Dynamic vehicle routing problems: Three decades and counting

Since the late 70s, much research activity has taken place on the class of dynamic vehicle routing problems (DVRP), with the time period after year 2000 witnessing a real explosion in related papers. Our paper sheds more light into work in this area over more than 3 decades by developing a taxonomy of DVRP papers according to 11 criteria. These are (1) type of problem, (2) logistical context, (3) transportation mode, (4) objective function, (5) fleet size, (6) time constraints, (7) vehicle capacity constraints, (8) the ability to reject customers, (9) the nature of the dynamic element, (10) the nature of the stochasticity (if any), and (11) the solution method. We comment on technological vis-à-vis methodological advances for this class of problems and suggest directions for further research. The latter include alternative objective functions, vehicle speed as decision variable, more explicit linkages of methodology to technological advances and analysis of worst case or average case performance of heuristics.© 2015 Wiley Periodicals, Inc

Integrating forecasting in metaheuristic methods to solve dynamic routing problems: evidence from the logistic processes of tuna vessels

The multiple Traveling Salesman Problem (mTSP) is a widespread phenomenon in real-life scenarios, and in fact it has been addressed from multiple perspectives in recent decades. However, mTSP in dynamic circumstances entails a greater complexity that recent approaches are still trying to grasp. Beyond time windows, capacity and other parameters that characterize the dynamics of each scenario, moving targets is one of the underdeveloped issues in the field of mTSP. The approach of this paper harnesses a simple prediction method to prove that integrating forecasting within a metaheuristic evolutionary-based method, such as genetic algorithms, can yield better results in a dynamic scenario than their simple non-predictive version. Real data is used from the retrieval of Fish Aggregating Devices (FADs) by tuna vessels in the Indian Ocean. Based on historical data registered by the GPS system of the buoys attached to the devices, their trajectory is firstly forecast to feed subsequently the functioning of a genetic algorithm that searches for the optimal route of tuna vessels in terms of total distance traveled. Thus, although valid for static cases and for the Vehicle Routing Problem (VRP), the main contribution of this method over existing literature lies in its application as a global search method to solve the multiple TSP with moving targets in many dynamic real-life optimization problems.Ministerio de Economía y Competitividad | Ref. ECO2016-76625-RXunta de Galicia | Ref. GRC2014/02

A decision support system for the management of smart mobility services

Master Dissertation (Master Degree in Engineering and Management of Information Systems)Nos dias que correm, a mobilidade assume especial importância no quotidiano das áreas metropolitanas em crescimento no país. . Com o notório crescimento das cidades, torna-se necessária e urgente uma transformação dos costumes e formas de mobilidade dentro das áreas urbanas, alterando as realidades aparentes que hoje conhecemos. Inseridos numa sociedade cada vez mais consciencializada e alerta para as questões ambientais, é essencial transportar esta mentalidade renovada para a resolução das problemáticas citadinas. Assim, o conceito de “Cidade Verde” levanta uma série de questões que exigem uma resposta eficaz para o bem-estar dos seus habitantes. Por entre as várias soluções apresentadas para estas patologias, uma das mais promissoras é, sem dúvida, o sistema de mobilidade partilhada. Pela sua dimensão, é pertinente expor o caso prático da cidade de Barcelona, em Espanha, explorando o seu sistema de partilha de scooters, um meio que adquire especial importância como meio de transporte urbano. Como qualquer sistema em constante aprimoramento, procura-se uma solução para a problemática da variação de procura, que apresenta oscilações constantes, tanto a nível temporal como geográfico, resultando na falta de veículos em algumas áreas e excesso noutras. Assim sendo, o rebalanceamento do sistema torna-se crucial para uma possível maximização na utilização de veículos, satisfazendo a procura e potenciando um aumento da sua utilização. No correr desta dissertação, foram estudados e utilizados vários métodos de otimização moderna (metaheurísticas) para a procura de soluções (sub)ótimas para o(s) percurso(s) a percorrer pelo(s) veículo(s) que executam a redistribuição das scooter/bicicletas pelas diversas áreas abrangidas pelo sistema de partilha. Deste modo, foi desenvolvido um sistema de apoio à decisão para satisfazer estas necessidades, garantindo ao utilizador toda a informação relevante para um trabalho mais eficiente e preciso.Nowadays, mobility is especially important in the daily life of the country growing metropolitan areas. With the increasing influx of people and development of these large cities, the reality of mobility that we know becomes increasingly unsustainable. Along with mobility, the environmental concerns are one of the main topics of discussion worldwide and the population is starting to act and change the way they live to find a more “green” and sustainable way of doing it. Several proposals have been put forward, trying to mitigate this issue and, one of the most promising is, undoubtedly, shared mobility systems. In this case study will be addressed the Barcelona scooter sharing system, characterized by its great size and importance as a mean of urban transport. One of the problems presented by these sharing services is that demand varies widely, both temporal and geographical. Thus, there are several cases where there is a lack of vehicles in some areas and an excess in others. The rebalancing of the system is crucial to maximize vehicle utilization and meet customer demand. In this thesis, several modern optimization methods (metaheuristics) were used to search for (sub)optimal solutions for the redistribution route(s). A decision support system was developed to meet this end, giving the end user relevant information for a more efficient and precise work

