scholarly journals Conflict-free dynamic route multi-AGV using dijkstra floyd-warshall hybrid algorithm with time windows

2020 ◽  
Vol 10 (4) ◽  
pp. 3596
Author(s):  
Solichudin Solichudin ◽  
Aris Triwiyatno ◽  
Munawar A. Riyadi
Keyword(s):  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Outdoor Environment ◽  
Route Problem ◽  
Shortest Route ◽  
Autonomous Guided Vehicle ◽  
Dynamic Route ◽  
Free Dynamic

Autonomous Guided Vehicle is a mobile robot that can move autonomously on a route or lane in an indoor or outdoor environment while performing a series of tasks. Determination of the shortest route on an autonomous guided vehicle is one of the optimization problems in handling conflict-free routes that have an influence on the distribution of goods in the manufacturing industry's warehouse. Pickup and delivery processes in the distribution on AGV goods such as scheduling, shipping, and determining the route of vehicle with short mileage characteristics, is very possible to do simulations with three AGV units. There is a windows time limit on workstations that limits shipping. The problem of determining the route in this study is considered necessary as a multi-vehicle route problem with a time window. This study aims to describe the combination of algorithms written based on dynamic programming to overcome the problem of conflict-free AGV routes using time windows. The combined approach of the Dijkstra and Floyd-Warshall algorithm results in the optimization of the closest distance in overcoming conflict-free routes.

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.

scholarly journals Lion optimization algorithm for team orienteering problem with time window

2021 ◽  
Vol 21 (1) ◽  
pp. 538
Author(s):  
Esam Taha Yassen ◽  
Alaa Abdulkhar Jihad ◽  
Sudad H. Abed
Keyword(s):  
Optimization Algorithm ◽  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Real Life ◽  
Search Space ◽  
Orienteering Problem ◽  
Team Orienteering Problem ◽  
Team Orienteering ◽  
Lion Optimization Algorithm

<span>Over the last decade, many nature-inspired algorithms have been received considerable attention among practitioners and researchers to handle several optimization problems. Lion optimization algorithm (LA) is inspired by a distinctive lifestyle of lions and their collective behavior in their social groups. LA has been presented as a powerful optimization algorithm to solve various optimization problems. In this paper, the LA is proposed to investigate its performance in solving one of the most popular and widespread real-life optimization problems called team orienteering problem with time windows (TOPTW). However, as any population-based metaheuristic, the LA is very efficient in exploring the search space, but inefficient in exploiting it. So, this paper proposes enhancing LA to tackle the TOPTW by utilizing its strong ability to explore the search space and improving its exploitation ability. This enhancement is achieved via improving a process of territorial defense to generate a trespass strong nomadic lion to prevail a pride by fighting its males. As a result of this improving process, an enhanced LA (ILA) emerged. The obtained solutions have been compared with the best known and standard results obtained in the former studies. The conducted experimental test verifies the effectiveness of the ILA in solving the TOPTW as it obtained a very competitive results compared to the LA and the state-of-the-art methods across all tested instances.</span>

Solving Fuzzy Problems in Operations Research

1999 ◽  
Vol 3 (3) ◽  
pp. 171-176
Author(s):  
James J. Buckley ◽  
◽  
Thomas Feuring ◽  
Yoichi Hayashi ◽  
◽  
...  
Keyword(s):  
Operations Research ◽  
Flow Problem ◽  
Optimization Problems ◽  
Fuzzy Optimization ◽  
Approximate Solutions ◽  
Fuzzy Regression ◽  
Neural Net ◽  
Hierarchical Analysis ◽  
Route Problem ◽  
Shortest Route

Fuzzy optimization problems to which traditional methods - calculus and crisp algorithms - are not directly applicable have not been completely solved. We used evolutionary algorithms to produce good approximate solutions to fuzzy optimization problems including fully fuzzified linear programming, nonlinear fuzzy regression, neural net training, and fuzzy hierarchical analysis. We applied our evolutionary algorithm package to generating good approximate solutions to fuzzy optimization problems in operations research including the fuzzy shortest route problem and the fuzzy min-cost capacitated flow problem.

