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 11

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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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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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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.
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An ant colony optimization method for the capacitated vehicle routing problem with stochastic demands
(2017-02-21)
Janjarassuk, Udom
;
Masuchun, Ruedee
The 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.
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A comparison of Latin hypercube sampling techniques for a supply chain network design problem
(2018-08-14)
Kaewsuwan, Kantapit
;
Yuangyai, Chumpol
;
Janjarassuk, Udom
;
Rienkhemaniyom, Kanokporn
Currently, 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.
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Locating an ambulance base by using social media: a case study in Bangkok
(2019-12-01)
Nilsang, Suriyaphong
;
Yuangyai, Chumpol
;
Cheng, Chen Yang
;
Janjarassuk, Udom
Response time reduction is a fundamental aspect of ambulance location management. To minimize patient mortality and disability, the response time of emergency medical services is critical. Therefore, real-time management is required to determine the location of an ambulance with a low response time or called or a dynamic allocation system. Dynamic allocation is moving the ambulance bases from low demand areas to high-demand areas that is useful in the operational level. However, the dynamic allocation model for real-time management requires re-allocation of ambulances, resulting in high costs and heavy workloads for the ambulance crews. This paper focuses on a covering model based on social media analysis. The model was used for developing an ambulance reallocation system. In addition to dynamic allocation, the proposed model considers real-time data from a social media application (Twitter) to minimize the response time and cost during emergencies and disasters. Twitter has been used in various ways to communicate during and manage emergencies. In this paper, we formulate the Maximal Covering Location Problem (MCLP), develop a solution procedure based on social media (Twitter application) and show the effect of the approach on the optimal solution by comparing it with the classical approach and also demonstrate our approach on Bangkok EMS.
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An interactive Analytic Hierarchy Process for product recommendation: A case study in studio recording
(2019-01-01)
Puengrusme, Sudatip
;
Janjarassuk, Udom
The Analytic Hierarchy Process (AHP) is a powerful tool for decision making. In this paper, we present an application of AHP used for product recommendation to assist customers in selecting power equipment for studio recording. The application is implemented in Microsoft Excel in an interactive manner where customers are asked to answer questions related to product requirements and customer preferences. The customer information is combined with the predefined judgments from the experts according to a given set of criteria for product selection. The score for each product is then calculated according to the AHP, and the top three products with the highest score are recommended for the customer. The proposed method is tested by the customers and the results are discussed.

Janjarassuk, Udom

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