Bidding

About: Bidding is a research topic. Over the lifetime, 15371 publications have been published within this topic receiving 294233 citations. The topic is also known as: competitive bidding.

Combinatorial Auctions for Transportation Service Procurement: The Carrier Perspective

Abstract: The procurement of transportation services is an important task for shippers because of the need to control costs at the same time as providing high service levels. When shippers with goods and/or materials to transport seek transportation services from outside companies they typically put out a request for quotes from a set of carriers. They then assign contracts based on negotiated service charges. This process is similar to a simple sealed-bid auction in which each bidder submits a sealed bid for a single item. In the past, when shippers need to procure transportation services for a set of distinctive delivery routes (called lanes) they would obtain quotes for each lane individually and repeat the simple auction process for each lane. Alternatively, they might negotiate for bundles of lanes with a single carrier at a time. However, in the last few years software has been developed to allow shippers to make all lanes available for bidding simultaneously and to allow carriers to simultaneously bid upon combinations of individual lanes. This method of awarding contracts, conventionally called a combinatorial auction, has been reported to result in significant cost savings for shippers. Our research examines the benefits of combinatorial auctions primarily from the carrier's perspective. Preliminary findings, based on a simple simulation model suggest that benefits for carriers can also be significant.

Fuzzy Adaptive Particle Swarm Optimization for Bidding Strategy in Uniform Price Spot Market

Abstract: In a deregulated electricity market, generators have to optimally bid to maximize their profit under incomplete information of other competing generators. This paper addresses an optimal bidding strategy of a thermal generator in a uniform price spot market considering a precise model of nonlinear operating cost function and minimum up/down constraints of unit commitment. The bidding behaviors of other competing generators are described using normal probability distribution function. Bidding strategy of a generator for each trading period in a day-ahead market is solved by fuzzy adaptive particle swarm optimization (FAPSO), where inertia weight is dynamically adjusted using fuzzy evaluation. FAPSO can dynamically follow the frequently changing market demand and supply in each trading interval. The effectiveness of the proposed approach is tested with examples and the results are compared with the solutions obtained using genetic algorithm (GA) approach and other versions of PSO.

Notice of Violation of IEEE Publication Principles A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market

Abstract: In the framework of competitive electricity markets, power producers and consumers need accurate price forecasting tools. Price forecasts embody crucial information for producers and consumers when planning bidding strategies in order to maximize their benefits and utilities, respectively. The choice of the forecasting model becomes the important influence factor on how to improve price forecasting accuracy. This paper provides a hybrid methodology that combines both autoregressive integrated moving average (ARIMA) and artificial neural network (ANN) models for predicting short-term electricity prices. This method is examined by using the data of Australian national electricity market, New South Wales, in the year 2006. Comparison of forecasting performance with the proposed ARIMA, ANN, and hybrid models are presented. Empirical results indicate that a hybrid ARIMA-ANN model can improve the price forecasting accuracy.