• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.44-51
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• 2017

In today's competitive environment, supply chain management is a major concern for a company. Two of the key issues in supply chain management are transportation and inventory management. To achieve significant savings, companies should integrate these two issues instead of treating them separately. In this paper we develop a framework for modeling stochastic programming in a supply chain that is subject to demand uncertainty. With reasonable assumptions, two stochastic programming models are presented, respectively, including a single-period and a multi-period situations. Our assumptions allow us to capture the stochastic nature of the problem and translate it into a deterministic model. And then, based on the genetic algorithm and stochastic simulation, a solution method is developed to solve the model. Finally, the computational results are provided to demonstrate the effectiveness of our model and algorithm.

• Journal of applied mathematics & informatics
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• 제19권1_2호
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• pp.397-414
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• 2005

In the existing literature, most of the purchasing models were developed only for retailers problem ignoring the constraint of storage capacity of retailers shop/showroom. In this paper, we have developed a deterministic model of wholesaler-retailers' problem of single product. The storage capacity of wholesaler's warehouse/showroom and retailers' showroom/shop are assumed to be finite. The items are transported from wholesaler's warehouse to retailers' Own Warehouse (OW) in a lot. The customer's demand is assumed to be displayed inventory level dependent. Demands are met from OW and that spaces of OW will immediately be filled by shifting the same amount from the Rented Warehouse (RW) till the RW is empty. The time duration between selling from OW and filling up its space by new ones from RW is negligible. According to relative size of the retailers' existing (own) warehouse capacity and the demand factors, different scenarios are identified. Our objectives are to optimize the cost functions of wholesaler and two retailers separately. To solve this problem, a real coded Genetic Algorithm (GA) with roulette wheel selection/reproduction, whole arithmetic crossover and non-uniform mutation is developed. Finally a numerical example is presented to illustrate the results for different scenarios. To compare the results of GA, Generalised Reduced Gradient Method has been used for the problem. Also, a sensitivity analysis has been performed to study the variations of the optimal average cost with respect to the different parameters.

• 대한산업공학회지
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• 제38권2호
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• pp.116-124
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• 2012

This paper concerns a joint replenishment problem for a single buyer who sells multiple types of items to end-customers. The buyer periodically replenishes the inventory of each item to a preset order-up-to-level to satisfy the end customers' demands, which may be non-stationary. A joint replenishment policy characterized by variable order-up-to-levels is proposed for the buyer who wishes to minimize the expected cost of operating the retail system. The proposed policy starts each period by calculating the expected cost of ordering and not ordering action based on the information of the current inventory position and forecasted demand for the upcoming period. It then takes advantage of an integer programming model to get a cost effective joint replenishment plan. Computer experiment was performed to test efficiency of the proposed policy. When compared with the most efficient policy currently available, our policy showed a considerable cost savings especially for the problems having non-stationary demands.

• 대한산업공학회지
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• 제40권5호
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• pp.477-480
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• 2014

The single-period production inventory control problem under random yield is considered to analyze the impact of the yield characteristics on the firm's profit. We use the stochastic comparison as a main vehicle to compare the profits resulted under different random yields. Commonly used stochastic orderings are addressed with an analysis of their implications on the firm's profit. Moreover, a distribution-free bound on the profit is derived.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2000년도 춘계공동학술대회 논문집
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• pp.61-64
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• 2000

This paper analyzes a dynamic lot-sizing problem, in which the order size of multiple products and a single container type are simultaneously considered. In the problem, each order (product) placed in a period is immediately shipped immediately by containers in the period and the total freight cost is proportional to the number of each container type employed. Also, it is assumed that backlogging is not allowed. The objective of this study is to determine the lot-sizes and the shipping policy that minimizes the total costs, which consist of ordering costs, inventory holding costs, and freight costs. Because this problem is NP-hard, we propose a heuristic algorithm with an adjustment mechanism, based on the optimal solution properties. The computational results from a set of simulation experiment are also presented.

• 제어로봇시스템학회논문지
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• 제18권9호
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• pp.854-862
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• 2012

The optimal design of batch-storage network by using periodic square wave model provides analytical lot sizing equations for a complex supply chain network characterized as multi-supplier, multi-product, multi-stage, non-serial, multi-customer, cyclic system including recycling and/or remanufacturing. The network structure includes multiple currency flows as well as material flows. The processes are represented by multiple feedstock/product materials with fixed composition which are very suitable for production processes. In this study, transportation processes that carry multiple materials with unknown composition are added and the time frame is changed from single period into multiple periods in order to represent nonperiodic parameter variations. The objective function of the optimization involves minimizing the opportunity costs of annualized capital investments and currency/material inventories minus the benefit to stockholders in the numeraire currency. The expressions for the Kuhn-Tucker conditions of the optimization problem are reduced to a multiperiod subproblem for average flow rates and analytical lot-sizing equations. The multiperiod lot sizing equations are different from single period ones. The effects of corporate income taxes, interest rates and exchange rates are incorporated.

