Optimal Inventory Level Research Articles

Decentralized Energy Allocation for Wireless Networks With Renewable Energy Powered Base Stations

In this paper, a green wireless communication system in which base stations are powered by renewable energy sources is considered. This system consists of a capacity-constrained renewable power supplier (RPS) and a base station (BS) that faces a predictable random connection demand from mobile user equipments (UEs). In this model, the BS, which is powered via a combination of a renewable power source and the conventional electric grid, seeks to specify the renewable power inventory policy, i.e., the power storage level. On the other hand, the RPS must strategically choose the energy amount that is supplied to the BS. An M/M/1 make-to-stock queuing model is proposed to investigate the decentralized decisions when the two parties optimize their individual costs in a noncooperative manner. The problem is formulated as a noncooperative game whose Nash equilibrium (NE) strategies are characterized to identify the causes of inefficiency in the decentralized operation. A set of simple linear contracts are introduced to coordinate the system so as to achieve an optimal system performance. The proposed approach is then extended to a setting with one monopolistic RPS and $N$ BSs that are privately informed of their optimal energy inventory levels. In this scenario, we show that the widely used proportional allocation mechanism is no longer socially optimal. To make the BSs truthfully report their energy demand, an incentive compatible (IC) mechanism is proposed for our model. Simulation results show that using the green energy can present significant traditional energy savings for the BS when the connection demand is not heavy. Moreover, the proposed scheme provides valuable energy cost savings by allowing the BSs to smartly use a combination of renewable and traditional energy, even when the BS has a heavy traffic of connections. Also, the results show that performance of the proposed IC mechanism will be close to the social optimal when the green energy production capacity increases.

Pricing and inventory management during new product introduction when shortage creates hype

AbstractIn this study, we analyze the joint pricing and inventory management during new product introduction when product shortage creates additional demand due to hype. We develop a two‐period model in which a firm launches its product at the beginning of the first period, before it observes sales in the two periods. The product is successful with an exogenous probability, or unsuccessful with the complementary probability. The hype in the second period is observed only when the product is successful. The firm learns the actual status of the product only after observing the first‐period demand. The firm must decide the stocking level and price of the product jointly at the beginning of each of the two periods. In this article, we derive some structural properties of the optimal prices and inventory levels, and show that (i) firms do not always exploit hype, (ii) firms do not always increase the price of a successful product in the second period, (iii) firms may price out an unsuccessful product in the first period if the success probability is above a threshold, and (iv) such a threshold probability is decreasing in the first‐period market potential of the successful product. © 2015 Wiley Periodicals, Inc. Naval Research Logistics 62: 304–320, 2015

Optimal inventory policy for two substitutable products with customer service objectives

We consider a firm facing stochastic demand for two products with downward, supplier-driven substitution and customer service objectives. We assume both products are perishable or prone to obsolescence, hence the firm faces a single period problem. The fundamental challenge facing the firm is to determine in advance of observing demand the profit maximizing inventory levels of both products that will meet given service level objectives. Note that while we speak of inventory levels, the products may be either goods or services. We characterize the firm’s optimal inventory policy with and without customer service objectives. Results of a numerical study reveal the benefits obtained from substitution and show how optimal inventory levels are impacted by customer service objectives.

Assortment Planning with Nested Preferences: Dynamic Programming with Distributions as States?

The main contribution of this paper is to develop new techniques in approximate dynamic programming, along with the notions of rounded distributions and inventory filtering, to devise a quasi-PTAS for the capacitated assortment planning problem, originally studied by Goyal, Levi, and Segev (2009). Motivated by real-life applications, their nested preference lists model stands as one of very few settings, where near-optimal assortments and inventory levels can be computed efficiently. However, these findings crucially depend on certain distributional assumptions, leaving the general problem wide open in terms of approximability prior to this work.In addition to proposing the first rigorous approach for handling the nested preference lists model in its utmost generality, from a technical perspective, we augment the existing literature on dynamic programming with a number of promising ideas. These are novel algorithmic tools for efficiently keeping approximate distributions as part of the state description, while losing very little information and while accumulating only small approximation errors throughout the overall computation. From a conceptual perspective, at the cost of losing an eps-factor in optimality, we show how to dramatically improve on the truly exponential nature of standard dynamic programs, which seem essential for the purpose of computing optimal inventory levels.

