The importance of information flows temporal attributes for the efficient scheduling of dynamic demand responsive transport services

The operation of a demand responsive transport service usually involves the management of dynamic requests. The underlying algorithms are mainly adaptations of procedures carefully designed to solve static versions of the problem, in which all the requests are known in advance. However there is no guarantee that the effectiveness of an algorithm stays unchanged when it is manipulated to work in a dynamic environment. On the other hand, the way the input is revealed to the algorithm has a decisive role on the schedule quality. We analyze three characteristics of the information flow (percentage of real-time requests, interval between call-in and requested pickup time and length of the computational cycle time), assessing their influence on the effectiveness of the scheduling proces

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

A hybrid metaheuristic for the time-dependent vehicle routing problem with hard time windows

This article paper presents a hybrid metaheuristic algorithm to solve the time-dependent vehicle routing problem with hard time windows. Time-dependent travel times are influenced by different congestion levels experienced throughout the day. Vehicle scheduling without consideration of congestion might lead to underestimation of travel times and consequently missed deliveries. The algorithm presented in this paper makes use of Large Neighbourhood Search approaches and Variable Neighbourhood Search techniques to guide the search. A first stage is specifically designed to reduce the number of vehicles required in a search space by the reduction of penalties generated by time-window violations with Large Neighbourhood Search procedures. A second stage minimises the travel distance and travel time in an ‘always feasible’search space. Comparison of results with available test instances shows that the proposed algorithm is capable of obtaining a reduction in the number of vehicles (4.15%), travel distance (10.88%) and travel time (12.00%) compared to previous implementations in reasonable tim

A tabu search heuristic for a dynamic transportation problem of patients between care units

29 pagesThe ambulance central station of the Hospital Complex of Tours (France) has to plan the transportation demands between care units which require a vehicle. Some demands are known in advance and the others arrive dynamically. Each demand requires a specific type of vehicle and a vehicle can transport only one person at a time. Moreover, transportations are subject to particular constraints: priority of urgent demands, disinfection of a vehicle after the transportation of a patient with contagious disease, respect of the type of vehicle, etc. This problem is related to the \emph{Dial A Ride Problem}. For solving the dynamic version of this problem, we propose a tabu search algorithm inspired by \cite{Gendreau99}. This method supports an adaptive memory which stores routes and consists in running a tabu search algorithm several times: one for improving the set of initial solutions, one for the neighborhood exploration and finally for improving the final solution. Computational experiments show that the method can provide high-quality solutions for this dynamic transportation problem

A Note on the Ichoua et al (2003) Travel Time Model.

In this paper we exploit some properties of the travel time model proposed by Ichoua et al (2003), on which most of the current time-dependent vehicle routing literature relies. Firstly, we prove that any continuous piecewise lin- ear travel time model can be generated by an appropriate Ichoua et al (2003) model. We also show that the model parameters can be obtained by solving a system of linear equations for each arc. Then such parameters are proved to be nonnegative if the continuous piecewise linear travel time model satis- es the FIFO property, which allows to interpret them as (dummy) speeds. Finally, we illustrate the procedure through a numerical example. As a by- product, we are able to link the travel time models of a road graph and the associated complete graph over which vehicle routing problems are usually formulated

Planning and Scheduling Transportation Vehicle Fleet in a Congested Traffic Environment

Transportation is a main component of supply chain competitiveness since it plays a major role in the inbound, inter-facility, and outbound logistics. In this context, assigning and scheduling vehicle routing is a crucial management problem. Despite numerous publications dealing with efficient scheduling methods for vehicle routing, very few addressed the inherent stochastic nature of travel times in this problem. In this paper, a vehicle routing problem with time windows and stochastic travel times due to potential traffic congestion is considered. The approach developed introduces mainly the traffic congestion component based on queueing theory. This is an innovative modeling scheme to capture the stochastic behavior of travel times. A case study is used both to illustrate the appropriateness of the approach as well as to show that time-independent solutions are often unrealistic within a congested traffic environment which is often the case on the european road networkstransportation; vehicle fleet; planning; scheduling; congested traffic

The dynamic nearest neighbor policy for the multi-vehicle pick-up and delivery problem

In this paper, a dynamic nearest neighbor (DNN) policy is proposed for operating a fleet of vehicles to serve customers, who place calls in a Euclidean service area according to a Poisson process. Each vehicle serves one customer at a time, who has a distinct origin and destination independently and uniformly distributed within the service area. The new DNN policy is a refined version of the nearest neighbor (NN) policy that is well known to perform sub-optimally when the frequency of customer requests is high. The DNN policy maintains geographically closest customer-to-vehicle assignments, due to its ability to divert/re-assign vehicles that may be already en-route to pick up other customers, when another vehicle becomes available or a new customer call arrives. Two other pertinent issues addressed include: the pro-active deployment of the vehicles by anticipating in which regions of the service area future calls are more likely to arise; and, imposition of limits to avoid prohibitively long customer wait times. The paper also presents accurate approximations for all the policies compared. Extensive simulations, some of which are included herein, clearly show the DNN policy to be tangibly superior to the first-comefirst-served (FCFS) and NN policies