A probabilistic approach to pickup and delivery problems with time window uncertainty

In this paper we study a dynamic and stochastic pickup and delivery problem proposed recently by Srour, Agatz and Oppen. We demonstrate that the cost structure of the problem permits an effective solution method without generating multiple scenarios. Instead, our method is based on a careful analysis of the transfer probability from one customer to the other. Our computational results confirm the effectiveness of our approach on the data set of Srour et al

Volumetric Techniques for Product Routing and Loading Optimisation in Industry 4.0: A Review

Industry 4.0 has become a crucial part in the majority of processes, components, and related modelling, as well as predictive tools that allow a more efficient, automated and sustainable approach to industry. The availability of large quantities of data, and the advances in IoT, AI, and data-driven frameworks, have led to an enhanced data gathering, assessment, and extraction of actionable information, resulting in a better decision-making process. Product picking and its subsequent packing is an important area, and has drawn increasing attention for the research community. However, depending of the context, some of the related approaches tend to be either highly mathematical, or applied to a specific context. This article aims to provide a survey on the main methods, techniques, and frameworks relevant to product packing and to highlight the main properties and features that should be further investigated to ensure a more efficient and optimised approach

A Data-Driven Based Dynamic Rebalancing Methodology for Bike Sharing Systems

Mobility in cities is a fundamental asset and opens several problems in decision making and the creation of new services for citizens. In the last years, transportation sharing systems have been continuously growing. Among these, bike sharing systems became commonly adopted. There exist two different categories of bike sharing systems: station-based systems and free-floating services. In this paper, we concentrate our analyses on station-based systems. Such systems require periodic rebalancing operations to guarantee good quality of service and system usability by moving bicycles from full stations to empty stations. In particular, in this paper, we propose a dynamic bicycle rebalancing methodology based on frequent pattern mining and its implementation. The extracted patterns represent frequent unbalanced situations among nearby stations. They are used to predict upcoming critical statuses and plan the most effective rebalancing operations using an entirely data-driven approach. Experiments performed on real data of the Barcelona bike sharing system show the effectiveness of the proposed approach

A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications

Particle swarm optimization (PSO) is a heuristic global optimization method, proposed originally by Kennedy and Eberhart in 1995. It is now one of the most commonly used optimization techniques. This survey presented a comprehensive investigation of PSO. On one hand, we provided advances with PSO, including its modifications (including quantum-behaved PSO, bare-bones PSO, chaotic PSO, and fuzzy PSO), population topology (as fully connected, von Neumann, ring, star, random, etc.), hybridization (with genetic algorithm, simulated annealing, Tabu search, artificial immune system, ant colony algorithm, artificial bee colony, differential evolution, harmonic search, and biogeography-based optimization), extensions (to multiobjective, constrained, discrete, and binary optimization), theoretical analysis (parameter selection and tuning, and convergence analysis), and parallel implementation (in multicore, multiprocessor, GPU, and cloud computing forms). On the other hand, we offered a survey on applications of PSO to the following eight fields: electrical and electronic engineering, automation control systems, communication theory, operations research, mechanical engineering, fuel and energy, medicine, chemistry, and biology. It is hoped that this survey would be beneficial for the researchers studying PSO algorithms