Modeling the Multicommodity Multimodal Routing Problem with Schedule-Based Services and Carbon Dioxide Emission Costs

We explore a freight routing problem wherein the aim is to assign optimal routes to move commodities through a multimodal transportation network. This problem belongs to the operational level of service network planning. The following formulation characteristics will be comprehensively considered: (1) multicommodity flow routing; (2) a capacitated multimodal transportation network with schedule-based rail services and time-flexible road services; (3) carbon dioxide emissions consideration; and (4) a generalized costs optimum oriented to customer demands. The specific planning of freight routing is thus defined as a capacitated time-sensitive multicommodity multimodal generalized shortest path problem. To solve this problem systematically, we first establish a node-arc-based mixed integer nonlinear programming model that combines the above formulation characteristics in a comprehensive manner. Then, we develop a linearization method to transform the proposed model into a linear one. Finally, a computational experiment from the Chinese inland container export business is presented to demonstrate the feasibility of the model and linearization method. The computational results indicate that implementing the proposed model and linearization method in the mathematical programming software Lingo can effectively solve the large-scale practical multicommodity multimodal transportation routing problem

The SNS logistics network design : location and vehicle routing.

Large-scale emergencies caused by earthquake, tornado, pandemic flu, terrorism attacks and so on can wreak havoc to communities. In order to mitigate the impact of the events, emergency stockpiles of food, water, medicine and other materials have been set up around the US to be delivered to the affected areas during relief operations. One type of stockpile is called the Strategic National Stockpile (SNS). The SNS logistics network is designed to have multiple stages of facilities, each of which is managed by different levels of governmental authorities - federal, state and local authorities. The design of a logistics network for delivery of the SNS materials within a state are explored in this dissertation. There are three major areas of focus in this dissertation: (1) the SNS facility location model, which is used to determine sites for locating Receiving, Staging and Storage (RSS) and Regional Distribution Nodes (RDNs) to form a logistics network to deliver relief material to Points of Demand (PODs), where the materials are directly delivered to the affected population; (2) the SNS Vehicle Routing Problem (VRP), which is used to assist the SNS staff in determining the numbers of various types of trucks, and the routing schedules of each truck to develop an operational plan for delivering the required relief materials to the assigned PODs within the required duration; (3) the location-routing analysis of emergency scenarios, in which the facility location model and the VRP solution are integrated through the use of a computer program to run on several assumed emergency scenarios. Using real data from the department of public health in the Commonwealth of Kentucky, a transshipment and location model is formulated to determine the facility locations and the transshipment quantities of materials; a multiple-vehicle routing model allowing split deliveries and multiple routes per vehicle that must be completed within a required duration is formulated to determine the routing and scheduling of trucks. The facility location model is implemented using Microsoft Solver Foundation and C#. An algorithm combining the Clark and Wright saving algorithm and Simulated Annealing is designed and implemented in C# to solve the VRP. The algorithm can determine whether there is shortage of transportation capacity, and if so, how many of various types of trucks should be added for optimal performance. All the solution algorithms are integrated into a web-based SNS planning tool. In the location-routing analysis of emergency scenarios, a binary location model and an algorithm for solving VRP solution are integrated as a computer program to forecast the feasibility of distribution plans and the numbers of required trucks of various types. The model also compares the costs and benefits of direct and indirect shipment. A large-scale emergency scenario in which a specific type of vaccine is required to be delivered to the entire state of Kentucky is considered. The experiments are designed based on the real data provided by the Kentucky state government. Thus the experimental results provide valuable suggestions for future SNS preparedness planning

Decision Support System for Container Port Selection using Multiple-Objective Decision Analysis