Inventory management for dual sales channels with inventory-level-dependent demand

This paper studies the inventory management problem of dual channels operated by one vendor. Demands of dual channels are inventory-level-dependent. We propose a multi-period stochastic dynamic programming model which shows that under mild conditions, the myopic inventory policy is optimal for the infinite horizon problem. To investigate the importance of capturing demand dependency on inventory levels, we consider a heuristic where the vendor ignores demand dependency on inventory levels, and compare the optimal inventory levels with those recommended by the heuristic. Through numerical examples, we show that the vendor may order less for dual channels than those recommended by the heuristic, and the difference between the inventory levels in the two cases can be so large that the demand dependency on inventory levels cannot be neglected. In the end, we numerically examine the impact of different ways to treat unmet demand and obtain some managerial insights.

Joint optimization of preventive maintenance and inventory policies for multi-unit systems subject to deteriorating spare part inventory

The interconnection of maintenance and spare part inventory management often puzzles managers and researchers. The deterioration of the inventory affects decision-making and increases losses. Block replacement and periodic review inventory policies were here used to evaluate a joint optimization problem for multi-unit systems in the presence of inventory deterioration. The deterministic deteriorating inventory (DDI) model was used to describe deteriorating inventory when deteriorating inventory data were available and the stochastic deteriorating inventory (SDI) model was used when they were not. Analytical joint optimization models were established, and the preventive replacement interval and the maximum inventory level served as decision variables to minimize the expected system total cost rate. This work proved the existence of the optimal maximum inventory level and gave the uniqueness condition under the DDI model. Numerical experiments based on the electric locomotives in Slovenian Railways were performed to confirm the effectiveness of the proposed models. Results showed the total cost rate to be sensitive to the maximum inventory level, which indicates that the research of this work is necessary. Further, the optimal preventive replacement interval was reduced and the optimal maximum inventory level was increased to balance the influence of deteriorating inventory. Monte Carlo experiments were used to show that the proposed policy is better than policies that do not take deteriorating inventory into account.

운영비용을 고려한 자전거 쉐어링의 최적 재고수준

In this paper, it shows optimal inventory level of bicycle sharing service terminal by maintaining the lowest costs. As the interest to environment and exhaustion of resource increases globally, investment to sustainable transportation increases around advanced countries and interest to efficient transportation, managing and consuming of vehicle increases also. Vehicle sharing service is a model of rental car where customer rent cars for short periods of time often by the hour and its users are increasing for the reason that it is more convenient than car rent. In addition, bicycle sharing service is one of the major parts in vehicle sharing program and many of country are already managing it. This paper proposes optimal ∙제1저자 : 김진식 ∙교신저자 : 이철웅 ∙투고일 : 2014. 11. 12, 심사일 : 2014. 11. 25, 게재확정일 : 2014. 12. 3. * 고려대학교 산업경영공학과 (Department of Industrial Management Engineering, Korea University 164 Journal of The Korea Society of Computer and Information January 2015 inventory levels of vehicle sharing service's terminal by using simulation calculating operation costs of vehicle sharing service. ▸

Performance Evaluation of a Hybrid Manufacturing Remanufacturing System Taking Into Account the Machine Degradation

This paper deals with a manufacturing remanufacturing system composed by two parallel machines, a serviceable inventory, a remanufacturing inventory and customers that demand a constant quantity of product. To describe the system, a stochastic fluid model is adopted that takes into account the return of the used products and the machines degradation. The objective of this paper is to evaluate the optimal serviceable inventory level which allows minimizing the sum of inventory, lost sales costs and the machines degradation cost. For the performance evaluation of the system, the stochastic fluid model is simulated in order to study the impact of the unit degradation costs on the optimal serviceable inventory.

Application of adaptive neuro-fuzzy inference system and artificial neural network in inventory level forecasting

Determining optimum level of inventory is very important for any organisation which depends on various factors. In this research, six main factors have been considered as input parameters and the inventory level has been considered as the single output for this inventory management problem. Price of raw material, demand of raw material, holding cost, setup cost, supplier's reliability and lead time are considered as input parameters. An adaptive neuro-fuzzy inference system ANFIS has been applied as the artificial intelligence technique for modelling the inventory problem. ANFIS results have been compared with results from another artificial intelligence technique, artificial neural network ANN, to validate the output results. Performance of both methods has been shown regarding different error measures. Comparison clearly shows the superiority of ANFIS results over ANN results and thus makes ANFIS a better choice for inventory level forecasting.

Single-period newsvendor modelling of flu vaccination order quantities

The objective of this paper is to determine optimal inventory levels of flu vaccines at one medical facility using the newsvendor model while also maintaining customer service levels and maximising profit. Using four years of empirical evidence, we illustrate the optimal ordering quantity of flu vaccine by healthcare managers to provide proper medical coverage. This research provides an initial step for ordering quantities of perishable biopharmaceuticals. The research also develops a framework for the logistics of ordering in the presence of multiple constraints throughout the supply chain network. An important contribution of this work is demonstrating the value of the posited logistics model within the context of a healthcare application that has the potential to influence practice.

