Ambulance routing problems with rich constraints and multiple objectives

Humanitäre non-profit Organisationen im Bereich des Patiententransports sehen sich dazu verpflichtet alle möglichen Einsparungs- und Optimierungspotentiale auszuloten um ihre Ausgaben zu reduzieren. Im Gegensatz zu Notfalleinsatzfahrten, bei denen ein Zusammenlegen mehrerer Transportaufträge normalerweise nicht möglich ist, besteht bei regulären Patiententransporten durchaus Einsparungspotential. Diese Tatsache gibt Anlass zur wissenschaftlichen Analyse jener Problemstellung, welche die täglich notwendige Planung regulärer Patiententransportaufträge umfasst. Solche Aufgabenstellungen werden als Dial-A-Ride-Probleme modelliert. Eine angemessene Service-Qualität kann entweder durch entsprechende Nebenbedingungen gewährleistet oder durch eine zusätzliche Zielfunktion minimiert werden. Beide Herangehensweisen werden hier untersucht. Zuerst wird eine vereinfachte Problemstellung aus der Literatur behandelt und ein kompetitives heuristisches Lösungsverfahren entwickelt. Diese vereinfachte Problemstellung wird in zwei Richtungen erweitert. Einerseits wird, zusätzlich zur Minimierung der Gesamtkosten, eine zweite benutzerorientierte Zielfunktion eingeführt. Andererseits werden eine heterogene Fahrzeugflotte und unterschiedliche Patiententypen in die Standardproblemstellung integriert. Letztendlich wird das reale Patiententransportproblem, basierend auf Informationen des Roten Kreuzes, definiert und gelöst. Neben heterogenen Fahrzeugen und unterschiedlichen Patienten, werden nun auch die Zuordnung von Fahrern und sonstigem Personal zu den verschiedenen Fahrzeugen, Mittagspausen und weitere Aufenthalte am Depot berücksichtigt. Alle eingesetzten exakten Methoden, obwohl sie auf neuesten Erkenntnissen aus der Literatur aufbauen, können Instanzen von realistischer Größe nicht lösen. Dieser Umstand macht die Entwicklung von passenden heuristischen Verfahren nach wie vor unumgänglich. In der vorliegenden Arbeit wird ein relativ generisches System basierend auf der Variable Neighborhood Search Idee entwickelt, das auf alle behandelten Einzielproblemversionen angewandt werden kann; auch für die bi-kriterielle Problemstellung, in Kombination mit Path Relinking, werden gute Ergebnisse erzielt.Humanitarian non-profit ambulance dispatching organizations are committed to look at cost reduction potentials in order to decrease their expenses. While in the context of emergency transportation cost reduction cannot be achieved by means of combined passenger routes, this can be done when dealing with regular patients. This research work is motivated by the problem situation faced by ambulance dispatchers in the field of patient transportation. Problems of this kind are modeled as dial-a-ride problems. In the field of patient transportation, the provision of a certain quality of service is necessary; the term “user inconvenience” is used in this context. User inconvenience can either be considered in terms of additional constraints or in terms of additional objectives. Both approaches are investigated in this book. The aim is to model and solve the real world problem based on available information from the Austrian Red Cross. In a first step, a competitive heuristic solution method for a simplified problem version is developed. This problem version is extended in two ways. On the one hand, besides routing costs, a user-oriented objective, minimizing user inconvenience, in terms of mean user ride time, is introduced. On the other hand, heterogeneous patient types and a heterogeneous vehicle fleet are integrated into the standard dial-a-ride model. In a final step, in addition to heterogeneous patients and vehicles, the assignment of drivers and other staff members to vehicles, the scheduling of lunch breaks, and additional stops at the depot are considered. All exact methods employed, although based on state of the art concepts, are not capable of solving instances of realistic size. This fact makes the development of according heuristic solution methods necessary. In this book a rather generic variable neighborhood search framework is proposed. It is able to accommodate all single objective problem versions and also proves to work well when applied to the bi-objective problem in combination with path relinking

Shared Mobility Optimization in Large Scale Transportation Networks: Methodology and Applications

abstract: Optimization of on-demand transportation systems and ride-sharing services involves solving a class of complex vehicle routing problems with pickup and delivery with time windows (VRPPDTW). Previous research has made a number of important contributions to the challenging pickup and delivery problem along different formulation or solution approaches. However, there are a number of modeling and algorithmic challenges for a large-scale deployment of a vehicle routing and scheduling algorithm, especially for regional networks with various road capacity and traffic delay constraints on freeway bottlenecks and signal timing on urban streets. The main thrust of this research is constructing hyper-networks to implicitly impose complicated constraints of a vehicle routing problem (VRP) into the model within the network construction. This research introduces a new methodology based on hyper-networks to solve the very important vehicle routing problem for the case of generic ride-sharing problem. Then, the idea of hyper-networks is applied for (1) solving the pickup and delivery problem with synchronized transfers, (2) computing resource hyper-prisms for sustainable transportation planning in the field of time-geography, and (3) providing an integrated framework that fully captures the interactions between supply and demand dimensions of travel to model the implications of advanced technologies and mobility services on traveler behavior.Dissertation/ThesisDoctoral Dissertation Civil, Environmental and Sustainable Engineering 201

