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
16 results
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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, Two-stage stochastic program for supply chain network design under facility disruptions(2021-03-01); 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Image analysis and high dimensional control chart for inspection of sausage color homogeneity and uniformity(2016-09-01); - 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 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. - Some of the metrics are blocked by yourconsent settings
Item type:Publication, LPG Leakage Risk Predictions from an IoT-Based Detection System Using Machine Learning(2023-01-01); LPG has the potential to ignite and explode if it spills since it is a flammable gas. Explosions and flames caused by LPG leaks can damage or kill people who are working in the hazardous area. The industrial sector currently lacks efficient warning systems to identify and anticipate leaks, necessitating efficient equipment to identify and monitor gas leaks. This work aims to investigate and identify a device designed to detect and keep track of LPG leaks by utilizing straightforward yet efficient IoT technologies. The experimental and carefully vetted data are applied to build an Artificial Neural Network (ANN) model for predicting the risk of gas leaks. Gas leaks in the workplace are monitored, alerted to, and controlled by using gas detection systems based on IoT technology. The dataset is then submitted to factor analysis for feature selection, which made use of the model's expertise from its examination of the information gathered from the detecting device in the Cloud system. Several measures were utilized to evaluate the model, including Accuracy, Precision, Recall, F1-Score and Area Under the Curve (AUC). The investigation led to clustering the Risk Ranking Number into three levels, which were then utilized in conjunction with the Risk Matrix to evaluate risk. The net processing time was 1.34 minutes, and the forecast accuracy was 96.05%. This study will assist in improving the model that establishes the alarm system's alert level.