scholarly journals Multiple Ant Colony Optimization for Single Depot Multiple Trip Vehicle Routing Problems

10.29007/8tjs ◽  
2018 ◽  
Author(s):  
Zhengmao Ye ◽  
Habib Mohamadian
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Urban Areas ◽  
Ant Colony ◽  
Ant Colony System ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Routing Problem ◽  
Single Vehicle

The multiple trip vehicle routing problem involves several sequences of routes. Working shift of single vehicle can be scheduled in multiple trips. It is suitable for urban areas where the vehicle has very limited size and capacity over short travel distances. The size and capacity limit also requires the vehicle should be vacated frequently. As a result, the vehicle could be used in different trips as long as the total time or distance is not exceeded. Various approaches are developed to solve the vehicle routing problem (VRP). Except for the simplest cases, VRP is always a computationally complex issue in order to optimize the objective function in terms or both time and expense. Ant colony optimization (ACO) has been introduced to solve the vehicle routing problem. The multiple ant colony system is proposed to search for alternative trails between the source and destination so as to minimize (fuel consumption, distance, time) among numerous geographically scattered routes. The objective is to design adaptive routing so as to balance loads among congesting city networks and to be adaptable to connection failures. As the route number increases, each route becomes less densely packed. It can be viewed as the vehicle scheduling problem with capacity constraints. The proposed scheme is applied to typical cases of vehicle routing problems with a single depot and flexible trip numbers. Results show feasibility and effectiveness of the approach.

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.

scholarly journals Pengembangan Algoritma Ant Colony System Pada Heterogeneous Vehicle Routing Problem with Soft Time Window

2020 ◽  
Vol 3 (2) ◽  
pp. 85-102
Author(s):  
Sonna Kristina ◽  
Ricky Sianturi ◽  
Valian Janelven Wijaya
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Ant Colony ◽  
Ant Colony System ◽  
Nearest Neighbour ◽  
Routing Problem ◽  
Total Cost ◽  
Heterogeneous Vehicle Routing Problem

Setiap perusahaan umumnya memiliki sistem distribusi dan transportasi dalam menunjang pengiriman barang kepada customer. Diperlukan sistem distribusi yang efektif dan efisien sehingga biaya dari transportasi dalam perusahaan dapat diminimasi. Penelitian ini bertujuan untuk mengembangkan algoritma Ant Colony System (ACS) untuk model matematis Heterogeneous VehicleRouting Problem with Soft Time Window (HVRPSTW) pada penentuan rute transportasi yang dapat meminimasi biaya pada perusahaan PT XYZ. HVRPSTW merupakan VRP yang mempertimbangkan kendaraan yang beragam dan jendela waktu dengan adanya biaya penalti yang dibebankan apabila kendaraan tiba di luar waktu yang telah ditentukan. Salah satu cara yang digunakan untuk menyelesaikan permasalahan VRP adalah metode metaheuristic ACS. Metode ACS diimplementasikan untuk menemukan rute kendaraan terbaik sesuai dengan kendala-kendala yang sudah ditentukan. Tahapan awal adalah mencari solusi awal menggunakan metode Nearest Neighbour yang akan digunakan sebagai pheromone awal. Proses pencarian rute pada ACS menggunakan tahapan tour construction lalu dilakukan update pheromone. Pemecahan masalah akan dilakukan dengan bantuan aplikasi Python. Hasil dari penelitian menunjukkan bahwa dihasilkan total jarak sebesar 1448,98 km dan total cost sebesar Rp. 3.582.367,86, di mana terjadi selisih jarak dengan penelitian sebelumnya menggunakan metode eksak sebesar 6,48 km (0,45%) dan selisih total biaya sebesar Rp. 42.248,86 (1,19%). Kata kunci: ant colony optimization, vehicle routing problem, kapasitas kendaraan yang beragam, jendela waktu, biaya transportasi.

Evolutionary Ant Colony Algorithm Using Firefly Based Transition for Solving Vehicle Routing Problems

2019 ◽  
Vol 10 (3) ◽  
pp. 46-60
Author(s):  
Rajeev Goel ◽  
Raman Maini
Keyword(s):  
Vehicle Routing ◽  
Ant Colony Algorithm ◽  
Optimization Problems ◽  
Search Space ◽  
Ant Colony ◽  
Ant Colony System ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Routing Problem ◽  
Firefly Optimization

Vehicle routing problems are a classical NP-hard optimization problem. In this article we propose an evolutionary optimization algorithm which adapts the advantages of ant colony optimization and firefly optimization to solve vehicle routing problem and its variants. Firefly optimization (FA) based transition rules and a novel pheromone shaking rule is proposed to escape local optima. Whereas the multi-modal nature of FA explores the search space, pheromone shaking avoids the stagnation of pheromones on the exploited paths. This is expected to improve working of an ant colony system (ACS). Performance of the proposed algorithm is compared with the performance of some of other currently available meta-heuristic approaches for solving vehicle routing problems (VRP) by applying it to certain standard benchmark datasets. Results show that the proposed approach is consistent and its convergence rate is faster. The results also demonstrate the superiority of the proposed approach over some of the other existing FA-based approaches for solving such type of discrete optimization problems.

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