Optimizing Multimodal Transportation Routes Considering Container Use

This study focuses on the route selection problem of multimodal transportation: When facing a shortage of containers, a transport plan must be designed for freight forwarders that realizes the optimal balance between transportation time and transportation cost. This problem is complicated by two important characteristics: (1) The use of containers is related to transport routes, and they interact with each other; and (2) Different types of containers should be used in different time ranges for different modes of transportation. To solve this problem, we establish a multi-objective optimization model for minimizing the total transportation time, transportation cost and container usage cost. To solve the multi-objective programming model, the normalized normal constraint method (NNCM) is used to obtain Pareto solutions. We conducted a case study considering the transportation of iron ore in Panzhihua City, Sichuan Province. The results demonstrate that using railway containers and railway transportation as much as possible in route selection can effectively solve the problem of container shortage and balance transportation time and transportation cost.

Multicriteria shape design of a sheet contour in stamping

One of the hottest challenges in automotive industry is related to weight reduction in sheet metal forming processes, in order to produce a high quality metal part with minimal material cost. Stamping is the most widely used sheet metal forming process; but its implementation comes with several fabrication flaws such as springback and failure. A global and simple approach to circumvent these unwanted process drawbacks consists in optimizing the initial blank shape with innovative methods. The aim of this paper is to introduce an efficient methodology to deal with complex, computationally expensive multicriteria optimization problems. Our approach is based on the combination of methods to capture the Pareto Front, approximate criteria (to save computational costs) and global optimizers. To illustrate the efficiency, we consider the stamping of an industrial workpiece as test-case. Our approach is applied to the springback and failure criteria. To optimize these two criteria, a global optimization algorithm was chosen. It is the Simulated Annealing algorithm hybridized with the Simultaneous Perturbation Stochastic Approximation in order to gain in time and in precision. The multicriteria problems amounts to the capture of the Pareto Front associated to the two criteria. Normal Boundary Intersection and Normalized Normal Constraint Method are considered for generating a set of Pareto-optimal solutions with the characteristic of uniform distribution of front points. The computational results are compared to those obtained with the well-known Non-dominated Sorting Genetic Algorithm II. The results show that our proposed approach is efficient to deal with the multicriteria shape optimization of highly non-linear mechanical systems.