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

The Dynamic Multi-objective Multi-vehicle Covering Tour Problem

This work introduces a new routing problem called the Dynamic Multi-Objective Multi-vehicle Covering Tour Problem (DMOMCTP). The DMOMCTPs is a combinatorial optimization problem that represents the problem of routing multiple vehicles to survey an area in which unpredictable target nodes may appear during execution. The formulation includes multiple objectives that include minimizing the cost of the combined tour cost, minimizing the longest tour cost, minimizing the distance to nodes to be covered and maximizing the distance to hazardous nodes. This study adapts several existing algorithms to the problem with several operator and solution encoding variations. The efficacy of this set of solvers is measured against six problem instances created from existing Traveling Salesman Problem instances which represent several real countries. The results indicate that repair operators, variable length solution encodings and variable-length operators obtain a better approximation of the true Pareto front

On the Development of a Multilayered Agent-based Heuristic System for Vehicle Routing Problem under Random Vehicle Breakdown

With the recent technological advancement, the Dynamic Vehicle Routing Problem (DVRP) is becoming more applicable but almost all of the research in this field limited the source of dynamism from the order side rather from the vehicle, in addition to the adoption of inflexible tools that are mainly designed for the static problem. Considering multiple random vehicle breakdowns complicates the problem of how to adapt and distribute the workload to other functioning vehicles. In this ongoing PhD research, a proposed multi-layered Agent-Based Model (ABM) along with a modelling framework on how to deal with such disruptive events in a reactive continuous manner. The model is partially constructed and experimented, with a developed clustering rule, on two randomly generated scenario for the purpose of validation. The rule achieved good order allocation to vehicles and reacted to different problem sizes by rejecting orders that are over the model capacity. This shows a promising path in fully adopting the ABM model in this dynamic problem

Optimization of vehicular networks in smart cities: from agile optimization to learnheuristics and simheuristics

Vehicular ad hoc networks (VANETs) are a fundamental component of intelligent transportation systems in smart cities. With the support of open and real-time data, these networks of inter-connected vehicles constitute an ‘Internet of vehicles’ with the potential to significantly enhance citizens’ mobility and last-mile delivery in urban, peri-urban, and metropolitan areas. However, the proper coordination and logistics of VANETs raise a number of optimization challenges that need to be solved. After reviewing the state of the art on the concepts of VANET optimization and open data in smart cities, this paper discusses some of the most relevant optimization challenges in this area. Since most of the optimization problems are related to the need for real-time solutions or to the consideration of uncertainty and dynamic environments, the paper also discusses how some VANET challenges can be addressed with the use of agile optimization algorithms and the combination of metaheuristics with simulation and machine learning methods. The paper also offers a numerical analysis that measures the impact of using these optimization techniques in some related problems. Our numerical analysis, based on real data from Open Data Barcelona, demonstrates that the constructive heuristic outperforms the random scenario in the CDP combined with vehicular networks, resulting in maximizing the minimum distance between facilities while meeting capacity requirements with the fewest facilities.Peer ReviewedPostprint (published version

