Janjarassuk, Udom
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Janjarassuk, Udom
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Janjarassuk, Udom
Janjarassuk, U.
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udom.ja@kmitl.ac.th
13 results
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Item type:Publication, Hybrid Ant Colony Optimization Method for the Traveling Salesman Problem(2024-01-01)The traveling salesman problem (TSP) is a classic problem in computer science and operations research which involves finding the shortest possible route that visits a given set of cities. In this paper, we propose a hybrid algorithm for solving such problem. The algorithm combines the ant colony optimization (ACO) method with the 2-opt heuristic to improve the efficiency for solving the TSP. Instances from the TSPLIB were used to test the algorithm. The results showed that the hybrid ACO algorithm was more effective in solving the TSP as compared to the traditional ACO or the 2-opt heuristic methods. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Product recommendation based on genetic algorithm(2019-07-01); Puengrusme, SudatipIn this paper, we propose a product recommendation system based on genetic algorithm to find the best recommendation for a combination of products to the customers. The model evaluation relies on customer preferences and product requirements as well as feature ratings from the product experts. The system is tested by using a case study from recommendation of power unit selection for recording studio. Experimental results are provided. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Hybrid Greedy Algorithm for the Capacitated Vehicle Routing Problem(2025-01-01)The Vehicle Routing Problem (VRP) is one of the most common problems in logistics and supply chain. In this study, we propose a hybrid greedy algorithm for the capacitated vehicle routing problem (CVRP) which is a variant of the VRP with vehicle capacity constraint. The algorithm adapts the minimal spanning tree algorithm to decompose the CVRP into many smaller traveling salesman problems (TSPs), and solves each sub-problem by using the Nearest Neighbor (NN) greedy algorithm. The hybrid algorithm is tested by using instances from the CVRPLIB library. The algorithm provides relatively good performances with optimality gaps varying from 4-30% across all tested instances, and the computational time is less than 1 second in all cases. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Ant Colony Optimization for Solving Electric Vehicle Traveling Salesman Problems(2026-01-01)Electric Vehicles (EVs) have become very popular nowadays due to their affordability, lower operating cost and maintenance cost, and environmental benefits. In this paper, we study an electric vehicle variant of the traveling salesman problem (EV-TSP) by using a modified ant colony optimization (ACO) method. Unlike the traditional traveling salesman problem (TSP), the electric vehicle variant of the TSP is more complex due to the limited battery range and the requirement of recharging along the trip. To tackle these difficulties, our proposed algorithm incorporates the battery level constraint as a heuristic information for choosing the next visiting node. We use a sigmoid function to capture the probability of node selection in addition to the pheromone value and visibility of ant. The type of nodes is also considered during the node selection. When the remaining battery level of the vehicle is low, the charging station nodes will have higher probability of being selected compared to the customer nodes, and vice versa. The algorithm was implemented in C++ and tested by using a number of benchmark instances from TSPLIB. The problem instances were modified by adding vehicle range and charging stations with their associated locations. Computational results were reported to show the effectiveness of the algorithm. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A Mathematical Programming Approach for Product Selection with Multiple Criteria(2019-10-25); Puengrusme, S.Product selection is very important for customer who looks for functionalities of the product. To get the best product with the available budget, multiple criteria should be considered simultaneously while making decision. In this paper, we propose a multi-objective decision model for selecting the most suitable product for the customers. The model is tested by using a real case of power unit selection in studio recording, and the results are presented. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A simulated annealing algorithm to the stochastic network interdiction problem(2016-01-18); Nakrachata-Amon, T.This paper proposes a solution algorithm for solving the stochastic network interdiction problem (SNIP). The problem is formulated as a two-stage stochastic integer program with an objective to minimize the expected maximum flow through the network, given that the success of interdicting an arc on the work is a Bernoulli random variable. The first-stage solution is obtained by using simulated annealing heuristic algorithm. In the second-stage problem, each scenario is a maximum network flow problem which is solved using the Ford-Fulkerson algorithm. The results show that our solution values of test instances are close to optimal. This is verified with a previous study in the literature that solved the same exact test instances and provided lower and upper bounds to their solutions. Furthermore, our average solution time is significantly lower. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Cost and reliability analyses for the vehicle routing problem(2017-11-03); Masuchun, RuedeeThe vehicle routing problem (VRP) is one of the central problems in logistics and supply chain management. In this paper, we present a simulation approach for cost and reliability analyses for the vehicle routing problem with stochastic demands. The objective of this study is to provide a method in evaluating the reliability and the associated cost of a VRP solution by using simulation. First, a deterministic variant of the capacitated vehicle routing problem (CVRP) is solved by using an ant colony optimization algorithm. Then the solution is evaluated under stochastic demands for the incurred cost and reliability. The reliability of a solution is estimated based on the number of route failures where customers' demands exceed the remaining capacity of the vehicles. When a failure occurs, a recourse action must be taken to fulfill the customer's requirement, which in turn increases the operating cost. We provide experiments for the proposed method by using standard benchmark problems for the CVRP. Computational results are given for the estimates by using a 95% confidence interval (CI) with 1% accuracy. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A comparative study of mixed-integer linear programming and genetic algorithms for solving binary problems(2018-06-15) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An ant colony optimization method for the capacitated vehicle routing problem with stochastic demands(2017-02-21); Masuchun, RuedeeThe ant colony optimization (ACO) method has been extensively studied recently for solving many combinatorial optimization problems. In this paper, an ant colony optimization method for solving the vehicle routing problem with stochastic demands is presented. The 2-opt local search is employed within the ACO algorithm to improve the qualities of the solutions. Simulation technique is used for the estimate of the expected cost under stochastic demands. Computational results are also reported. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A comparison of Latin hypercube sampling techniques for a supply chain network design problem(2018-08-14); ; Rienkhemaniyom, KanokpornCurrently, supply chain network design becomes more complex. In designing a supply chain network to withstand changing events, it is necessary to consider the uncertainties and risks that cause network disruptions from unexpected events. The current research related to the designing problem considers network disruptions using Monte Carlo Sampling (MCS) or Latin Hypercube Sampling (LHS) techniques. Both have a disadvantage that sample points or disruption locations are not scattered entirely sample space leading to high variation in objective function values. The purpose of this study is to apply a modified LHS or Improved Distributed Hypercube Sampling (IHS) techniques to reduce the variation. The results show that IHS techniques provide smaller standard deviation than that of the LHS technique. In addition, IHS can reduce not only the number of sample size but also and the computational time.