Browsing by Subject "Inventory Management"

Now showing 1 - 3 of 3

Open Access
Inventory management problem for two substitutable products : a bayesian approach
(2016-07) Tekin, Burcu
Accurate estimation of demand and the inventory levels are must for customer satisfaction and the protability of a business. In this thesis, we consider two main topics: First, the joint estimation of the demand arrival rate, primary and substitute demand rates in a lost sales environment with two products is provided using a Bayesian methodology. It is assumed that demand arrivals for the products follow a Poisson process where the unknown arrival rate is a random variable. An arriving customer requests one of the two products with a certain probability and may substitute the other product if the primary demand is not available. We consider a general mixture gamma density for prior of the Poisson arrival rate and a general prior for the joint distribution of the primary and substitute demand probabilities. It is observed that the resulting marginal posterior and the conditional posterior density of demand arrival rate given the other parameters, after observation of n-period data, are again in the structure of gamma mixtures. Then, we calculated the inventory levels of the products dynamically with the help of rst part. Using the updated posterior demand rates on the observed data from the previous period, we revised the inventory levels for the following period to maximize the prot. Finally, some numerical results for both parts are presented to illustrate the performance of the estimations.
Open Access
The newsvendor problem with multiple inputs and environment sensitive customers
(2015) Sönmez, Nazlı
Motivated by the global aim and trends to reduce carbon emissions, in this thesis we investigate the effects of carbon sensitivity on the operations management in the context of inventory management. We assume the newsvendor setting under multiple substitutable inputs with varying carbon emission levels, and carbon sensitive random demand. The Cobb-Douglas production function is used which provides a link between the production quantity and the inputs. Our goal is to determine the optimal production quantity under two different supply chain models. In the decentralized model, we consider an independent manufacturer and a retailer, where the retailer orders Q units to the manufacturer and the manufacturer produces these items in such a way that he minimizes his production cost. In the integrated production or the centralized model, the manufacturer and the retailer act as a centralized system and the aim is to find the production quantity that maximizes the expected profit of the integrated system. Exact expressions for the expected profits of both models are derived and analytical results regarding the optimal solutions are presented. Numerical results are also provided to illustrate the effects of the system parameters and carbon sensitivity levels.
Open Access
On the newsvendor problem with multiple inputs under a carbon emission constraint
(2012) Sözüer, Sibel
In this thesis, we consider two problems in the newsvendor setting with multiple inputs, under a carbon emission constraint and non-linear production functions. In the first problem, we assume a strict carbon cap and find the optimal production quantity and input allocation that will maximize the expected profit under this constraint. In the second problem, we consider an emission trading scheme where an advance purchase of carbon emission permits is made at an initial price before the random demand is realized. When the demand is realized and new carbon trade prices are revealed, it is possible to buy additional permits or to sell an excess amount. The aim is to decide on the optimal allocation of the inputs as well as the carbon trading policy so as to maximize the expected profit. In both problems, the production quantity is linked to multiple inputs via the Cobb-Douglas and Leontief production functions. Optimal policy structures are derived and numerical examples are provided.