Optimal control of decoupling point with deteriorating items

Purpose: The aim of this paper is to develop a dynamic model to simultaneously determine the optimal position of the decoupling point and the optimal path of the production rate as well as the inventory level in a supply chain. With the objective to minimize the total cost of the deviation from the target setting, the closed forms of the optimal solution are derived over a finite planning horizon with deterioration rate under time-varying demand rate. Design/methodology/approach: The Pontryagin's Maximum Principle is employed to explore the optimal position of decoupling point and the optimal production and inventory rate for the proposed dynamic models. The performances of parameters are illustrated through analytical and numerical approaches. Findings: The results denote that the optimal production rate and inventory level are closely related to the target setting which are highly dependent on production policy; meanwhile the optimal decoupling point is exist and unique with the fluctuating of deteriorating rate and product life cycle. The further analyses through both mathematic and numerical approaches indicate that the shorten of product life cycle shifts the optimal decoupling point forward to the end customer meanwhile a backward shifting appears when the deterioration rate increase. Research limitations/implications: There is no shortage allowed and the replacement policy is not taken into account. Practical implications: Solutions derived from this study of the optimal production-inventory plan and decoupling point are instructive for operation decision making. The obtained knowledge about the performance of different parameters is critical to deteriorating supply chains management. Originality/value: Many previous models of the production-inventory problem are only focused on the cost. The paper introduces the decoupling point control into the production and inventory problem such that a critical element-customer demand, can be taken into account. And the problem is solved as dynamic when the production rate, inventory level and the position of the decoupling point are all regarded as decision variables.

A Simulation Model for Optimal Inventory Level of R.O.K Navy’s Repair Parts

The weapon system of The Navy is the small quantity producing system on multiple kinds. It is consisted of various equipment and the subordinate parts of those which can repair the damaged part. The operating procedure concerning warship's repair parts managed under these systems is as follows. Firstly, if demand of repair parts occurs from warship which is the operating unit of weapon, then the Fleet(the repair & supply support battalion) is in charge of dealing with these requests. If certain request from warship is beyond the battalion's capability, it is delivered directly to the Logistic Command. In short, the repair and supply support system of repair parts can be described as the multi-level support system. The various theoretical researches on inventory management of Navy's repair parts and simulation study that reflects reality in detail have been carried out simultaneously. However, the majority of existing research has been conducted on aircraft and tank's repairable items, in that, the studies is woefully deficient in the area concerning Navy's inventory management. For that reason, this paper firstly constructs the model of consumable items that is frequently damaged reflecting characteristics of navy's repair parts inventory management using ARENA simulation. After that, this paper is trying to propose methodology to analyze optimal inventory level of each supply unit through OptQuest, the optimization program of ARENA simulation.

Joint pricing and inventory control for additive demand models with reference effects

We study a periodic review joint inventory and pricing problem of a single item with stochastic demand subject to reference effects. The random demand is contingent on the current price and the reference price that acts a benchmark against which customers compare the price of a product. Randomness is introduced with an additive random term. The customers perceive the difference between the price and the reference price as a loss or a gain. Hence, they have different attitudes towards them, such as loss aversion, loss neutrality or loss seeking. We model the problem using safety stock as the decision variable and show that the problem can be decomposed into two subproblems for all demand models under mild conditions. Using the decomposition, we show that a steady state solution exists for the infinite horizon problem and we characterize the steady state solution. Defining the modified revenue as revenue less the production cost, we show that a state-dependent order-up-to policy is optimal for concave demand models with concave modified revenue functions and provide example demand models with absolute difference reference effects and loss-averse customers. We also show that the optimal inventory level increases with the reference price. All of our results hold for finite and infinite horizon problems.

A Simulation Model for Optimal Inventory Level of R.O.K Navy’s Repair Parts

The weapon system of The Navy is the small quantity producing system on multiple kinds. It is consisted of various equipment and the subordinate parts of those which can repair the damaged part. The operating procedure concerning warship’s repair parts managed under these systems is as follows. Firstly, if demand of repair parts occurs from warship which is the operating unit of weapon, then the Fleet(the repair & supply support battalion) is in charge of dealing with these requests. If certain request from warship is beyond the battalion’s capability, it is delivered directly to the Logistic Command. In short, the repair and supply support system of repair parts can be described as the multi-level support system. The various theoretical researches on inventory management of Navy’s repair parts and simulation study that reflects reality in detail have been carried out simultaneously. However, the majority of existing research has been conducted on aircraft and tank’s repairable items, in that, the studies is woefully deficient in the area concerning Navy’s inventory management. For that reason, this paper firstly constructs the model of consumable items that is frequently damaged reflecting characteristics of navy’s repair parts inventory management using ARENA simulation. After that, this paper is trying to propose methodology to analyze optimal inventory level of each supply unit through OptQuest, the optimization program of ARENA simulation.

