A metaheuristic for the time-dependent pollution-routing problem

We propose a metaheuristic for the Time-Dependent Pollution-Routing Problem, which consists of routing a number of vehicles to serve a set of customers and determining their speed on each route segment with the objective of minimizing the cost of driver’s wage and greenhouse gases emissions. The vehicles face traffic congestion which, at peak periods, significantly restricts vehicle speeds and leads to increased emissions. Our algorithm is based on an adaptive large neighborhood search heuristic and uses new removal and insertion operators which significantly improve the quality of the solution. A previously developed departure time and speed optimization procedure is used as a subroutine to optimize departure times and vehicle speeds. Results from extensive computational experiments demonstrate the effectiveness of our algorithm

A collaborative stakeholder decision-making approach for sustainable urban logistics

Cities strongly rely on efficient urban logistics to ensure their attractiveness, quality of life, and economic development. In the same time, they strive to ensure livable and safe environments around its road network, where the increased presence of light and heavy goods vehicles raises questions of regarding safety and environmental impacts. Recent literature has well-recognized the need to consider different stakeholders’ perspectives on these issues, in order to achieve desired outcomes. In this paper, we introduce a collaborative stakeholders’ decision-making approach for sustainable urban logistics, and demonstrate its applicability on a real-life example. The suggested approach extends existing route planning approaches by considering route sustainability as a part of an arc’s traversal cost. The integration of route sustainability is based on the adoption of a multi-criterial decision-making approach, with the possibility of including different stakeholders’ points of view, and evaluating the sustainability cost concerning the route’s spatial context. To demonstrate the applicability of the suggested approach, we extract the route sustainability cost from the traffic sign database, and implement the findings on a real-life example. Furthermore, the suggested approach exhibits a high level of transferability to various local contexts, where local stakeholders might have a different view on the route sustainability than is the case in our exampl

Integrating operations research into green logistics:A review

Logistical activities have a significant global environmental impact, necessitating the adoption of green logistics practices to mitigate environmental effects. The COVID-19 pandemic has further emphasized the urgency to address the environmental crisis. Operations research provides a means to balance environmental concerns and costs, thereby enhancing the management of logistical activities. This paper presents a comprehensive review of studies integrating operations research into green logistics. A systematic search was conducted in the Web of Science Core Collection database, covering papers published until June 3, 2023. Six keywords (green logistics OR sustainable logistics OR cleaner logistics OR green transportation OR sustainable transportation OR cleaner transportation) were used to identify relevant papers. The reviewed studies were categorized into five main research directions: Green waste logistics, the impact of costs on green logistics, the green routing problem, green transport network design, and emerging challenges in green logistics. The review concludes by outlining suggestions for further research that combines green logistics and operations research, with particular emphasis on investigating the long-term effects of the pandemic on this field.</p

The continuous pollution routing problem

In this paper, we presented an ε-accurate approach to conduct a continuous optimization on the pollution routing problem (PRP). First, we developed an ε-accurate inner polyhedral approximation method for the nonlinear relation between the travel time and travel speed. The approximation error was controlled within the limit of a given parameter ε, which could be as low as 0.01% in our experiments. Second, we developed two ε-accurate methods for the nonlinear fuel consumption rate (FCR) function of a fossil fuel-powered vehicle while ensuring the approximation error to be within the same parameter ε. Based on these linearization methods, we proposed an ε-accurate mathematical linear programming model for the continuous PRP (ε-CPRP for short), in which decision variables such as driving speeds, travel times, arrival/departure/waiting times, vehicle loads, and FCRs were all optimized concurrently on their continuous domains. A theoretical analysis is provided to confirm that the solutions of ε-CPRP are feasible and controlled within the predefined limit. The proposed ε-CPRP model is rigorously tested on well-known benchmark PRP instances in the literature, and has solved PRP instances optimally with up to 25 customers within reasonable CPU times. New optimal solutions of many PRP instances were reported for the first time in the experiments

Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem

The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process

A study on the heterogeneous fleet of alternative fuel vehicles: Reducing CO2 emissions by means of biodiesel fuel

In the context of home healthcare services, patients may need to be visited multiple times by different healthcare specialists who may use a fleet of heterogeneous vehicles. In addition, some of these visits may need to be synchronized with each other for performing a treatment at the same time. We call this problem the Heterogeneous Fleet Vehicle Routing Problem with Synchronized visits (HF-VRPS). It consists of planning a set of routes for a set of light duty vehicles running on alternative fuels. We propose three population-based hybrid Artificial Bee Colony metaheuristic algorithms for the HF-VRPS. These algorithms are tested on newly generated instances and on a set of homogeneous VRPS instances from the literature. Besides producing quality solutions, our experimental results illustrate the trade-offs between important factors, such as CO2 emissions and driver wage. The computational results also demonstrate the advantages of adopting a heterogeneous fleet rather than a homogeneous one for the use in home healthcare services

A review of recent advances in the operations research literature on the green routing problem and its variants

