Vehicle Routing Problems: Decision Support Systems and Distributed Approaches

Modern logistics received increasing attention for planning and scheduling operations of transport systems that have to be resource efficient, environmentally sustainable, and compatible with workers\u2019 rights. In particular, modern timeliness requirements and technological advances respectively call for and enable new formulations and solutions for the classical vehicle routing problem (VRP). Indeed, companies and service supplier need of real-time data and fast procedure to face uncertainty and meet people\u2019s expectative, and Information Communication Technologies make this information increasingly available. In other word, some topics emerge: dynamic VRP (DVRP), need of decision support systems (DSS), distributed approaches, balancing workloads for drivers. Firstly, the thesis exposes a review of recent contributions about DVRP, enlightening classifications by source of dynamism (factor of uncertainty), applications, methodologies. A particular attention is paid to distributed approaches, which still represent a minority part of literature. Secondly, due to the complexity and the urgency character of real-world application, the thesis proposes an architecture for a Decision Support System (DSS) that includes a fast VRP module devoted to critical services in city logistics, such as waste collection. The module can be fed with data that are tailored on different scenarios and can be customized for different logistics services. The core of the module is a two-phase heuristic algorithm able to solve a VRP with work shifts constraints for a waste collection service involving large network of pick-up locations. The algorithm is assessed by comparisons with Mixed Integer Linear Problems (MILP) and by the application to real case studies. Thirdly, the thesis proposes a distributed approach for VRP with time windows constraints (VRPTW) in a static and dynamic setting, which also takes in account workload balancing. In particular, the distributed approach is applied to a VRPTW and a multi-depot VRPTW (MDVRPTW), which can respectively act as the initial component and the ongoing component of a DVRP, in which the source of dynamism is the arrival of new service requests. The general strategy is the "cluster first, route second" and the core of the approach consists of an asynchronous, randomized and distributed algorithm. More precisely, vehicles reach the final assignment by iteratively solving local Graph Partitioning problems, in the form of Local-Integer Linear Programming problems (L-ILP), with randomly selected neighbor agents. Afterwards, each vehicle can optimize the route into its own cluster, by solving a small instance of the Traveling Salesman Problem with Time Windows. The proposed approach is assessed for both VRPTW and MDVRPTW by comparisons with exact and centralized approaches with particular regard to balanced workloads in terms of average traveling times, average vehicle loads and their standard deviations. Moreover, an example inspired by a transport company shows the applicability of the proposed approach in real-world scenarios

A Heuristic Method for Task Selection in Persistent ISR Missions Using Autonomous Unmanned Aerial Vehicles

The Persistent Intelligence, Surveillance, and Reconnaissance (PISR) problem seeks to provide timely collection and delivery of data from prioritized ISR tasks using an autonomous Unmanned Aerial Vehicle (UAV). In the literature, PISR is classified as a type of Vehicle Routing Problem (VRP), often called by other names such as persistent monitoring, persistent surveillance, and patrolling. The objective of PISR is to minimize the weighted revisit time to each task (called weighted latency) using an optimal task selection algorithm. In this research, we utilize the average weighted latency as our performance metric and investigate a method for task selection called the Maximal Distance Discounted and Weighted Revisit Period (MD2WRP) utility function. The MD2WRP function is a heuristic method of task selection that uses n+1 parameters, where n is the number of PISR tasks. We develop a two-step optimization method for the MD2WRP parameters to deliver optimal latency performance for any given task configuration, which accommodates both single and multi-vehicle scenarios. To validate our optimization method, we compare the performance of MD2WRP to common Traveling Salesman Problem (TSP) methods for PISR using different task configurations. We find that the optimized MD2WRP function is competitive with the TSP methods, and that MD2WRP often results in steady-state task visit sequences that are equivalent to the TSP solution for a single vehicle. We also compare MD2WRP to other utility methods from the literature, finding thatMD2WRP performs on par with or better than these other methods even when optimizing only one of its n + 1 parameters. To address real-world operational factors, we test MD2WRP with Dubins constraints, no-y zones in the operational area, return-to-base requirements, and the addition and removal of vehicles and tasks mid-mission. For each operational factor, we demonstrate its effect on PISR task selections using MD2WRP and how MD2WRP needs to be modified, if at all, to compensate. Finally, we make practical suggestions about implementing MD2WRP for flight testing, outline potential areas for future study, and offer recommendations about the conduct of PISR missions in general

AIRO 2016. 46th Annual Conference of the Italian Operational Research Society. Emerging Advances in Logistics Systems Trieste, September 6-9, 2016 - Abstracts Book

The AIRO 2016 book of abstract collects the contributions from the conference participants. The AIRO 2016 Conference is a special occasion for the Italian Operations Research community, as AIRO annual conferences turn 46th edition in 2016. To reflect this special occasion, the Programme and Organizing Committee, chaired by Walter Ukovich, prepared a high quality Scientific Programme including the first initiative of AIRO Young, the new AIRO poster section that aims to promote the work of students, PhD students, and Postdocs with an interest in Operations Research. The Scientific Programme of the Conference offers a broad spectrum of contributions covering the variety of OR topics and research areas with an emphasis on “Emerging Advances in Logistics Systems”. The event aims at stimulating integration of existing methods and systems, fostering communication amongst different research groups, and laying the foundations for OR integrated research projects in the next decade. Distinct thematic sections follow the AIRO 2016 days starting by initial presentation of the objectives and features of the Conference. In addition three invited internationally known speakers will present Plenary Lectures, by Gianni Di Pillo, Frédéric Semet e Stefan Nickel, gathering AIRO 2016 participants together to offer key presentations on the latest advances and developments in OR’s research

