Inventory Control Model Research Articles

Under the Environment of Agile Supply Chain Inventory Control Research

Agile supply chain is composed of different enterprises in different areas formed. In the process of agile supply chain; each member enterprises should make a rapid reaction based on the changing market situation, the customer demand and the changing of product information. For a long time, a bottleneck in the overall performance of the supply chain is the inventory, which is only occupy a lot of money, but also affect the accuracy and timeliness of information supply chain channel. The inventory cost control model and inventory time model are built in this paper to achieve minimized inventory costs among manufacturers, distributors and retailers.

A hybrid two-stock inventory control model for a reverse supply chain

This paper develops an inventory control model for a reverse supply chain with separate serviceable and remanufacturable inventory stock points. Return rate is expressed stochastically as a function of product demand. Variation in demand distribution during product life-cycle is modeled using a new order-up-to replenishment policy incorporating five maximum inventory levels corresponding to five product life cycle stages. A near optimum solution to this problem is sought using a hybrid solution method integrating a discrete event simulation with a meta-heuristic search method. Real data from an Australian case company is utilized to design test experiments for model validation and evaluation.

A JUMP-FLUID PRODUCTION–INVENTORY MODEL WITH A DOUBLE BAND CONTROL

We consider a production–inventory control model with two reflecting boundaries, representing the finite storage capacity and the finite maximum backlog. Demands arrive at the inventory according to a Poisson process, their i.i.d. sizes having a common phase-type distribution. The inventory is filled by a production process, which alternates between two prespecified production rates ρ1 and ρ2: as long as the content level is positive, ρ1 is applied while the production follows ρ2 during time intervals of backlog (i.e., negative content). We derive in closed form the various cost functionals of this model for the discounted case as well as under the long-run-average criterion. The analysis is based on a martingale of the Kella–Whitt type and results for fluid flow models due to Ahn and Ramaswami.

Application of linear programming to optimization of production and transportation plans in supply chain

This article presents the substantiation of expediency and efficiency of linear programming models application for joint optimization of production plans of suppliers and manufacturer plans, transportation plans for finished product delivery at destinations. It is supposed that there are a set of suppliers and one manufacturer. The method of synergetic effect from integration of supply chain participants is proposed on the basis of above linear programming model. The numerical results are given.Also in the article the stochastic optimization model for joint optimization plans of supply and finished product production under random demand is built. It is shown that deterministic equivalent of stochastic optimization problem is reduced to a convex programming problem.The results obtained are the basis for further investigations and generalizations, for example, application of dynamic models of inventory control (the Wagner-Whitin type model).

A sharp bound for the ergodic distribution of an inventory control model under the assumption that demands and inter-arrival times are dependent

In this study, a stochastic process which represents a single-item inventory control model with (s,S)-type policy is constructed when the demands of a costumer are dependent on the inter-arrival times between consecutive arrivals. Under the assumption that the demands can be expressed as a monotone convex function of the inter-arrival times, it is proved that this process is ergodic, and closed form of the ergodic distribution is given. Moreover, a sharp lower bound for this distribution is obtained.

Study on VMI Inventory Control Mode based on the Third-Party Logistics

Adding the third party logistics enterprises between the suppliers and the retailers is a kind of the development of VMI mode, in this mode; inventory pressure is transferred to the third party logistics enterprise. In view of this situation, the VMI inventory control model which treats total inventory control costs as the objective function is built based on from four dimensions: the inventory holding costs, the fixed delivery costs, replenishment costs and customer waiting costs. After solving the model and sensitivity analyzing related parameters, it can be inferred that related parameters in the VMI model of the introduction of the third party logistics have effects on inventory control costs.

Infinite Horizon Strategies for Replenishment Systems with a General Pool of Suppliers

We consider a general infinite horizon inventory control model that combines demand and supply risks and the firm's ability to mitigate the supply risks by diversifying its procurement orders among a set of N potential suppliers. Supply risks arise because only a random percentage of any given replenishment order is delivered as useable units. The suppliers are characterized by the price they charge and the distribution of their yield factor. Assuming unsatisfied demand is backlogged, the firm incurs, as in standard inventory models, three types of costs: (i) procurement costs, (ii) inventory carrying costs for units carried over from one period to the next, and (iii) backlogging costs. We establish the existence of an optimal stationary policy, under both the long-run discounted and average cost criteria, and characterize its structure. Assuming each period's inventory level distribution can be approximated as a normal, we develop an efficient solution method identifying additional structural properties. Finally, we identify a simple class of heuristic policies that come close to being optimal.

