Analysis of travelling salesman problem
01 Nov 2017-Vol. 263, Iss: 4, pp 042085
TL;DR: In this article, the authors investigated different algorithms utilized as a part of writing to understand multiple traveling salesman problem (mTSP) and found that the measure of algorithm time to take care of this issue develops exponentially as number of urban areas builds thus, the metaheuristic streamlining algorithms, for example, Genetic Algorithm (GAs) are should have been investigated.
Abstract: The multiple Traveling Salesman Problem (mTSP) is the general type of TSP, in which at least one than one sales representatives can be utilized as a part of the arrangement set. The Constraint in the improvement undertaking is that every sales representative comes back to beginning stage at end of outing, heading out to a particular arrangement of urban areas in the middle of and with the exception of the first, every last city is gone to by precisely one sales representative. The thought is to scan for the briefest course that is the slightest separation required for every salesperson to go from the beginning area to individual urban areas and back to the area from where he has begun. It is an intricate NP-Hard issue and has different applications for the most part in the field of planning and steering. The measure of algorithm time to take care of this issue develops exponentially as number of urban areas builds thus, the meta-heuristic streamlining algorithms, for example, Genetic Algorithm (GAs) are should have been investigated. The objective of this paper is to discover different algorithms utilized as a part of writing to understand mTSP.
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01 Mar 2020
TL;DR: Ad-hoc protocol for distributed object search by multi-agent robotic system in urban environment by muti-agent drone system and communication model for the system was proposed and ad-hOC protocol for communication between drones has been developed.
Abstract: In the paper ad-hoc protocol for distributed object search by multi-agent robotic system in urban environment are described. For this purpose, exploration and mapping process by the muti-agent drone system has been discussed and appropriate algorithms has been analyzed. Based on this, communication model for the system was proposed and ad-hoc protocol for communication between drones has been developed.
3 citations
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30 Apr 2020
TL;DR: In this article, the authors used the Saving Matrix method to schedule limit-determined transportation vehicles to deliver goods from one facility to various customers, and used the Farthest Insert, Nearest Insert and Nearest Neighbour method.
Abstract: PT. Star Spart Indonesia in Sidoarjo, East Java is an automotive and spare part company which sells various national and international quality automotive products such as baterries, oil, tyres, and other spare parts. In promoting the products, this company is facing many competitors, so it has to collaborate with other retail shops and main dealers to win the competition. Therefore, the company is planning a proper marketing strategy to market the products in order to strive the organizational goals (to survive, to grow, and to multiply). This research is using Saving Matrix method to schedule limitied transportation vehicles to deliver goods from one facility to various customers. Another method is using the Farthest Insert, Nearest Insert and Nearest Neighbour method. Using the Saving Matrix method, the company can spend Rp. 45,802,836 for transportion costs using company's vehicles. If the company rents the vehicles to other outsourcing company, the transportation costs will be Rp. 49,000,000. There is a saving of Rp. 3,197,164 if the company uses own vehicles. From changing the route, there will be another cost saving from fuel as much as Rp. 409,115. Keywords: Saving Matrix, Farthest Insert, Nearest Insert, Nearest neighbour, Distribution
1 citations
Cites background from "Analysis of travelling salesman pro..."
...[8] Belal Ahmed, Shivank Singh Chouhan, Subham Biswas, P Gayathri, H Santhi....
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...depot untuk mengunjungi beberapa outlet dimana setiap outlet yang ada hanya dikunjungi satu kali dan akhirnya kembali ke depot semula [8]....
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TL;DR: In this article , a real-time separating system, which detects the products in the factory band with object recognition methods and enables fast positioning of the separating tool on the products, works simultaneously with object detection and traveling salesman problem algorithms has been created.
Abstract: The stage before the conversion of agricultural products into post-harvest consumer products is the process of separating the raw products into appropriate classes. Today, this difficult manual separating process is a process in which a large number of workers work at an intense pace on the product line and the workforce is intensively spent. Disruptions in separating as a result of carelessness cause product loss, loss of time and cost increases. In this study, as an alternative to manual separating processes, a real-time separating system, which detects the products in the factory band with object recognition methods and enables fast positioning of the separating tool on the products, works simultaneously with object recognition and traveling salesman problem algorithms has been created. In this way, a low-budget separating system is recommended for large selecting processes with a time- and cost-effective selecting model. In the study, the creation of a real-time fast separating system with the support of the traveling salesman algorithm, performance evaluation and research and findings on the fast separating model are presented.
