• 산업공학
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• 제17권spc호
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• pp.90-96
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• 2004

In this paper, we consider the joint replenishment problem of a one-warehouse, n-retailer system. We introduce a joint replenishment and delivery strategy of a warehouse and develop a mathematical model based on the proposed strategy. Two efficient algorithms are presented and compared using numerical examples. The proposed strategy is compared with the common cycle strategy for 1,600 randomly generated problems, and has been proven to be superior to the common cycle strategy.

• 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.

• Industrial Engineering and Management Systems
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• 제14권1호
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• pp.1-10
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• 2015

In this paper, we consider multi-item inventory management. When managing a multi-item inventory, we coordinate replenishment orders of items supplied by the same supplier. The associated problem is called the joint replenishment problem (JRP). One often-used approach to the JRP is to apply a can-order policy. Under a can-order policy, some items are re-ordered when their inventory level drops to or below their re-order level, and any other item with an inventory level at or below its can-order level can be included in this order. In the present paper, we propose a method for finding the optimal parameter of a can-order policy, the can-order level, for each item in a lost-sales model. The main objectives in our model are minimizing the number of ordering, inventory, and shortage (i.e., lost-sales) respectively, compared with the conventional JRP, in which the objective is to minimize total cost. In order to solve this multi-objective optimization problem, we apply a genetic algorithm. In a numerical experiment using actual shipment data, we simulate the proposed model and compare the results with those of other methods.

• 한국경영과학회:학술대회논문집
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• 대한산업공학회/한국경영과학회 2004년도 춘계공동학술대회 논문집
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• pp.429-432
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• 2004

다수의 품목을 개별적으로 주문하기 보다는 묶어서 한꺼번에 주문하는 경우 수송비용과 주문비용을 줄일 수 있으며, 같은 공급자에게서 구매하는 경우에는 가격할인까지 기대할 수 있다. 이와 같이 하나의 공급자들 통해 다수의 품목을 구매하는 경우에 대한 최적의 구매전략을 다룬 문제가 다품목 일괄 주문모형으로 Joint Replenishment Problem(JRP)으로 잘 알려져 있으며 지난 수십 여년간 많은 연구가 이루어진 생산재고관리 영역의 문제들과는 달리 일반적인 JRP를 해결하기 위한 발견적 기법들에 대한 연구는 수없이 많은 반면 JRP를 현실적으로 확장한 연구는 국내외적으로 전무한 형편이다. 본 연구에서는 일반적인 JRP를 제약식을 고려한 문제로 확장하여 유전자 알고리즘을 이용한 해법을 개발하고 이 문제의 확장 가능성에 대해 소개하고자 한다.

• 경영과학
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• 제29권3호
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• pp.107-120
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• 2012

In this paper we propose an effective genetic algorithm for solving the integrated inventory and routing problem of supply chain composed of multi-warehouses and multi-retailers. Unlike extant studies dealing with integrated inventory and routing problem of supply chain, our model incorporates more realistic aspect such as positive inventory at the multi-warehouses under the assumption of inventory policy of power of two-replenishment-cycle. The objective is to determine replenishment intervals for the retailers and warehouses as well as the vehicles routes so that the total cost of delivery and inventory cost is minimized. A notable feature of our algorithm is that the procedure for evaluating the fitness of objective function has the computational complexity closing to linear function. Computational results show effectiveness of our algorithm.

• 한국경영과학회지
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• 제14권2호
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• pp.75-86
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• 1989

Joint stocking and preventive age replacement policy is considered for non-repairable items assuming instantaneous replenishment. A recursive relationship among the optimal preventive replacement ages is obtained, which shows that the preventive replacement ages in a replenishment cycle form an increasing sequence due to the inventory carrying cost. Using this relationship, a procedure is given for determining how many units to purchase on each order and when to replace each unit after it has begun operating so as to minimize the total cost per unit time over an infinite time span. The problem can be simplified if equal preventive replacement ages are assumed, and the solution is very close to that of the original unconstrained problem.

• 한국경영과학회지
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• 제36권3호
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• pp.91-105
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• 2011

In this paper, we analyze a logistic system involving a supplier who produces and delivers multiple types of items and a buyer who receives and sells the products to end customers. The buyer controls the inventory level by replenishing each product item up to a given order-up-to-level to cope with stochastic demand of end customers. In response to the buyer's order, the supplier produces or outsources the ordered item and delivers them at the start of each period. For the system described above, a mathematical model for a single type of item was developed from the buyer's perspective. Based on the model, an efficient method to find the cycle length and safety factor which correspond to a local minimum solution is proposed. This single product model was extended to cover a multiple item situation. From the model, algorithms to decide the base cycle length and order interval of each item were proposed. The results of the computational experiment show that the algorithms were able to determine the global optimum solution for all tested cases within a reasonable amount of time.

• 품질경영학회지
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• 제40권1호
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• pp.88-91
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• 2012

Joint optimization of preventive age replacement and inventory policy is considered in this paper. There are three decision variables in the problem: (i) preventive replacement age of the operating unit, (ii) order quantity per order and (iii) reorder point for spare replenishment. Preventive replacement age and order quantity are jointly determined so as to minimize the expected cost rate, and then the reorder point for meeting a desired service level is found. A numerical example is included to explain the joint optimization model.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제13권7호
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• pp.3412-3432
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• 2019

Recent advances in radio frequency (RF) power transfer provide a promising technology to power sensor nodes. Adoption of mobile chargers to replenish the nodes' energy has recently attracted a lot of attention and the mobility assisted energy replenishment provides predictable and sustained power service. In this paper, we study the joint optimization of mobile charging and data gathering in sensor networks. A wireless multi-functional vehicle (WMV) is employed and periodically moves along specified trajectories, charge the sensors and gather the sensed data via one-hop communication. The objective of this paper is to maximize the uplink throughput by optimally allocating the time for the downlink wireless energy transfer by the WMV and the uplink transmissions of different sensors. We consider two scenarios where the WMV moves in a straight line and around a circle. By time discretization, the optimization problem is formulated as a 0-1 programming problem. We obtain the upper and lower bounds of the problem by converting the original 0-1 programming problem into a linear programming problem and then obtain the optimal solution by using branch and bound algorithm. We further prove that the network throughput is independent of the WMV's velocity under certain conditions. Performance of our proposed algorithm is evaluated through extensive simulations. The results validate the correctness of our proposed theorems and demonstrate that our algorithm outperforms two baseline algorithms in achieved throughput under different settings.