Economic model predictive control for inventory management in supply chains

In this paper, we propose economic model predictive control with guaranteed closed-loop properties for supply chain optimization. We propose a new multiobjective stage cost that captures economics as well as risk at a node, using a weighted sum of an economic cost and a tracking stage cost. We also demonstrate integration of scheduling with control using a supply chain example. We integrate a scheduling model for a multiproduct batch plant with a control model for inventory control in a supply chain. We show recursive feasibility of such integrated control problems by developing simple terminal conditions.

Effect of rework, rejects and inspection on lot size with work-in-process inventory

The economic order quantity and economic production quantity models are the most commonly used inventory models in production environments for the calculation of optimum lot size. However, these models are based on the unrealistic assumption that every process produces good quality products every time. Moreover, the impact of inspection is neglected in all extended inventory control models involving work in process inventory. By taking both imperfect production and lot size inspection into consideration, this paper presents a more realistic approach for the modelling of optimum lot size and total cost with a focus on the work in process inventory. A mathematical model is derived for optimum lot size based on the minimisation of the average cost. Our approach incorporates the effect of rework, rejects and inspection on work in process inventory. The significant effect of imperfect production and inspection on optimum lot size is evaluated via numerical examples. In comparison to existing models, the proposed model is a more generalised and flexible form of inventory model for independent demands.

Stochastic inventory model with finite production rate and partial backorders

One of the most widespread stochastic inventory models is the classical stochastic continuous review inventory control (Q, R) model. However, there are some restrictive assumptions in deriving this model. One of these assumptions is that the production rate is infinite which has motivated many researchers to develop (Q, R) models with finite production rate. In these models, the randomness of demand is indirectly introduced in the models by assuming that the safety stock is equal to a safety factor multiplied by the standard deviation of lead time demand. In this paper, the classical stochastic continuous review inventory control (Q, R) model is generalised for the case when the production rate is finite and unmet demand is partially backordered. Unlike the models presented in the literature, the probabilistic nature of the demand is reflected directly in the formulation of the proposed model. Furthermore, the optimal lot size and reorder point are determined for this model. Two types of items are investigated; fast-moving and slow-moving items. Also, the effect of the production rate on the optimal solution is studied through numerical examples. The results show that the production rate impacts the inventory and production decisions significantly.

Optimal dynamic pricing and replenishment policies for deteriorating items

Article history: Received January 22 2014 Received in Revised Format June 6 2014 Accepted June 18 2014 Available online June 23 2014 Marketing strategies and proper inventory replenishment policies are often incorporated by enterprises to stimulate demand and maximize profit. The aim of this paper is to represent an integrated model for dynamic pricing and inventory control of deteriorating items. To reflect the dynamic characteristic of the problem, the selling price is defined as a time-dependent function of the initial selling price and the discount rate. In this regard, the price is exponentially discounted to compensate negative impact of the deterioration. The planning horizon is assumed to be infinite and the deterioration rate is time-dependent. In addition to price, the demand rate is dependent on advertisement as a powerful marketing tool. Several theoretical results and an iterative solution algorithm are developed to provide the optimal solution. Finally, to show validity of the model and illustrate the solution procedure, numerical results are presented. © 2014 Growing Science Ltd. All rights reserved

Research on Multi-Leg Inventory Control Based on Passenger Choice

To remedy the lack of mathematical programming and the Expected Marginal Seat Revenue (EMSR) model for multi-leg seat inventory control, this paper proposes a method based on passenger choice. Except for data about which seats passengers decide to opt for, there is no need to obtain distributions of passengers' demands or other “a priori” information. The proposed method can discover the real factors that affect passengers' choices, and then estimate the probabilities of seat choices and the revenue according to the weights of the factors. Simulated experiments and comparison with the shadow price method and the virtual “bucket” method confirm the feasibility and effectiveness of the proposed method in seat inventory control for multi-leg flights.

Optimal control of a continuous review production inventory system with Gamma distributed deterioration

This paper develops the production inventory models with and without Gamma distributed deteriorating items. The production system with inventory level dependent demand is considered here and the Pontryagin maximum principle is used to determine the optimal control, which is the production rate that minimizes the optimal inventory control model, while satisfying the system dynamics. The necessary optimality conditions associated with and without Gamma distributed deteriorating items are derived from the Hamiltonian functions. The optimal inventory levels and the optimal production rates are derived and numerical simulations are used to illustrate the theoretical results.

Applicable Inventory Control Models for Agricultural Business Managers: Issues and Concerns

Applicable Inventory Control Models for Agricultural Business Managers: Issues and Concerns

A Simultaneous Pricing and Inventory Control Model for a Single Product with Capacitated Working Capital

The simultaneous stochastic pricing and inventory management with capacitated cash-on-hand is a profit maximization problem faced by a firm, which has limited working capital for its liabilities. In addition to this working capital constraint, the company has to dynamically price its products and determine inventory levels to minimize its expected cost. In our study, we tackle this complicated problem by assuming a single product, periodic review inventory model with finite multiple periods, and imperfect market information. We also assume no fixed cost in order to relax the complexity of the problem and perform numerical analysis for providing managerial implications. We investigate several research questions to extend our understanding of this complicated but very practical problem faced by numerous decision-makers in organizations. A discussion of possible optimal policies for this complicated unanswered problem with working capital constraints is explored.

