This page has only limited features, please log in for full access.

Unclaimed
Jiseong Noh
Institute of Knowledge Services, Hanyang University, Erica, Ansan 15588, Korea

Honors and Awards

The user has no records in this section


Career Timeline

The user has no records in this section.


Short Biography

The user biography is not available.
Following
Followers
Co Authors
The list of users this user is following is empty.
Following: 0 users

Feed

Journal article
Published: 15 June 2020 in Sustainability
Reads 0
Downloads 0

Recently, as global warming has become a major issue, many companies have increased their efforts to control carbon emissions in green supply chain management (GSCM) activities. This paper deals with the multi-item replenishment problem in GSCM, from both economic and environmental perspectives. A single buyer orders multiple items from a single supplier, and simultaneously considers carbon cap-and-trade under limited storage capacity and limited budget. In this case we can apply a can-order policy, which is a well-known multi-item replenishment policy. Depending on the market characteristics, we develop two mixed-integer programming (MIP) models based on the can-order policy. The deterministic model considers a monopoly market in which a company fully knows the market information, such that both storage capacity and budget are already determined. In contrast, the fuzzy model considers a competitive or a new market, in which case both of those resources are considered as fuzzy numbers. We performed numerical experiments to validate and assess the efficiency of the developed models. The results of the experiments showed that the proposed can-order policy performed far better than the traditional can-order policy in GSCM. In addition, we verified that the fuzzy model can cope with uncertainties better than the deterministic model in terms of total expected costs.

ACS Style

Jiseong Noh; Jong Soo Kim; Seung-June Hwang. A Multi-Item Replenishment Problem with Carbon Cap-And-Trade under Uncertainty. Sustainability 2020, 12, 4877 .

AMA Style

Jiseong Noh, Jong Soo Kim, Seung-June Hwang. A Multi-Item Replenishment Problem with Carbon Cap-And-Trade under Uncertainty. Sustainability. 2020; 12 (12):4877.

Chicago/Turabian Style

Jiseong Noh; Jong Soo Kim; Seung-June Hwang. 2020. "A Multi-Item Replenishment Problem with Carbon Cap-And-Trade under Uncertainty." Sustainability 12, no. 12: 4877.

Journal article
Published: 11 April 2020 in Mathematics
Reads 0
Downloads 0

Product demand forecasting plays a vital role in supply chain management since it is directly related to the profit of the company. According to companies’ concerns regarding product demand forecasting, many researchers have developed various forecasting models in order to improve accuracy. We propose a hybrid forecasting model called GA-GRU, which combines Genetic Algorithm (GA) with Gated Recurrent Unit (GRU). Because many hyperparameters of GRU affect its performance, we utilize GA that finds five kinds of hyperparameters of GRU including window size, number of neurons in the hidden state, batch size, epoch size, and initial learning rate. To validate the effectiveness of GA-GRU, this paper includes three experiments: comparing GA-GRU with other forecasting models, k-fold cross-validation, and sensitive analysis of the GA parameters. During each experiment, we use root mean square error and mean absolute error for calculating the accuracy of the forecasting models. The result shows that GA-GRU obtains better percent deviations than other forecasting models, suggesting setting the mutation factor of 0.015 and the crossover probability of 0.70. In short, we observe that GA-GRU can optimally set five types of hyperparameters and obtain the highest forecasting accuracy.

ACS Style

Jiseong Noh; Hyun-Ji Park; Jong Soo Kim; Seung-June Hwang. Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management. Mathematics 2020, 8, 565 .

AMA Style

Jiseong Noh, Hyun-Ji Park, Jong Soo Kim, Seung-June Hwang. Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management. Mathematics. 2020; 8 (4):565.

Chicago/Turabian Style

Jiseong Noh; Hyun-Ji Park; Jong Soo Kim; Seung-June Hwang. 2020. "Gated Recurrent Unit with Genetic Algorithm for Product Demand Forecasting in Supply Chain Management." Mathematics 8, no. 4: 565.

Journal article
Published: 01 January 2019 in European J. of Industrial Engineering
Reads 0
Downloads 0

This paper develops a two-echelon supply chain model with a single manufacturer and a single retailer, where the demand is sensitive to advertising and retail price. To resolve the supply chain coordination, three strategies are introduced as retailer leader-manufacturer follower, manufacturer leader-retailer follower, and centralised supply chain. Based on these strategies, this paper suggests an optimal production rate, a production lot size, shortage level, an advertising expenditure, and retail price. Stackelberg approach is employed for solving leader-follower game to obtain the maximum profit of both manufacturer and retailer. The improved algorithm is developed to obtain the numerical results. For testing the model, this paper considers several numerical experiments, graphical illustrations, and sensitivity analysis. The result shows that the strategy of retailer leader-manufacturer follower obtains the highest profit than other strategies. [Received: 23 September 2017; Revised: 28 November 2017; Revised: 29 April 2018; Revised: 19 July 2018; Revised: 21 October 2018; Accepted: 21 October 2018]

ACS Style

Jiseong Noh; Jong Soo Kim; Biswajit Sarkar. Two-echelon supply chain coordination with advertising-driven demand under Stackelberg game policy. European J. of Industrial Engineering 2019, 13, 213 .

AMA Style

Jiseong Noh, Jong Soo Kim, Biswajit Sarkar. Two-echelon supply chain coordination with advertising-driven demand under Stackelberg game policy. European J. of Industrial Engineering. 2019; 13 (2):213.