Demand-Responsive Transport: Models and Algorithms

Demand-responsive transport is a form of public transport between bus and taxi services, involving flexible routing of small or medium sized vehicles. This dissertation presents mathematical models for demand-responsive transport and methods that can be used to solve combinatorial problems related to vehicle routing and journey planning in a transport network. Public transport can be viewed as a market where demand affects supply and vice versa. In the first part of the dissertation related to vehicle routing, we show how a given demand for transportation can be satisfied by using a fleet of vehicles, assuming that the demand is known at the individual level. In the second part, by considering the journey planning problem faced by commuters, we study how the demand adapts to the supply of transport services, assuming that the supply remains unchanged for a short period of time. We also present a stochastic network model for determining the economic equilibrium, that is, the point at which the demand meets the supply, by assuming that commuters attempt to minimize travel time and transport operators aim to maximize profit. The mathematical models proposed in this work can be used to simulate the operations of public transport services in a wide range of scenarios, from paratransit services for the elderly and disabled to large-scale demand-responsive transport services designed to compete with private car traffic. Such calculations can provide valuable information to public authorities and planners of transportation services, regarding, for example, regulation and investments. In addition to public transport, potential applications of the proposed methods for solving vehicle routing and journey planning problems include freight transportation, courier and food delivery services, military logistics and air traffic.Kysyntäohjautuvalla joukkoliikenteellä tarkoitetaan bussi- ja taksipalvelujen välimuotoa, joka perustuu pienten tai keskisuurten ajoneuvojen joustavaan reititykseen. Tässä väitöskirjassa esitetään matemaattisia malleja kysyntäohjautuvalle joukkoliikenteelle, ja menetelmiä, joilla voidaan ratkaista ajoneuvojen reitinlaskentaan ja matkansuunnitteluun liittyviä kombinatorisia ongelmia liikenneverkossa. Joukkoliikennettä voidaan tarkastella markkinana, jossa kysyntä vaikuttaa tarjontaan ja päinvastoin. Väitoskirjan ensimmäisessa osassa, joka käsittelee ajoneuvojen reitinlaskentaa, näytetään miten tunnettuun kysyntään voidaan vastata käyttämällä tiettyä ajoneuvokantaa, kun oletetaan kysyntä tunnetuksi yhden matkustajan tarkkuudella. Toisessa osassa tarkastellaan matkustajien matkansuunnittelua joukkoliikenneverkossa, eli sitä miten kysyntä mukautuu liikennepalvelujen tarjonnan mukaan, kun oletetaan tarjonta muuttumattomaksi lyhyellä aikavälillä. Lopuksi esitetään menetelmä taloudellisen tasapainopisteen, eli kysynnän ja tarjonnan kohtaamispisteen, määrittämiseksi, kun oletetaan että matkustajat pyrkivät minimoimaan matka-aikaa ja liikennepalvelujen tarjoajat pyrkivät maksimoimaan taloudellista voittoa. Tässä työssä esiteltyjen mallien avulla voidaan simuloida useita erityyppisiä liikennepalvelujavanhuksille ja liikuntarajoitteisille suunnatuista kutsulinjoista henkilöautoliikenteen kanssa kilpaileviin laajamittaisiin kysyntäohjautuviin joukkoliikennejärjestelmiin. Mallien avulla tehdyt laskelmat voivat tuottaa arvokasta tietoa viranomaisille ja liikennepalvelujen suunnittelijoille liikenteen säännöstelyyn ja investointeihin liittyen. Joukkoliikenteen lisäksi esiteltyjä reitinlaskenta- ja matkansuunnittelumenetelmiä voidaan soveltaa muun muassa rahti- ja lentoliikenteessä, lähetti- ja ruoankuljetuspalveluissa sekä sotilaslogistiikassa

Urban Public Transportation Planning with Endogenous Passenger Demand

An effective and efficient public transportation system is crucial to people\u27s mobility, economic production, and social activities. The Operations Research community has been studying transit system optimization for the past decades. With disruptions from the private sector, especially the parking operators, ride-sharing platforms, and micro-mobility services, new challenges and opportunities have emerged. This thesis contributes to investigating the interaction of the public transportation systems with significant private sector players considering endogenous passenger choice. To be more specific, this thesis aims to optimize public transportation systems considering the interaction with parking operators, competition and collaboration from ride-sharing platforms and micro-mobility platforms. Optimization models, algorithms and heuristic solution approaches are developed to design the transportation systems. Parking operator plays an important role in determining the passenger travel mode. The capacity and pricing decisions of parking and transit operators are investigated under a game-theoretic framework. A mixed-integer non-linear programming (MINLP) model is formulated to simulate the player\u27s strategy to maximize profits considering endogenous passenger mode choice. A three-step solution heuristic is developed to solve the large-scale MINLP problem. With emerging transportation modes like ride-sharing services and micro-mobility platforms, this thesis aims to co-optimize the integrated transportation system. To improve the mobility for residents in the transit desert regions, we co-optimize the public transit and ride-sharing services to provide a more environment-friendly and equitable system. Similarly, we design an integrated system of public transit and micro-mobility services to provide a more sustainable transportation system in the post-pandemic world

A Tabu Search Based Metaheuristic for Dynamic Carpooling Optimization

International audienceThe carpooling problem consists in matching a set of riders' requests with a set of drivers' offers by synchronizing their origins, destinations and time windows. The paper presents the so-called Dynamic Carpooling Optimization System (DyCOS), a system which supports the automatic and optimal ridematching process between users on very short notice or even en-route. Nowadays, there are numerous research contributions that revolve around the carpooling problem, notably in the dynamic context. However, the problem's high complexity and the real time aspect are still challenges to overcome when addressing dynamic carpooling. To counter these issues, DyCOS takes decisions using a novel Tabu Search based metaheuristic. The proposed algorithm employs an explicit memory system and several original searching strategies developed to make optimal decisions automatically. To increase users' satisfaction, the proposed metaheuristic approach manages the transfer process and includes the possibility to drop off the passenger at a given walking distance from his destination or at a transfer node. In addition, the detour concept is used as an original aspiration process, to avoid the entrapment by local solutions and improve the generated solution. For a rigorous assessment of generated solutions , while considering the importance and interaction among the optimization criteria, the algorithm adopts the Choquet integral operator as an aggregation approach. To measure the effectiveness of the proposed method, we develop a simulation environment based on actual carpooling demand data from the metropolitan area of Lille in the north of France

