Scheduling deliveries under uncertainty

Quite often transportation companies face two types of jobs, ones which they can plan themselves and ones which have to be done on call. In this paper we study the scheduling of these jobs, while we assume that job durations are known beforehand as well as windows in which the jobs need to be done. We develop several heuristics to solve the problem at hand. The most successful are based on defining an appropriate buffer. The methods are assessed in extensive experiments on two aspects, viz. efficiency, in the sense that they carry out many jobs and certainty, in the sense that they provide information beforehand about which jobs they will execute

Scheduling vehicles in automated transportation systems : algorithms and case study

One of the major planning issues in large scale automated transportation systems is so-called empty vehicle management, the timely supply of vehicles to terminals in order to reduce cargo waiting times. Motivated by a Dutch pilot project on an underground cargo transportation system using Automated Guided Vehicles CAGV s), we developed several rules and algorithms for empty vehicle management, varying from trivial First-Come, First-Served (FCFS) via look-ahead rules to integral planning. For our application, we focus on attaining customer service levels in the presence of varying order priorities, taking into account resource capacities and the relation to other planning decisions, such as terminal management We show how the various rules are embedded in a framework for logistics control of automated transportation networks. Using simulation, the planning options are evaluated on their performance in terms of customer service levels, AGV requirements and empty travel distances. Based on our experiments, we conclude that look-ahead rules have significant advantages above FCFS. A more advanced so-called serial scheduling method outperforms the look-ahead rules if the peak demand quickly moves amongst routes in the system

THREE BUSINESS ANALYTICS ESSAYS ON TRANSPORTATION MANAGEMENT

Transportation service has a major impact on economic development and growth. Efficient management in transportation allows organizations to handle complicated situations. The studies in my dissertation focus on developing novel methodologies, strategies and decisions to help address three different practical demands from organizations by using business analytical tools. Although we present our methodologies in three particular business contexts, the frameworks of three essays can be easily generalized to other industries. The first essay is to explore the strategy for global market expansion of a private air medical company. We assess the global medical aviation market and identify the most suitable regions for the company. We combine the Analytic Hierarchy Process (AHP) and the Grey Number theory (GN) to analyze the potential foreign market. We evaluate all countries and areas in the world and make our recommendations through our novel AHP-GN model. The second essay targets a booming industry-the China express delivery industry. With rapid development of e-commerce in China, its express industry has experienced phenomenal growth in recent years. Investors are particularly eager to discover how to gain a better understanding of the market and compare between operating express delivery firms to understand their respective strengths and weaknesses. We investigate the top 12 express delivery companies in China and evaluate their independent business performance. The Analytical Network Process (ANP), a multi-criteria decision-making methodology, is used to develop an evaluation framework. In addition to the ANP method, we employ the center-point triangular whitenization weight function to convert uncertain information into a unique value and rank the 12 express delivery companies. The third essay studies the car-sharing industry. The three fundamental management issues in car-sharing industry are: 1). Branch Station Location Selection; 2). Station Size/Capacity; 3). Strategies for imbalance of vehicle distribution at each station. In this study, we develop novel approaches to address these questions. Our models require few inputs and offer quick analytic results. Application of the models to the Zipcar setting is used to illustrate our models and to derive managerial insights

Profit Based Simulation Model for The Rail Transportation Industry

Schedules often conflict in the rail transportation industry. Operations managers assign resources and make scheduling decisions with no visibility of the revenue, cost, and profitability characteristics of the route they are manipulating. Transit speed decisions focus on ensuring trains safely reach their destination on time with little regard given to the actual service needs of the customer. Although all customers want on-time deliveries, few actually pay a premium to garner this level of preferential treatment. Operating in this type of environment results in decisions that severely erode profits. In this dissertation, a simulation model referred to as the Rail Profit Model (RPM) is developed to test three transit strategies that reveal how transit speed decisions impact supply chain and rail service provider profits and to lay the groundwork to challenge the cultural premise that the rail industry must behave like the trucking industry in order to thrive. In fact, the Rail Profit Model demonstrates that most trains should maintain the most economical speed to maximize profits. The model also identifies specific scenarios where increasing speed to arrive on time is the most profitable solution, contributing to the ability to leverage revenue management techniques to ensure customers pay the adequate premium that on-time delivery requires. Equipped with the Rail Profit Model, operations managers can now examine transit speed decisions and de-conflict competing resources to form recommended solutions that preserve maximum profits for the rail service provider and supply chain

