Usage of background knowledge about events and their relations to other concepts in the application domain, can improve the quality of event processing. In this paper, we describe a system for knowledge-based event detection of complex stock market events based on available background knowledge about stock market companies. Our system profits from data fusion of live event streams and backgroun...

For many people the term sustainable mining is an oxymoron. After all, mining entails the exploitation of non-renewable resources. Eventually these resources will be gone and mining will have to cease. As a result, many concerned individuals urge society to conserve non-renewable resources and where possible to use renewable resources instead. Drawing on what we have learned from the debate ove...

Stock market prediction is regarded as a challenging task in financial time-series forecasting. The central idea to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. To achieve these purposes this article presents an integrated approach based on genetic fuzzy systems (GFS) and artificial neural networks (ANN)...

All intensively studied and widely applied inventory control policies satisfy demand in accordance with the First-Come-First-Served (FCFS) rule, whether this demand is in backorder or not. Interestingly, this rule is sub-optimal when the fill-rate is constrained or when the backorder cost structure includes fixed costs per backorder and costs per backorder per unit time. In this paper we study ...

We consider a system in which a single finished good is assembled from two components. Demand for the finished product is stochastic and stationary, and procurement and assembly leadtimes are constant. Unsatisfied demand is backordered. The inventory of each component or assembly is controlled by a separate firm using a base-stock policy. Each firm is charged holding costs on its own inventory ...

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a “data mining-based evolutionary fuzzy expert system” (DEFE...

Motivated by practices in modern supply chains, we consider capacity-inventory joint management for a make-to-stock manufacturing system operating under a base stock policy. The production facility is modeled as multiple servers operating in parallel. The number of servers corresponds to the capacity decision and the base stock level is the inventory decision. The main problem which we consider...

W consider the optimal production and inventory control of an assemble-to-order system with m components, one end-product, and n customer classes. A control policy specifies when to produce each component and, whenever an order is placed, whether or not to satisfy it from on-hand inventory. We formulate the problem as a Markov decision process and characterize the structure of an optimal policy...

We study single and multistage inventory systems with stochastic lead times. We study a class of stochastic lead time processes, which we refer to as exogenous lead times. This class of lead time processes includes as special cases all lead time models from existing literature (such as Kaplan’s lead times with no order crossing or independent and identically distributed lead times with order cr...

We study a multi-item stochastic inventory system in which customers may order different but possibly overlapping subsets of items, such as a multiproduct assemble-to-order system. The goal is to determine the right base-stock level for each item and to identify the key driving factors. We formulate a cost-minimization model with order-based backorder costs and compare it with the standard sing...