Optimizing agents with genetic programming : an evaluation of hyper-heuristics in dynamic real-time logistics

Dynamic pickup and delivery problems (PDPs) require online algorithms for managing a fleet of vehicles. Generally, vehicles can be managed either centrally or decentrally. A common way to coordinate agents decentrally is to use the contract-net protocol (CNET) that uses auctions to allocate tasks among agents. To participate in an auction, agents require a method that estimates the value of a task. Typically this method is an optimization algorithm. Recently, hyper-heuristics has been proposed for automated design of heuristics. Two properties of automatically designed heuristics are particularly promising: 1) a generated heuristic computes quickly, it is expected therefore that hyper-heuristics heuristics perform especially well for urgent problems, and 2) by using simulationbased evaluation, hyper-heuristics can learn from the past and can therefore create a ‘rule of thumb’ that anticipates situations in the future. In the present paper we empirically evaluate whether hyper-heuristics, more specifically genetic programming (GP), can be used to improve agents decentrally coordinated via CNET. We compare several GP settings and compare the resulting heuristic with existing centralized and decentralized algorithms on a dynamic PDP dataset with varying levels of dynamism, urgency, and scale. The results indicate that the evolved heuristic always outperforms the optimization algorithm in the decentralized MAS and often outperforms the centralized optimization algorithm. Our paper shows that designing MASs using genetic programming is an effective way to obtain competitive performance compared to traditional operational research approaches. These results strengthen the relevance of decentralized agent based approaches in dynamic logistics

A mathematical programming approach for dispatching and relocating EMS vehicles.

We consider the problem of dispatching and relocating EMS vehicles during a pandemic outbreak. In such a situation, the demand for EMS vehicles increases and in order to better utilize their capacity, the idea of serving more than one patient by an ambulance is introduced. Vehicles transporting high priority patients cannot serve any other patient, but those transporting low priority patients are allowed to be rerouted to serve a second patient. We have considered three separate problems in this research. In the first problem, an integrated model is developed for dispatching and relocating EMS vehicles, where dispatchers determine hospitals for patients. The second problem considers just relocating EMS vehicles. In the third problem only dispatching decisions are made where hospitals are pre-specified by patients not by dispatchers. In the first problem, the objective is to minimize the total travel distance and the penalty of not meeting specific constraints. In order to better utilize the capacity of ambulances, we allow each ambulance to serve a maximum of two patients. Considerations are given to features such as meeting the required response time window for patients, batching non-critical and critical patients when necessary, ensuring balanced coverage for all census tracts. Three models are proposed- two of them are linear integer programing and the other is a non-linear programing model. Numerical examples show that the linear models can be solved using general-purpose solvers efficiently for large sized problems, and thus it is suitable for use in a real time decision support system. In the second problem, the goal is to maximize the coverage for serving future calls in a required time window. A linear programming model is developed for this problem. The objective is to maximize the number of census tracts with single and double coverage, (each with their own weights) and to minimize the travel time for relocating. In order to tune the parameters in this objective function, an event based simulation model is developed to study the movement of vehicles and incidents (911 calls) through a city. The results show that the proposed model can effectively increase the system-wide coverage by EMS vehicles even if we assume that vehicles cannot respond to any incidents while traveling between stations. In addition, the results suggest that the proposed model outperforms one of the well-known real time repositioning models (Gendreau et al. (2001)). In the third problem, the objective is to minimize the total travel distance experienced by all EMS vehicles, while satisfying two types of time window constraints. One requires the EMS vehicle to arrive at the patients\u27 scene within a pre-specified time, the other requires the EMS vehicle to transport patients to their hospitals within a given time window. Similar to the first problem, each vehicle can transport maximum two patients. A mixed integer program (MIP) model is developed for the EMS dispatching problem. The problem is proved to be NP-hard, and a simulated annealing (SA) method is developed for its efficient solution. Additionally, to obtain lower bound, a column generation method is developed. Our numerical results show that the proposed SA provides high quality solutions whose objective is close to the obtained lower bound with much less CPU time. Thus, the SA method is suitable for implementation in a real-time decision support system