OPTIMIZATION MODELS AND METHODOLOGIES TO SUPPORT EMERGENCY PREPAREDNESS AND POST-DISASTER RESPONSE

This dissertation addresses three important optimization problems arising during the phases of pre-disaster emergency preparedness and post-disaster response in time-dependent, stochastic and dynamic environments. The first problem studied is the building evacuation problem with shared information (BEPSI), which seeks a set of evacuation routes and the assignment of evacuees to these routes with the minimum total evacuation time. The BEPSI incorporates the constraints of shared information in providing on-line instructions to evacuees and ensures that evacuees departing from an intermediate or source location at a mutual point in time receive common instructions. A mixed-integer linear program is formulated for the BEPSI and an exact technique based on Benders decomposition is proposed for its solution. Numerical experiments conducted on a mid-sized real-world example demonstrate the effectiveness of the proposed algorithm. The second problem addressed is the network resilience problem (NRP), involving an indicator of network resilience proposed to quantify the ability of a network to recover from randomly arising disruptions resulting from a disaster event. A stochastic, mixed integer program is proposed for quantifying network resilience and identifying the optimal post-event course of action to take. A solution technique based on concepts of Benders decomposition, column generation and Monte Carlo simulation is proposed. Experiments were conducted to illustrate the resilience concept and procedure for its measurement, and to assess the role of network topology in its magnitude. The last problem addressed is the urban search and rescue team deployment problem (USAR-TDP). The USAR-TDP seeks an optimal deployment of USAR teams to disaster sites, including the order of site visits, with the ultimate goal of maximizing the expected number of saved lives over the search and rescue period. A multistage stochastic program is proposed to capture problem uncertainty and dynamics. The solution technique involves the solution of a sequence of interrelated two-stage stochastic programs with recourse. A column generation-based technique is proposed for the solution of each problem instance arising as the start of each decision epoch over a time horizon. Numerical experiments conducted on an example of the 2010 Haiti earthquake are presented to illustrate the effectiveness of the proposed approach

The Fleet-Sizing-and-Allocation Problem: Models and Solution Approaches

Transportation is one of the most vital services in modern society. It makes most of the other functions of society possible. Real transportation systems are so large and complex that in order to build the science of transportation systems it will be necessary to work in many areas, such as: Modeling, Optimization and Simulation. We are interested in solutions for the so-called fleet-sizing-and-allocation problem (FSAP). Fleet sizing and allocation problems are one of the most interesting and hard to solve logistic problems. A fleet sizing and allocation problem consists of two interdependent parts. The fleet sizing problem is to determine a number of transportation units that optimally balances service requirements against the cost of purchasing and maintaining the transportation units. The allocation problem is dealing with the repositioning of transportation units to serve future transportation demand. To make the fleet sizing and allocation problem a little bit more tractable we concentrate on logistic systems with a special hub-and-spoke structure. We start with a very simple fleet sizing of one-to-one case. This case will cause us to focus attention on several key issues in fleet sizing. Afterwards, the generalization of the one-to-one system is the one-to-many system. As a simple example can serve the continuous time situation where a single origin delivers items to many destinations. For the case that items are produced in a deterministic production cycle and transportation times are stochastic. We also studied a hub-and-spoke problem with continuous time and stochastic demand. To solve this problem, based on Marginal Analysis, we applied queueing theory methods. The investigation of the fleet-sizing-and-allocation problem for hub-and-spoke systems is started for a single-period, deterministic-demand model. In that the model hub has to decide how to use a given number of TU’s to satisfy a known (deterministic) demand in the spokes. We consider two cases: 1. Renting of additional TU’s from outside the system is not possible, 2. Renting of additional TU’s from outside the system is possible. For each case, based on Marginal Analysis, we developed a simple algorithm, which gives us the cost-minimal allocation. Since the multi-period, deterministic demand problem is NP-hard we suggest to use Genetic Algorithms. Some building elements for these are described. For the most general situation we also suggest to use simulation optimization. To realize the simulation optimization approach we could use the software tool “Calculation Assessment Optimization System” (CAOS). The idea of CAOS is to provide a software system, which separates the optimization process from the optimization problem. To solve an optimization problem the user of CAOS has to build up a model of the system to which the problem is related. Furthermore he has to define the decision parameters and their domain. Finally, we used CAOS for two classes of hub-and-spoke system: 1. A single hub with four spokes, 2. A single hub with fifty spokes. We applied four optimizers – a Genetic Algorithm, Tabu Search, Hybrid Parallel and Hybrid Serial with two distributions (Normal Distribution and Exponential Distribution) for a customer interarrival times and their demand

Liner ship fleet planning with uncertain container shipment demand

Ph.DDOCTOR OF PHILOSOPH