Using group role assignment to solve Dynamic Vehicle Routing Problem

The Dynamic Vehicle Routing Problem (DVRP) is a more complex problem than the traditional Vehicle Routing Problem (VRP) in the combinatorial optimization of operations research. With more degrees of freedom, DVRP introduces new challenges while judging the merit of a given route plan. This thesis utilized the time slice strategy to solve dynamic and deterministic routing problems. Based on Group Role Assignment (GRA) and two different routing methods (Modified Insertion heuristic routing and Modified Composite Pairing Or-opt routing), a new ridesharing system has been designed to provide services in the real world. Simulation results are presented in this thesis. A qualitative comparison has been made to outline the advantages and performance of our solution framework. From the numerical results, the proposed method has a great potential to put into operation in the real world and provides a new transit option for the public.Master of Science (MSc) in Computational Scienc

Desenvolvimento de uma metodologia baseada em um modelo exato para resolver o picker routing problem em um caso real

Orientador: Prof. Dr. Cassius Tadeu ScarpinDissertação (mestrado) - Universidade Federal do Paraná, Setor de Ciências Sociais Aplicadas, Programa de Pós-Graduação em Gestão de Organizações, Liderança e Decisão. Defesa : Curitiba, 14/10/2022Inclui referênciasResumo: Neste trabalho apresenta-se uma aplicação real de um modelo exato para o Problema de Roteamento de Separadores de Pedidos, também conhecido com Picker Routing Problem (PRP), em uma Rede varejista do setor supermercadista. O estudo de caso feito na pesquisa foi no Centro de Distribuição desta rede supermercadista. O PRP consiste em determinar a menor rota a ser percorrida por um separador em um Centro de Distribuição (CD) de forma a coletar manualmente todos os produtos contidos em um determinado pedido. Tem-se como objetivo a aplicação de um modelo de Programação Linear Inteira Mista (PLIM), encontrado na literatura, e a comparação dos resultados obtidos com o atual método utilizado na empresa, a heurística SShape. Para isso, dados reais de pedidos de um determinado período foram coletados e algumas suposições relativas ao tamanho do problema e ao leiaute do CD foram feitas para gerar os 65 cenários de testes estabelecidos. Para atingir o objetivo almejado, foi necessário elaborar um algoritmo em três etapas, em linguagem de programação C#. A primeira etapa é o tratamento de dados e ajuste do leiaute para a elaboração do modelo Matemático. Com uso do solver GUROBI para a resolução dos testes, realizou-se a segunda etapa. A terceira etapa consistiu na aplicação da heurística S-Shape para possibilitar a comparação entre os métodos. As comparações entre o modelo aplicado e a heurística da empresa foram avaliadas em termos de economias (em metros) do trajeto gerado e tempo de resolução. Em 81,54% dos testes, o modelo obteve melhores resultados, gerando rotas com distâncias menores. Os outros 18,46% ambos os métodos retornaram o mesmo resultado. A melhoria média geral ficou em 8,41%. O modelo com parâmetro alterado resolveu 87,69% dos testes em até 30 minutos, considerado como tempo aceitável em termos práticos operacionais. Para os 12,31% dos testes resolvidos acima de 30 minutos, uma manipulação nos dados para contornar essa situação foi sugerida. Dessa forma, foi considerada como vantajosa a aplicação do modelo para o problema real de roteamento de pickers.Abstract: This work presents a real application of an exact model for the Picker Routing Problem (PRP), in a retail chain in the supermarket sector. The case study done in the research was in the Distribution Center of this supermarket chain. The PRP consists of determining the shortest route to be taken by a picker in a Distribution Center (DC) in order to manually collect all the products contained in a given order. The objective is to apply a Mixed Integer Linear Programming (MILP) model, found in the literature, and to compare the results obtained with the current method used in the company, the SShape heuristic. For this, actual order data for a given period was collected and some assumptions regarding the size of the problem and the CD layout were made to generate the 65 established test scenarios. To achieve the desired goal, it was necessary to develop an algorithm in three steps, in C # programming language. The first step is the data treatment and adjustment of the layout for the elaboration of the Mathematical model. Using the GUROBI solver to solve the tests, the second step was performed. The third step consisted of applying the S-Shape heuristic to make it possible to compare the methods. The comparisons between the applied model and the company's heuristic were evaluated in terms of savings (in meters) of the generated route and resolution time. In 81.54% of the tests, the model obtained better results, generating routes with shorter distances. The other 18.46% both methods returned the same result. The overall average improvement was 8.41%. The model with an altered parameter solved 87.69% of the tests within 30 minutes, considered an acceptable timeframe in operational practical terms. For the 12.31% of the tests resolved over 30 minutes, a manipulation of the data to get around this situation was suggested. Thus, it was considered advantageous to apply the model to the real problem of picker routing

