263 research outputs found
Approximate Order-up-to Policies for Inventory Systems with Binomial Yield
This paper studies an inventory policy for a retailer who orders his products from a supplier whose deliveries only partially satisfy the quality require- ments. We model this situation by an infinite-horizon periodic-review model with binomial random yield and positive lead time. We propose an order- up-to policy based on approximating the inventory model with unreliable supplier by a model with a reliable supplier and suitably modified demand distribution. The performance of the order-up-to policy is verified by com- paring it with both the optimal policy and the safety stock policy proposed in Inderfurth & Vogelgesang (2013). Further, we extend our approximation to a dual-sourcing model with two suppliers: the first slow and unreliable, and the other fast and fully reliable. Compared to the dual-index order- up-to policy for the model with full information on the yield, the proposed approximation gives promising results
Safety Stocks in Centralized and Decentralized Supply Chains under Different Types of Random Yields
Safety stock planning with focus on risk protection to cope with demand uncertainties is a very well researched topic in the field of supply chain management, in central as well as in local decision making systems. In contrast, there is only few knowledge about safety stock management in situations where supply risks have to be covered that are caused by uncertainties with respect to production yields. In this study, a two-stage manufacturer-retailer supply chain is considered in a single-period context that allows for an analytical study of the impact of yield randomness on safety stock determination. In order to concentrate the analysis on the effects of yield uncertainty demand will be assumed to be deterministic. We consider three basic types of yield randomness which represent different reasons for yield losses in production processes each, namely the stochastically proportional, binomial, and interrupted geometric yield type. It will be shown that these different yield risk specifications can bring about completely different properties with regard to the way safety stocks depend on various input parameters in supply chain planning. This holds especially for the impact of the demand size and for the influence of the level of product profitability in a supply chain. In an analytical model-based investigation it is demonstrated that these safety stock properties not only differ between the respective yield types, but also between systems of central and decentralized supply chain decision making. Thus, this study presents general insights into the importance of a correct yield type specification for an effective safety stock management and explains necessary differences in the stock distribution across supply chain stages in both centralized and decentralized settings
Confidence-based Optimization for the Newsvendor Problem
We introduce a novel strategy to address the issue of demand estimation in
single-item single-period stochastic inventory optimisation problems. Our
strategy analytically combines confidence interval analysis and inventory
optimisation. We assume that the decision maker is given a set of past demand
samples and we employ confidence interval analysis in order to identify a range
of candidate order quantities that, with prescribed confidence probability,
includes the real optimal order quantity for the underlying stochastic demand
process with unknown stationary parameter(s). In addition, for each candidate
order quantity that is identified, our approach can produce an upper and a
lower bound for the associated cost. We apply our novel approach to three
demand distribution in the exponential family: binomial, Poisson, and
exponential. For two of these distributions we also discuss the extension to
the case of unobserved lost sales. Numerical examples are presented in which we
show how our approach complements existing frequentist - e.g. based on maximum
likelihood estimators - or Bayesian strategies.Comment: Working draf
Parametric Distributionally Robust Optimisation Models for Budgeted Multi-period Newsvendor Problems
In this paper, we consider a static, multi-period newsvendor model under a
budget constraint. In the case where the true demand distribution is known, we
develop a heuristic algorithm to solve the problem. By comparing this algorithm
with off-the-shelf solvers, we show that it generates near-optimal solutions in
a short time. We then consider a scenario in which limited information on the
demand distribution is available. It is assumed, however, that the true demand
distribution lies within some given family of distributions and that samples
can be obtained from it. We consider the cases of normal and Poisson demands.
For each case, we show that using maximum likelihood estimates in place of the
true parameters can lead to poor estimates of the true cost associated with an
order quantity. Hence, we make use of likelihood inference to develop
confidence sets for the true parameters. These are used as ambiguity sets in a
distributionally robust model, where we enforce that the worst-case
distribution lies in the same family as the true distribution. We solve these
models by discretising the ambiguity set and reformulating them as piecewise
linear models. We show that these models quickly become large as the ambiguity
set grows, resulting in long computation times. To overcome this, we propose a
heuristic cutting surface algorithm that exploits theoretical properties of the
objective function to reduce the size of the ambiguity set. We illustrate that
our cutting surface algorithm solves orders of magnitude faster than the
piecewise linear model, while generating very near-optimal solutions
Dynamic Stochastic Inventory Management in E-Grocery Retailing: The Value of Probabilistic Information
Inventory management optimisation in a multi-period setting with dependent
demand periods requires the determination of replenishment order quantities in
a dynamic stochastic environment. Retailers are faced with uncertainty in
demand and supply for each demand period. In grocery retailing, perishable
goods without best-before-dates further amplify the degree of uncertainty due
to stochastic spoilage. Assuming a lead time of multiple days, the inventory at
the beginning of each demand period is determined jointly by the realisations
of these stochastic variables. While existing contributions in the literature
focus on the role of single components only, we propose to integrate all of
them into a joint framework, explicitly modelling demand, supply shortages, and
spoilage using suitable probability distributions learned from historic data.
As the resulting optimisation problem is analytically intractable in general,
we use a stochastic lookahead policy incorporating Monte Carlo techniques to
fully propagate the associated uncertainties in order to derive replenishment
order quantities. We develop a general inventory management framework and
analyse the benefit of modelling each source of uncertainty with an appropriate
probability distribution. Additionally, we conduct a sensitivity analysis with
respect to location and dispersion of these distributions. We illustrate the
practical feasibility of our framework using a case study on data from a
European e-grocery retailer. Our findings illustrate the importance of properly
modelling stochastic variables using suitable probability distributions for a
cost-effective inventory management process
The Value of Information in Reverse Logistics
We explore the value of information in the context of a remanufacturer that faces uncertainty with respect to demand, product return, and product recovery (yield loss). We assume a single period model in which the operational decision of interest is the quantity of new product to order. Our objective is to evaluate the absolute and relative value of the different types of information that such a firm may choose to invest in order to reduce the uncertainty it experiences in matching supply with demand. The different types of information include demand, return, and yield loss. Our results are extensive and reveal that the value for any specific type of information depends both on the overall level of uncertainty and the level of uncertainty that is attributed to the information for which it explains. We develop and test a theoretical model that is predictive of 1) the value of each type of information, 2) the conditions that give rise to the value for each type of information, and 3) the relative value for each type of information
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