PARALLEL COOPERATIVE SAVINGS BASED ANT COLONY OPTIMIZATION — MULTIPLE SEARCH AND DECOMPOSITION APPROACHES

2006 ◽  
Vol 16 (03) ◽  
pp. 351-369 ◽  
Author(s):  
KARL F. DOERNER ◽  
RICHARD F. HARTL ◽  
SIEGFRIED BENKNER ◽  
MARIA LUCKA
Keyword(s):  
Domain Decomposition ◽  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Search Strategies ◽  
Ant System ◽  
Solution Quality ◽  
Routing Problem ◽  
Low Level ◽  
Ant System Algorithm

In this paper we study different parallel implementations of the Savings based Ant System algorithm developed for solving the Vehicle Routing Problem. We analyze the effects of low-level parallelization, multiple search strategies and domain decomposition approaches. For the different strategies speedup and efficiency as well as solution quality are reported. Different information exchanges are analyzed within the multiple search strategies.

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.

Solving the Mixed Backhauling Vehicle Routing

2013 ◽  
Vol 2 (2) ◽  
pp. 62-77 ◽  
Author(s):  
Niaz A. Wassan ◽  
Said Salhi ◽  
Gábor Nagy ◽  
Naveed Wassan ◽  
Anne C. Wade
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Data Sets ◽  
Computational Results ◽  
Ant System ◽  
Distribution Problem ◽  
Routing Problem ◽  
Benchmark Data ◽  
Look Ahead ◽  
Ant System Algorithm

The mixed vehicle routing problem with backhauls is investigated using ant system heuristic. This distribution problem seems to suffer from a lack of published work even though it has immense practical applicability especially within logistic systems. Some enhancements to the basic ant system algorithm are embedded into the search. In particular a focus is on the choice in the placement of ants, the use of site-dependent candidate list, the introduction of a look ahead-based visibility, and appropriate strategies for updating local and global trails. Encouraging computational results are reported when tested on benchmark data sets.

scholarly journals Current State of Dynamic Vehicle Routing Problems Solved by Ant Colony Optimization Algorithm

2021 ◽  
Vol 15 (3) ◽  
pp. 429-434
Author(s):  
Luka Olivari ◽  
Goran Đukić
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Optimization Algorithm ◽  
Vehicle Routing Problem ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Dynamic Vehicle Routing ◽  
Routing Problems ◽  
Routing Problem ◽  
Solution Methods

Dynamic Vehicle Routing Problem is a more complex version of Vehicle Routing Problem, closer to the present, real-world problems. Heuristic methods are used to solve the problem as Vehicle Routing Problem is NP-hard. Among many different solution methods, the Ant Colony Optimization algorithm is proven to be the efficient solution when dealing with the dynamic version of the problem. Even though this problem is known to the scientific community for decades, the field is extremely active due to technological advancements and the current relevance of the problem. As various sub-types of routing problems and solution methods exist, there is a great number of possible problem-solution combinations and research directions. This paper aims to make a focused review of the current state in the field of Dynamic Vehicle Routing Problems solved by Ant Colony Optimization algorithm, to establish current trends in the field.

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