Data envelopment analysis models play an important role in decision making. In this paper, one-stage and two-stage nonlinear programming problems are investigated in order to evaluate the efficiency of two types of network data envelopment analysis model. The first type of network data envelopment analysis model has a series structure with three stages and a feedback between the last step and the middle step, the second model has a three-stage series structure with two feedback between the final step and the first step and the middle step. By examining the overall efficiency of the models based on the one-stage programming problem, a two-stage programming problem is also applied in order to evaluate the efficiency of each step. In order to solve one-stage nonlinear programming problems and two-stage linear and nonlinear programming problems derived from modeling, a linearization method based on coordinate transformation, and constant assumption and gradual growth of some variables is presented. In the last section, the proposed methods have been discussed using some numerical examples.

As an attractive feature for modern transportation systems, the potential of the transfers capability (the load/passenger transfer between the two vehicles in its route) in reducing costs, increasing customer satisfaction and increasing the flexibility of the system, has been approved. But how profitable it could be under different circumstances? In other words, to which factors its influence depends on? what are its benefits versus its costs? The present research aimed to give a relatively comprehensive answer to these questions using a mathematical model of the pickup and delivery system with transfers. According to the model results under different situations, many factors such as modeling assumptions, system goals, transportation network scheme, vehicle fleet in terms of capacity, cost rate, and time window of activity and requests in terms of the length (direct distance between the pickup and delivery points), time windows and the volume to vehicle capacity ratio, affect the transfers benefits. As the small-scale numerical results indicate, we have an average of 5.7% reduction in the trip cost under normal conditions, which increases with the heterogeneity of vehicles, shorter time windows, and an increase in the length of the request. On the other hand, it is expected that profitability increases by problem size.

Weather forecast has been a major concern in various industries such as agriculture, aviation, maritime, tourism, transportation, etc. A good weather prediction may reduce natural disasters and unexpected events. This paper presents an empirical investigation to predict weather temperature using minimization of continuous ranked probability score (CRPS). The mean and standard deviation of normal density function are linear combination of the components of ensemble system. The resulted optimization model has been solved using particle swarm optimization (PSO) and the results are compared with Broyden–Fletcher–Goldfarb–Shanno (BFGS) method. The preliminary results indicate that the proposed PSO provides better results in terms of CRPS deviation criteria than the alternative BFGS method.