• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.20 no.42
• /
• pp.21-30
• /
• 1997

This research fundamentally deals with an analysis of service level for a multi-level inventory distribution system which is consisted of a central distribution center and several branches being supplied stocks from the distribution center, Under continuous review policy, the distribution center places an order for planned order quantity to an outside supplier, and the order quantity is received after a certain lead time. Also, each branch places an order for particular quantity to its distribution center, and receives the order quantity after a lead time. In most practical distribution environment, demands and lead times are generally not fixed or constant, but variable. And these variabilities make the analysis more complicated. Thus, the main objective of this research is to suggest a method to compute the service level at each depot, that is, the distribution center and each branch with variable demands and variable lead times. Further, the model will give an idea to keep the proper level of safety stocks that can attain effective or expected service level for each depot.

• Journal of the Korea Safety Management & Science
• /
• v.3 no.3
• /
• pp.65-75
• /
• 2001

This research fundamentally deals with an analysis of service level for a multi-level inventory distribution system which is consisted of a central distribution center and several branches being supplied stocks from the distribution center, Under continuous review policy, the distribution center places an order for planned order quantity to an outside supplier, and the order quantity is received after a certain lead time. Also, each branch places an order for particular quantity to its distribution center, and receives the order quantity after a lead time. In most practical distribution environment, demands and lead times are generally not fixed or constant, but variable. And these variabilities make the analysis more complicated. Thus, the main objective of this research is to suggest a method to compute the service level at each depot, that is, the distribution center and each branch with variable demands and variable lead times. Further, the model will give an idea to keep the proper level of safety stocks that can attain effective or expected service level for each depot.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.59 no.3
• /
• pp.97-110
• /
• 2017

This study investigates the efficiencies of machine learning models, including artificial neural network (ANN), generalized regression neural network (GRNN), adaptive neuro-fuzzy inference system (ANFIS) and random forest (RF), for reservoir water level forecasting in the Chungju Dam, South Korea. The models' efficiencies are assessed based on model efficiency indices and graphical comparison. The forecasting results of the models are dependent on lead times and the combination of input variables. For lead time t = 1 day, ANFIS1 and ANN6 models yield superior forecasting results to RF6 and GRNN6 models. For lead time t = 5 days, ANN1 and RF6 models produce better forecasting results than ANFIS1 and GRNN3 models. For lead time t = 10 days, ANN3 and RF1 models perform better than ANFIS3 and GRNN3 models. It is found that ANN model yields the best performance for all lead times, in terms of model efficiency and graphical comparison. These results indicate that the optimal combination of input variables and forecasting models depending on lead times should be applied in reservoir water level forecasting, instead of the single combination of input variables and forecasting models for all lead times.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.10 no.16
• /
• pp.143-147
• /
• 1987

Safety stocks constitute one of the major means of dealing with the uncertainties associated with variation in demand and lead time. Adeguate safety facilitate production activities and help to assure customers if good service on the other hand, carrying safety storks ties up working capital on goods that sit idle. The major problem of safety stocks management thus of consists of trying to achieve an optimal balance between the other carrying cost and the costs of stock shortage. Therefore, this study aims to find safety stock level of the fixed reorder quantity system and the fixed reorder cycle system of minimizing total cost when both demand and lead time are variable. (The distribution of demand and lead time is a mere assumption that follows the normal distribution) The results can be summarized as follows. i) Safety factor on the safety stock is determined by carrying cost and the costs of stock shortage: An optimal safety stick=the costs of stork shortage($C_s$) (the carrying cost($C_h$)+the costs of stock storage($C_s$). ii) The safety stock level of the fixed reorder quantity system is ($a{\;}_p\sqrt{L}{\sigma}$) under uncertainties. iii) The safety stock level of the fixed reorder cycle system is ($a{\;}_p\sqrt{R+L{\sigma}}$) under uncertain demand and constant lead time. ($a{\;}_p\sqrt{L{\sigma}_d{\;^2+{\mu}^2L{\sigma}^2}$) under demand and lead time uncertainties.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.18 no.36
• /
• pp.93-103
• /
• 1995

This paper des with an economic production quantity model with partial backorders for the situation in which production lead time is deterministic and demand during lead time follows a continuous distribution. In the model, an objective function is formulated In minimize an average annual inventory cost. And then the procedure of iterative solution method for the model is developed to find both production reorder point and production quantity. Finally, sensitivity analysis for various partial backorder ratios and standard deviations of demand during production lead time are presented.

