نتایج جستجو برای: bi product multi echelon inventory planning

تعداد نتایج: 1000386  

Journal: :Computers & Industrial Engineering 2023

With today's rapidly changing supply chain environment, it is essential to include uncertainty in an explicit manner planning models. Therefore, we propose a stochastic model for tactical of the Crude Oil Supply Chain (COSC) under cost and demand uncertainties. The mathematical considers multi-echelon with multi-products multi-period horizon. It integrates inventory backorder penalties. A Sampl...

2007
J. P. E. Hodgson R. D. H. Warburton

We consider multi-echelon supply chains, typically consisting of retailer, distributor, manufacturer, etc. The inventory of each actor is subject to its own delay in delivery time, and each chooses its own inventory replenishment strategy. We model this interaction with a system of linear differential delay equations. When put in matrix form the system is triangular and we derive an exact solut...

2014
Oussama Ben Ammar Alexandre Dolgui Hélène Marian

The aim of this paper is to propose tools to adapt and parameterize the Material Requirement Planning (MRP) method under lead time uncertainty. We study multi-level assembly systems with one type of finished products and several types of components. We consider that each component has a fixed unit inventory cost and the finished product has a backlogging cost per unit of time. The lead times of...

Journal: :Operations Research 2001
Gérard P. Cachon

This paper studies a two-echelon supply chain with stochastic and discrete consumer demand, batch order quantities, periodic inventory review, and deterministic transportation times. Reorder point policies manage inventories at every location. Average inventory, backorders and fill rates are evaluated exactly for each location. Safety stock is evaluated exactly at the lower echelon and a good a...

2009
Steven David Prestwich Armagan Tarim Roberto Rossi Brahim Hnich

Stochastic inventory control in multi-echelon systems poses hard problems in optimisation under uncertainty. Stochastic programming can solve small instances optimally, and approximately solve large instances via scenario reduction techniques, but it cannot handle arbitrary nonlinear constraints or other nonstandard features. Simulation optimisation is an alternative approach that has recently ...

Journal: :Global Journal of Engineering and Technology Advances 2021

Product recovery has become significant business strategies to increase a competitive edge in and also the society. Parts from discarded products due rapid advancement post-consumer before & after end-of-life (EOL) are recovered reduce landfill waste have part of circular economy. is made possible with help Closed-loop supply chain (CLSC). This paper concentrates on multi-period, multi-prod...

Journal: :Sustainability 2022

In this paper, the two-echelon multi-period multi-product location–inventory problem with partial facility closing and reopening is studied. For each product period, plants serve warehouses, which consolidation hubs, service customers independent, normally distributed demands. The schedule of construction, temporary closing, modular capacities facilities, continuous-review inventory control pol...

2016
Cong Guo Xueping Li

This article addresses the development of an integrated supplier selection and inventory control problems in supply chain management by developing a mathematical model for a multi-echelon system. In particular, a buyer firm that consists of one warehouse and N identical retailers wants to procure a product from a group of potential suppliers, which may require different price, ordering cost, le...

2003
P. J. Sánchez D. Ferrin Heng Cao Haifeng Xi Stephen F. Smith

We have used Reinforcement Learning together with Monte Carlo simulation to solve a multi-period production planning problem in a two-stage hybrid manufacturing process (a combination of build-to-plan with build-to-order) with a capacity constraint. Our model minimizes inventory and penalty costs while considering real-world complexities such as different component types sharing the same manufa...

2004
Heng Cao Haifeng Xi

We have used Reinforcement Learning together with Monte Carlo simnlation to solve a multi-period production planning problem in a westage hybrid manufacturing process (a combination of build-to-plan with build-to-order) with a capacity constraint. Our model minimizes inventory and penalty costs while considering real-world complexities such as different component types sharing the same manufact...

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