scholarly journals A Vehicle Routing Problem with Consideration of Green Transportation

2017 ◽  
Vol 7 (4) ◽  
pp. 89 ◽  
Author(s):  
Che-Fu Hsueh
Keyword(s):  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Service Providers ◽  
Environmental Cost ◽  
Fixed Costs ◽  
Routing Problem ◽  
Fuel Consumption Rate ◽  
Logistics Company ◽  
Fuel Consumption And Emissions

This study aims to investigate the Green Vehicle Routing Problem (GVRP), which considers stochastic traffic speeds, so that fuel consumption and emissions can be reduced. Considering a heterogeneous fleet, the fuel consumption rate differs due to several factors, such as vehicle types and conditions, travel speeds, roadway gradients, and payloads. A mathematical model was proposed to deal with the GVRP, and its objective is to minimize the sum of the fixed costs and the expected fuel consumption costs. A customized genetic algorithm was proposed for solving the model. The computational experiments confirm the efficiency of the algorithm and show that the solution of GVRP is quite different from that of the traditional vehicle routing problem. We suggest that a company should use light vehicles to service the customers situated at higher terrains. The customers with higher demands can be visited earlier, but the customers situated at higher terrains or far away from the depot should be visited later. The study also found that the fixed costs of dispatching vehicles are critical in GVRP; a logistics company may thus tend to use large vehicles, despite that it may cause higher fuel consumption and emissions. The proposed model and algorithm are capable of suggesting a guidance for green logistics service providers to adopt a beneficial vehicle routing plan so as to eventually achieve a low economic and environmental cost.

Valid inequalities for the fleet size and mix vehicle routing problem with fixed costs

Networks ◽  
2009 ◽  
Vol 54 (4) ◽  
pp. 178-189 ◽  
Author(s):  
Roberto Baldacci ◽  
Maria Battarra ◽  
Daniele Vigo
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Valid Inequalities ◽  
Fixed Costs ◽  
Routing Problem ◽  
Fleet Size ◽  
Fleet Size And Mix

scholarly journals An Efficient Two-Objective Hybrid Local Search Algorithm for Solving the Fuel Consumption Vehicle Routing Problem

2016 ◽  
Vol 2016 ◽  
pp. 1-16 ◽  
Author(s):  
Weizhen Rao ◽  
Feng Liu ◽  
Shengbin Wang
Keyword(s):  
Local Search ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Search Algorithm ◽  
Classical Model ◽  
Mixed Integer ◽  
Local Search Algorithm ◽  
Routing Problem ◽  
Total Vehicle

The classical model of vehicle routing problem (VRP) generally minimizes either the total vehicle travelling distance or the total number of dispatched vehicles. Due to the increased importance of environmental sustainability, one variant of VRPs that minimizes the total vehicle fuel consumption has gained much attention. The resulting fuel consumption VRP (FCVRP) becomes increasingly important yet difficult. We present a mixed integer programming model for the FCVRP, and fuel consumption is measured through the degree of road gradient. Complexity analysis of FCVRP is presented through analogy with the capacitated VRP. To tackle the FCVRP’s computational intractability, we propose an efficient two-objective hybrid local search algorithm (TOHLS). TOHLS is based on a hybrid local search algorithm (HLS) that is also used to solve FCVRP. Based on the Golden CVRP benchmarks, 60 FCVRP instances are generated and tested. Finally, the computational results show that the proposed TOHLS significantly outperforms the HLS.

scholarly journals Multi-Depot Joint Distribution Vehicle Routing Problem Considering Energy Consumption with Time-Dependent Networks

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2082
Author(s):  
Dengkai Hou ◽  
Houming Fan ◽  
Xiaoxue Ren
Keyword(s):  
Energy Consumption ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Joint Distribution ◽  
Road Network ◽  
Time Dependent ◽  
Neighborhood Search ◽  
Routing Problem ◽  
The Road

This paper studies the multi-depot joint distribution vehicle routing problem considering energy consumption with time-dependent networks (MDJDVRP-TDN). Aiming at the multi-depot joint distribution vehicle routing problem where the vehicle travel time depends on the variation characteristics of the road network speed in the distribution area, considering the influence of the road network on the vehicle speed and the relationship between vehicle load and fuel consumption, a multi-depot joint distribution vehicle routing optimization model is established to minimize the sum of vehicle fixed cost, fuel consumption cost and time window penalty cost. Traditional vehicle routing problems are modeled based on symmetric graphs. In this paper, considering the influence of time-dependent networks on routes optimization, modeling is based on asymmetric graphs, which increases the complexity of the problem. A hybrid genetic algorithm with variable neighborhood search (HGAVNS) is designed to solve the model, in which the nearest neighbor insertion method and Logistic mapping equation are used to generate the initial solution firstly, and then five neighborhood structures are designed to improve the algorithm. An adaptive neighborhood search times strategy is used to balance the diversification and depth search of the population. The effectiveness of the designed algorithm is verified through several groups of numerical instances with different scales. The research can enrich the relevant theoretical research of multi-depot vehicle routing problems and provide the theoretical basis for transportation enterprises to formulate reasonable distribution schemes.

