Competitive bidding strategies for online linear optimization with inventory management constraints

Prediction of cost is the most imperative task and the reason for settling on choices in competitive bidding strategies. Reliability, Robustness and optimal benefits for the market players are the fundamental concerns which can be accomplished by a point value anticipating module constitute of diminutive prediction errors, reduced complexity and lesser computational time. Thus in this work, a coordinated methodology dependent on Artificial Neural Networks (ANN) prepared with Particle Swarm Optimization (PSO) is proposed for momentary market clearing costs anticipating in pool based electricity markets. The proposed methodology overcomes the difficulties like trapping towards local minima and moderate convergence as in existing techniques. The work was speculated on territory Spain electricity markets and the outcomes obtained are compared with hybrid models presented in the previous literature. The response shows decline in forecasting errors that are recognized in price forecasting. The total research may help the ISO in finding the key factors that are fit for expectation with low errors.

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Markov game approach for multi-agent competitive bidding strategies in electricity market

IET Generation Transmission & Distribution ◽

10.1049/iet-gtd.2016.0075 ◽

2016 ◽

Vol 10 (15) ◽

pp. 3756-3763 ◽

Cited By ~ 14

Author(s):

Navid Rashedi ◽

Mohammad Amin Tajeddini ◽

Hamed Kebriaei

Keyword(s):

Electricity Market ◽

Competitive Bidding ◽

Bidding Strategies ◽

Markov Game ◽

Multi Agent

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Workload Distribution Within An Extensive Case Pedagogy: A Competitive Bidding Strategy

College Teaching Methods & Styles Journal (CTMS) ◽

10.19030/ctms.v1i3.5235 ◽

2011 ◽

Vol 1 (3) ◽

pp. 13

Author(s):

David Malone

Keyword(s):

Bounded Rationality ◽

Research Question ◽

Basic Research ◽

Competitive Bidding ◽

Bidding Strategy ◽

Intensive Courses ◽

Bidding Strategies ◽

Workload Distribution ◽

Bidding Mechanism ◽

Bidding Process

This paper presents a bidding strategy that may be incorporated into case-intensive courses. The purpose of the bidding process is to equitably distribute credit when students are assigned cases of differing degrees of difficulty. The paper also collects data to help answer a basic research question regarding this device: Is there evidence of bounded rationality among students in executing their bidding strategies? While there does appear to be evidence of some bounded rationality, the bidding mechanism appears to distribute workload and credit rationally.

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Efficient Online Linear Optimization with Approximation Algorithms

Mathematics of Operations Research ◽

10.1287/moor.2020.1053 ◽

2020 ◽

Author(s):

Dan Garber

Keyword(s):

Approximation Algorithms ◽

Optimization Problems ◽

Linear Optimization ◽

Natural Extension ◽

First Results ◽

Linear Optimization Problems ◽

Oracle Complexity ◽

Approximation Guarantee ◽

Factor Α ◽

Online Linear Optimization

We revisit the problem of online linear optimization in the case where the set of feasible actions is accessible through an approximated linear optimization oracle with a factor α multiplicative approximation guarantee. This setting in particular is interesting because it captures natural online extensions of well-studied offline linear optimization problems that are NP-hard yet admit efficient approximation algorithms. The goal here is to minimize the α-regret, which is the natural extension to this setting of the standard regret in online learning. We present new algorithms with significantly improved oracle complexity for both the full-information and bandit variants of the problem. Mainly, for both variants, we present α-regret bounds of [Formula: see text], were T is the number of prediction rounds, using only [Formula: see text] calls to the approximation oracle per iteration, on average. These are the first results to obtain both the average oracle complexity of [Formula: see text] (or even polylogarithmic in T) and α -regret bound [Formula: see text] for a constant c > 0 for both variants.

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