• International Journal of Computer Science & Network Security
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• v.22 no.10
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• pp.352-358
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• 2022

In this paper, we study the relationship between infection rates of covid 19 and the precautionary measures and strict protocols taken by Saudi Arabia to combat the spread of the coronavirus disease and minimize the number of infected people. Based on the infection rates and the timetable of precautionary measures, the best framework of precautionary measures was identified by applying the traveling salesman problem (TSP) that relies on ant colony optimization (ACO) algorithms. The proposed algorithm was applied to daily infected cases data in Saudi Arabia during three periods of precautionary measures: partial curfew, whole curfew, and gatherings penalties. The results showed the partial curfew and the whole curfew for some cities have the minimum total cases over other precautionary measures. The gatherings penalties had no real effect in reducing infected cases as the other two precautionary measures. Therefore, in future similar circumstances, we recommend first applying the partial curfew and the whole curfew for some cities, and not considering the gatherings penalties as an effective precautionary measure. We also recommend re-study the application of the grouping penalty, to identify the reasons behind the lack of its effectiveness in reducing the number of infected cases.

• Tunnel and Underground Space
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• v.24 no.1
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• pp.11-20
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• 2014

This study analyzed the optimal routes of auxiliary vehicles in an open-pit mine that need to traverse the entire mine through many working points. Unlike previous studies which usually used the Dijkstra's algorithm, this study utilized a heuristic algorithm for the Traveling Salesman Problem(TSP). Thus, the optimal routes of auxiliary vehicles could be determined by considering the visiting order of multiple working points. A case study at the Pasir open-pit coal mine, Indonesia was conducted to analyze the travel route of an auxiliary vehicle that monitors the working condition by traversing the entire mine without stopping. As a result, we could know that the heuristic TSP algorithm is more efficient than intuitive judgment in determining the optimal travel route; 20 minutes can be shortened when the auxiliary vehicle traverses the entire mine through 25 working points according to the route determined by the heuristic TSP algorithm. It is expected that the results of this study can be utilized as a basis to set the direction of future research for the system optimization of auxiliary vehicles in open-pit mines.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.1
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• pp.18-24
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• 2001

This paper presents a model and a heuristic algorithm for the Traveling Salesman Problem with Time Windows(TSPTW). The main difference of our model compared with the previous ones lies in that the time windows we are concerned are more flexible and realistic than the previous ones. In the typical TSPTW, the service at a node must begin within the time grid called the time window that is defined by the earliest and the latest time to start the service at each node. But, in real business practice, a lateness cost is usually penalized rather than the service is prohibited at all when a vehicle arrives after the latest time. Considering this situation, we develop a model with a new time window that allows an arrival after the latest time and penalizes the late arrival by charging a lateness cost. A two-phased heuristic algorithm is proposed for the model and is extensively tested to verify the accuracy and efficiency of the algorithm.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.149.3-149
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• 2001

Task allocation is a key problem in multiagent systems. The importance of automated negotiation protocols for solving the task allocation problem is increasing as a consequence of increased multi-agent applications. In this paper, we introduce the multiagent Traveling Salesman Problem(TSP) as an example of task reallocation problem, and suggest Vickery auction as an inter-agent coordination mechanism for solving this problem. In order to apply this market-based coordination mechanism into multiagent TSPs, we define the profit of each agent, the ultimate goal of negotiation, cities to be traded out through auctions, the bidding strategy, and the order of auctions. The primary advantage of such approach is that it can find an optimal task allocation ...

