Abstract
Neural regression provides a rapid solution to modeling complex systems with minimal computation effort. Recurrent structures such as fuzzy cognitive map (FCM) enable for drawing cause effect relationships among system variables assigned to graph nodes. Accordingly, the obtained matrix of edges, known as adjacency model, represents the overall behavior of the system. With this, there are many applications of semantic networks in data mining, computational geometry, physics-based modeling, pattern recognition, and forecast. This article examines a methodology for drawing application-specific adjacency models. The idea is to replace crisp neural weights with functions such as polynomials of desired degree, a property beyond the current scope of neural regression. The notion of natural adjacency matrix is discussed and examined as an alternative to classic neural adjacency matrix. There are examples of stochastic and complex engineering systems mainly in the context of modeling residential electricity demand to examine the proposed methodology.

Abstract
Logistics network design is a major strategic issue due to its impact on the efficiency and responsiveness of the supply chain. This paper focuses on strategic and tactical design of steel supply chain (SSC) networks. Ever-increasing demand for steel products enforces the steel producers to expand their production and storage capacities. The main purpose of the paper includes preparing a countrywide production, inventory, distribution, and capacity expansion plan to design an SSC network. The SSC networks consist of iron ore mines as suppliers, raw steel producer companies as producers, and downstream steel companies as customers. Demand is assumed stochastic with normal distribution and known at the beginning of planning horizon. To achieve the service level of interest, a potential production capacity along with two kinds of safety stocks including emergency and shared safety stocks are suggested by the authors. A mixed integer nonlinear programming (MINLP) model and a mixed integer linear programming (MILP) model are presented to design dynamic multi-commodity SSC networks. To evaluate the performance of the MILP model, a real case of SSC network design is solved. Furthermore, solving two proposed models by using a commercial solver for a set of numerical test cases shows that the MILP model outperforms MINLP in medium- and large-scale problems in terms of computational time. Finally, the complexity of the linear model is investigated by relaxing some major assumptions.

Abstract
Commonly in container terminals, the containers are stored in yards on top of each other using yard cranes. The split-platform storage/retrieval system (SP-AS/RS) has been invented to store containers more efficiently and to access them more quickly. The integrated scheduling of quay cranes, automated guided vehicles and handling platforms in SP-AS/RS has been formulated and solved using the simulated annealing algorithm in previous literatures. This paper presents a genetic algorithm (GA) to solve this problem more accurately and precisely. The GA includes a new operator to make a random string of tasks observing the precedence relations between the tasks. For evaluating the performance of the GA, 10 small size test cases were solved by using the proposed GA and the results were compared to those from the literature. Results show that the proposed GA is able to find fairly near optimal solutions similar to the existing simulated annealing algorithm. Moreover, it is shown that the proposed GA outperforms the existing algorithm when the number of tasks in the scheduling horizon increases (e.g. 30 to 100).

Abstract
The use of process discovery algorithms is in practice hindered by many factors, being the algorithm's representational bias, parameter configuration and algorithm's capabilities the most important ones. Nowadays, a user of these algorithms needs an expert knowledge in order to successfully apply them. In this demo, we present the RS4PD, a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Copyright © 2014 for this paper by its authors. Copying permitted for private and academic purposes.

Abstract
Over the last decade, several algorithms for process discovery and process conformance have been proposed. Still, it is well-accepted that there is no dominant algorithm in any of these two disciplines, and then it is often difficult to apply them successfully. Most of these algorithms need a close-to expert knowledge in order to be applied satisfactorily. In this paper, we present a recommender system that uses portfolio-based algorithm selection strategies to face the following problems: to find the best discovery algorithm for the data at hand, and to allow bridging the gap between general users and process mining algorithms. Experiments performed with the developed tool witness the usefulness of the approach for a variety of instances. © 2014 Springer International Publishing Switzerland.

Abstract
This paper describes the major elements of the Goldratt's framework - the Theory of Constraints (TOC) - in the banking sector, and examines the factors involved in the decision to adopt the TOC by companies in this sector. Through a deep literature review, analyzing similar cases that apply the Goldratt's framework in services and in manufacturing and the several views of its components, we aim at formulating a framework specifically for the banking system. The study uses a qualitative methodology supported by the information extracted from reality as it is framed in a multi-case study model. As part of the quantitative approach, we test several research hypotheses raised from the review of existing studies in the area. The main factors that influence the decision to adopt the TOC are the nature and the characteristics of the banking service, the attitude towards change, the leadership and the commitment of the entire institution. By using the Goldratt's approach outlined in this article, through the location of the constraints and develop practical measurement to facilitate the banking process improvements, banks can improve resource utilization, revenues and employee satisfaction.