scholarly journals A P-Based Hybrid Evolutionary Algorithm for Vehicle Routing Problem with Time Windows

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Yunyun Niu ◽  
Juanjuan He ◽  
Zhongyu Wang ◽  
Jianhua Xiao
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Membrane Computing ◽  
Optimization Problems ◽  
Time Windows ◽  
Genetic Operators ◽  
Improvement Strategy ◽  
Solution Quality ◽  
Routing Problem ◽  
Speed Up

The ability to solve optimization problems using membrane algorithms is an important application of membrane computing. This work combines membrane systems and genetic operators to build an approximated algorithm for the vehicle routing problem with time windows. The algorithm is based on a tissue-like membrane structure combined with cell separation rules and communication rules; during such processes membranes collect and disperse information. Genetic operators are used as the system's subalgorithms. We also design a special improvement strategy to speed up the search process in subsystems. The experimental results show that the solution quality from the proposed algorithm is competitive with other heuristic or metaheuristic algorithms in the literature.

Optimization of Multi-Depot Heterogeneous Incident Vehicle Routing Problem with Soft Time Windows Based on an Improved Ant Colony Optimization

2014 ◽  
Vol 1061-1062 ◽  
pp. 1108-1117
Author(s):  
Ya Lian Tang ◽  
Yan Guang Cai ◽  
Qi Jiang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Ant Colony ◽  
Genetic Operators ◽  
Solution Quality ◽  
Routing Problem ◽  
Soft Time Windows ◽  
Heterogeneous Vehicle Routing Problem

Aiming at vehicle routing problem (VRP) with many extended features is widely used in actual life, multi-depot heterogeneous vehicle routing problem with soft time windows (MDHIVRPSTW) mathematical model is established. An improved ant colony optimization (IACO) is proposed for solving this model. Firstly, MDHIVRPSTW was transferred into different groups according to nearest depot method, then constructing the initial route by scanning algorithm (SA). Secondly, genetic operators were introduced, and then adjusting crossover probability and mutation probability adaptively in order to improve the global search ability of the algorithm. Moreover, smooth mechanism was used to improve the performance of ant colony optimization (ACO). Finally, 3-opt strategy was used to improve the local search ability. The proposed IACO has been tested on a 32-customer instance which was generated randomly. The experimental results show that IACO is superior to other three algorithms in terms of convergence speed and solution quality, thus the proposed method is effective and feasible, and the proposed model is better than conventional model.

Efficient Golden-Ball Algorithm Based Clustering to solve the Multi-Depot VRP With Time Windows

2018 ◽  
Vol 9 (1) ◽  
pp. 1-16 ◽  
Author(s):  
Lahcene Guezouli ◽  
Mohamed Bensakhria ◽  
Samir Abdelhamid
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Clustering Algorithm ◽  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Routing Problem ◽  
Combinatorial Optimization Problems ◽  
Acceptable Quality ◽  
Golden Ball

In this article, the authors propose a decision support system which aims to optimize the classical Capacitated Vehicle Routing Problem by considering the existence of multiple available depots and a time window which must not be violated, that they call the Multi-Depot Vehicle Routing Problem with Time Window (MDVRPTW), and with respecting a set of criteria including: schedules requests from clients, the capacity of vehicles. The authors solve this problem by proposing a recently published technique based on soccer concepts, called Golden Ball (GB), with different solution representation from the original one, this technique was designed to solve combinatorial optimization problems, and by embedding a clustering algorithm. Computational results have shown that the approach produces acceptable quality solutions compared to the best previous results in similar problem in terms of generated solutions and processing time. Experimental results prove that the proposed Golden Ball algorithm is efficient and effective to solve the MDVRPTW problem.

