Computing replenishment cycle policy parameters for a perishable item

In many industrial environments there is a significant class of problems for which the perishable nature of the inventory cannot be ignored in developing replenishment order plans. Food is the most salient example of a perishable inventory item. In this work, we consider the periodic-review, single-location, single-product production/inventory control problem under non-stationary stochastic demand and service level constraints. The product we consider can be held in stock for a limited amount of time after which it expires and it must be disposed of at a cost. In addition to wastage costs, our cost structure comprises fixed and unit variable ordering costs, and inventory holding costs. We propose an easy-to-implement replenishment cycle inventory control policy that yields at most 2N control parameters, where N is the number of periods in our planning horizon. We also show, on a simple numerical example, the improvement brought by this policy over two other simpler inventory control rules of common use

Inventory control for a non-stationary demand perishable product: comparing policies and solution methods

This paper summarizes our findings with respect to order policies for an inventory control problem for a perishable product with a maximum fixed shelf life in a periodic review system, where chance constraints play a role. A Stochastic Programming (SP) problem is presented which models a practical production planning problem over a finite horizon. Perishability, non-stationary demand, fixed ordering cost and a service level (chance) constraint make this problem complex. Inventory control handles this type of models with so-called order policies. We compare three different policies: a) production timing is fixed in advance combined with an order up-to level, b) production timing is fixed in advance and the production quantity takes the age distribution into account and c) the decision of the order quantity depends on the age-distribution of the items in stock. Several theoretical properties for the optimal solutions of the policies are presented. In this paper, four different solution approaches from earlier studies are used to derive parameter values for the order policies. For policy a), we use MILP approximations and alternatively the so-called Smoothed Monte Carlo method with sampled demand to optimize values. For policy b), we outline a sample based approach to determine the order quantities. The flexible policy c) is derived by SDP. All policies are compared on feasibility regarding the α-service level, computation time and ease of implementation to support management in the choice for an order policy.National project TIN2015-66680-C2-2-R, in part financed by the European Regional Development Fund (ERDF)

Basics of inventory management (Part 6: The (R,s,S)-model)

Inventory Models;management science

Consumption and Investment

This paper presents an overview of current models of consumption and investment behavior. First, the stochastic implications of the permanent income model and empirical tests of these implications are discussed. Then the simple theoretical model is extended to include expenditure on consumer durables. In addition, the implications of liquidity constraints and the unpredictability of the rate of return on wealth are discussed. The overview of consumption behavior closes with a critical discussion of the Ricardia Equivalence Theorem. Investment behavior is analyzed using a dynamic optimization model of a firm facing costs of adjustment. This framework integrates the accelerator model, the neoclassical model and the q theory. The model is then used to analyze the interaction of corporate taxes, inflation and investment and also to analyze the effects of uncertainty on investment. The overview of investment concludes with a discussion of inventory investment.

Search Frictions on Product and Labor markets : Money in the Matching Function

This paper builds a macroeconomic model of equilibrium unemployment in which firms persistently face difficulties in selling their production and this affects their decisions to create jobs. Due to search-frictins on the product market, equilibrium unemployment is a U-shaped function of the ratio of total demand to total supply on this market. When prices are at their Competitive Search Equilibrium values, the unemployment rate is minimized. Yet, the Competitive Search Equilibrium is not efficient. Inflation is detrimental to unemployment.Equilibrium unemployment, Matching, Inflation, Demand Constraints

Basics of inventory management (Part 4: The (s,S)-model)

Inventory Models;management science