Vehicle routing and tour planning problem: a cement industry case study

Dissertação de mestrado em Engenharia de SistemasThe transportation, being part of the logistics field, plays a crucial role in the business world. Its impact in the costs and service quality is an increasingly imperative topic. In industry, transportation systems are equally important and can represent a large improvement in the management of the plants and in the service quality of the products, thus bringing advantages for the companies and for the clients. The cement industry is not an exception. Cement is the second most consumed substance in the world and with the great number of trucks arriving at cement facilities, every day, the supply chain management of this industry must encompass this management as well. With the lack of assistance and guidance clients have inside the cement facilities, both companies incur in additional costs and clients experience reduced levels of service quality. To overcome these issues, three algorithms were developed and implemented. Each algorithm has different specifications and different goals. However, all the developed algorithms improve the service quality, guiding the truck drivers – the clients – inside the plants and giving the routes in shorter periods of time. One algorithm guides the trucks through the minimum distance route and will serve as a comparison term for the other two. The other two algorithms, named equilibrium approaches, are the main contribution of this dissertation. These dynamic algorithms consider not only the traveled distance, but also the workload both in the servers and in the roads. The entrance management in the facilities is also a crucial aspect cement companies must be aware of. Several thought policies are presented and an algorithm for the entrance management is developed and implemented. With a simulation software, the developed algorithms were tested and simulated. The simulation results are reported and discussed.A indústria do transporte desempenha um papel crucial no mundo empresarial. O seu impacto nos custos e na qualidade de serviço são um tópico cada vez mais importante. Na indústria, os sistemas de transporte são igualmente importantes e podem representar uma grande melhoria na gestão das fábricas e na qualidade do serviço dos produtos, trazendo vantagens tanto para as empresas como para os clientes. A indústria cimenteira não é uma exceção. O cimento é a segunda comodidade mais consumida em todo o mundo, e com o grande número de camiões que chegam às fábricas de cimento todos os dias, a gestão da cadeia de abastecimento desta indústria deve, também, incorporar esta gestão. Com a falta de assistência na orientação que os clientes têm dentro das fábricas, tanto as fábricas incorrem em custos acrescidos como os clientes experienciam uma qualidade de serviço reduzida. Para abordar este problema, três algoritmos foram desenvolvidos e implementados. Cada algoritmo tem objetivos e especificações diferentes. No entanto, todos os algoritmos implementados melhoram a qualidade de serviço guiando os camiões dos clientes dentro das plantas, e calculando as rotas em curtos períodos de tempo. Um dos algoritmos guia os camiões pela rota que permite a menor distância percorrida, e servirá como termo de comparação para os outros dois. Os outros dois algoritmos, chamados abordagens de equilíbrio, são a grande contribuição desta dissertação. Estes algoritmos dinâmicos consideram a ocupação dos servidores e das estradas, além da distância percorrida. A gestão de entrada nas fábricas é também um aspeto importante que as fábricas de cimento devem ter atenção. Diversas políticas de entrada são apresentadas e um algoritmo para a gestão de entrada na fábrica é também desenvolvido e implementado. Com um software de simulação, os algoritmos desenvolvidos foram testados e simulados. Os resultados das simulações são apresentados e discutidos

Multi-Robot Path Planning for Persistent Monitoring in Stochastic and Adversarial Environments

In this thesis, we study multi-robot path planning problems for persistent monitoring tasks. The goal of such persistent monitoring tasks is to deploy a team of cooperating mobile robots in an environment to continually observe locations of interest in the environment. Robots patrol the environment in order to detect events arriving at the locations of the environment. The events stay at those locations for a certain amount of time before leaving and can only be detected if one of the robots visits the location of an event while the event is there. In order to detect all possible events arriving at a vertex, the maximum time spent by the robots between visits to that vertex should be less than the duration of the events arriving at that vertex. We consider the problem of finding the minimum number of robots to satisfy these revisit time constraints, also called latency constraints. The decision version of this problem is PSPACE-complete. We provide an O(log p) approximation algorithm for this problem where p is the ratio of the maximum and minimum latency constraints. We also present heuristic algorithms to solve the problem and show through simulations that a proposed orienteering-based heuristic algorithm gives better solutions than the approximation algorithm. We additionally provide an algorithm for the problem of minimizing the maximum weighted latency given a fixed number of robots. In case the event stay durations are not fixed but are drawn from a known distribution, we consider the problem of maximizing the expected number of detected events. We motivate randomized patrolling paths for such scenarios and use Markov chains to represent those random patrolling paths. We characterize the expected number of detected events as a function of the Markov chains used for patrolling and show that the objective function is submodular for randomly arriving events. We propose an approximation algorithm for the case where the event durations for all the vertices is a constant. We also propose a centralized and an online distributed algorithm to find the random patrolling policies for the robots. We also consider the case where the events are adversarial and can choose where and when to appear in order to maximize their chances of remaining undetected. The last problem we study in this thesis considers events triggered by a learning adversary. The adversary has a limited time to observe the patrolling policy before it decides when and where events should appear. We study the single robot version of this problem and model this problem as a multi-stage two player game. The adversary observes the patroller’s actions for a finite amount of time to learn the patroller’s strategy and then either chooses a location for the event to appear or reneges based on its confidence in the learned strategy. We characterize the expected payoffs for the players and propose a search algorithm to find a patrolling policy in such scenarios. We illustrate the trade off between hard to learn and hard to attack strategies through simulations

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes