• Journal of Korean Institute of Industrial Engineers
• /
• v.31 no.2
• /
• pp.120-126
• /
• 2005

Until now, the traditional production models and logistics have developed a broader strategic approach called supply chain. However, there are some obstacles to apply industry practice because of unrealistic assumptions. The most serious of them is that they assume the lead times are integer multiples of the planning time grid. This assumption makes it difficult to express the processing and transportation lags correctly. Thus, in this paper, we propose a new methodology for the integrated production/distribution model having non-integer time lags using the concept of dynamic production function. In case that the time lags are integer or non-integer, the dynamic production function reflects well the situation under given environments. Experiments show that the proposed model can express the real system more accurately than the prior model can.

• IE interfaces
• /
• v.12 no.2
• /
• pp.222-236
• /
• 1999

This paper presents a design and implementation of an intelligent-integrated production-logistics systems. The situation considered here is that there are multiple manufacturing plants and multiple distribution centers. Effective distribution resource and production planning are required to reduce inventory cost and to avoid inventory shortage. We propose an intelligent forecasting scheme of each distribution centers, adaptive inventory replenishment planning, distribution resource planning, and integrated production planning system. In forecasting a huge number of on-line model identification is performed using neural network approximation capability. An efficient adaptive replenishment planning and distribution resource planning are also presented in connection with forecasting scheme. An appropriate production is also requested based on production lead-time and the results of distribution planning. Experimental simulations are presented to verify the proposed approach using real data.

• Proceedings of the Korea Society for Simulation Conference
• /
• 2000.11a
• /
• pp.165-173
• /
• 2000

Analytic models have been developed to solve integrated production-distribution problems in supply chain management (SCM). As one of major constraints in analytic models, capacity, which is the total operation time in this paper has mostly been known or disregarded assuming infinite capacity. Also, as major factors, machine processing time to fabricate or assemble a part or product at a certain machine center in production system and vehicle processing time to deliver a product to a customer by a certain vehicle in distribution system have been fixed and regarded as a static factor, But in the real systems significant differences exit between capacity and the required time to achieve the production-distribution plan and between processing time and consumed time to process a part or product. In this paper, capacity and processing times in the analytic model are considered as dynamic factors and adjusted by the results from independently developed simulation model, which includes general production-distribution characteristics. Through experiments, we obtain the more realistic solutions reflecting stochastic natures by performing the iterative analytic-simulation procedure.

• Industrial Engineering and Management Systems
• /
• v.6 no.1
• /
• pp.1-10
• /
• 2007

The objective of this study is to develop an optimal joint cost from the perspectives of both the manufacturer and the retailer. The integrated production-inventory model with Weibull distribution deteriorating items is assumed to have a constant demand rate. A limited retailer storage space and multiple delivery per order are considered in this model. A numerical example including the sensitivity analysis is given to validate the results of the production-inventory model.

• Journal of the Korea Safety Management & Science
• /
• v.22 no.4
• /
• pp.45-52
• /
• 2020

Recently, a multi facility, multi product and multi period industrial problem has been widely investigated in Supply Chain Network(SCN). One of keys issues in the current SCN research area involves minimizing both production and distribution costs. This study deals with finding an optimal solution for minimizing the total cost of production and distribution problems in supply chain network. First, we presented an integrated mathematical model that satisfies the minimum cost in the supply chain. To solve the presented mathematical model, we used a genetic algorithm with an excellent searching ability for complicated solution space. To represent the given model effectively, the matrix based real-number coding schema is used. The difference rate of the objective function value for the termination condition is applied. Computational experimental results show that the real size problems we encountered can be solved within a reasonable time.

