The introduction of deregulation and the subsequent open access policy in electricity sector have opened up the door for power transactions between generators and bulk consumers under many different market-driven contractual forms including bilateral contracts. Long-term bilateral contracts are attractive to many parties who want to avoid price volatility. With bilateral contracts it becomes necessary to allocate transmission loss to respective transactions. An artificial neural network based transmission loss allocation method is presented in this paper. The method is computationally efficient and can provide solutions on a realtime basis. Most independent system variables can be used as inputs to this neural network which in turn makes the loss allocation process responsive to practical situations. Training and testing of this network have been done with the help of the IEEE 24-bus test system. A technique has been developed to expedite the convergence and to improve the accuracy of the results. Numerical examples on loss allocations for both peak and off-peak hours have been provided and compared with those obtained using another technique.

This paper aims to design an inventory model for a retail enterprise with a profit maximization objective using the opportunity for a price discount facility given by a supplier. In the profit maximization objective, the demand should be increased. The demand can be boosted by lowering the selling price. However, lowering the selling price may not always give the best profit. Impreciseness plays a vital role during such decision-making. The decision-making and managerial activities may be imprecise due to some decision variables. For instance, the selling price may not be deterministic. A vague selling price will make the retail decision imprecise. To achieve this goal, the retailer must minimize impreciseness as much as possible. Learning through repetition may be a practical approach in this regard. This paper investigates the impact of fuzzy impreciseness and triangular dense fuzzy setting, which dilutes the impreciseness involved with managerial decisions. Based on the mentioned objectives, this article considers an inventory model with price-dependent demand and time and a purchasing cost-dependent holding cost in an uncertain phenomenon. This paper incorporates the all-units discount policy into the unit purchase cost according to the order quantity. In this paper, the sense of learning is accounted for using a dense fuzzy set by considering the unit selling price as a triangular dense fuzzy number to lessen the impreciseness in the model. Four fuzzy optimization methods are used to obtain the usual extreme profit when searching for the optimal purchasing cost and sale price. It is perceived from the numerical outcomes that a dense fuzzy environment contributes the best results compared to a crisp and general fuzzy environment. Managerial insights from this paper are that learning from repeated dealing activities contributes to enhancing profitability by diluting impreciseness about the selling price and demand rate and taking the best opportunity from the discount facility while purchasing.