An-Najah Staff

Optimization of hybrid photovoltaic/wind system for Kuala Terengganu, Malaysia has been implemented by considering optimal sizing of photovoltaic array, wind turbine, and battery. The optimization technique is done based on loss of load probability and system cost. Simulation models of hybrid photovoltaic/wind system are developed by using daily solar energy and wind speed records and considering many configurations of photovoltaic array, wind turbine, and storage battery. The optimization performed in this research aims to select the optimal capacities of photovoltaic array and wind turbine, which give minimum system cost.

This article presents a method for optimizing the tilt angle of photovoltaic module/array installed in the five sites in Malaysia. The optimization method is based on the Liu and Jordan model for solar energy incident on a tilt surface considering monthly and seasonal tilt angles. The optimization results showed that a seasonal optimum tilt angle change is recommended for the peninsular Malaysia, while a monthly optimum tilt angle change is recommended for east Malaysia comprising the states of Sabah and Sarawak. By applying the monthly optimum tilt angle, the collected yields by the PV module/array in Kuala Lumpur, Johor Bharu, Ipoh, Kuching, and Alor Setar increased by 5.03, 5.02, 5.65, 7.96, and 6.13%, respectively. On the other hand, applying the seasonal optimum tilt angle for the same regions increased the collected yields by 4.54, 4.58, 5.70, 4.11, and 5.85%, respectively.

This thesis has been realized as a part of the project GOCD (French acronym for Management and optimization of document life cycle) and within the context of the French competitive cluster PICOM. The project aims to design and develop a new paperless workflow system and decision making tool to replace the current paper based system. The new workflow system must manage and optimize received credit demands at COFIDIS.The first part of this thesis presents and discusses a framework to model and implement workflow systems. The proposed framework allows more flexibility in workflow reengineering process and operational analysis for different business process. The proposed framework uses the most recent and promising language to model and execute workflow the Business Process Modeling Notation (BPMN) and Business Process Execution Language (BPEL).The flexibility offered by BPMN can also lead to undesirable properties for business process such as deadlocks and unreachablity. More, BPMN notation was designed to model business process, and little consideration was concentrated to represent data and resources. As a result, carrying out performance analysis on a BPMN model is also limited.To overcome these problems, we propose two additional phases in the reengineering process. They are applied to the target BPMN model. The first phase is verification and validation and the second one is optimization. These two phases are realized by transforming the BPMN model to a formal language, Petri nets. As for optimization, a new variant of bin packing problem has been defined. And we propose to integrate its resolution in a decision making tool.

A maximum partition of a directed weighted graph is partitioning the nodes into two sets such that it maximizes the total weights of edges between the two sets. In this study a genetic algorithm is proposed to solve the maximum partition problem. Experiments performed on randomly generated graphs of different sizes show that the proposed algorithm converges to an optimal solution faster than the existing heuristic algorithm.

This paper presents a Genetic Algorithm for Production Systems Optimization (GAPSO). The GAPSO finds an ordering of Condition Elements (CEs) in the rules of a Production System (PS) that results in a (near) optimal PS with respect to execution time. Finding such an ordering can be difficult since there is often a large number of ways to order CEs in the rules of a PS. Additionally, existing heuristics to order CEs in many cases conflict with each other. The GAPSO is applicable to PSs in general and no assumptions are made about the matching algorithm or the interpreter that executes the PS. The results of applying the GAPSO to some example PSs are presented. In all examples, the GAPSO found an optimal ordering of CEs in a small number of iterations.

Current signal control strategies tend to ignore the pedestrian delays that may be imposed by reducing traffic delays. Such an objective is reasonable for motorways and rural roads where vehicular traffic is dominant over pedestrian traffic. However, it is not the case in metropolitan cities with large volume of pedestrian demands. This paper developed a traffic signal optimization strategy that considers both vehicular and pedestrian flows. The objective of the proposed model is to minimize the weighted vehicular and pedestrian delays. The deterministic queuing model is used to calculate vehicular traffic delay and pedestrian delay on sidewalk. Pedestrian delay on crosswalk is calculated based on an empirical pedestrian speed model, which considers interactions of pedestrian platoons and their impacts on average walking speed. A Japanese Intersection is utilized as a numerical case study to evaluate the proposed model. MATLAB is used to solve the optimization problem and to output a set of measures of effectiveness (MOEs). The results show that the proposed model improved average person delay (APRD) by 10% without changing the existing cycle length. Moreover, the model can optimize the cycle length and further improve APRD by as much as 44%. In order to demonstrate the applicability of the proposed model for general cases, this paper also conducted sensitivity analysis. The results showed that the proposed model is most significant and necessary for two circumstances: (1) metropolitan areas with high pedestrian demands and (2) major urban arterials with high pedestrian demands crossing major streets.

We present an integrated methodology for the optimal management of nitrate contamination of ground water combining environmental assessment and economic cost evaluation through multi-criteria decision analysis. The proposed methodology incorporates an integrated physical modeling framework accounting for on-ground nitrogen loading and losses, soil nitrogen dynamics, and fate and transport of nitrate in ground water to compute the sustainable on-ground nitrogen loading such that the maximum contaminant level is not violated. A number of protection alternatives to stipulate the predicted sustainable on-ground nitrogen loading are evaluated using the decision analysis that employs the importance order of criteria approach for ranking and selection of the protection alternatives. The methodology was successfully demonstrated for the Sumas–Blaine aquifer in Washington State. The results showed the importance of using this integrated approach which predicts the sustainable on-ground nitrogen loadings and provides an insight into the economic consequences generated in satisfying the environmental constraints. The results also show that the proposed decision analysis framework, within certain limitations, is effective when selecting alternatives with competing demands.