• Journal of the Korea Safety Management & Science
• /
• v.22 no.4
• /
• pp.65-74
• /
• 2020

Many of companies have made significant improvements for globalization and competitive business environment The supply chain management has received many attentions in the area of that business environment. The purpose of this study is to generate realistic production and distribution planning in the supply chain network. The planning model determines the best schedule using operation sequences and routing to deliver. To solve the problem a hybrid approach involving a genetic algorithm (GA) and computer simulation is proposed. This proposed approach is for: (1) selecting the best machine for each operation, (2) deciding the sequence of operation to product and route to deliver, and (3) minimizing the completion time for each order. This study developed mathematical model for production, distribution, production-distribution and proposed GA-Simulation solution procedure. The results of computational experiments for a simple example of the supply chain network are given and discussed to validate the proposed approach. It has been shown that the hybrid approach is powerful for complex production and distribution planning in the manufacturing supply chain network. The proposed approach can be used to generate realistic production and distribution planning considering stochastic natures in the actual supply chain and support decision making for companies.

• IE interfaces
• /
• v.13 no.3
• /
• pp.320-327
• /
• 2000

In this research report, we consider multi stage supply chain optimization modeling techniques, and propose a new integrated model. The stages that are normally associated with a supply chain include procurement, production and distribution. In general, there are two distinct set of approaches which manage whole supply chain. One approach is the plant decision and subsequently distribution and inventory decision while the second approach is to address all decisions simultaneously through the integrated model. First, we present a survey of existing models that are treated by independently. And then, we propose an integrated model that is in a moderate size and easy to implement in practice. Finally, we address possible solution methodologies and present some computational test results using CPLEX to see the computational burden from which new algorithmic insight might be come up.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
• /
• v.15 no.3
• /
• pp.124-132
• /
• 2010

In spite of the global importance of primary production of phytoplankton, some primary production data in Korean coastal waters still need to be better processed. The daily rates of water column primary production is generally estimated by integrating the primary production per unit volume over time and depth, but efforts for time integration algorithm have been conducted insufficiently. In this study a mathematical equation evaluating daily primary production integrated over time of a day is proposed and the effectiveness of the model is tested on Saemangeum Lake. The daily primary productions computed with the proposed equation were nearly the same with the results numerically integrated by substituting solar irradiance data. It was suggested that better estimation of primary production would be obtained by using monthly or weekly means of solar irradiance rather than more variable daily data. Because of the vertically heterogenous distribution of phytoplankton, it's hard to integrate the equation over depth to give the daily rates of primary production per unit area of water surface. However, the problem would be solved if, after the vertical distribution of phytoplankton was classified into several patterns and reduced to mathematical formula, every composite function of time integrated equation and chlorophyll distribution equation was integrated successfully.

• Journal of Korean Society of Transportation
• /
• v.29 no.2
• /
• pp.81-89
• /
• 2011

Trip distribution is the second step of the conventional travel demand estimation process, which connects trips between origin and destination, while transport mode choice is the third step of the process, which chooses transport mode among several modes serving for each origin-destination pair. Although these two steps have closely connected, they have been estimated independently each other in the estimation procedure. This paper presents an integrated model combining trip distribution and transport mode choice, and also presents its solution algorithm. The model integrates gravity model adopted for the trip distribution process with logit model employed for the mode choice process. The model would be expected to cope with the inconsistency issue existing in the conventional travel demand estimation procedure. This paper also presents an equilibrium condition, sensitivity of the model, and compares them with those of existing models.

• Proceedings of the Korean Society for Technology of Plasticity Conference
• /
• 2007.10a
• /
• pp.96-99
• /
• 2007

The microstructure with fine and uniform AGS(austenite grain size) along thickness direction over no recrystallization temperature is strongly required for production of the high strength steels. The previous AGS prediction only based on the average strain improves to find the rolling conditions for accomplishment of the fine grain, but cannot find those for uniform grain. In this paper, an integrated mathematical model for prediction of the strain distribution along thickness direction is developed by carrying out finite element simulation for a series of rolling conditions. Also, the AGS distribution after rough rolling is predicted by applying the proposed model with AGS prediction model.