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.

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.

scholarly journals Ant Colony Optimization for Multiple Pickup and Multiple Delivery Vehicle Routing Problem with Time Window and Heterogeneous Fleets

Logistics ◽  
2021 ◽  
Vol 5 (2) ◽  
pp. 28
Author(s):  
Phan Nguyen Ky Phuc ◽  
Nguyen Le Phuong Thao
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Service Providers ◽  
Time Window ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Delivery Vehicle ◽  
Routing Problem

This study focuses on solving the vehicle routing problem (VRP) of E-logistics service providers. In our problem, each vehicle must visit some pick up nodes first, for instance, warehouses to pick up the orders then makes deliveries for customers in the list. Each pickup node has its own list of more than one customers requiring delivery. The objective is to minimize the total travelling cost while real-world application constraints, such as heterogeneous vehicles, capacity limits, time window, driver working duration, etc. are still considered. This research firstly proposes a mathematical model for this multiple pickup and multiple delivery vehicle routing problem with time window and heterogeneous fleets (MPMDVRPTWHF). In the next step, the ant colony optimization algorithm is studied to solve the problem in the large-scale.

An ant colony optimization algorithm with time window constraints relaxation for solving the vehicle routing problem

2018 ◽  
pp. 49-62
Author(s):  
Ольга Эдуардовна Долгова ◽  
Владимир Викторович Пересветов
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Hybrid Method ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Decision Time ◽  
Ant Colony ◽  
Ant Colony Optimization Algorithm ◽  
Routing Problem ◽  
Time Window Constraints

Рассмотрена задача маршрутизации транспорта с ограничениями по временным окнам. Требовалось составить план доставки товара клиентам, построив маршруты движения идентичных транспортных средств так, чтобы общая длина пройденного пути была минимальной. Для решения задачи разработан гибридный алгоритм. Он состоит из методов построения исходных решений, муравьиного алгоритма и локального поиска. В муравьином алгоритме в процессе формирования маршрутов разрешается нарушение временных ограничений при условии добавления штрафа в целевую функцию. Предложенный метод показал высокую эффективность при решении задач кластерного типа и задач с долгосрочным горизонтом планирования. The purpose of this paper is to improve the performance of a hybrid method based on ant colony optimization (ACO) that finds approximate solutions of the vehicle routing problem with time windows (VRPTW). In order to solve this problem it is required to design a plan for goods delivery to the customers generating the routes of identical vehicles so that the total travelled distance is minimal. For the VRPTW solving, the hybrid method is developed in which a usage of trial solutions makes it possible to explore the most promising parts of the search space. The initial methods for solution construction, an ant colony optimization (ACO) algorithm and local search are proposed in the framework of the hybrid method. In the ACO algorithm, when generating the routes, it is allowed to violate the time window constraints. A method to restore the feasibility of solutions is implemented within the relaxation scheme under “returns in time” principle. Numerical results for solving all problems with 25, 50 and 100 customers from the Solomon test set are obtained. We provide the results on the time and deviation of the solution of these problems in comparison with the results of other authors. Some problems and their classes were solved much faster by the algorithm proposed in this paper. Relative deviations from optimal values of the objective function for the most complex tasks decrease with increasing decision time. The proposed approach can be considered to be an additional or an alternative algorithm for solving the cluster type and the long-term planning horizon problems of the VRPTW.

scholarly journals A Comprehensive Study of Vehicle Routing Problem With Time Windows Using Ant Colony Optimization Techniques

2018 ◽  
Vol 7 (2.32) ◽  
pp. 80 ◽  
Author(s):  
Avirup Guha Neogi ◽  
Singamreddy Mounika ◽  
Salagrama Kalyani ◽  
S A. Yogananda Sai
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Optimization Techniques ◽  
Ant Colony ◽  
Routing Problem ◽  
Time Window Constraints

Ant Colony Optimization (ACO) is a nature-inspired swarm intelligence technique and a metaheuristic approach which is inspired by the foraging behavior of the real ants, where ants release pheromones to find the best and shortest route from their nest to the food source. ACO is being applied to various optimization problems till date and has been giving good quality results in the field. One such popular problem is known as Vehicle Routing Problem(VRP). Among many variants of VRP, this paper presents a comprehensive survey on VRP with Time Window constraints(VRPTW). The survey is presented in a chronological order discussing which of the variants of ACO is used in each paper followed by the advantages and limitations of the same.  

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.

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