A Hybrid Meta-Heuristic Algorithm for Vehicle Routing Problem with Time Windows

2015 ◽  
Vol 24 (06) ◽  
pp. 1550021 ◽  
Author(s):  
Esam Taha Yassen ◽  
Masri Ayob ◽  
Mohd Zakree Ahmad Nazri ◽  
Nasser R. Sabar
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Heuristic Algorithm ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Time Windows ◽  
Harmony Search ◽  
Harmony Search Algorithm ◽  
Routing Problem

Harmony search algorithm, which simulates the musical improvisation process in seeking agreeable harmony, is a population based meta-heuristics algorithm for solving optimization problems. Although it has been successfully applied on various optimization problems; it suffers the slow convergence problem, which greatly hinders its applicability for getting good quality solution. Therefore, in this work, we propose a hybrid meta-heuristic algorithm that hybridizes a harmony search with simulated annealing for the purpose of improving the performance of harmony search algorithm. Harmony search algorithm is used to explore the search spaces. Whilst, simulated annealing algorithm is used inside the harmony search algorithm to exploit the search space and further improve the solutions that are generated by harmony search algorithm. The performance of the proposed algorithm is tested using the Solomon's Vehicle Routing Problem with Time Windows (VRPTW) benchmark. Numerical results demonstrate that the hybrid approach is better than the harmony search without simulated annealing and the hybrid also proves itself to be more competent (if not better on some instances) when compared to other approaches in the literature.

scholarly journals Sequential Simulated Annealing for the Vehicle Routing Problem with Time Windows

2009 ◽  
Vol 3 (2) ◽  
pp. 87-100 ◽  
Author(s):  
Marcin Woch ◽  
Piotr Łebkowski
Keyword(s):  
Simulated Annealing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Simulated Annealing Algorithm ◽  
Optimization Problems ◽  
Time Windows ◽  
High Quality ◽  
Routing Problem ◽  
Annealing Algorithm ◽  
Very High

This article presents a new simulated annealing algorithm that provides very high quality solutions to the vehicle routing problem. The aim of described algorithm is to solve the vehicle routing problem with time windows. The tests were carried out with use of some well known instances of the problem defined by M. Solomon. The empirical evidence indicates that simulated annealing can be successfully applied to bi-criterion optimization problems.

scholarly journals Advanced loading constraints for 3D vehicle routing problems

OR Spectrum ◽  
2021 ◽  
Author(s):  
Corinna Krebs ◽  
Jan Fabian Ehmke ◽  
Henriette Koch
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Comprehensive Evaluation ◽  
Time Windows ◽  
Complex Loading ◽  
Solution Quality ◽  
Routing Problem ◽  
Loading Constraints ◽  
New Variant ◽  
Neighbourhood Search

AbstractGiven automated order systems, detailed characteristics of items and vehicles enable the detailed planning of deliveries including more efficient and safer loading of distribution vehicles. Many vehicle routing approaches ignore complex loading constraints. This paper focuses on the comprehensive evaluation of loading constraints in the context of combined Capacitated Vehicle Routing Problem and 3D Loading (3L-CVRP) and its extension with time windows (3L-VRPTW). To the best of our knowledge, this paper considers the currently largest number of loading constraints meeting real-world requirements and reducing unnecessary loading efforts for both problem variants. We introduce an approach for the load bearing strength of items ensuring a realistic load distribution between items. Moreover, we provide a new variant for the robust stability constraint enabling better performance and higher stability. In addition, we consider axle weights of vehicles to prevent overloaded axles for the first time for the 3L-VRPTW. Additionally, the reachability of items, balanced loading and manual unloading of items are taken into account. All loading constraints are implemented in a deepest-bottom-left-fill algorithm, which is embedded in an outer adaptive large neighbourhood search tackling the Vehicle Routing Problem. A new set of 600 instances is created, published and used to evaluate all loading constraints in terms of solution quality and performance. The efficiency of the hybrid algorithm is evaluated by three well-known instance sets. We outperform the benchmarks for most instance sets from the literature. Detailed results and the implementation of loading constraints are published online.

