A matheuristic approach for the Pollution-Routing Problem

This paper deals with the Pollution-Routing Problem (PRP), a Vehicle Routing Problem (VRP) with environmental considerations, recently introduced in the literature by [Bektas and Laporte (2011), Transport. Res. B-Meth. 45 (8), 1232-1250]. The objective is to minimize operational and environmental costs while respecting capacity constraints and service time windows. Costs are based on driver wages and fuel consumption, which depends on many factors, such as travel distance and vehicle load. The vehicle speeds are considered as decision variables. They complement routing decisions, impacting the total cost, the travel time between locations, and thus the set of feasible routes. We propose a method which combines a local search-based metaheuristic with an integer programming approach over a set covering formulation and a recursive speed-optimization algorithm. This hybridization enables to integrate more tightly route and speed decisions. Moreover, two other "green" VRP variants, the Fuel Consumption VRP (FCVRP) and the Energy Minimizing VRP (EMVRP), are addressed. The proposed method compares very favorably with previous algorithms from the literature and many new improved solutions are reported.Comment: Working Paper -- UFPB, 26 page

On green routing and scheduling problem

The vehicle routing and scheduling problem has been studied with much interest within the last four decades. In this paper, some of the existing literature dealing with routing and scheduling problems with environmental issues is reviewed, and a description is provided of the problems that have been investigated and how they are treated using combinatorial optimization tools

Internalizing negative externalities in vehicle routing problems through green taxes and green tolls

Road freight transportation includes various internal and external costs that need to be accounted for in the construction of efficient routing plans. Typically, the resulting optimization problem is formulated as a vehicle routing problem in any of its variants. While the traditional focus of the vehicle routing problem was the minimization of internal routing costs such as travel distance or duration, numerous approaches to include external factors related to environmental routing aspects have been recently discussed in the literature. However, internal and external routing costs are often treated as competing objectives. This paper discusses the internalization of external routing costs through the consideration of green taxes and green tolls. Numeric experiments with a biased-randomization savings algorithm, show benefits of combining internal and external costs in delivery route planning.Peer Reviewe

Survey on Ten Years of Multi-Depot Vehicle Routing Problems: Mathematical Models, Solution Methods and Real-Life Applications

A crucial practical issue encountered in logistics management is the circulation of final products from depots to end-user customers. When routing and scheduling systems are improved, they will not only improve customer satisfaction but also increase the capacity to serve a large number of customers minimizing time. On the assumption that there is only one depot, the key issue of distribution is generally identified and formulated as VRP standing for Vehicle Routing Problem. In case, a company having more than one depot, the suggested VRP is most unlikely to work out. In view of resolving this limitation and proposing alternatives, VRP with multiple depots and multi-depot MDVRP have been a focus of this paper. Carrying out a comprehensive analytical literature survey of past ten years on cost-effective Multi-Depot Vehicle Routing is the main aim of this research. Therefore, the current status of the MDVRP along with its future developments is reviewed at length in the paper

Performance Comparison of Two-phase LP-based Heuristic Methods for Capacitated Vehicle Routing Problem with Three Objectives

This paper develops a two-phase LP-based heuristic for the Capacitated Vehicle Routing Problem (CVRP). It considers three objectives: (1) minimizing the total costs of fuel consumption and overtime, (2) maximizing the total personal relationships between customers and drivers, and (3) balancing the delivery weights of vehicles. The two-phase LP-based heuristic (cluster-first route-second) is proposed. First, in the clustering stage, three LP-based clustering models (denoted by C1, C2, and C3) are developed. Customers are grouped into clusters based on real distances between the customers for C1, personal relationships between the customers and drivers for C2, and the delivery weights of vehicles for C3. Second, in the routing stage, an LP-based traveling salesman problem model is used to form a route for each cluster, to minimize the total costs of fuel consumption and overtime labor. The experimental results from a case study of Thai SMEs show that when the C2 clustering model is applied, the performances are the best. Significant contributions of this paper include: (1) it is an original paper that proposes the C2 clustering model, and it has the best performances based on the experimental results, and (2) the proposed two-phase LP-based heuristic methods are suitable for practical use by SMEs since the required computational time is short, and it has multiple models with different objectives that can be selected to match a user's requirements

Multi-objective Vehicle Routing Problem with Cost and Emission Functions

AbstractAmong the logistics activities, transportation, is presented as a major source of air pollution in Europe, generating harmful levels of air pollutants and is responsible for up to 24% of greenhouse gases (GHGs) emissions in the European Union. The growing environmental concern related to the economic activity has been transferred to the field of transport and logistics in recent decades. Therefore, environmental targets are to be added to economic targets in the decision-making, to find the right balance between these two dimensions. In real life, there are many situations and problems that are recognized as multi-objective problems. This type of problems containing multiple criteria to be met or must be taken into account. Often these criteria are in conflict with each other and there is no single solution that simultaneously satisfies everyone. Vehicle routing problems (VRP) are frequently used to model real cases, which are often established with the sole objective of minimizing the internal costs. However, in real life other factors could be taken into account, such as environmental issues. Moreover, in industry, a fleet of vehicles is rarely homogeneous. The need to be present in different segments of the market, forcing many companies to have vehicles that suit the type of goods transported. Similarly, to have vehicles of different load capacities enables a better adaptation to the customer demand. This paper proposes a multi-objective model based on Tchebycheff methods for VRP with a heterogeneous fleet, in which vehicles are characterized by different capacities, costs and emissions factors. Three objective functions are used to minimize the total internal costs, while minimizing the CO2 emissions and the emission of air pollutants such as NOx. Moreover, this study develops an algorithm based on C&W savings heuristic to solve the model when time windows are not considered. Finally, a real case application is analyzed to confirm the practicality of the model and the algorithm