• Industrial Engineering and Management Systems
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• v.12 no.3
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• pp.172-180
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• 2013

We herein consider a stochastic multi-item inventory management problem in which a warehouse sells multiple items with stochastic demand and periodic replenishment from a supplier. Inventory management requires the timing and amounts of orders to be determined. For inventory replenishment, trucks of finite capacity are available. Most inventory management models consider either a single item or assume that multiple items are ordered independently, and whether there is sufficient space in trucks. The order cost is commonly calculated based on the number of carriers and the usage fees of carriers. In this situation, we can reduce future shipments by supplementing items to an order, even if the item is not scheduled to be ordered. On the other hand, we can reduce the average number of items in storage by reducing the order volume and at the risk of running out of stock. The primary variables of interest in the present research are the average number of items in storage, the stock-out volume, and the number of carriers used. We formulate this problem as a multi-objective optimization problem. In a numerical experiment based on actual shipment data, we consider the item shipping characteristics and simulate the warehouse replenishing items coordinately. The results of the simulation indicate that applying a conventional ordering policy individually will not provide effective inventory management.

• Industrial Engineering and Management Systems
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• v.14 no.1
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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.

• Journal of Korean Institute of Industrial Engineers
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• v.34 no.4
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• pp.425-432
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• 2008

While the Vendor-Managed Inventory(VMI) is a convenient inventory replenishment policy for the customer company, the supplier usually bears the burden of higher inventory and urgent shipments to avoid shortage. Recently some manufacturers begin to fix the production schedule for the next few days (such as three days). Utilizing that information can improve the efficiency of the VMI. In this study, we present a myopic optimization model using a mixed inter programming; and a heuristics algorithm. We compare the performance of the two proposed methods with the existing (s, S) reorder policy. We consider the total cost as the sum of transportation cost and inventory cost at the customer's site. Numerical tests indicate that the two proposed methods significantly reduce the total cost over the (s, S) policy.

• Journal of Korean Society for Quality Management
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• v.40 no.1
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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.

• Journal of the Korean Operations Research and Management Science Society
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• v.36 no.3
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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.

• The Journal of Society for e-Business Studies
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• v.26 no.1
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• pp.67-78
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• 2021

The number of domestic e-commerce transactions has been breaking its own record by an annual average growth rate of over 20% based on volume for the past 5 years. Due to the rapid increase in e-commerce market, retail companies that have difficulty meeting consumers in person are in fierce competition to take the lead in the last mile service, which is the only point of contact with customers. Especially in the delivery area, where competition is most intense, the role of the fulfillment center is very important for service differentiation. It must be capable of fast product preparation ordered by consumers in accordance with the delivery service level. This study focuses on the order picking system for rapid order processing in the fulfillment center as an alternative for companies to gain competitive advantage in the e-commerce market. A mixed integer programming model was developed and implemented to optimize the stock replenishment in order picking facilities. The effectiveness was scientifically and objectively verified by simulation using the actual operation process and data.

• Journal of the Korea Society for Simulation
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• v.22 no.2
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• pp.41-51
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• 2013

Tendency for small changes in end-consumer demand to be amplified as one moves further up the supply chain is known as bullwhip effect (BE). BE is usually defined as variance(order)/variance(demand). Since such distorted information throughout the supply chain can lead to inefficiencies, many studies to reduce variance(order) have been performed. However, in this study, we show that minimization of BE may increase inefficiencies of the supply chain. We introduce a new objective function to increase system efficiency using smoothed ordering policies. Simulation optimization is utilized to find optimal smoothed ordering policies.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.38 no.4
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• pp.98-108
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• 2015

This paper introduces the existence of purchase dependence that was identified during the analysis of inventory operations practice at a sales agency of dealing with spare parts for ship engines and generators. Purchase dependence is an important factor in designing an inventory replenishment policy. However, it has remained mostly unaddressed. Purchase dependence is different from demand dependence. Purchase dependence deals with the purchase behavior of customers, whereas demand dependence deals with the relationship between item-demands. In order to deal with purchase dependence in inventory operations practice, this paper proposes (Q, r) models with the consideration of purchase dependence. Through a computer simulation experiment, this paper compares performance of the proposed (Q, r) models to that of a (Q, r) model ignoring purchase dependence. The simulation experiment is conducted for two cases : a case of using a lost sale cost and a case of using a service level. For a case of using a lost sale cost, this paper calculates an order quantity, Q and a reorder point, r using the iterative procedure. However, for a case of using a service level, it is not an easy task to find Q and r. The complexity stems from the interactions among inventory replenishment policies for items. Thus, this paper considers the genetic algorithm (GA) as an optimization method. The simulation results demonstrates that the proposed (Q, r) models incur less inventory operations cost (satisfies better service levels) than a (Q, r) model ignoring purchase dependence. As a result, the simulation results supports that it is important to consider purchase dependence in the inventory operations practice.

• Applied Microscopy
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• v.45 no.4
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• pp.249-253
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• 2015

Microorganisms specifically groups of bacteria exhibiting physiological activities of production of acids are a major cause of concern because of their ability to induce corrosion in oil field pipelines and metal systems involved in water handling. Water Injection Stations as a means of secondary recovery from existing oil producing reservoirs, are often employed in most upstream oil and gas industries to ensure replenishment of voidage, maintenance of reservoir pressure and optimization of crude emulsion throughput. In the present study, scanning electron microscopy of macroscopic orange coloured slime formations sampled from leaking valves on the flow-lines of a Water Injection Stations of Oil India Limited revealed the presence of filamentous bacterial mats in association with diatoms. The species composition of the acidic slime formations from the sampled locations reveal the possible role of acid producing iron oxidizing bacteria (IOB) like Acidithiobacillus ferrooxidans in association with Gomphonema sp. in creating conditions for bio-corrosion.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.4
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• pp.116-123
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• 2023

In supply chain, most partners except the top level suppliers have inbound and outbound logistics. For example, toll manufacturing companies get unprocessed materials from a requesting company and send the processed materials back to the company after toll processing. Accordingly, those companies have inbound and outbound transportation costs in their total logistics costs. For many cases, the company may make the schedule of distributions by considering only the due delivery dates. However, the inbound and outbound transportation costs could significantly affect the total logistics costs. Thus, this paper considers the inbound and outbound transportation costs to find the optimal distribution plans. In addition, we have considered the inventory holding costs as well with transportation costs. From the experimental results, we have provided the optimal strategies for the distributions of replenishment as well as deliveries.