Service Consistency in Vehicle Routing

This thesis studies service consistency in the context of multi-period vehicle routing problems (VRP) in which customers require repeatable services over a planning horizon of multiple days. Two types of service consistency are considered, namely, driver consistency and time consistency. Driver consistency refers to using the fewest number of different drivers to perform all of the visits required by a customer over a planning horizon and time consistency refers to visiting a customer at roughly the same time on each day he/she needs service. First, the multi-objective consistent VRP is defined to explore the trade-offs between the objectives of travel cost minimization and service consistency maximization. An improved multi-objective optimization algorithm is proposed and the impact of improving service consistency on travel cost is evaluated on various benchmark instances taken from the literature to facilitate managerial decision making. Second, service consistency is introduced for the first time in the literature to the periodic vehicle routing problem (PVRP). In the PVRP, customers may require multiple visits over a planning horizon, and these visits must occur according to an allowable service pattern. A service pattern specifies the days on which the visits required by a customer are allowed to occur. A feasible service pattern must be determined for each customer before vehicle routes can be optimized on each day. Various multi-objective optimization approaches are implemented to evaluate their comparative competitiveness in solving this problem and to evaluate the impact of improving service consistency on the total travel cost. Third, a branch-and-price algorithm is developed to solve the consistent vehicle routing problem in which service consistency is enforced as a hard constraint. In this problem, the objective is to minimize the total travel cost. New constraints are devised to enhance the original mixed integer formulation of the problem. The improved formulation outperforms the original formulation regarding CPLEX solution times on all benchmark instances taken from the literature. The proposed branch-and-price algorithm is shown to be able to solve instances with more than fourteen customers more efficiently than either the existing mixed integer formulation or the one we propose in this paper

A robust solving strategy for the vehicle routing problem with multiple depots and multiple objectives

This document presents the development of a robust solving strategy for the Vehicle Routing Problem with Multiple Depots and Multiple Objectives (MO-MDVRP). The problem tackeled in this work is the problem to minimize the total cost and the load imbalance in vehicle routing plan for distribution of goods. This thesis presents a MILP mathematical model and a solution strategy based on a Hybrid Multi- Objective Scatter Search Algorithm. Several experiments using simulated instances were run proving that the proposed method is quite robust, this is shown in execution times (less than 4 minutes for an instance with 8 depots and 300 customers); also, the proposed method showed good results compared to the results found with the MILP model for small instances (up to 20 clients and 2 depots).MaestríaMagister en Ingeniería Industria

Optimal patrol routing and scheduling for parking enforcement considering drivers' parking behavior

Logistics costs constitute a considerable proportion of overall daily expenses for many public sectors, among which parking enforcement agencies are some of the most prominent examples. While currently there is little research about the planning of efficient parking enforcement patrol operations, this work presents several models to generate patrol schemes that help parking departments achieve low operational costs and effective enforcement. This thesis considers two levels of problems: i) parking behavior of drivers based on given patrol frequency (but not schedule), and ii) parking enforcement patrol routing and scheduling based on the parking behavior of drivers. Driver determines optimal payment based on the distribution of parking duration, parking prices, citation fines, and patrol frequencies via a newsvendor model. As the intensity of parking enforcement increases, illegal parking is expected to occur less frequently. However, improving parking enforcement sometimes requires more frequent patrols that lead to higher agency costs. In order to find the optimal trade-off point, the problem is further formulated into a Vehicle Routing Problem (VRP). Solving this bi-level optimization problem means that the cost is reduced while anticipated parking offenses are limited to a certain level. We present a traditional discrete mixed-integer programming model, and a continuous approximation model based on the method of continuum approximation. Numerical tests are performed in order to examine the performance of these two models using randomly-generated datasets. Sensitivity analyses show that as parking price or demand increases, or citation fine decreases, more frequent patrols are required to maintain the healthy operation of the parking lots. The results also validate that the method of continuum approximation can offer good estimation of the agency cost for the parking patrol problem with comparatively minimal runtime

Exact Models, Heuristics, and Supervised Learning Approaches for Vehicle Routing Problems

This dissertation presents contributions to the field of vehicle routing problems by utilizing exact methods, heuristic approaches, and the integration of machine learning with traditional algorithms. The research is organized into three main chapters, each dedicated to a specific routing problem and a unique methodology. The first chapter addresses the Pickup and Delivery Problem with Transshipments and Time Windows, a variant that permits product transfers between vehicles to enhance logistics flexibility and reduce costs. To solve this problem, we propose an efficient mixed-integer linear programming model that has been shown to outperform existing ones. The second chapter discusses a practical workforce scheduling problem, formulated as a specific type of vehicle routing problem. The objective here is to efficiently assign consultants to various clients and plan their trips. This computational challenge is addressed by using a two-stage approach: the first stage employs a mathematical model, while the second stage refines the solution with a heuristic algorithm. In the final chapter, we explore methods that integrate machine learning with traditional approaches to address the Traveling Salesman Problem, a foundational routing challenge. Our goal is to utilize supervised learning to predict information that boosts the efficiency of existing algorithms. Taken together, these three chapters offer a comprehensive overview of methodologies for addressing vehicle routing problems

A metaheuristic for a teaching assistant assignment-routing problem

The Flemish Ministry of Education promotes the integrated education of disabled children by providing educational opportunities in common schools. In the current system, disabled children receive ambulant help from a teaching assistant (TA) employed at an institute for extra-ordinary education. The compensation that the TAs receive for driving to visit the students is a major cost factor for the institute that provides the assistance, therefore its management desires a schedule that minimizes the accumulated distance traveled by all TAs combined. We call this optimization problem the teaching assistants assignment routing problem (TAARP). It involves three decisions that have to be taken simultaneously: (1) pupils have to be assigned to TAs; (2) pupils assigned to a given TA have to be spread over the TA's different working days; and (3) the order in which to visit the pupils on each day has to be determined. We propose a solution strategy based on an auction algorithm and a variable neighborhood search which exhibit an excellent performance both in simulated and real instances. The total distance traveled in the solution obtained for the real data set improves the current solution by about 22% which represents a saving of around 9% on the annual budget of the institute.