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 A Heuristic Algorithm for the Routing and Scheduling Problem with Time Windows: A Case Study of the Automotive Industry in Mexico

Algorithms ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 111 ◽  
Author(s):  
Marco Antonio Juárez Pérez ◽  
Rodolfo Eleazar Pérez Loaiza ◽  
Perfecto Malaquias Quintero Flores ◽  
Oscar Atriano Ponce ◽  
Carolina Flores Peralta
Keyword(s):  
Heuristic Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Second Phase ◽  
Two Phase ◽  
Routing And Scheduling ◽  
Logistics Company ◽  
Vehicle Production ◽  
Time Window Constraints

This paper investigates a real-world distribution problem arising in the vehicle production industry, particularly in a logistics company, in which cars and vans must be loaded on auto-carriers and then delivered to dealerships. A solution to the problem involves the loading and optimal routing, without violating the capacity and time window constraints for each auto-carrier. A two-phase heuristic algorithm was implemented to solve the problem. In the first phase the heuristic builds a route with an optimal insertion procedure, and in the second phase the determination of a feasible loading. The experimental results show that the purposed algorithm can be used to tackle the transportation problem in terms of minimizing total traveling distance, loading/unloading operations and transportation costs, facilitating a decision-making process for the logistics company.

scholarly journals IMPLEMENTASI ALGORITMA HILL CLIMBING PADA PENENTUAN JARAK TERPENDEK KOTA WISATA DI INDONESIA

2019 ◽  
Vol 1 (3) ◽  
pp. 127-132
Author(s):  
Desti Fitriati ◽  
Nura Meutia Nessrayasa
Keyword(s):  
Optimization Problems ◽  
Complex Problem ◽  
Hill Climbing ◽  
Hill Climbing Algorithm ◽  
Steepest Ascent ◽  
Shortest Route ◽  
Shortest Distance ◽  
Optimization Function ◽  
The Right

Searching and determining the shortest route is a complex problem, looking for the shortest route from a number of attractions and the distance between attractions. With varying access paths, the shortest route search becomes the right choice using a website-based app that provides the closest route on a map using the SAHC (Steepest Ascent Hill Climbing) algorithm. Steepest Ascent Hill Climbing is a method of an algorithm that is widely used for optimization problems. One application is to find the shortest route by maximizing or minimizing the value of the existing optimization function. In research ii study using 34 provinces in Indonesia and every province, there are 5 most popular tour, accuracy value obtained in research determination of the shortest distance of tourist city in Indonesia is 93,3%.  

A Route Optimizing Model and Algorithm for Pickup and Delivery Problem with Time Window

2013 ◽  
Vol 321-324 ◽  
pp. 2060-2064
Author(s):  
Ting Ting Wu
Keyword(s):  
Optimization Problems ◽  
Time Window ◽  
Time Windows ◽  
Minimum Cost ◽  
Pickup And Delivery ◽  
Delivery Problem ◽  
Soft Time Windows ◽  
Routing Optimization ◽  
Optimizing Model ◽  
Vehicle Routing Optimization

Choosing in the more practical soft time Windows the logistics pickup and delivery path choice is discussed. Setting up a more comprehensive model to get the minimum cost including the distance and time. Then improving the genetic algorithm to solve the vehicle routing optimization problems better.

