Study on the Pricing and Path Scheme Comparison of Transit Freight

This paper aims to optimize the transportation network and transportation organization strategy of Transport through China, enabling operators to obtain greater profits, improving the efficiency of transit freight transport, and solving the problem of transportation pricing and route selection of transit goods. In this paper, the growth trend of transit transport demand is firstly determined. On this basis, the ultimate goal is to maximize the transport profit of the operator. In-depth analysis is made from the perspectives of transport income and transport cost. In addition, through combing existing international transportation routes, the overall transit network map of transit China to central Asian and European countries is drawn. In order to achieve the goal of minimizing transportation expenditure, the model of comparing freight routes is established. The customer is also classified by matrix model. Finally, with the transit transportation from Japan, Korea and other countries as examples, the model in this paper is verified, and the optimal transportation path is obtained through software solution. Compared with the current scheme, it has saved operating costs

Integer programming and ant colony optimization for planning intermodal freight transportation operations

In this paper we deal with the operational planning of transportation operations in an intermodal network. The objective is to satisfy a given transportation demand by using road vehicles and trains, in order to minimize the total transportation cost and meeting a set of operational constraints. We propose a linear integer programming model and an Ant Colony Optimization metaheuristic approach in a pure and a hybrid version. We present and compare the results obtained testing all the approaches on a benchmark set made of randomly generated problem instances. The tests show the appreciable behavior of the IP model, that however requires a considerable amount of time, and the ability of the hybrid ACO to generate high quality solutions for all the benchmark instances with a quite reduced computational effort

Global supply chain optimization: a machine learning perspective to improve caterpillar's logistics operations

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University London.Supply chain optimization is one of the key components for the effective management of a company with a complex manufacturing process and distribution network. Companies with a global presence in particular are motivated to optimize their distribution plans in order to keep their operating costs low and competitive. Changing condition in the global market and volatile energy prices increase the need for an automatic decision and optimization tool. In recent years, many techniques and applications have been proposed to address the problem of supply chain optimization. However, such techniques are often too problemspecific or too knowledge-intensive to be implemented as in-expensive, and easy-to-use computer system. The effort required to implement an optimization system for a new instance of the problem appears to be quite significant. The development process necessitates the involvement of expert personnel and the level of automation is low. The aim of this project is to develop a set of strategies capable of increasing the level of automation when developing a new optimization system. An increased level of automation is achieved by focusing on three areas: multi-objective optimization, optimization algorithm usability, and optimization model design. A literature review highlighted the great level of interest for the problem of multiobjective optimization in the research community. However, the review emphasized a lack of standardization in the area and insufficient understanding of the relationship between multi-objective strategies and problems. Experts in the area of optimization and artificial intelligence are interested in improving the usability of the most recent optimization algorithms. They stated the concern that the large number of variants and parameters, which characterizes such algorithms, affect their potential applicability in real-world environments. Such characteristics are seen as the root cause for the low success of the most recent optimization algorithms in industrial applications. Crucial task for the development of an optimization system is the design of the optimization model. Such task is one of the most complex in the development process, however, it is still performed mostly manually. The importance and the complexity of the task strongly suggest the development of tools to aid the design of optimization models. In order to address such challenges, first the problem of multi-objective optimization is considered and the most widely adopted techniques to solve it are identified. Such techniques are analyzed and described in details to increase the level of standardization in the area. Empirical evidences are highlighted to suggest what type of relationship exists between strategies and problem instances. Regarding the optimization algorithm, a classification method is proposed to improve its usability and computational requirement by automatically tuning one of its key parameters, the termination condition. The algorithm understands the problem complexity and automatically assigns the best termination condition to minimize runtime. The runtime of the optimization system has been reduced by more than 60%. Arguably, the usability of the algorithm has been improved as well, as one of the key configuration tasks can now be completed automatically. Finally, a system is presented to aid the definition of the optimization model through regression analysis. The purpose of the method is to gather as much knowledge about the problem as possible so that the task of the optimization model definition requires a lower user involvement. The application of the proposed algorithm is estimated that could have saved almost 1000 man-weeks to complete the project. The developed strategies have been applied to the problem of Caterpillar’s global supply chain optimization. This thesis describes also the process of developing an optimization system for Caterpillar and highlights the challenges and research opportunities identified while undertaking this work. This thesis describes the optimization model designed for Caterpillar’s supply chain and the implementation details of the Ant Colony System, the algorithm selected to optimize the supply chain. The system is now used to design the distribution plans of more than 7,000 products. The system improved Caterpillar’s marginal profit on such products by a factor of 4.6% on average.Caterpillar Inc