• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.3
• /
• pp.431-437
• /
• 2010

It's strongly needed to minimize the logistics response time for supporting military operations in wartime. In this paper, we suggest the ILP formulation for minimizing logistics response time in wartime. 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 general TPP, objective function is to minimize the sum of traveling cost and purchase cost. But, in this study, objective function is to minimize traveling cost only. That's why it's more important to minimize traveling cost(time or distance) than purchase cost in wartime. We find out optimal solution of this problem by using ILOG OPL STUDIO(CPLEX v.11.1) and do the sensitive analysis about computing time according to number of operated vehicles.

• IE interfaces
• /
• v.24 no.1
• /
• pp.51-57
• /
• 2011

The traveling purchaser problem (TPP) is a generalization of the well-known traveling salesman problem (TSP), which has many real-world applications such as purchasing the required raw materials for the manufacturing factories and the scheduling of a set of jobs over some machines, and many others. In the last decade, TPP has received some attention of the researchers in the operational research area. However, all of the past researches for TPP are restricted on a single purchaser (vehicle). It could be the limitation to solve the real world problem. The purpose of this paper is to suggest the capacitated multiple TPP (CMTPP). It could be used in inbound logistics optimization in supply chain management area and many others. Since TPP is known as NP-hard, we also developed the heuristic algorithm to solve the CMTPP.

• The KIPS Transactions:PartB
• /
• v.11B no.1
• /
• pp.77-82
• /
• 2004

Reinforcement learning takes reward about selecting action when agent chooses some action and did state transition in Present state. this can be the important subject in reinforcement learning as temporal-credit assignment problems. In this paper, by new meta heuristic method to solve hard combinational optimization problem, examine Ant-Q learning method that is proposed to solve Traveling Salesman Problem (TSP) to approach that is based for population that use positive feedback as well as greedy search. And, suggest Ant-TD reinforcement learning method that apply state transition through diversification strategy to this method and TD-error. We can show through experiments that the reinforcement learning method proposed in this Paper can find out an optimal solution faster than other reinforcement learning method like ACS and Ant-Q learning.

• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.15 no.3
• /
• pp.211-218
• /
• 2015

This paper introduces a 'divide-and-conquer' algorithm to the travelling salesman problem (TSP). Top 10n are selected beforehand from a pool of n(n-1) data which are sorted in the ascending order of each vertex's distance. The proposed algorithm then firstly selects partial paths that are interconnected with the shortest distance $r_1=d\{v_i,v_j\}$ of each vertex $v_i$ and assigns them as individual regions. For $r_2$, it connects all inter-vertex edges within the region and inter-region edges are connected in accordance with the connection rule. Finally for $r_3$, it connects only inter-region edges until one whole Hamiltonian cycle is constructed. When tested on TSP-1(n=26) and TSP-2(n=42) of real cities and on a randomly constructed TSP-3(n=50) of the Euclidean plane, the algorithm has obtained optimal solutions for the first two and an improved one from that of Valenzuela and Jones for the third. In contrast to the brute-force search algorithm which runs in n!, the proposed algorithm runs at most 10n times, with the time complexity of $O(n^2)$.

• Journal of Institute of Control, Robotics and Systems
• /
• v.8 no.12
• /
• pp.1048-1055
• /
• 2002

The problem of generating torch paths for 2D laser cutting of a stock plate nested with a set of free-formed parts is investigated. The objective is to minimize the total length of the torch path starting from a blown depot, then visiting all the given Parts, and retuning back to the depot. A torch Path consists of the depot and Piercing Points each of which is to be specified for cutting a part. The torch path optimization problem is shown to be formulated as an extended version of the standard travelling salesman problem To solve the problem, a hybrid genetic algorithm is proposed. In order to improve the speed of evolution convergence, the algorithm employs a genetic algorithm for global search and a combination of an optimization technique and a genetic algorithm for local optimization. Traditional genetic operators developed for continuous optimization problems are used to effectively deal with the continuous nature of piercing point positions. Computational results are provided to illustrate the validity of the proposed algorithm.

• International Journal of Fuzzy Logic and Intelligent Systems
• /
• v.8 no.1
• /
• pp.61-67
• /
• 2008

This paper introduces the multi-vehicle routing problem(MRP) which is different from the traveling sales problem(TSP), and presents the ant system(AS) applied to the MRP. The proposed MRP is a distributive model of TSP since many vehicles are used, not just one salesman in TSP and even some constraints exist. In the AS, a set of cooperating agents called vehicles cooperate to find good solutions to the MRP. To make the proposed MRP extended more, Tokyo city model(TCM) is proposed. The goal in TCM is to find a set of routes that minimizes the total traveling time such that each vehicle can reach its destination as soon as possible. The results show that the AS can effectively find a set of routes minimizing the total traveling time even though the TCM has some constraints.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.33 no.4
• /
• pp.201-208
• /
• 2010

This research is about developing an efficient solution procedure for the vehicle routing problem under varying vehicle moving speeds for hour-based time interval. Different moving speeds for every hour is too difficult condition to solve for this type of combinatorial optimization problem. A methodology to divide the 12 hour based time interval offered by government into 5 different time intervals and then divide delivery area into 12 small divisions first and then re-organizing them into 5 groups. Then vehicle moving speeds are no longer varying in each of the 5 divisions. Therefore, a typical TSP solution procedure may be applied to find the shortest path for all 5 divisions and then connect the local shortest paths to form a delivery path for whole area. Developed solution procedures are explained in detail with 60 points example.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.25 no.4
• /
• pp.97-109
• /
• 2000

This work proposes a neural network approach to solve vehicle routing problems which have diverse application areas such as vehicle routing and robot programming. In solving these problems, classical mathematical approaches have many difficulties. In particular, it is almost impossible to implement a real-time vehicle routing with multiple vehicles. Recently, many researchers proposed methods to overcome the limitation by adopting heuristic algorithms, genetic algorithms, neural network techniques and others. The most basic model for path planning is the Travelling Salesman Problem(TSP) for a minimum distance path. We extend this for a problem with dynamic upcoming of new positions with multiple vehicles. In this paper, we propose an algorithm based on SOM(Self-Organization Map) to obtain a sub-optimal solution for a real-time vehicle routing problem. We develope a model of a generalized multiple TSP and suggest and efficient solving procedure.

• Journal of Korean Institute of Industrial Engineers
• /
• v.22 no.2
• /
• pp.255-263
• /
• 1996

Optical fiber systems play an essential role In today's telecommunications networks. The recently standardized SONET technology has made a ring structure the preferred architecture for inter-city communication networks. In designing a SONET with ring structure, we consider inserting optional cites, which are not necessary in constructing the SONET, but cost-effective in connecting essential nodes in the ring. This problem is modeled as Steiner ring problem. Efficient heuristic procedures are developed based on the procedures for the traveling salesman problem. Computational results show that the proposed algorithm is excellent compared to the optimal solution. The error bound by the proposed method is 2 - 6% in experimented problems.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.33 no.3
• /
• pp.30-39
• /
• 2010

This paper considers a problem of optimizing torch paths to cut stock plates nested with open contours. For each contour, one of the two ending points is to be selected as a starting point of cutting with the other being the exit point. A torch path is composed of a single depot and a series of starting and ending points of contours to be cut. The torch path optimization problem is shown to be formulated as an extended version of the standard travelling salesman problem. To solve the problem, a hybrid genetic algorithm with the local search of torch paths is proposed. The genetic algorithm is tested for hypothetical problems whose optimal solutions are known in advance due to the special structure of them. The computational results show that the algorithm generates very near optimal solutions for most cases of the test problems, which verifies the validity of the algorithms.