Multi-Leg Seat Inventory Control Based on EMSU and Virtual Bucket

Expected marginal seat revenue (EMSR) is a well-known method for airline seat inventory control airlines. However, this method employs a static model to study the dynamic reservation process, and does not take into account the risk tolerance of policy makers. Expected marginal seat utility (EMSU) replaces revenue by utility, which addresses the real situation of seat inventory control. However, there is still a lack of multi-leg seat control algorithms based on EMSU. Therefore, using EMSU and bucket algorithms, this paper applies the Markov decision-making process to simulate the flight reservation process and builds a dynamic multi-leg seat inventory control model. Experimental results validate the effectiveness of the proposed method.

Multi-item multiperiodic inventory control problem with variable demand and discounts: a particle swarm optimization algorithm.

A multi-item multiperiod inventory control model is developed for known-deterministic variable demands under limited available budget. Assuming the order quantity is more than the shortage quantity in each period, the shortage in combination of backorder and lost sale is considered. The orders are placed in batch sizes and the decision variables are assumed integer. Moreover, all unit discounts for a number of products and incremental quantity discount for some other items are considered. While the objectives are to minimize both the total inventory cost and the required storage space, the model is formulated into a fuzzy multicriteria decision making (FMCDM) framework and is shown to be a mixed integer nonlinear programming type. In order to solve the model, a multiobjective particle swarm optimization (MOPSO) approach is applied. A set of compromise solution including optimum and near optimum ones via MOPSO has been derived for some numerical illustration, where the results are compared with those obtained using a weighting approach. To assess the efficiency of the proposed MOPSO, the model is solved using multi-objective genetic algorithm (MOGA) as well. A large number of numerical examples are generated at the end, where graphical and statistical approaches show more efficiency of MOPSO compared with MOGA.

Pricing and inventory control policy for non-instantaneous deteriorating items with time- and price-dependent demand and partial backlogging

Article history: Received October 15, 2013 Received in revised format March 6 2014 Accepted March 24, 2014 Available online March 26 2014 Determining the optimal inventory control and selling price for deteriorating items is of great significance. In this paper, a joint pricing and inventory control model for deteriorating items with priceand time-dependent demand rate and time-dependent deteriorating rate with partial backlogging is considered. The objective is to determine the optimal price, the replenishment time, and economic order quantity such that the total profit per unit time is maximized. After modeling the problem, an algorithm is proposed to solve the resulted problem. We also prove that the problem statement is concave function and the optimal solution is indeed global. © 2014 Growing Science Ltd. All rights reserved.

A decision support system for mean–variance analysis in multi-period inventory control

Traditionally inventory management models have focused on risk-neutral decision making with the objective of maximizing the expected rewards or minimizing costs over a specified time horizon. However, for items marked by high demand volatility such as fashion goods and technology products, this objective needs to be balanced against the risk associated with the decision. Depending on how the product performs vis-à-vis the seller's original forecast, the seller could end up with losses due to either short or surplus supply. Unfortunately, traditional models do not address this issue. Stochastic dynamic programming models have been extensively used for sequential decision making in the context of multi-period inventory management, but in the traditional way where one either minimizes costs or maximizes profits. Risk is implicitly considered by accounting for stock-out costs. Considering risk and reward simultaneously and explicitly in a stochastic dynamic setting is a cumbersome task and often difficult to implement for practical purposes, since dynamic programming is designed to optimize on one variable, not two. In this paper we develop an algorithm, Variance-Retentive Stochastic Dynamic Programming that tracks variance as well as expected reward in a stochastic dynamic programming model for inventory control. We use the mean–variance solutions in a heuristic, RiskTrackr, to construct efficient frontiers which could be an ideal decision support tool for risk-reward analysis.

Research on Food Processing Engineering with an Inventory Model for Foodstuffs with Service Level

As the raw grain processing, the problems of foodstuffs storage deterioration gradually appear, it will affect the enterprise operation costs. The paper analyzed the factors affecting inventory cost with deterioration rate, shortage cost and probabilities of stock on hand and so on, established an inventory control model based on service level constraints. The relationship of deterioration costs and inventory costs were researched on the basis of the model, and gave a method in order to solve the problem of optimization inventory and minimum costs. With the simulation case, the model can effectively solve the problem better.