Chicago/Turabian Style

Jiseong Noh; Jong Soo Kim; Biswajit Sarkar. 2019. "Two-echelon supply chain coordination with advertising-driven demand under Stackelberg game policy." European J. of Industrial Engineering 13, no. 2: 213.

Journal article
Published: 13 December 2018 in Mathematics
Reads 0
Downloads 0

We consider a buyer’s decision problem of sustainable supplier selection and order allocation (SSS & OA) among multiple heterogeneous suppliers who sell multiple types of items. The buyer periodically orders items from chosen suppliers to refill inventory to preset levels. Each supplier is differentiated from others by the types of items supplied, selling price, and order-related costs, such as transportation cost. Each supplier also has a preset requirement for minimum order quantity or minimum purchase amount. In the beginning of each period, the buyer constructs an SSS & OA plan considering various information from both parties. The buyer’s planning problem is formulated as a mathematical model, and an efficient algorithm to solve larger instances of the problem is developed. The algorithm is designed to take advantage of the branch-and-bound method, and the special structure of the model. We perform computer experiments to test the accuracy of the proposed algorithm. The test result confirmed that the algorithm can find a near-optimal solution with only 0.82 percent deviation on average. We also observed that the use of the algorithm can increase solvable problem size by about 2.4 times.

ACS Style

Jong Soo Kim; Eunhee Jeon; Jiseong Noh; Jun Hyeong Park. A Model and an Algorithm for a Large-Scale Sustainable Supplier Selection and Order Allocation Problem. Mathematics 2018, 6, 325 .

AMA Style

Jong Soo Kim, Eunhee Jeon, Jiseong Noh, Jun Hyeong Park. A Model and an Algorithm for a Large-Scale Sustainable Supplier Selection and Order Allocation Problem. Mathematics. 2018; 6 (12):325.

Chicago/Turabian Style

Jong Soo Kim; Eunhee Jeon; Jiseong Noh; Jun Hyeong Park. 2018. "A Model and an Algorithm for a Large-Scale Sustainable Supplier Selection and Order Allocation Problem." Mathematics 6, no. 12: 325.

Journal article
Published: 25 October 2018 in Journal of Cleaner Production
Reads 0
Downloads 0

Concerns about environmentally sustainable supply chain management have increased widely in recent years. As a consequence, supply chain members have cooperated with one another to make efficient contracts, frequently called green supply-chain management contracts. The purpose of this paper is to investigate one such contract between a single manufacturer and multiple retailers with limited resources for several types of products under greenhouse-gas emission regulations. Each retailer orders the products regularly within a limited budget and warehouse capacity. In response to orders, the manufacturer produces products and ships them after inspections. Demand for the products can be either known or have some uncertainty, which can best be represented using fuzzy number demand. To reflect demand properties, this paper introduces two nonlinear integer programming models, a crisp model and a fuzzy model. A genetic algorithm (GA) and hybrid genetic algorithm-pattern search (HGAS) are developed to solve the models. Numerical experiments evaluating the efficiency of the algorithms showed that the HGAS method performed better than the GA. Also observed is that the crisp model's average total costs were lower than those of the fuzzy model. The results as a whole indicate that the models can evaluate the performance of contracts and optimize cooperative green supply chain management.

ACS Style

Jiseong Noh; Jong Soo Kim. Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand. Journal of Cleaner Production 2018, 208, 1421 -1435.

AMA Style

Jiseong Noh, Jong Soo Kim. Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand. Journal of Cleaner Production. 2018; 208 ():1421-1435.

Chicago/Turabian Style

Jiseong Noh; Jong Soo Kim. 2018. "Cooperative green supply chain management with greenhouse gas emissions and fuzzy demand." Journal of Cleaner Production 208, no. : 1421-1435.

Original paper
Published: 05 December 2016 in Operational Research
Reads 0
Downloads 0

This paper analyzes a logistics system involving a supplier who produces and delivers multiple types of items and a buyer who receives and sells to end customers. The buyer controls the inventory of each item by ordering at a preset time interval, which is an integer multiple of a base cycle, to meet the stochastic demands of the end customers. The supplier makes contracts with the buyer that specify that the ordered amount is delivered at the start of each period at a unit price determined by a quantity discount schedule. The contract also specifies that a buyer’s order should exceed a minimum order quantity. To analyze the system, a mathematical model describing activities for replenishing a single type of item is developed from the buyer’s perspective. An efficient method to determine the base cycle length and safety factor that minimizes the buyer’s total cost is then proposed. The single item model is extended to a multiple items joint replenishment model, and algorithms for finding a cost-minimizing base cycle, order interval multipliers, and safety factors are proposed. The result of computational experiments shows that the algorithms can find near-optimal solutions to the problem.

ACS Style

Jiseong Noh; Jong Soo Kim; Biswajit Sarkar. Stochastic joint replenishment problem with quantity discounts and minimum order constraints. Operational Research 2016, 19, 151 -178.

AMA Style

Jiseong Noh, Jong Soo Kim, Biswajit Sarkar. Stochastic joint replenishment problem with quantity discounts and minimum order constraints. Operational Research. 2016; 19 (1):151-178.

Chicago/Turabian Style

Jiseong Noh; Jong Soo Kim; Biswajit Sarkar. 2016. "Stochastic joint replenishment problem with quantity discounts and minimum order constraints." Operational Research 19, no. 1: 151-178.