Distributed Order-Up-To Inventory Control in Networked Supply Systems With Delay

In this work, the dynamics of networked goods distribution systems subject to the control of a continuous-review order-up-to inventory policy are investigated. In the analytical study, as opposed to the earlier models constrained to the serial and arborescent interconnection structures, an arbitrary multiechelon topology is considered. This external, uncertain demand,following any distribution, may be imposed on all network nodes,not just conveniently selected contact points. As in the physical systems, stock relocation to refill the reserves is subject to nonnegligible delay, which poses a severe stability threat and may lead to cost-inefficient decisions. A state-space model is created and used as the framework for analyzing system properties. In particular, it is formally demonstrated that despite unpredictable demand fluctuations, a feasible(nonnegative and bounded)reserves replenishment signal is generated at all times, and the stock gathered at the nodes does not surpass a finite, precisely determined level. The theoretical content is illustrated with a case study of the Chinese oil supply system.

Three-stage decision approach of network dynamic pricing and seat inventory control

The dynamic joint pricing and seat inventory control is more practical but complicated in both formulation and solving.This paper presents a three-stage decision approach(TSDA)to attack this problem.In the first stage,the relationship between dynamic prices and their pre-sale periods is built.The game process between passengers and the airline based on maximisation of both passenger utility and airline’s network revenue is applied.Passenger’s booking and cancellation processes are simulated according to respective distributions.In the second stage,the seat allocation model for different itineraries is built based on presented unified prices.It greatly decreases computation complexity since prices,itinerary legs and pre-sale time periods are excluded from combination.In the third stage,itinerary-based revenue management model is built with embedding the nested control strategy inside.Meanwhile,some practical factors such as cancellation and no-show are considered.The experimental computation results show TSDA is effective.

Inventory Management of a Plant Spare Parts Supply Chain

In spare parts industries, firms are dealing with a situation which is more and more uncertain due to the supply chain structure and variable demands. This paper presents a Bayesian approach to forecast demand and subsequently determine the appropriate parameter S of an （ S - 1 ; S） inventory system for controlling plant spare parts. We apply the Bayesian approach in an innovative way to specify the initial prior distributions of the failure rates, using the initial estimates and the failure history of similar items. According to the proposed method, a lower base stock than the one currently used is sufficient to achieve the desired service level.

Distribution Inventory Cost Optimization Under Grey and Fuzzy Uncertainty

The grey fuzzy variable was defined for the two fold uncertain parameters combining grey and fuzziness factors. On the basis of the credibility and chance measure of grey fuzzy variables, the distribution center inventory uncertain programming model was presented. The grey fuzzy simulation technology can generate input-output data for the uncertain functions. The neural network trained from the inputoutput data can approximate the uncertain functions. The designed hybrid intelligent algorithm by embedding the trained neural network into genetic algorithm can optimize the general grey fuzzy programming problems. Finally, one numerical example is provided to illustrate the effectiveness of the model and the hybrid intelligent algorithm.

New bounds of (s,S) policies in periodical review inventory systems

We study the stochastic inventory problem with optimal （s,S） policies.In a finite horizon model with lost sales,we establish new lower and upper bounds of s and S.These bounds have structural implications for the optimal solutions.Consequently,when demand has a generalized phase type distribution,there are no more than a pre-determined number of minima.Similar bounds can also be found for the system where unsatisfied demand is backordered instead of lost sales.

A Kind of Backwards Thinking-fast Moving Consumer Goods (FMCG) Stock-controlling Analysis

To control the material purchase of solve the problem of purchase planning, this a fast moving consumer goods （FMCG） enterprise warehouse and paper formulates an integrated system of reverse logistics operated with a demand forecast model and an economic storage model. This is based on the supply chain and focused on the marketing terminal. By analyzing the integrated system reverse logistics and the timely information marketing terminal, the purpose is to effectively alleviate or solve the terminal stock-out which needs to and oversupply which needs to return. According to the system, it can provide a theoretical basis for the inventory cost control from the replenish enterprise

Soft Computing Based Procurement Planning of Time-variable Demand in Manufacturing Systems

Procurement planning with discrete time varying demand is an important problem in Enterprise Resource Planning （ERP）. It can be described using the non-analytic mathematical programming model proposed in this paper. To solve the model we propose to use a fuzzy decision embedded genetic algorithm. The algorithm adopts an order strategy selection to simplify the original real optimization problem into binary ones. Then, a fuzzy decision quantification method is used to quantify experience from planning experts. Thus, decision rules can easily be embedded in the computation of genetic operations. This approach is applied to purchase planning problem in a practical machine tool works, where satisfactory results have been achieved.

