Abstract
This paper describes an approach to the short term operation planning of hydro stations in market environment. The developed approach is based on the solution of an optimization problem to maximize the profit of a generation agent along a planning period discretized in hourly steps using a Genetic Algorithm. This problem includes the possibility of pumping since this is an important resource in the scope of electricity markets. The scheduling problem was developed starting with an initial simplified version in which the head loss is neglected and the head is assumed constant. Then, it was implemented a second model in which the nonlinear relation between the head, the hydro power and the water discharge is retained and finally an approach in which the hydro schedule obtained in a given step is used to update the hourly electricity prices used to compute the profit of the generation agent. The short term hydro scheduling problem is illustrated using two Case Studies - the first one was designed to run a set of initial tests to the developed algorithm and the second one refers to a set of hydro stations that mirrors a cascade of 8 stations in Portugal. © 2013 IEEE.

Abstract

Abstract
A model to optimize the water use in the irrigation of a farm field via optimal control (water flow) that take into account the evapotranspiration, rainfall, and losses by filtration and runoff was developed in [1]. Here we improve the previous model to take into account real data of rainfall. Model predictive control is applied to replan. We test and compare different nonlinear constrained optimization techniques for solving the nonlinear constrained optimization problem that arises from the discretization of the proposed optimal control problem. Furthermore, we test different time discretization steps.

Abstract

Abstract
This work deals with the optimal short-term scheduling of general multipurpose batch plants, considering multiple operational characteristics such as sequence-dependent changeovers, temporary storage in the processing units, lots blending, and material flows traceability. A novel Mixed Integer Linear Programming (MILP) discrete-time formulation based on the State-Task Network (STN) is proposed, with new types of constraints for modeling changeovers and storage. We also propose some model extensions for addressing changeovers start; nonpreemptive lots; lots start and sizes; alternative task-unit and task-unit-layout assignments. Computational tests have shown that the proposed model is more effective than a similar model based on the Resource-Task Network (RTN).

Abstract
Purpose - This article seeks to contribute to a better understanding of company social networks (CSNs), which constitute people connected to a company or brand through a social networking site. To this end, the research addresses both participation goals and CSN attributes that drive participation in CSNs. Design/methodology/approach - With a grounded theory approach, this research begins with an exploratory study of the page maintained by a large retailer for six months, followed by a qualitative study featuring in-depth individual interviews and focus groups with 26 members of the CSN. Findings - The results highlight differences between CSNs and other types of online communities (OCs). Members rely on the company to help them achieve their goals; few count on their CSN peers, with whom they maintain weak ties. Unlike in brand communities (BCs), most members are not enthusiasts but instead engage in a pragmatic relationship with the brand. Practical implications - CSNs can create value for both the host company and its members; active management is necessary to unlock that potential. The implications for CSN management include strategies to foster participation and increase value for companies and members. Originality/value - Although the previous research has studied OCs, specific research on CSNs is scarce. This study characterizes CSNs and provides details regarding participation factors in this new context, as well as relevant implications for CSN management and service research.