An Optimization Framework for a Dynamic Multi-Skill Workforce Scheduling and Routing Problem with Time Windows and Synchronization Constraints

This article addresses the dynamic multi-skill workforce scheduling and routing problem with time windows and synchronization constraints (DWSRP-TW-SC) inherent in the on-demand home services sector. In this problem, new service requests (tasks) emerge in real-time, necessitating a constant reevaluation of service team task plans. This reevaluation involves maintaining a portion of the plan unaltered, ensuring team-task compatibility, addressing task priorities, and managing synchronization when task demands exceed a team's capabilities. To address the problem, we introduce a real-time optimization framework triggered upon the arrival of new tasks or the elapse of a set time. This framework redesigns the routes of teams with the goal of minimizing the cumulative weighted throughput time for all tasks. For the route redesign phase of this framework, we develop both a mathematical model and an Adaptive Large Neighborhood Search (ALNS) algorithm. We conduct a comprehensive computational study to assess the performance of our proposed ALNS-based reoptimization framework and to examine the impact of reoptimization strategies, frozen period lengths, and varying degrees of dynamism. Our contributions provide practical insights and solutions for effective dynamic workforce management in on-demand home services

An event-driven optimization framework for dynamic vehicle routing

International audienceThe real-time operation of a fleet of vehicles introduces challenging optimization problems. In this work, we propose an event-driven framework which anticipates unknown changes arising in the context of dynamic vehicle routing. The framework is intrinsically parallelized to take advantage of modern multi-core and multi-threaded computing architectures. It is also designed to be easily embeddable in decision support systems that cope with a wide range of contexts and side constraints. We illustrate the flexibility of the framework by showing how it can be adapted to tackle the dynamic vehicle routing problem with stochastic demands

The time-dependent vehicle routing problem

In der Tourenplanung wird meistens angenommen, dass die Reisezeiten während des gesamten Planungshorizonts konstant sind. In der Realität ist es jedoch so, dass es während des Tages zu variablen Reisezeiten kommt. Vor allem im urbanen Bereich führen Staus zu längeren Reisezeiten. Im tageszeitabhängigen Tourenplanungsproblem wird dieser Aspekt berücksichtigt indem man annimmt, dass die Reisezeiten von der Tageszeit abhängen. Die vorliegende Diplomarbeit gibt einen Überblick über die tageszeitabhängige Tourenplanung und präsentiert die Ergebnisse einer experimentellen Studie. Im ersten Teil dieser Arbeit werden das klassische Tourenplanungsproblem und verschiedene Lösungsverfahren vorgestellt. Danach wird das tageszeitabhängige Tourenplanungsproblem beschrieben. Im zweiten Teil wird zunächst ein Algorithmus basierend auf der Tabu Suche entwickelt um das kapazitierte Tourenplanungsproblem zu lösen. Die Lösungen werden dann mit tageszeitabhängigen Szenarien evaluiert, wobei jedes Szenario einen anderen Grad an Zeitabhängigkeit repräsentiert. Es wird gezeigt, dass die Gesamtkosten im Vergleich zu den ursprünglichen Kosten steigen. Desweiteren werden die Tourlängenbeschränkungen von vielen Touren nicht mehr erfüllt. Schließlich wird der ursprüngliche Algorithmus adaptiert um das tageszeitabhängige kapazitierte Tourenplanungsproblem zu lösen. Es wird gezeigt, dass die Gesamtkosten verbessert werden können wenn man tageszeitabhängige Reisezeiten einsetzt. Die Verbesserung ist umso stärker, je höher der Grad an Zeitabhängigkeit. Zusätzlich erfüllen die neuen Lösungen alle Tourlängenbeschränkungen.Most vehicle routing models assume constant travel times throughout the whole planning horizon. In reality, however, travel times vary during the day. This is especially true for urban areas where daily traffic congestion leads to longer travel times. The time-dependent vehicle routing problem (TDVRP) takes this aspect into account by assuming that travel times depend on the time of the day. This diploma thesis gives an overview of the TDVRP and presents the results of an experimental study. The first part introduces the VRP and different solution methods. This is followed by a detailed description of the TDVRP. The second part of the thesis presents an algorithm based on tabu search to solve the capacitated VRP (CVRP). Afterwards, the best solutions of the CVRP are evaluated with five time-dependent scenarios, each representing a different degree of time-dependency. Compared to the original CVRP results, the total costs increase significantly and several routes become infeasible. In the next step, the original algorithm is adapted to solve the TD-CVRP. It is shown that the total costs can be improved when assuming time-dependent travel times. The improvement is higher, the higher the degree of time-dependency. Furthermore, the new solutions satisfy all tour length constraints