• 산업경영시스템학회지
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• 제35권3호
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• pp.204-209
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• 2012

본 논문은 전국적인 소매업체의 각 지점별 고객 수요가 불확실한 상황에서 고객 서비스 목표 수준을 충족하는 최적재고 수준을 결정하는 문제에 대해 연구하였다. 이를 위해 전국에 분포한 지점에서 물품을 판매하는 베스트바이, 월마트, 혹은 시어스와 같은 전국적인 소매업체 관점에서 사용할 수 있는 핵심 관리 지표(KPI)로서 ISR(In-Stock Ratio)를 정의하였으며, 전국적인 소매업체가 평균 ISR로 정의되는 고객 서비스 목표 수준을 충족하면서 각 지점 보유 재고의 총합을 최소화할 수 있는 최적화 모델을 수립하였다. 본 논문은 해당 모델에 항상 최적해가 존재함을 증명하고 해당 최적해를 Karush-Kuhn-Tucker 조건을 사용하여 고객 수요의 확률분포의 형태에 상관없이 일반화된 형태로 표현하였다. 또한 본 논문은 고객 수요가 정규분포와 같은 특정 확률분포를 따르는 경우에 대해 연구하였으며, 이 경우에 대한 최적 재고수준을 나타내는 식을 도출하였다. 마지막으로 본 논문에서는 상기 기술된 상황에 대한 수리적인 예제를 통하여 최적재고 수준과 확률분포 파라미터들간의 관계를 분석하였다.

• 대한산업공학회지
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• 제24권1호
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• pp.157-165
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• 1998

This paper considers the single-product production and transportation problem with discrete time, dynamic demand and finite time horizon, an extension of classical dynamic lot-sizing model. In the model, multiple freight container types are allowed as the transportation mode and each order (product) placed in a period is shipped immediately by containers in the period. Moreover, each container has type-dependent carrying capacity restriction and at most one container type is allowed in each shipping period. The unit freight cost for each container type depends on the size of its carrying capacity. The total freight cost is proportional to the number of each container type employed. Such a freight cost is considered as another set-up cost. Also, it is assumed in the model that production and inventory cost functions are dynamically concave and backlogging is not allowed. The objective of this study is to determine the optimal production policy and the optimal transportation policy simultaneously that minimizes the total system cost (including production cost, inventory holding cost, and freight cost) to satisfy dynamic demands over a finite time horizon. In the analysis, the optimal solution properties are characterized, based on which a dynamic programming algorithm is derived. The solution algorithm is then illustrated with a numerical example.

• Industrial Engineering and Management Systems
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• 제11권3호
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• pp.288-298
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• 2012

Lot sizing and shipment scheduling are two interrelated decisions made by a manufacturing plant and a third-party logistics distribution center. This paper analyzes a dynamic inbound ordering problem and shipment problem with a freight container cost, in which the order size of multiple products and single container type are simultaneously considered. In the problem, each ordered product placed in a period is immediately shipped by some freight containers in the period, and the total freight cost is proportional to the number of containers employed. It is assumed that the load size of each product is equal and backlogging is not allowed. The objective of this study is to simultaneously determine the lot-sizes and the shipment schedule that minimize the total costs, which consist of production cost, inventory holding cost, and freight cost. Because the problem is NP-hard, we propose three meta-heuristic algorithms: a simulated annealing algorithm, a genetic algorithm, and a new population-based evolutionary meta-heuristic called self-evolution algorithm. The performance of the meta-heuristic algorithms is compared with a local search heuristic proposed by the previous paper in terms of the average deviation from the optimal solution in small size problems and the average deviation from the best one among the replications of the meta-heuristic algorithms in large size problems.

• 대한산업공학회지
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• 제32권1호
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• pp.9-17
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• 2006

This paper deals with a discrete simulation optimization method for designing a complex probabilistic discrete event simulation. The developed algorithm uses the configuration algorithm that can change decision variables and the stopping algorithm that can end simulation in order to satisfy the given objective value during single run. It tries to estimate an auto-regressive model for evaluating correctly the objective function obtained by a small amount of output data. We apply the proposed algorithm to M/M/s model, (s, S) inventory model, and known-function problem. The proposed algorithm can't always guarantee the optimal solution but the method gives an approximate feasible solution in a relatively short time period. We, therefore, show the proposed algorithm can be used as an initial feasible solution of existing optimization methods that need multiple simulation run to search an optimal solution.