High Penalty Costs Research Articles

Sensitivity of UHF PD measurements in power transformers

The reliability of electrical energy networks depends on the quality and availability of electrical equipment like power transformers. Local failures inside their insulation may lead to catastrophic breakdowns and might cause high outage and penalty costs. To prevent these destructive events power transformers are e.g. tested for partial discharge (PD) activity before commissioning and currently also during service. The current work deals with the electromagnetic PD detection method, also known as UHF method. The disadvantage of the UHF method is still the missing possibility for a calibration or at least a verification of the sensitivity. The so-called Sensitivity Check might show in future a relation between unconventionally measured UHF quantities to the apparent charge level in pico Coulomb. An important aspect is the attenuation of UHF signals within power transformers which is investigated in this paper.

An optimal critical level policy for inventory systems with two demand classes

AbstractTraditional inventory systems treat all demands of a given item equally. This approach is optimal if the penalty costs of all customers are the same, but it is not optimal if the penalty costs are different for different customer classes. Then, demands of customers with high penalty costs must be filled before demands of customers with low penalty costs. A commonly used inventory policy for dealing with demands with different penalty costs is the critical level inventory policy. Under this policy demands with low penalty costs are filled as long as inventory is above a certain critical level. If the inventory reaches the critical level, only demands with high penalty costs are filled and demands with low penalty costs are backordered. In this article, we consider a critical level policy for a periodic review inventory system with two demand classes. Because traditional approaches cannot be used to find the optimal parameters of the policy, we use a multidimensional Markov chain to model the inventory system. We use a sample path approach to prove several properties of this inventory system. Although the cost function is not convex, we can build on these properties to develop an optimization approach that finds the optimal solution. We also present some numerical results. © 2008 Wiley Periodicals, Inc. Naval Research Logistics, 2008

Production strategies for coal quality improvement at SLE, Turkey

The Seyitömer Lignite Enterprise of the Turkish National Coal Board (SLE) is one of the largest coal producers in Turkey and produces coal from an open cast mine close to the city of Kütahya. The most significant problem that the enterprise currently faces is the low quality run of mine coal which is mainly sold to the nearby power station. The excavation of the partings in the seam together with coal increases the ash content of the coal produced, which in turn decreases the efficiency of the coal burning process at the power station and results in high penalty costs for the mine.Since the main problems at SLE were caused by the current coal quality, not meeting the standards required by the power plant and the market, and the need for balanced pit development an attempt was made at quality planning to meet the various requirements. Factors related to quality improvement for current and proposed production strategies were determined, according to the customers' quality demands and the saleable coal quality reserves. Afterwards, the necessity and the degree of selectivity for each strategy and determining of an optimum excavation configuration was defined. The result was high selectivity mining at the B3 seam to increase the power plant coal quality up to 1761 kcal kg−1 using a production strategy based on maximum coal supply to the market.

Intelligent supply chain management using adaptive critic learning

A set of neural networks is employed to develop control policies that are better than fixed, theoretically optimal policies, when applied to a combined physical inventory and distribution system in a nonstationary demand environment. Specifically, we show that model-based adaptive critic approximate dynamic programming techniques can be used with systems characterized by discrete valued states and controls. The control policies embodied by the trained neural networks outperformed the best, fixed policies (found by either linear programming or genetic algorithms) in a high-penalty cost environment with time-varying demand.

A single-period model for conjunctive use of ground and surface water under severe overdrafts and water deficit

We develop a model for a multi-reservoir system, where the inflow to the main reservoir and the demand for irrigation water at local areas are stochastic. The controlled releases from the main reservoir to local ones can be supplemented by groundwater from an aquifer suffering from severe overdrafts. We consider high penalty costs for pumping groundwater in order to reduce the risk of total depletion of the aquifer as well as quality degradation and seawater intrusion. We analyze the problem for a single period with a single decision-maker approach. We allow deficit irrigation in the problem of maximizing expected total profit for the entire region. We show that the formulated non-linear stochastic problem is concave with linear constraints and propose an iterative procedure that generates an optimal operating policy. We illustrate the model through a hypothetical example.