An Improved Ant Colony Optimization for VRP with Time Windows

2012 ◽  
Vol 263-266 ◽  
pp. 1609-1613 ◽  
Author(s):  
Su Ping Yu ◽  
Ya Ping Li
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Distribution Systems ◽  
Optimization Problems ◽  
Time Windows ◽  
Optimal Solution ◽  
Ant Colony ◽  
Routing Problem ◽  
Combinatorial Optimization Problems

The Vehicle Routing Problem (VRP) is an important problem occurring in many distribution systems, which is also defined as a family of different versions such as the Capacitated Vehicle Routing Problem (CVRP) and the Vehicle Routing Problem with Time Windows (VRPTW). The Ant Colony Optimization (ACO) is a metaheuristic for combinatorial optimization problems. Given the ACO inadequacy, the vehicle routing optimization model is improved and the transfer of the algorithm in corresponding rules and the trajectory updated regulations is reset in this paper, which is called the Improved Ant Colony Optimization (I-ACO). Compared to the calculated results with genetic algorithm (GA) and particle swarm optimization (PSO), the correctness of the model and algorithm is verified. Experimental results show that the I-ACO can quickly and effectively obtain the optimal solution of VRFTW.

scholarly journals Mathematical models for the periodic vehicle routing problem with time windows and time spread constraints

2020 ◽  
Vol 11 (1) ◽  
pp. 10-23
Author(s):  
Hande Öztop ◽  
Damla Kizilay ◽  
Zeynel Abidin Çil
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Valid Inequalities ◽  
Time Windows ◽  
Mixed Integer ◽  
Routing Problem ◽  
Periodic Vehicle Routing Problem ◽  
Time Spread ◽  
Periodic Vehicle Routing

The periodic vehicle routing problem (PVRP) is an extension of the well-known vehicle routing problem. In this paper, the PVRP with time windows and time spread constraints (PVRP-TWTS) is addressed, which arises in the high-value shipment transportation area. In the PVRP-TWTS, period-specific demands of the customers must be delivered by a fleet of heterogeneous capacitated vehicles over the several planning periods. Additionally, the arrival times to a customer should be irregular within its time window over the planning periods, and the waiting time is not allowed for the vehicles due to the security concerns. This study, proposes novel mixed-integer linear programming (MILP) and constraint programming (CP) models for the PVRP-TWTS. Furthermore, we develop several valid inequalities to strengthen the proposed MILP and CP models as well as a lower bound. Even though CP has successful applications for various optimization problems, it is still not as well-known as MILP in the operations research field. This study aims to utilize the effectiveness of CP in solving the PVRP-TWTS. This study presents a CP model for PVRP-TWTS for the first time in the literature to the best of our knowledge. Having a comparison of the CP and MILP models can help in providing a baseline for the problem. We evaluate the performance of the proposed MILP and CP models by modifying the well-known benchmark set from the literature. The extensive computational results show that the CP model performs much better than the MILP model in terms of the solution quality.

A HYBRID SEARCH ALOGRITHM FOR THE VEHICLE ROUTING PROBLEM WITH TIME WINDOWS

2001 ◽  
Vol 10 (03) ◽  
pp. 431-449 ◽  
Author(s):  
WEE-KIT HO ◽  
JUAY CHIN ANG ◽  
ANDREW LIM
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Search Space ◽  
Hybrid Search ◽  
Solution Quality ◽  
Routing Problem ◽  
Complete Problems ◽  
Search Heuristics ◽  
The Individual

The vehicle routing problem with time windows (VRPTW) is an extension of the well-known vehicle routing problem (VRP). It involves a fleet of homogeneous vehicles, originating and terminating at a central depot, with limited capacity and maximum travel time to service a set of customers with known demands and service-time windows. The objective is to find a set of feasible routes that minimizes the total costs using some measures of solution quality. This paper focuses on the study of a hybrid of two search heuristics, Tabu Search (TS) and Genetic Algorithm (GA) on VRPTW. TS is a local search technique that has been successfully applied to many NP-complete problems. On the other hand, GA which is capable of searching multiple search areas in a search space is good in diversification. In this paper, we create a hybrid that combines the strengths of the two search heuristics. Experimental results indicate that such a hybrid outperforms the individual heuristics alone.

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