Janjarassuk, Udom

Preferred name

Janjarassuk, Udom

Alternative Name

Janjarassuk, Udom

Janjarassuk, U.

Main Affiliation

School of Engineering

udom.ja@kmitl.ac.th

Now showing 1 - 10 of 16

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Hybrid Ant Colony Optimization Method for the Traveling Salesman Problem
(2024-01-01)
Janjarassuk, Udom
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.
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Product recommendation based on genetic algorithm
(2019-07-01)
Janjarassuk, Udom
;
Puengrusme, Sudatip
In 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.
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A Hybrid Greedy Algorithm for the Capacitated Vehicle Routing Problem
(2025-01-01)
Janjarassuk, Udom
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.
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Two-stage stochastic program for supply chain network design under facility disruptions
(2021-03-01)
Kungwalsong, Kanokporn
;
Cheng, Chen Yang
;
Yuangyai, Chumpol
;
Janjarassuk, Udom
A supply chain disruption is an unanticipated event that disrupts the flow of materials in a supply chain. Any given supply chain disruption could have a significant negative impact on the entire supply chain. Supply chain network designs usually consider two stage of decision process in a business environment. The first stage deals with strategic levels, such as to determine facility locations and their capacity, while the second stage considers in a tactical level, such as production quantity, delivery routing. Each stage’s decision could affect the other stage’s result, and it could not be determined individual. However, supply chain network designs often fail to account for supply chain disruptions. In this paper, this paper proposed a two-stage stochastic programming model for a four-echelon global supply chain network design problem considering possible disruptions at facilities. A modified simulated annealing (SA) algorithm is developed to determine the strategic decision at the first stage. The comparison of traditional supply chain network decision framework shows that under disruption, the stochastic solutions outperform the traditional one. This study demonstrates the managerial viability of the proposed model in designing a supply chain network in which disruptive events are proactively accounted for.
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Image analysis and high dimensional control chart for inspection of sausage color homogeneity and uniformity
(2016-09-01)
Kaewsuwan, Piraya
;
Yuangyai, Chumpol
;
Cheng, Chen Yang
;
Janjarassuk, Udom
Sausage color usually influences consumers' selection due to the perceptions of quality. Extensive studies have applied image processing to capture the characteristics of food products according to the high-dimensional nature of the resultant images. However, the color homogeneity (i. e. "within pack" variation) and uniformity (i. e. "between-pack" variation) have rarely been studied. Therefore, this paper proposes a new framework to detect both variations using images. In addition, a new approach has been developed to deal with high-dimension data involving colorimetric characteristics, namely L∗, a∗, b∗, hue (h) and chroma (C∗). These high-dimensional data are transformed to represent color homogeneity and uniformity. Hotelling T2 chart is used to detect color abnormalities. Our approach indicates that the out-of-control items can be identified with the control chart signals. Nonetheless, the out-of-control signals alone are inadequate for determination of the possible causes. Then, the proposed analysis framework was subsequently applied to identify possible causes that contributed to the process deviations. Furthermore, prior to the experiments with sausages, the image inspection device was tested for gauge repeatability and reproducibility.
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Ant Colony Optimization for Solving Electric Vehicle Traveling Salesman Problems
(2026-01-01)
Janjarassuk, Udom
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.
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A Mathematical Programming Approach for Product Selection with Multiple Criteria
(2019-10-25)
Janjarassuk, U.
;
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.
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A simulated annealing algorithm to the stochastic network interdiction problem
(2016-01-18)
Janjarassuk, U.
;
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.
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Cost and reliability analyses for the vehicle routing problem
(2017-11-03)
Janjarassuk, Udom
;
Masuchun, Ruedee
The 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.
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A comparative study of mixed-integer linear programming and genetic algorithms for solving binary problems
(2018-06-15)
Kuendee, Punyisa
;
Janjarassuk, Udom
This paper aims to investigate the capability of mixed-integer linear programming (MILP) method and genetic algorithm (GA) to solve binary problem (BP). A comparative study on the MILP method and GA with default and tuned setting to find out an optimal solution is presented. The mixed-integer programming library (MIPLIB 2010) is used to test and evaluate algorithms. The evaluation is shown in quality of the solution and the execution time of computation. The results show that GA is superior to MILP in execution time with inconsistent results. However, MILP is superior to GA in quality of the solution with more stable results.

Janjarassuk, Udom

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