Controlling Stock and Other Inventory Issues

Inventory control is one of the most important business processes during the operation of a trading or manufacturing company as it relates to purchases,sales and logistic activities.In order to have clear inventory management,a company should not only focus on logistic management but also on sales and purchase management. Commonly,we think of the warehouse as the most important component of inventory management and the accounting department is responsible for the inventory management. However,inventory control is not only the responsibility of the accounting department and the warehouse,but also the responsibility of the entire organization.Actually,there are many departments involved in the inventory control process,such as sales,purchasing, production,logistics and accounting.All these departments must work together in or- der to achieve effective inventory controls.

LOT SIZING WITH NON-ZERO SETUP TIMES FOR REWORK

In this paper we consider a single machine multi-product lot scheduling problem in which defective items are produced in any production run of each product. In each cycle after the normal production of each product the machine is setup for the rework of the defectives of the same product and then the rework process starts. We assume that the setup time for the normal production process as well as the rework process is non-zero. Further we consider the waiting time cost of defectives for rework. This paper has two objectives. The first objective is to obtain the economic batch quantity （EBQ） for a single product. The second objective is to extend the result of the first objective to the multi-product case. Adopting the common cycle scheduling policy we obtain optimal batch sizes for each product such that the total cost of the system per unit time is minimized.

AN ALGORITHM FOR THE DETERMINATION OF THE ECONOMIC ORDER QUANTITY IN A TWO-LEVEL SUPPLY CHAIN WITH TRANSPORTATION COSTS:COMPARISON OF DECENTRALIZED WITH CENTRALIZED DECISION

This paper considers a two-level supply chain consisting of one warehouse and one retailer. In this model we determine the optimal ordering policy according to inventory and transportation costs. We assume that the demand rate by the retailer is known. Shortages are allowed neither at the retailer nor at the warehouse. We study this model in two cases; decentralized and centralized. In the decentralized case the retailer and the warehouse independently minimize their own costs; while in the centralized case the warehouse and the retailer are considered as a whole firm. We propose an algorithm to find economic order quantifies for both the retailer and the warehouse which minimize the total system cost in the centralized case. The total system cost contains the holding and ordering costs at the retailer and the warehouse as well as the transportation cost from the warehouse to the retailer. The application of this model into the pharmaceutical downstream supply chain of a public hospital allows obtaining significant savings. By numerical examples, the costs are computed in MATLAB to compare the costs in the centralized case with decentralized one and to propose a saving-sharing mechanism through quantity discount.

Identifying strategic maintenance capacity for accidental damage occurrence in aircraft operations

Airline operators face accidental damages on their fleet of aircraft as part of operational practice.Individual occurrences are hard to predict;consequently,the approach towards repairing accidental damage is reactive in aircraft maintenance practice.However,by aggregating occurrence data and predicting future occurrence rates,it is possible to predict future long-term(strategic)demand for maintenance capacity.In this paper,a novel approach for integration of reliability modelling and inventory control is presented.Here,the concept of a base stock policy has been translated to the maintenance slot capacity problem to determine long-term cost-optimal capacity.Demand has been modelled using a superposed Non-homogeneous Poisson Process(NHPP).A case study has been performed on damage data from a fleet of Boeing 777 aircraft.The results prove the feasibility of adopting an integrated approach towards strategic capacity identification,using real-life data to predict future damage occurrence and associated maintenance slot requirements.

The newsvendor model revisited： the impacts of high unit holding costs on the accuracy of the classic model

The newsvendor problem has been applied in various business settings. It is often assumed that the decision variable, i.e., order-up-to level, has no impacts on the holding costs for average inventory cycled in a given period, which is the difference between beginning and ending inventory levels on hand in that period. The average holding cost for this portion of inventory is conveniently and approximately calcutated as half the product of the unit holding cost and the expectation of the demand in one period if it is assumed that the inventory is approximately evenly consumed. It is a good approximation when the unit holding cost is significantly lower than the unit backorder cost as this optimal solution to inventory level is able to guarantee a low probability of understocking. However, if this condition does not hold, the approximation may deviate from the actual cost and cannot measure the expected holding cost for this portion of inventory. This paper examines the impact of the cycle stock holding cost on the newsvendor model and the conditions under which this portion of cost is not negligible.