• Architectural research
• /
• v.11 no.2
• /
• pp.43-49
• /
• 2009

Since the late 1980s, the schedule technique applied to the construction industry around the world has rapidly changed from the traditional ADM (Arrow Diagramming Method) to the PDM (Precedence Diagramming Method) technique. The main reason for this change is to overcome the limits and inconveniences of the traditional ADM technique. The time-cost trade-off is one of the core scheduling techniques to establish the best optimized combination plan in terms of a relationship between the cost and schedule. However, most of the schedule-related textbooks and research papers have discussed and proposed applications of a time-cost trade-off technique based only on the Finish to Start relationship. Therefore, there are almost no consideration and discussion of problems or restrictions that emerge when the time-cost trade-off technique is applied to the PDM network that has overlapping relationships. This paper proposes the lead-time adjustment method as a methodology for overcoming some restrictions that are encountered when the time-cost trade-off technique is applied to the overlapping relationships of the PDM network.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.28 no.2
• /
• pp.146-151
• /
• 2005

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application. We here assume that customer order sizes are described by the Poisson distribution with the random parameter following a gamma distribution. This implies in turn that the negative binomial distribution is obtained by mixing the mean of the Poisson distribution with a gamma distribution. The purpose of this paper is to give an interpretation of the negative binomial demand process by considering the sources of variability in the unknown Poisson parameter. Such variability comes from the unknown demand rate and the unknown lead time interval.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2003.11a
• /
• pp.342-345
• /
• 2003

Due to the rapid development of manufacturing and information technology, traditional supply chain scheme has been changed dramatically Most companies have been forced to relocate or redesign their logistics network in different countries. A supply chain partnership is a relationship formed between two independent members in supply chain through information sharing to achieve specific objectives and benefits in terms of reductions in total costs and inventories. This study illustrates the benefits of supply chain partnerships based on information sharing and lead-time patterns. We consider three level of information sharing: (1) immediate order information; (2) demand information; (3) inventory information. Given a fixed total lead-time, how lead-time distribution will affect the bullwhip effect and inventory cost under information sharing strategies. The results can help improving supply chain performance and selecting suitable direction for the re-configuration of supply chain network.

• The Journal of Information Systems
• /
• v.10 no.2
• /
• pp.129-150
• /
• 2001

In this study, we model a decentralized supply chain system which is managed by four types of centers, sequentially located: Retailer, Wholesaler, Distributor, and Factory Each center contributes to enhancing the performance of the supply chain system individually with its own local inventory information. Through experiments which are performed with a programmed simulation (like the MIT beer game), we investigate how the information lead time improvement in each center affects the whole system. And we show that the impact of the lead time improvement in the downstream, like retailers, affects more to the system than the one in the upstream, like factories, in a cost-effective way. Moreover, by using information lead time for each center, we analyze how much the extent of the improvement affects the whole system, especially for the total cost and the order level.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.28 no.4
• /
• pp.79-84
• /
• 2005

Some distributions have been used for diagnosing the lead time demand distribution in inventory system. In this paper, we describe the negative binomial distribution as a suitable demand distribution for a specific retail inventory management application. We here assume that customer order sizes are described by the Poisson distribution with the random parameter following a gamma distribution. This implies in turn that the negative binomial distribution is obtained by mixing the mean of the Poisson distribution with a gamma distribution. The purpose of this paper is to give an interpretation of the negative binomial demand process by considering the sources of variability in the unknown Poisson parameter. Such variability comes from the unknown demand rate and the unknown lead time interval.