scholarly journals A Problem-Reduction Evolutionary Algorithm for Solving the Capacitated Vehicle Routing Problem

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Wanfeng Liu ◽  
Xia Li
Keyword(s):  
Evolutionary Algorithm ◽  
Vehicle Routing ◽  
Optimization Problem ◽  
Optimal Solution ◽  
Solution Quality ◽  
Routing Problem ◽  
Individual Solution ◽  
Problem Reduction ◽  
Evaluation Phase ◽  
Capacitated Vehicle

Assessment of the components of a solution helps provide useful information for an optimization problem. This paper presents a new population-based problem-reduction evolutionary algorithm (PREA) based on the solution components assessment. An individual solution is regarded as being constructed by basic elements, and the concept of acceptability is introduced to evaluate them. The PREA consists of a searching phase and an evaluation phase. The acceptability of basic elements is calculated in the evaluation phase and passed to the searching phase. In the searching phase, for each individual solution, the original optimization problem is reduced to a new smaller-size problem. With the evolution of the algorithm, the number of common basic elements in the population increases until all individual solutions are exactly the same which is supposed to be the near-optimal solution of the optimization problem. The new algorithm is applied to a large variety of capacitated vehicle routing problems (CVRP) with customers up to nearly 500. Experimental results show that the proposed algorithm has the advantages of fast convergence and robustness in solution quality over the comparative algorithms.

A Fast and Scalable Heuristic for the Solution of Large-Scale Capacitated Vehicle Routing Problems

2021 ◽  
Author(s):  
Luca Accorsi ◽  
Daniele Vigo
Keyword(s):  
Vehicle Routing ◽  
Large Scale ◽  
Computing Time ◽  
Search Space ◽  
Iterated Local Search ◽  
Solution Quality ◽  
Routing Problem ◽  
Acceptance Criterion ◽  
Acceleration Techniques ◽  
Capacitated Vehicle

In this paper, we propose a fast and scalable, yet effective, metaheuristic called FILO to solve large-scale instances of the Capacitated Vehicle Routing Problem. Our approach consists of a main iterative part, based on the Iterated Local Search paradigm, which employs a carefully designed combination of existing acceleration techniques, as well as novel strategies to keep the optimization localized, controlled, and tailored to the current instance and solution. A Simulated Annealing-based neighbor acceptance criterion is used to obtain a continuous diversification, to ensure the exploration of different regions of the search space. Results on extensively studied benchmark instances from the literature, supported by a thorough analysis of the algorithm’s main components, show the effectiveness of the proposed design choices, making FILO highly competitive with existing state-of-the-art algorithms, both in terms of computing time and solution quality. Finally, guidelines for possible efficient implementations, algorithm source code, and a library of reusable components are open-sourced to allow reproduction of our results and promote further investigations.

Meta-heuristic Approach to Solve Mixed Vehicle Routing Problem with Backhauls in Enterprise Information System of Service Industry

2010 ◽  
pp. 210-225
Author(s):  
S. P. Anbuudayasankar ◽  
K. Ganesh ◽  
Tzong-Ru Lee
Keyword(s):  
Vehicle Routing ◽  
Enterprise Resource Planning ◽  
Vehicle Routing Problem ◽  
Service Industry ◽  
Optimal Solution ◽  
Resource Planning ◽  
Enterprise Information ◽  
Solution Quality ◽  
Routing Problem ◽  
Support Tool

This chapter presents the development of simulated annealing (SA) for a health care application which is modeled as Single Depot Vehicle routing problem called Mixed Vehicle Routing Problem with Backhauls (MVRPB), an extension of Vehicle Routing Problem with Backhauls (VRPB). This variant involves both delivery and pick-up customers and sequence of visiting the customers is mixed. The entire pick-up load should be taken back to depot. The latest rapid advancement of meta-heuristics has shown that it can be applied in practice if they are personified in packaged information technology (IT) solutions along with the combination of a Supply Chain Management (SCM) application integrated with an enterprise resource planning (ERP) resulted to this decision support tool. This chapter provides empirical proof in sustain of the hypothesis, that a population extension of SA with supportive transitions leads to a major increase of efficiency and solution quality for MVRPB if and only if the globally optimal solution is located close to the center of all local optimal solutions.

scholarly journals MENYELESAIKAN VEHICLE ROUTING PROBLEM MENGGUNAKAN ALGORITMA FUZZY EVOLUSI

2018 ◽  
Vol 7 (3) ◽  
pp. 252
Author(s):  
I PUTU ARYA YOGA SUMADI ◽  
I PUTU EKA NILA KENCANA ◽  
LUH PUTU IDA HARINI
Keyword(s):  
Evolutionary Algorithm ◽  
Vehicle Routing ◽  
Relative Error ◽  
Vehicle Routing Problem ◽  
Average Relative Error ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Optimal Value ◽  
Evolution Algorithm ◽  
Capacitated Vehicle

The purpose of this research is to know the performance of Fuzzy Evolutionary Algorithm in solving one type of Vehicle Routing Problem that is Capacitated Vehicle Routing Problem (CVRP). There are 8 different CVRP data to be solved. The performance of the algorithm can be determined by comparing the value obtained by AFE with the optimal value of the data. The result of this research is fuzzy evolution algorithm yields the best average relative error from all data for distance that is equal to 69,5855% and for minimum vehicle equal to 26%.

scholarly journals A Hybrid Algorithm Based on ACO and PSO for Capacitated Vehicle Routing Problems

2012 ◽  
Vol 2012 ◽  
pp. 1-17 ◽  
Author(s):  
Yucheng Kao ◽  
Ming-Hsien Chen ◽  
Yi-Ting Huang
Keyword(s):  
Swarm Intelligence ◽  
Vehicle Routing ◽  
Hybrid Algorithm ◽  
Optimization Problem ◽  
Benchmark Problems ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Routing Problem ◽  
Logistic Management ◽  
Capacitated Vehicle

The vehicle routing problem (VRP) is a well-known combinatorial optimization problem. It has been studied for several decades because finding effective vehicle routes is an important issue of logistic management. This paper proposes a new hybrid algorithm based on two main swarm intelligence (SI) approaches, ant colony optimization (ACO) and particle swarm optimization (PSO), for solving capacitated vehicle routing problems (CVRPs). In the proposed algorithm, each artificial ant, like a particle in PSO, is allowed to memorize the best solution ever found. After solution construction, only elite ants can update pheromone according to their own best-so-far solutions. Moreover, a pheromone disturbance method is embedded into the ACO framework to overcome the problem of pheromone stagnation. Two sets of benchmark problems were selected to test the performance of the proposed algorithm. The computational results show that the proposed algorithm performs well in comparison with existing swarm intelligence approaches.

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