sales discount can help the sellers earn maximum profit with constraints of fixed order quantities. Most of the research articles available in the literature assumed that the sellers are always in optimal situation. In fact, sellers’ decisions rely on suppliers’ decisions in most cases. In this paper, determination of sales discounts can be seen as sellers compromise to suppliers by seeking the optimal strategy with downstream customers. In view of this fact, a mathematical model is developed considering sales price dependent linear demand and sellers’ profit. Very few researchers have investigated optimal sales discount decision system with fixed order quantities; although it is a real situation the sellers should be faced.

We analyze the retailer’s optimal order with capital constraint under stochastic demand. We consider the impact of external financing, that is inventory financing, and the internal financing, that is trade credit, on the order strategy of retailer separately. We establish revenue model of retailer and solve, and give revenue of retailer and supplier respectively. Finally, we give the impact of own funds on the retailer’s order quantity, retailer’s revenue, supplier’s revenue, as well as the impact on the overall revenue gains of the supply chain. And some analysis and statement are also given in the end.

The traffic congestion often occurs in urban road network. When one of the sections becomes congested, it will lead to a series of congestions in other sections. The traffic congestion spreads rapidly until part of road network becomes congestion ultimately. In this case, the paper investigates the mechanism of the traffic congestion in urban road network and points out that subsystems of the traffic congestion always perform completive and cooperative functions in the process of traffic congestion. The process behaves in a manner of self-organized criticality, which can be forecasted. The paper also establishes synergetic predictive models based on self-organized criticality of the synergetic theory. Finally, the paper takes Beijing road network as an example to forecast the widespread traffic congestion. The result shows that the established models are accuracy, and the traffic congestion is featured of self-organized criticality.

The maximum assembling for some period on nested routing platforms of urban rail transit line were calculated by trend analysis method. Based on influencing factors of maximum assembling on platform, relative hypotheses were given. Platforms were divided to eight types by relations between platform and routing train. Interaction of passenger, train and platform, assembling change process, and calculation frames of maximum assembling on different type platforms carried out to be maximum assembling were concluded by trend analysis method. The numerical example application revealed that trend analysis method to calculate maximum assembling on nested routing platforms is feasible, and the final results by trend analysis method are more accurate than by one of codes for design of metro.