The Map-Based Shortest Route Selection by Using Ant Colony Optimization Algorithm

2018 ◽  
Vol 1 (1) ◽  
pp. 15
Author(s):  
Achmad Fanany Onnilita Gaffar ◽  
Agusma Wajiansyah ◽  
Supriadi Supriadi
Keyword(s):  
Ant Colony Optimization ◽  
Shortest Path ◽  
Optimization Algorithm ◽  
Optimization Problems ◽  
Google Earth ◽  
Ant Colony ◽  
Food Sources ◽  
Shortest Route ◽  
Study Results ◽  
Destination Point

The shortest path problem is one of the optimization problems where the optimization value is a distance. In general, solving the problem of the shortest route search can be done using two methods, namely conventional methods and heuristic methods. The Ant Colony Optimization (ACO) is the one of the optimization algorithm based on heuristic method. ACO is adopted from the behavior of ant colonies which naturally able to find the shortest route on the way from the nest to the food sources. In this study, ACO is used to determine the shortest route from Bumi Senyiur Hotel (origin point) to East Kalimantan Governor's Office (destination point). The selection of the origin and destination points is based on a large number of possible major roads connecting the two points. The data source used is the base map of Samarinda City which is cropped on certain coordinates by using Google Earth app which covers the origin and destination points selected. The data pre-processing is performed on the base map image of the acquisition results to obtain its numerical data. ACO is implemented on the data to obtain the shortest path from the origin and destination point that has been determined. From the study results obtained that the number of ants that have been used has an effect on the increase of possible solutions to optimal. The number of tours effect on the number of pheromones that are left on each edge passed ant. With the global pheromone update on each tour then there is a possibility that the path that has passed the ant will run out of pheromone at the end of the tour. This causes the possibility of inconsistent results when using the number of ants smaller than the number of tours.

Enterprise distribution routing optimization with soft time windows based on genetic algorithm

2021 ◽  
pp. 1-11
Author(s):  
Kaixian Gao ◽  
Guohua Yang ◽  
Xiaobo Sun
Keyword(s):  
Genetic Algorithm ◽  
Customer Satisfaction ◽  
Optimization Problems ◽  
Rapid Development ◽  
Time Windows ◽  
Theory And Practice ◽  
Routing Problem ◽  
Logistics Industry ◽  
Routing Optimization ◽  
A Company

With the rapid development of the logistics industry, the demand of customer become higher and higher. The timeliness of distribution becomes one of the important factors that directly affect the profit and customer satisfaction of the enterprise. If the distribution route is planned rationally, the cost can be greatly reduced and the customer satisfaction can be improved. Aiming at the routing problem of A company’s vehicle distribution link, we establish mathematical models based on theory and practice. According to the characteristics of the model, genetic algorithm is selected as the algorithm of path optimization. At the same time, we simulate the actual situation of a company, and use genetic algorithm to plan the calculus. By contrast, the genetic algorithm suitable for solving complex optimization problems, the practicability of genetic algorithm in this design is highlighted. It solves the problem of unreasonable transportation of A company, so as to get faster efficiency and lower cost.

scholarly journals A hybrid ant colony algorithm based on multiple strategies for the vehicle routing problem with time windows

2021 ◽  
Author(s):  
Hongguang Wu ◽  
Yuelin Gao ◽  
Wanting Wang ◽  
Ziyu Zhang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Ant Colony Algorithm ◽  
Time Window ◽  
Time Windows ◽  
Ant Colony ◽  
Routing Problem ◽  
Multiple Strategies ◽  
Hard Time ◽  
Mutation Operation

AbstractIn this paper, we propose a vehicle routing problem with time windows (TWVRP). In this problem, we consider a hard time constraint that the fleet can only serve customers within a specific time window. To solve this problem, a hybrid ant colony (HACO) algorithm is proposed based on ant colony algorithm and mutation operation. The HACO algorithm proposed has three innovations: the first is to update pheromones with a new method; the second is the introduction of adaptive parameters; and the third is to add the mutation operation. A famous Solomon instance is used to evaluate the performance of the proposed algorithm. Experimental results show that HACO algorithm is effective against solving the problem of vehicle routing with time windows. Besides, the proposed algorithm also has practical implications for vehicle routing problem and the results show that it is applicable and effective in practical problems.

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