Abstract
In the fiercely competitive era induced by expansion of open business archetypes, the managerial aspects of Extended Manufacturing Environments (EMEs) are experiencing growing concerns. There is no scope of leaving a possible operational improvement unexplored. For enhanced operational efficiency and capacity utilization the balancing and scheduling problems of EMEs are, therefore, rightfully considered and an integer programme is proposed in this paper. The model is designed in a spread sheet and solved through What'sBest optimizer. The model capabilities is assessed through a test problem. The results have demonstrated that the model is capable of defining optimized production schedules for EMEs. © 2015 AISTI.

Abstract
In this paper, a multi-period integrated approach is developed for Generation Expansion Planning (GEP), Transmission Expansion Planning (TEP) and Natural Gas Grid Expansion Planning (NGGEP) problems for large scale systems. New nodal generation requirements, new transmission lines and Natural Gas (NG) pipelines are simultaneously obtained in a multi-period planning horizon. In order to solve the large scale mixed integer nonlinear problem, an approach is developed based on genetic algorithms. The proposed approach performance is investigated by applying it to a typical electric-NG combined grid. Moreover, in order to evaluate the effectiveness of the proposed approach for real-world systems, it has been applied to the Iranian power and NG system. The results indicate that the proposed approach is applicable for large scale systems.

Abstract

Abstract
Manufacturing environments require a real-time adaptation and optimization method to dynamically and intelligently maintain the current scheduling plan feasible. This way, the organization keeps clients satisfied and achieves its objectives (costs are minimized and profits maximized). This paper proposes an optimization approach - Selection Constructive based Hyper-heuristic for Dynamic Scheduling - to deal with these dynamic events, with the main goal of maintaining the current scheduling plan feasible and robust as possible. The development of this dynamic adaptation approach is inspired on evolutionary computation and hyper-heuristics. Our empirical results show that a selection constructive hyperheuristic could be advantageous on solving dynamic adaptation optimization problems.

Abstract

Abstract
This short note compares two different methods for exploring type-inhabitation in the simply typed lambda-calculus, highlighting their similarities.