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.4
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• pp.80-87
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• 2022

Lack of sufficient battery capacity is one of the most important challenges impeding the development of wireless sensor networks (WSNs). Recent innovations in the areas of wireless energy transfer and rechargeable batteries have made it possible to advance WSNs. Therefore, in this article, we propose an energy-efficient charging of sensors in a WSN scenario. First, we have formulated the problem as an integer linear programming (ILP) problem. Then a utility function-based greedy algorithm named UGreedy/UF1 is proposed for solving the problem. Finally, the performance of UGreedy/UF1 is analyzed along with other baseline algorithms: UGreedy/UF2, 2-opt TSP, and Greedy TSP. The simulation results show that UGreedy/UF1 performs better than others both in terms of the deadline missing ratio of sensors and the total energy consumption of UAVs.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.143-146
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• 1996

In this paper, we propose a hopfield model for solving the part-matching which is the number of parts and positions are changed. The goal of this paper is to minimize part-connection in pairs and net total path of part-connection. Therefore, this kind of problem is referred to as a combinatorial optimization problem. First of all, we review the theoretical basis for hopfield model to optimization and present two method of part-matching; Traveling Salesman Problem (TSP) and Weighted Matching Problem (WMP). Finally, we show demonstration through computer simulation and analyzes the stability and feasibility of the generated solutions for the proposed connection methods.

• Journal of the Korean Society for Precision Engineering
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• v.9 no.3
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• pp.122-131
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• 1992

In this paper an approximate solution method for the large-scale Traveling Salesman Problem (TSP) is presented. The method starts with the subdivision of the problem domain into a number of cluster by considering their geometric characteristic. Each cluster has a limited number of nodes so as to get a local solution. They are linked go give the least pathe which covers the whole domain and become TSPs solution with start-and end-node. The approximate local solution in each cluster are obtained based on geometrical properties of the cluster, and combined to give an overall approximate solution for the larte-scale TSP.

• Industrial Engineering and Management Systems
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• v.9 no.2
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• pp.80-87
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• 2010

As express courier market expands rapidly, companies are exposed to fierce competition. To cope with struggle for their survival, they are continuously making efforts to improve their service system. Even if most of service centers are directly linked to a consolidation terminal in courier service network, some of them with regional disadvantages are operated in milk run type from/to the consolidation terminal, which is a traditional PDP (Pick-up and Delivery Problem). This study suggests an approach to solve the PDP with the objective of maximizing the incremental profit, which belongs to PTP (Profitable Tour Problem) class. After the PTP is converted to TSP (Traveling Salesman Problem) with the same objective, a heuristic algorithm based on GA (Genetic Algorithm) is developed and examined through an example problem in practice of a courier service company in Korea.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.6
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• pp.2269-2275
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• 2010

The performance of Genetic Algorithms (GA) is affected by various factors such as parameters, genetic operators and strategies. The traditional approach with random initial population is efficient however the whole initial population may contain many infeasible solutions. Thus it would take a long time for GA to produce a good solution. The GA have been modified in various ways to achieve faster convergence and it was particularly recognized by researchers that initial population greatly affects the performance of GA. This study proposes modified GA with sorted initial population and applies it to solving Travelling Salesman Problem (TSP). Normally, the bigger the initial the population is the more computationally expensive the calculation becomes with each generation. New approach allows reducing the size of the initial problem and thus achieve faster convergence. The proposed approach is tested on a simulator built using object-oriented approach and the test results prove the validity of the proposed method.

• Journal of the Korea Institute of Military Science and Technology
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• v.14 no.3
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• pp.458-466
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• 2011

In war time, minimizing the logistics response time for supporting military operations is strongly needed. In this paper, i propose the mathematical formulation for minimizing the maximal acquisition completion time in wartime or during a state of emergency. The main structure of this formulation is based on the traveling purchaser problem (TPP), which is a generalized form of the well-known traveling salesman problem (TSP). In the case of the general TPP, an objective function is to minimize the sum of the traveling cost and the purchase cost. However, in this study, the objective function is to minimize the traveling cost only. That's why it's more important to minimize the traveling cost (time or distance) than the purchase cost in wartime or in a state of emergency. I generate a specific instance and find out the optimal solution of this instance by using ILOG OPL STUDIO (CPLEX version 11.1).