scholarly journals A A Multi-Objective Mathematical Model for Vehicle Routing Problem Considering the Time Window and Economic and Environmental Objectives Using the Metaheuristic Algorithm Based on Pareto Archive

2020 ◽  
Vol 1 (1) ◽  
pp. 58-68
Author(s):  
Elnaz Asadifard ◽  
Maryam Adlifard ◽  
Mohammad Taghipour ◽  
Nader Shamami
Keyword(s):  
Mathematical Model ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Fixed Costs ◽  
Metaheuristic Algorithm ◽  
Routing Problem ◽  
Total Distance ◽  
Environmental Objectives ◽  
Single Objective

Purpose:  The purpose of this study is the well-known Heterogeneous Fleet Vehicle Routing Problem (HVRP) is one of the developed problems of vehicle routing, which involves optimizing a set of routes for a fleet of vehicles with different costs and capacities. Methods: HVRP is usually modeled as a single objective that aims to minimize overall transportation costs (total fixed costs and costs commensurate with total distance). Results: These vehicles are located in the central depot and serve customers’ demands. Implications: In this case, the number of vehicles available (of any type) may be limited.

scholarly journals TDGVRPSTW of Fresh Agricultural Products Distribution: Considering Both Economic Cost and Environmental Cost

2021 ◽  
Vol 11 (22) ◽  
pp. 10579
Author(s):  
Daqing Wu ◽  
Chenxiang Wu
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Economic Cost ◽  
Agricultural Products ◽  
Time Dependent ◽  
Environmental Cost ◽  
Routing Problem ◽  
Measure Function

The time-dependent vehicle routing problem of time windows of fresh agricultural products distribution have been studied by considering both economic cost and environmental cost. A calculation method for road travel time across time periods is designed in this study. A freshness measure function of agricultural products and a measure function of carbon emission rate are employed by considering time-varying vehicle speeds, fuel consumptions, carbon emissions, perishable agricultural products, customers’ time windows, and minimum freshness. A time-dependent green vehicle routing problem with soft time windows (TDGVRPSTW) model is formulated. The object of the TDGVRPSTW model is to minimize the sum of economic cost and environmental cost. According to the characteristics of the model, a new variable neighborhood adaptive genetic algorithm is designed, which integrates the global search ability of the genetic algorithm and the local search ability of the variable neighborhood descent algorithm. Finally, the experimental data show that the proposed approaches effectively avoid traffic congestions, reduce total distribution costs, and promote energy conservation and emission reduction.

scholarly journals Tighter Analysis of an Approximation for the Cumulative VRP

2017 ◽  
Author(s):  
Mauro Henrique Mulati ◽  
Flávio Keidi Miyazawa
Keyword(s):  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Scaling Factor ◽  
Traveling Salesperson Problem ◽  
Routing Problem

We deal with the cumulative vehicle routing problem (VRP), a generalization of the capacitated VRP, which objective is to minimize the fuel consumption. Gaur et al. in 2013 gave a 4-approximation based on a well-known partition heuristic to the traveling salesperson problem (TSP). We present a tighter analysis obtaining a 4 3s34Q2 -approximation, where Q is the capacity of the vehicle and s is a scaling factor. To the best of our knowledge, this is the best proved approximation for the cumulative VRP so far.

The Robust Vehicle Routing Problem with Time Window Assignments

2021 ◽  
Vol 55 (2) ◽  
pp. 395-413
Author(s):  
Maaike Hoogeboom ◽  
Yossiri Adulyasak ◽  
Wout Dullaert ◽  
Patrick Jaillet
Keyword(s):  
Travel Time ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Service Providers ◽  
Time Window ◽  
Time Windows ◽  
Branch And Cut ◽  
Optimal Time ◽  
Routing Problem ◽  
Solution Approach

In practice, there are several applications in which logistics service providers determine the service time windows at the customers, for example, in parcel delivery, retail, and repair services. These companies face uncertain travel times and service times that have to be taken into account when determining the time windows and routes prior to departure. The objective of the proposed robust vehicle routing problem with time window assignments (RVRP-TWA) is to simultaneously determine routes and time window assignments such that the expected travel time and the risk of violating the time windows are minimized. We assume that the travel time probability distributions are not completely known but that some statistics, such as the mean, minimum, and maximum, can be estimated. We extend the robust framework based on the requirements’ violation index, which was originally developed for the case where the specific requirements (time windows) are given as inputs, to the case where they are also part of the decisions. The subproblem of finding the optimal time window assignment for the customers in a given route is shown to be convex, and the subgradients can be derived. The RVRP-TWA is solved by iteratively generating subgradient cuts from the subproblem that are added in a branch-and-cut fashion. Experiments address the performance of the proposed solution approach and examine the trade-off between expected travel time and risk of violating the time windows.

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