Optimization for Decision Making II

In the current context of the electronic governance of society, both administrations and citizens are demanding the greater participation of all the actors involved in the decision-making process relative to the governance of society. This book presents collective works published in the recent Special Issue (SI) entitled “Optimization for Decision Making II”. These works give an appropriate response to the new challenges raised, the decision-making process can be done by applying different methods and tools, as well as using different objectives. In real-life problems, the formulation of decision-making problems and the application of optimization techniques to support decisions are particularly complex and a wide range of optimization techniques and methodologies are used to minimize risks, improve quality in making decisions or, in general, to solve problems. In addition, a sensitivity or robustness analysis should be done to validate/analyze the influence of uncertainty regarding decision-making. This book brings together a collection of inter-/multi-disciplinary works applied to the optimization of decision making in a coherent manner

From metaheuristics to learnheuristics: Applications to logistics, finance, and computing

Un gran nombre de processos de presa de decisions en sectors estratègics com el transport i la producció representen problemes NP-difícils. Sovint, aquests processos es caracteritzen per alts nivells d'incertesa i dinamisme. Les metaheurístiques són mètodes populars per a resoldre problemes d'optimització difícils en temps de càlcul raonables. No obstant això, sovint assumeixen que els inputs, les funcions objectiu, i les restriccions són deterministes i conegudes. Aquests constitueixen supòsits forts que obliguen a treballar amb problemes simplificats. Com a conseqüència, les solucions poden conduir a resultats pobres. Les simheurístiques integren la simulació a les metaheurístiques per resoldre problemes estocàstics d'una manera natural. Anàlogament, les learnheurístiques combinen l'estadística amb les metaheurístiques per fer front a problemes en entorns dinàmics, en què els inputs poden dependre de l'estructura de la solució. En aquest context, les principals contribucions d'aquesta tesi són: el disseny de les learnheurístiques, una classificació dels treballs que combinen l'estadística / l'aprenentatge automàtic i les metaheurístiques, i diverses aplicacions en transport, producció, finances i computació.Un gran número de procesos de toma de decisiones en sectores estratégicos como el transporte y la producción representan problemas NP-difíciles. Frecuentemente, estos problemas se caracterizan por altos niveles de incertidumbre y dinamismo. Las metaheurísticas son métodos populares para resolver problemas difíciles de optimización de manera rápida. Sin embargo, suelen asumir que los inputs, las funciones objetivo y las restricciones son deterministas y se conocen de antemano. Estas fuertes suposiciones conducen a trabajar con problemas simplificados. Como consecuencia, las soluciones obtenidas pueden tener un pobre rendimiento. Las simheurísticas integran simulación en metaheurísticas para resolver problemas estocásticos de una manera natural. De manera similar, las learnheurísticas combinan aprendizaje estadístico y metaheurísticas para abordar problemas en entornos dinámicos, donde los inputs pueden depender de la estructura de la solución. En este contexto, las principales aportaciones de esta tesis son: el diseño de las learnheurísticas, una clasificación de trabajos que combinan estadística / aprendizaje automático y metaheurísticas, y varias aplicaciones en transporte, producción, finanzas y computación.A large number of decision-making processes in strategic sectors such as transport and production involve NP-hard problems, which are frequently characterized by high levels of uncertainty and dynamism. Metaheuristics have become the predominant method for solving challenging optimization problems in reasonable computing times. However, they frequently assume that inputs, objective functions and constraints are deterministic and known in advance. These strong assumptions lead to work on oversimplified problems, and the solutions may demonstrate poor performance when implemented. Simheuristics, in turn, integrate simulation into metaheuristics as a way to naturally solve stochastic problems, and, in a similar fashion, learnheuristics combine statistical learning and metaheuristics to tackle problems in dynamic environments, where inputs may depend on the structure of the solution. The main contributions of this thesis include (i) a design for learnheuristics; (ii) a classification of works that hybridize statistical and machine learning and metaheuristics; and (iii) several applications for the fields of transport, production, finance and computing

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

Mathematical Methods and Operation Research in Logistics, Project Planning, and Scheduling

In the last decade, the Industrial Revolution 4.0 brought flexible supply chains and flexible design projects to the forefront. Nevertheless, the recent pandemic, the accompanying economic problems, and the resulting supply problems have further increased the role of logistics and supply chains. Therefore, planning and scheduling procedures that can respond flexibly to changed circumstances have become more valuable both in logistics and projects. There are already several competing criteria of project and logistic process planning and scheduling that need to be reconciled. At the same time, the COVID-19 pandemic has shown that even more emphasis needs to be placed on taking potential risks into account. Flexibility and resilience are emphasized in all decision-making processes, including the scheduling of logistic processes, activities, and projects