We propose to train trading systems and portfolios by optimizing objective functions that directly measure trading and investment performance. Rather than basing a trading system on forecasts or training via a supervised learning algorithm using labelled trading data, we train our systems using recurrent reinforcement learning (RRL) algorithms. The performance functions that we consider for rei...

This study models a joint pricing, inventory, and preservation decision-making problem for deteriorating items subject to stochastic demand and promotional effort. The generalized price-dependent stochastic demand, time proportional deterioration, and partial backlogging rates are used to model the inventory system. The objective is to find the optimal pricing, replenishment, and preservation t...

Private companies (PCs) in restructured electricity industries determine facility investment timing and sizing. Such decisions maximize the PC’s expected profit (rather than social welfare) under uncertainty. By anticipating incentives, a welfare-maximizing transmission system operator (TSO) shapes network to align public private objectives. Via an option-based approach, we first quantify welfa...

A major obstacle in data mining applications is the gap between the statistic-based pattern extraction and the value-based decision-making. “Profit mining” aims to reduce this gap. In profit mining, given a set of past transactions and pre-determined target items, we like to build a model for recommending target items and promotion strategies to new customers, with the goal of maximizing profit...

INTRODUCTION A major obstacle in data mining applications is the gap between the statistic-based pattern extraction and the value-based decision-making. Profit mining aims to reduce this gap. In profit mining, given a set of past transactions and predetermined target items, we like to build a model for recommending target items and promotion strategies to new customers, with the goal of maximiz...

Recently, market globalization and competition have forced companies to find alternative means to boost sales and revenue. The use of the cash flow is increasingly becoming a viable alternative for managers to improve their company’s profitability in a supply chain. In today’s business transactions, a supplier usually asks a manufacturer to pay via the advance-cash-credit (ACC) payment scheme i...

We introduce and study the range contract, which allows a buyer to procure from a supplier at a prescribed price any amountwithin a specified range. In return, the supplier is compensated up front for thewidth of the rangewith a range fee. This fee canbe viewed as the buyer tradingmonetary value for reduceduncertainty. The range contract generalizes and unifiesmany common contracts, such as fix...

This dissertation examines a profit-maximizing project selection and scheduling problem. Assume that a set of potentially profitable projects are available, yet limited available resources may not allow all of them to be pursued. Profit profiles for projects are assumed to be non-increasing functions of project completion times, i.e. profit returns are sensitive to time-to-market. Decision need...

In this paper an application of a combined method for the profit based unit commitment problem (PBUC) using Genetic Algorithm and Lagrangian Relaxation (LR) is presented. The algorithm is proposed to solve PBUC under deregulated environment with the objective of maximizing GENCO’s profit and minimizing the operating cost. The problem formulation of the unit commitment takes into consideration t...

This paper proposes a novel hybrid Monte Carlo simulation-genetic approach (MCS-GA) for optimal operation of a distribution network considering renewable energy generation systems (REGSs) and battery energy storage systems (BESSs). The aim of this paper is to design an optimal charging /discharging scheduling of BESSs so that the total daily profit of distribution company (Disco) can be maximiz...