Since early 2010s, the Green Routing Problem (GRP) has dominated the literature of logistics and transportation. The problem itself consists of finding a set of vehicle routes for a set of customers while minimizing the detrimental effects of transportation activities. These negative externalities have been intensively tackled in the last decade. Operations research studies have particularly focused on minimizing the energy consumption and emissions. As a result, the rich literature on GRPs has already reached its peak, and several early literature reviews have been conducted on various aspects of related vehicle routing and scheduling problem variants. The major contribution of this paper is that it represents a comprehensive review of the current reviews on GRP studies. In addition to that, it is an up-to-date review based on a new chronological taxonomy of the literature. The detailed analysis provides a useful framework for understanding the research gaps for the future studies and the potential impacts for the academic community

Last-mile logistics optimization in the on-demand economy

L'abstract è presente nell'allegato / the abstract is in the attachmen

Problèmes de tournées avec gestion de stock et prise en compte explicite de la consommation d'énergie

Dans le problème de tournées avec gestion de stock ou "Inventory Routing Problem" (IRP), le fournisseur a pour mission de surveiller les niveaux de stock d'un ensemble de clients et gérer leur approvisionnement en prenant simultanément en compte les coûts de transport et de stockage. Etant données les nouvelles exigences de développement durable et de transport écologique, nous étudions l'IRP sous une perspective énergétique, peu de travaux s'étant intéressés à cet aspect. Plus précisément, la thèse identifie les facteurs principaux influençant la consommation d'énergie et évalue les gains potentiels qu'une meilleure planification des approvisionnements permet de réaliser. Un problème relatif à l'approvisionnement en composants de chaînes d'assemblage d'automobiles est tout d'abord considéré pour lequel la masse transportée, la dynamique du véhicule et la distance parcourue sont identifiés comme les principaux facteurs impactant la consommation énergétique. Ce résultat est étendu à l'IRP classique et les gains potentiels en termes d'énergie sont analysés. Un problème industriel de tournées avec gestion de stock est ensuite étudié et résolu, notamment à l'aide d'une méthode de génération de colonnes. Ce problème met en évidence les limitations du modèle IRP classique, ce qui nous a amené à définir un modèle d'IRP plus réaliste. Finalement, une méthode de décomposition basée sur la relaxation lagrangienne est développée pour la résolution de ce problème dans le but de minimiser la consommation énergétique.The thesis studies the Inventory Routing Problem (IRP) with explicit energy consideration. Under the Vendor Managed Inventory (VMI) model, the IRP is an integration of the inventory management and routing, where both inventory storage and transportation costs are taken into account. Under the new sustainability paradigm, green transport and logistics has become an emerging area of study, but few research focus on the ecological aspect of the classical IRP. Since the classical IRP concentrates solely on the economic benefits, it is worth studying under the energy perspective. The thesis gives an estimation of the energetic gain that a better supplying plan can provide. More specifically, this thesis integrates the energy consumption into the decision of the inventory replenishment and routing. It starts with a part supplying problem in car assembly lines, where the transported mass, the vehicle dynamics and the travelled distance are identified as main energy influencing factors. This result is extended to the classical IRP with energy objective to show the potential energy reduction that can be achieved. Then, an industrial challenge of IRP is presented and solved using a column generation approach. This problem put the limitations of the classical IRP model in evidence, which brings us to define a more realistic IRP model on a multigraph. Finally, a Lagrangian relaxation method is presented for solving this new model with the aim of energy minimization

Recharge strategies for the electric vehicle routing problem with time windows in deterministic and stochastic environments

Due to increasing concerns about greenhouse gas emissions in recent years, many companies have had an interest in using alternative fuel vehicles in their fleets. Electric vehicles (EVs) are one of these vehicles and they have various advantages such as zero tailpipe emissions, low maintenance costs and low energy consumption. However, their acquisition costs are higher compared to the conventional vehicles and recharging the battery may take significant amount of time compared to the short fueling times. Hence, to overcome these challenges, logistics decisions have to be made effectively. The problem of planning EVs’ activities has been introduced to the literature as the Electric Vehicle Routing Problem (EVRP), which is a special case of the classical VRP where the fleet consists of EVs. The difference between this problem and the classical VRP is that vehicles have batteries as the energy source and the battery is being discharged while the EV is traveling. Hence, the EVs may recharge their batteries at the recharging stations to continue their routes. These stations are located at distant locations and there are few of them compared to the common fuel stations. Recharging may be performed at any level of the battery and the recharging time increases with the recharge amount. In some stations, there may be different chargers which vary in terms of charging speed. For instance, fast chargers recharge the battery faster, but they incur higher cost. Furthermore, EVs may wait in the queue at the stations since there may be other EVs which arrive earlier and wait for ii service. In this dissertation, we address four problems which consider these different features of the EVRP. First, we study the EVRP with Time Windows where the batteries can be recharged partially at the recharging stations. Second, we extend this problem where the recharging stations are equipped with multiple types of chargers which differ by recharging rates and unit recharging costs. Next, we consider a stochastic environment where an EV may wait in the queue before recharging due to other EVs that have arrived earlier at that station. The waiting times depend on the time of the visit during the day, i.e., they are longer in the rush hours. Furthermore, the recharging time is assumed to be a nonlinear function of the energy recharged. In the final problem, we consider random waiting times at the recharging stations. In this case, the EVs do not have information about the queue lengths of the stations before they arrive at. We propose Adaptive Large Neighborhood Search heuristics and matheuristics to solve these problems effectivel