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18 Apr 2005
TL;DR: This paper attempts to examine the claim that PSO has the same effectiveness (finding the true global optimal solution) as the GA but with significantly better computational efficiency by implementing statistical analysis and formal hypothesis testing.
Abstract: Particle Swarm Optimization (PSO) is a relatively recent heuristic search method whose mechanics are inspired by the swarming or collaborative behavior of biological populations. PSO is similar to the Genetic Algorithm (GA) in the sense that these two evolutionary heuristics are population-based search methods. In other words, PSO and the GA move from a set of points (population) to another set of points in a single iteration with likely improvement using a combination of deterministic and probabilistic rules. The GA and its many versions have been popular in academia and the industry mainly because of its intuitiveness, ease of implementation, and the ability to effectively solve highly nonlinear, mixed integer optimization problems that are typical of complex engineering systems. The drawback of the GA is its expensive computational cost. This paper attempts to examine the claim that PSO has the same effectiveness (finding the true global optimal solution) as the GA but with significantly better computational efficiency (less function evaluations) by implementing statistical analysis and formal hypothesis testing. The performance comparison of the GA and PSO is implemented using a set of benchmark test problems as well as two space systems design optimization problems, namely, telescope array configuration and spacecraft reliability-based design.
1,221 citations
TL;DR: A novel particle swarm optimization (PSO)-based algorithm for the traveling salesman problem (TSP) is presented and it has been shown that the size of the solved problems could be increased by using the proposed algorithm.
401 citations
TL;DR: Experimental results show that TCX can improve the solution quality of the GA compared to three existing crossover approaches and is evaluated and compared with three different crossover methods for two MTSP objective functions.
129 citations
TL;DR: This chapter is to review how genetic algorithms can be applied to solve these problems, and propose a novel, easily interpretable and problem-oriented representation and operators, that can easily handle constraints on the tour lengths, and the number of salesmen can vary during the evolution.
Abstract: The Vehicle Routing Problem (VRP) is a complex combinatorial optimization problem that can be described as follows: given a fleet of vehicles with uniform capacity, a common depot, and several requests by the customers, find a route plan for the vehicles with overall minimum route cost (eg. distance traveled by vehicles), which service all the demands. It is well known that multiple Traveling Salesman Problem (mTSP) based algorithms can also be utilized in several VRPs by incorporating some additional constraints, it can be considered as a relaxation of the VRP, with the capacity restrictions removed. The mTSP is a generalization of the well known traveling salesman problem (TSP), where more than one salesman is allowed to be used in the solution. Because of the fact that TSP is already a complex, namely an NP-hard problem, heuristic optimization algorithms, like genetic algorithms (GAs) need to be taken into account. The extension of classical GA tools for mTSP is not a trivial problem, it requires special, interpretable encoding and genetic operators to ensure efficiency. The aim of this chapter is to review how genetic algorithms can be applied to solve these problems, and propose a novel, easily interpretable and problem-oriented representation and operators, that can easily handle constraints on the tour lengths, and the number of salesmen can vary during the evolution. The elaborated heuristic algorithm is demonstrated by a complete realistic example.
53 citations
Dissertation•
01 Jan 2003TL;DR: This work presents a new modeling methodology for setting up the multiple traveling salesmen problem to be solved using a GA and proves itself to be an effective method to model the MTSP for solving with GAs.
Abstract: The multiple traveling salesmen problem (MTSP) is an extension of the traveling salesman problem with many production and scheduling applications. The TSP has been well studied including methods of solving the problem with genetic algorithms. The MTSP has also been studied and solved with GAs in the form of the vehicle-scheduling problem. This work presents a new modeling methodology for setting up the MTSP to be solved using a GA. The advantages of the new model are compared to existing models both mathematically and experimentally. The model is also used to model and solve a multi line production problem in a spreadsheet environment. The new model proves itself to be an effective method to model the MTSP for solving with GAs. The concept of the MTSP is then used to model and solve with a GA the use of one salesman make many tours to visit all the cities instead of using one continuous trip to visit all the cities. While this problem uses only one salesman, it can be modeled as a MTSP and has many applications for people who must visit many cities on a number of short trips. The method used effectively creates a schedule while considering all required constraints.
21 citations