Solving the TSP using Traditional Computing Approach

Baidoo, Evans; Oppong, Stephen Opoku

doi:10.5120/ijca2016911906

Cited by 5 publications

(3 citation statements)

References 11 publications

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“…), combined methods (Ant Colony Optimization, Memetic Algorithms, Genetic algorithms, Tabu-search, Simulated Annealing, Firefly Algorithm etc.) [14]. Permutations, loop reversal and combined methods are often used to improve the basic route [15,16].…”

Section: Methodsmentioning

confidence: 99%

Modeling of transportation logistics processes for the urban environment

Romanov

Romanova

2019

E3S Web Conf.

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The article deals with the approach to modeling the road transport movement in large cities (with a population of over 100 thousand people) for the delivery of goods from a large warehouse to stores belonging to a trading network company, with the task of optimizing these movements. The main purposes of this optimization task are: to reduce the transportation time; to conduct a rational distribution of vehicles; to reduce the number of required vehicles involved in these transportations; to reduce operating costs for the maintenance of vehicles. The problem statement and its solution on the basis of heuristic algorithms of the Traveling Salesman Problem are given. The article presents a comparative analysis of the most popular methods for solving the Traveling Salesman Problem (Greedy Approach, Modified Greedy Approach, Minimum Spanning Tree, Monte Carlo Simplification Model, Ant Colony Optimization, Algorithm of Little) on the basis of experimental research and simulation. As a result of the analysis, it is proposed to use the Algorithm of Little for optimizing of road transport movement in the delivery of goods. The article provides an example of solving a specific problem using the developed calculation procedure and a computer program “Traveling Salesman Problem” (developed in Pascal in the software environment Delphi 7).

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Section: Methodsmentioning

confidence: 99%

Modeling of transportation logistics processes for the urban environment

Romanov

Romanova

2019

E3S Web Conf.

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“…Moreover, to confirm that the solution obtained by solving the models proposed in this study is truly optimal and unaffected by any mistake in the solver package or programming code, several optimization studies employing the brute-force search method [18][19][20][21] were used as a baseline. The brute-force search method enumerates all possible candidate solutions and then checks whether each candidate satisfies the problem statement.…”

Section: Literature Reviewmentioning

confidence: 99%

Economic Order Picker Routing Considering Travel Time and Vehicular Energy Consumption with Varying Aisle Traffic

Rojanapitoon¹,

Teeravaraprug²

2020

IJIES

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Warehousing can be expensive because order picking requires considerable vehicular movement and labor hours. Although many previous studies have focused on order picker routing, there exists a lack of research on the simultaneous increase of order picking speed and energy reduction in rectangular warehouses with varying levels of traffic in each aisle. This study accordingly developed and evaluated a mathematical model for determining optimal picker routes considering the total travel time and energy consumed. The results were validated using the brute-force search method and benchmarked with the time-staged (TS) model. The energy savings were determined by comparing a time-optimized use-case (T) with one optimized for both time and energy (TE). Both use-cases provided routes up to 44% faster than the TS and avoided more than 50% of congested paths, and TE which represents the full functionality of our model provided a possible energy savings of up to 17% over T.

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“…and each route will be compared between one another to find the best route. The number of combinations significantly influences the computational time required (Baidoo, 2016).…”

Section: Introductionmentioning

confidence: 99%

The Average Value Algorithm from The Distance Matrix for Traveling Salesman Problem

Setiawan,

Aditya,

Heryati

2023

J. Ilm. Inform. Glob.

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The Traveling Salesman Problem (TSP) is a popular problem, but until now there is no algorithm that has the same search results as brute force with a fast search time. Many algorithms have been made previously related to solving this problem with the aim of finding the shortest route through a number of nodes to finally return to the initial node. The purpose of this research is to create an algorithm that can optimize the search for the shortest route with a fast search time. The approach taken is to find the average value of the distance matrix and look for routes with links that have values below the average value. Each route that has been passed will be marked and compared so that it can facilitate the search with a shorter processing time. In this paper the best and effective routes are limited to 12 nodes. The results obtained show that the Average Score Algorithm provides a relatively stable processing time from node 4 to node 12. The proposed algorithm has a tendency of decreasing processing capacity with increasing number of nodes.

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Solving the TSP using Traditional Computing Approach

Cited by 5 publications

References 11 publications

Modeling of transportation logistics processes for the urban environment

Modeling of transportation logistics processes for the urban environment

Economic Order Picker Routing Considering Travel Time and Vehicular Energy Consumption with Varying Aisle Traffic

The Average Value Algorithm from The Distance Matrix for Traveling Salesman Problem

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