The time-consistent dial-a-ride problem

peer reviewedIn the context of door-to-door transportation of people with disabilities, service quality considerations such as maximum ride time and service time consistency are critical requirements. To identify a good trade-off between these considerations and economic objectives, we define a new variant of the multiperiod dial-a-ride problem called the time-consistent dial-a-ride problem. A transportation planning is supposed to be time consistent if for each passenger, the same service time is used all along the planning horizon. However, considering the numerous variations in transportation demands over a week, designing consistent plan for all passengers can be too expensive. It is therefore necessary to find a compromise solution between costs and time-consistency objectives. The time-consistent dial-a-ride problem is solved using an epsilon-constraint approach to illustrate the trade-off between these two objectives. It computes an approximation of the Pareto front, using a matheuristic framework that combines a large neighbourhood search with the solution of set partitioning problems. This approach is benchmarked on time-consistent vehicle routing problem literature instances. Experiments are also conducted in the context of door-to-door transportation for people with disabilities, using real data. These experiments support managerial insights regarding the inter-relatedness of costs and quality of service

A concise guide to existing and emerging vehicle routing problem variants

Vehicle routing problems have been the focus of extensive research over the past sixty years, driven by their economic importance and their theoretical interest. The diversity of applications has motivated the study of a myriad of problem variants with different attributes. In this article, we provide a concise overview of existing and emerging problem variants. Models are typically refined along three lines: considering more relevant objectives and performance metrics, integrating vehicle routing evaluations with other tactical decisions, and capturing fine-grained yet essential aspects of modern supply chains. We organize the main problem attributes within this structured framework. We discuss recent research directions and pinpoint current shortcomings, recent successes, and emerging challenges

New Formulations and Solution Methods for the Dial-a-ride Problem

The classic Dial-A-Ride Problem (DARP) aims at designing the minimum-cost routing solution that accommodates a set of user requests under constraints at the operations planning level. It is a highly constrained combinatorial optimization problem initially designed for providing door-to-door transportation for people with limited mobility (e.g. the elderly or disabled). It consists of routing and scheduling a fleet of capacitated vehicles to service a set of requests with specified pickup and drop-off locations and time windows. With the details of requests obtained either beforehand (static DARP) or en-route (dynamic DARP), dial-a-ride operators strive to deliver efficient and yet high-quality transport services that satisfy each passenger's individual travel needs. The goal of this thesis is threefold: (1) to propose rich DARP formulations where users' preferences are taken into account, in order to improve service quality of Demand-Responsive Transport (DRT) services and promote ridership strategically; (2) to develop novel and efficient solution methods where local search, column generation, metaheuristics and machine learning techniques are integrated to solve large-scale DARPs; and (3) to conduct real-life DARP case studies (using data extracted from NYC Yellow Taxi trip records) to test the practicality of proposed models and solution methods, as well as to emphasise the importance of connecting algorithms with real-world datasets. These aims are achieved and presented in the three core chapters of this thesis. In the first core chapter (Chapter 3), two Mixed Integer Programming (MIP) formulations (link-based and path-based) of DARP are presented, alongside with their objective functions and standard solution methods. This chapter builds the foundation of the thesis by elaborating the base models and algorithms that this thesis is based on, and by running benchmark experiments and reporting numerical results as the base line of the whole thesis. In the second core chapter (Chapter 4), two DARP models (one deterministic, one stochastic) integrated with users' preferences from dial-a-ride service operators' perspective are proposed, facilitating them to optimise their overall profit while maintaining service quality. In these models, users' utility users' preferences are considered within a dial-a-ride problem. A customized local search based heuristic and a matheuristic are developed to solve the proposed Chance-Constrained DARP (CC-DARP). Numerical results are reported for both DARP benchmark instances and a realistic case study based on New York City yellow taxi trip data. This chapter also explores the design of revenue/fleet management and pricing differentiation. The proposed chance-constrained DARP formulation provides a new decision-support tool to inform on revenue and fleet management, including fleet sizing, for DRT systems at a strategic planning level. In the last core chapter (Chapter 5), three hybrid metaheuristic algorithms integrated with Reinforcement Learning (RL) techniques are proposed and implemented, aiming to increase the scale-up capability of existing DARP solution methods. Machine learning techniques and/or a branching scheme are incorporated with various metaheuristic algorithms including VNS and LNS, providing innovative methodologies to solve large-instance DARPs in a more efficient manner. Thompson Sampling (TS) is applied to model dual values of requests under a column generation setting to negate the effect of dual oscillation (i.e. promote faster converging). The performance of proposed algorithms is tested benchmark datasets, and strengths and weaknesses across different algorithms are reported

Home health care logistics planning: a review and framework

Home Health Care (HHC) is a growing industry in the medical services business, mainly in Europe and North America. These care services are provided at patients’ home by a multidisciplinary team using a distribution network. In this paper, an overview of the HHC services in Portugal and Brazil is presented. Additionally, a review is also presented to identify the main logistics problems associated with HHC services such as districting, routing and inventory management and the lack of integrated approaches to address them, as well as the best practices of management in the area. A framework is proposed to represent the main elements and characteristics of HHC services and their relationships. The framework suggests the use of a Decision Support System (DSS) based on optimization models and simulation approaches to overcome some of the main challenges associated to integrated approaches to address main problems, filling the gaps in the current literature. With the development of this DSS it will be possible to assist in the logistic planning of HHC teams, especially in countries like Brazil and Portugal.This work has been supported by CNPq (National Counsel of Technological and Scientific Development, Brazil) and COMPETE: POCI-01-0145-FEDER-007043 and FCT – Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.info:eu-repo/semantics/publishedVersio