A Hybrid Genetic Algorithm for Multi-Trip Green Capacitated Arc Routing Problem in the Scope of Urban Services (original) (raw)
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Applied Soft Computing, 2015
We present a green vehicle routing and scheduling problem (GVRSP) considering general time-dependent traffic conditions with the primary objective of minimizing CO 2 emissions and weighted tardiness. A new mathematical formulation is proposed to describe the GVRSP with hierarchical objectives and weighted tardiness. The proposed formulation is an alternative formulation of the GVRSP in the way that a vehicle is allowed to travel an arc in multiple time periods. The schedule of a vehicle is determined based on the actual distance that the vehicle travels each arc in each time period instead of the time point when the vehicle departs from each node. Thereby, more general time dependent traffic patterns can be considered in the model. The proposed formulation is studied using various objectives functions, such as minimizing the total CO 2 emissions, the total travel distance, and the total travel time. Computational results show that up to 50% reduction in CO 2 emissions can be achieved with average reductions of 12% and 28% compared to distance-oriented solutions and travel-time-oriented solutions, respectively. In addition, a simulated annealing (SA) algorithm is introduced to solve large-sized problem instances. To reduce the search space, the SA algorithm searches only for vehicle routes and rough schedules, and a straightforward heuristic procedure is used to determine near-optimal detailed schedules for a given set of routes. The performance of the SA algorithm is tested on large-sized problems with up to 100 nodes and 10 time periods.
A Simulated Annealing Heuristic for the Capacitated Green Vehicle Routing Problem
Mathematical Problems in Engineering, 2019
This research studies the capacitated green vehicle routing problem (CGVRP), which is an extension of the green vehicle routing problem (GVRP), characterized by the purpose of harmonizing environmental and economic costs by implementing effective routes to meet any environmental concerns while fulfilling customer demand. We formulate the mathematical model of the CGVRP and propose a simulated annealing (SA) heuristic for its solution in which the CGVRP is set up as a mixed integer linear program (MILP). The objective of the CGVRP is to minimize the total distance traveled by an alternative fuel vehicle (AFV). This research conducts a numerical experiment and sensitivity analysis. The results of the numerical experiment show that the SA algorithm is capable of obtaining good CGVRP solutions within a reasonable amount of time, and the sensitivity analysis demonstrates that the total distance is dependent on the number of customers and the vehicle driving range.
A Genetic Algorithm-based optimization model for supporting green transportation operations
Expert Systems with Applications, 2014
Green Logistics (GL) has emerged as a trend in the management of the distribution of goods and the collection of end-of-life products. With its focus on maximizing the economic and environmental value by means of recycling and emission control, GL contributes to the sustainable development of industry but also requires a more comprehensive transportation scheme when conducting logistics services. This study is motivated by the practice of delivering and collecting water carboys. In this paper, a Genetic Algorithm-based optimization model (GOM) is proposed for designing a green transportation scheme of economic and environmental cost efficiency in forward and reverse logistics. Two vehicle routing models with simultaneous delivery and pickup (full or partial pickup) are formulated and solved by a Genetic Algorithm. A cost generation engine is designed to perform a comprehensive cost comparison and analysis based on a set of economic and environmental cost factors, so as to examine the impact of the two models and to suggest optimal transportation schemes. The computational experiments show that the overall cost is evidently lower in the full pickup model. Notably, the impact of product cost after recycling and reusing empty carboys on total cost is more significant than the impact of transportation cost and CO 2 emission cost. In summary, the proposed GOM is capable of suggesting a guidance for the logistics service providers, who deal with green operations, to adopt a beneficial transportation scheme so as to eventually achieve a low economic and environmental cost.