Ports are essential for maritime transportation and global supply chains since they are nodes that connect the sea- and land-based modes of transportation. With containerization and supply chains stimulating global trade, ports are challenged to adjust to changes in the market to create value to their customers. Therefore, this dissertation research focuses on the container port selection decision analysis to provide information to help shipping lines select the best port for their shipping networks. Since the problem is complex, dynamic, and involves multiple and conflicting criteria, the research proposes to use the multi-objective decision analysis with Value-Focused Thinking approach. The first chapter analyzes the port selection literature by timeline, journals, geographical location, and focus of the studies. Also, the research identifies the multiple criteria used in the port selection literature, as well as the models and approaches used for the analysis of the port selection decision problem. The second chapter develops a container port selection decision model for shipping lines using ports in West Africa. This model uses a multi-attribute value theory with valued-focused thinking and Alternative-Focused Thinking methodologies. The third chapter develops a port selection decision support system for shipping lines to select the best port in the U.S. Gulf Coast considering the impact of the Panama Canal’s expansion. The decision support system uses the multi-objective decision analysis with Value-Focused Thinking approach, incorporating the opinion of an industry expert for the development of the value model. It also includes a cost model to quantify the cost of the alternatives. A Monte Carlo simulation is used to help decision makers understand the value and cost risks of the decision. The contribution of this research is that it provides a tool to decision makers of the shipping lines industry to improve the decision making process to select the port that will add the most affordable value to the global supply chains of their customers. In addition, researchers can use the proposed methodology for future port selection studies in other regions and from the perspectives of other stakeholders

Community Vulnerability Perspectives on Infrastructure Network Resilience Decision Making

Critical interdependent infrastructure networks such as water distribution, natural gas pipeline, electricity power, communication and transportation systems provide the essential necessities for societies and their utilization is the backbone of everyday processes such as production, health, convenience and many more. Often cascading dysfunctionality or disruption in these critical infrastructure networks triggers chain reactions of blackouts or blockages through the system of highly interconnected infrastructure networks and the inevitable collapse of surrounding societies. For the planning of restoration processes and resilience of these, social aspects and demographics should also be considered to assign and mitigate the possible social risks associated with these disruptions. Additionally, it is crucial to identify the most critical components of these networks which are the components that have the largest impact on the performance of both their and other networks that are operationally dependent. These critical components have the largest impact on society in terms of serving its needs so that its recovery can be completed in a timely manner after a disruption. This research studies the restoration planning of critical interdependent infrastructure networks after a possible disruptive event by mainly emphasizing on the vulnerability indices of interacting society. The methodology integrates (i) a resilience-driven multi-objective mixed-integer programming formulation to schedule the restoration process of disrupted network components in each network, and (ii) a component importance measure that quantifies the impact of equitable restoration activities on components with (iii) an index of social vulnerability that is geographically distributed. An illustrative example of the proposed integrated model that focuses on studying the community resilience in Shelby County, TN, United States is also represented

Applications of Genetic Algorithm and Its Variants in Rail Vehicle Systems: A Bibliometric Analysis and Comprehensive Review

Railway systems are time-varying and complex systems with nonlinear behaviors that require effective optimization techniques to achieve optimal performance. Evolutionary algorithms methods have emerged as a popular optimization technique in recent years due to their ability to handle complex, multi-objective issues of such systems. In this context, genetic algorithm (GA) as one of the powerful optimization techniques has been extensively used in the railway sector, and applied to various problems such as scheduling, routing, forecasting, design, maintenance, and allocation. This paper presents a review of the applications of GAs and their variants in the railway domain together with bibliometric analysis. The paper covers highly cited and recent studies that have employed GAs in the railway sector and discuss the challenges and opportunities of using GAs in railway optimization problems. Meanwhile, the most popular hybrid GAs as the combination of GA and other evolutionary algorithms methods such as particle swarm optimization (PSO), ant colony optimization (ACO), neural network (NN), fuzzy-logic control, etc with their dedicated application in the railway domain are discussed too. More than 250 publications are listed and classified to provide a comprehensive analysis and road map for experts and researchers in the field helping them to identify research gaps and opportunities

Aspiration Based Decision Support Systems

This book focuses the methodology of decision analysis and support related to the principle of reference point optimization (developed by the editors of this volume and called also variously: aspiration-led decision support, quasi-satisfying framework of rationality, DIDAS methodology etc.). The selection principle applied for this volume was to concentrate on advances of theory and methodology, related to the focusing theme, to supplement them by experiences and methodological advances gained through wide applications and tests in one particular application area - the programming of development of industrial structures in chemical industry, and finally to give a very short description of various software products developed in the contracted study agreement