Approximate dynamic programming modeling for a typical blood platelet bank

This paper introduces a workable model for the establishment of an inventory bank holding perishable blood platelets with a short shelf life. The model considers a blood platelet bank with eight blood types, stochastic demand, stochastic supply, and deterministic lead time. The model is formulated using approximate dynamic programming. The model is evaluated in terms of four measures of effectiveness: blood platelet shortage, outdating, inventory level, and reward gained. Moreover, several alternative inventory control policies are analyzed. The order quantity decision is taken using a news-vendor model. In addition, the variation of the O− percentage is studied. This study confirms that the blood platelet bank reward can be maximized by operating at the optimal inventory level, thereby minimizing the number of outdated units as well as shortages. In addition, the suitable O− percentage within the blood platelet bank inventory was studied. As the O− blood type inventory levels increase to 40%, shortages drop from 3.9% to 1.5%. Outdated units drop from 4.6% to 1.8%. Furthermore, when the order quantity is received twice a day, shortages drop to 1.8% and outdated units drop to 2.1%.

Two-Echelon Inventory Stochastic Model of Supply Chain Based on Service Level Constraints

The study on multi-echelon inventory of supply chain is becoming more and more important in E-business era. This paper proposes a two-echelon inventory model with one supplier and several retailers, in which a certain service level has to be satisfied and the goal is to minimize the total inventory cost. In addtion it puts forward an effective algorithm for this model to obtain the optimal replenishment period and inventory level of each supply chain node.

INVENTORY CONTROL USING ABC AND HML ANALYSIS – A CASE STUDY ON A MANUFACTURING INDUSTRY

Inherent uncertainties in demands and supply make it difficult for supply chains to achieve optimum inventory replenishment, resulting in loss of sales or keeping excessive inventories. An unkempt inventory can take up to one-third of an organization’s annual investment. Therefore, in order to compete with invariably erratic demands, it is not only challenging to develop an intelligent system to maintain and control an optimum level of inventory but has also become mandatory. Here we have tried to study the inventory control system of an EMU coach manufacturing industry using ABC and HML analysis method.

Adaptive inventory control system for material items with continuous non-stationary demand

Abstract This paper describes an algorithm of dynamic inventory control system for large numbers of material items with continuous non-stationary demand. It uses principles of pull inventory control systems, statistical inventory analysis and joint replenishment inventory systems. This algorithm was processed in software module (VBA for Excel) and applied in practice. The target of described algorithm is to keep an optimum inventory level and optimum customer service level in terms of inventory control of items with non-stationary demand.

PROBABILISTIC PERIODIC REVIEW INVENTORYMODELUSING LAGRANGE TECHNIQUE AND FUZZY ADAPTIVE PARTICLE SWARM OPTIMIZATION

The integration between inventory model and Artificial Intelligent (AI) represents the rich area of research since last decade. In this study we investigate probabilistic periodic review <Qm, N> inventory model with mixture shortage (backorder and lost sales) using Lagrange multiplier technique and Fuzzy Adaptive Particle Swarm Optimization (FAPSO) under restrictions. The objective of these algorithms is to find the optimal review period and optimal maximum inventory level which will minimize the expected annual total cost under constraints. Furthermore, a numerical example is applied and the experimental results for both approaches are reported to illustrate the effectiveness of overcoming the premature convergence and of improving the capabilities of searching to find the optimal results in almost all distributions.

Updating Inventories of Substitutable Resources in Response to Forecast Updates

We show simple yet optimal results to update the inventory/capacity levels, expected profit, fill rates, and service levels of substitutable resources in response to an updating of the mean demand forecasts for the resources. We find that a change in the mean demand of one resource does not affect the optimal inventory level of any other resource. The results are obtained for demands with location‐scale distribution, and for a revenue structure satisfying a triangle property such that the manager will always use the inventory of a resource to meet her own demand first before using it for substitution. The results for updating the performance measures also extend to managers who maintain non‐optimal inventory/capacity levels. Implications for procurement, sales and operational planning, and multi‐store operations are discussed.