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Multi-Objective Genetic Algorithms for the Green Vehicle Routing Problem : A Comparative Study
2017
The Green Vehicle Routing Problem (GVRP) is an extension of the standard VRP taking into account the awareness of companies and governments of the dangerous effect of gases emissions. The primary objective of the GVRP is to minimize the volume of emitted carbon dioxide (co2) in adding to the optimization of the traveled distance and other functional objectives. In this paper, we model the GVRP as a bi-objective optimization problem for which many solving algorithms can be adapted and applied including deferent variants and extensions of Multi-Objective Genetic Algorithms (MOGAs). We select three elitist MOGAs: Non-dominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm II (SPEA-II) and the Indicator-Based Evolutionary Algorithm (IBEA) to evaluate the quality of the returned Pareto fronts using deferent metrics: computation time, traveled distance, emissions volume, generational distance, spacing, entropy, and contribution. The comparison is performed...
A Survey on Environmentally Friendly Vehicle Routing Problem and a Proposal of Its Classification
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The growth of environmental awareness and more robust enforcement of numerous regulations to reduce greenhouse gas (GHG) emissions have directed efforts towards addressing current environmental challenges. Considering the Vehicle Routing Problem (VRP), one of the effective strategies to control greenhouse gas emissions is to convert the fossil fuel-powered fleet into Environmentally Friendly Vehicles (EFVs). Given the multitude of constraints and assumptions defined for different types of VRPs, as well as assumptions and operational constraints specific to each type of EFV, many variants of environmentally friendly VRPs (EF-VRP) have been introduced. In this paper, studies conducted on the subject of EF-VRP are reviewed, considering all the road transport EFV types and problem variants, and classifying and discussing with a single holistic vision. The aim of this paper is twofold. First, it determines a classification of EF-VRP studies based on different types of EFVs, i.e., Alterna...
A matheuristic approach for the Pollution-Routing Problem
European Journal of Operational Research, 2015
This paper deals with the Pollution-Routing Problem (PRP), a Vehicle Routing Problem (VRP) with environmental considerations, recently introduced in the literature by , 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.
A Memetic Algorithm With Competition for the Capacitated Green Vehicle Routing Problem
IEEE/CAA Journal of Automatica Sinica, 2019
In this paper, a memetic algorithm with competition (MAC) is proposed to solve the capacitated green vehicle routing problem (CGVRP). Firstly, the permutation array called traveling salesman problem (TSP) route is used to encode the solution, and an effective decoding method to construct the CGVRP route is presented accordingly. Secondly, the k-nearest neighbor (kNN) based initialization is presented to take use of the location information of the customers. Thirdly, according to the characteristics of the CGVRP, the search operators in the variable neighborhood search (VNS) framework and the simulated annealing (SA) strategy are executed on the TSP route for all solutions. Moreover, the customer adjustment operator and the alternative fuel station (AFS) adjustment operator on the CGVRP route are executed for the elite solutions after competition. In addition, the crossover operator is employed to share information among different solutions. The effect of parameter setting is investigated using the Taguchi method of design-of-experiment to suggest suitable values. Via numerical tests, it demonstrates the effectiveness of both the competitive search and the decoding method. Moreover, extensive comparative results show that the proposed algorithm is more effective and efficient than the existing methods in solving the CGVRP.
Determination of green vehicle routing problem via differential evolution
International Journal of Logistics Systems and Management, 2019
This paper presents the comparison of pickup and delivery with time window (PDPTW) and green vehicle routing for pickup and delivery problems, with time windows (Green-PDPTW) by using differential evolution (DE) algorithm. The main idea of PDPTW is to design the optimal route for transportation by minimising the total cost. Green-PDPTW aims to design the route by minimising the emission of direct greenhouse gases, i.e., carbon dioxide (CO2), methane (CH4), and nitrous oxide (N2O). These two concepts were verified by eight standard benchmark instances. DE algorithm is proposed to design the optimal route for these two problems. The computational experiments demonstrate that designing route by minimising greenhouse gases emission provides cleaner routes than designing routes by minimising total cost. However, it is not as economical as considering the minimum total cost as the objective function since it requires more vehicles and total distance than route that designed based on the minimum total cost concept.