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This paper presents a new modeling approach called Progressive Modeling (PM) and demonstrates it by solving the Assembly Line Balancing Type II (ALBP-II) problem. PM introduces some new concepts that make the modeling process of large-scale complex industrial problems more systematic and their solution algorithms much faster and easily maintained. In the context of SALBP-II, PM introduces a component model to deploy the problem logic and its solution algorithm into several interacting components. The problem is represented as an object-oriented graph G(V,E,W) of vertices, edges, and workstations which enables our solutions to start anywhere. The novel representation relaxes the only forward and backward tracking approach used in the assembly line balancing problems in the related literature. The developed problem analysis and solution algorithms demonstrate why the new modeling paradigm should be promising and capable of handling more complex real-world assembly balancing problems. A set of wellreported problems in the literature is reported and solved. The paper concludes by demonstrating the efficiency of the new modeling approach and future extensions. [ABSTRACT FROM AUTHOR]

This paper presents a new modeling approach called Progressive Modeling (PM) and demonstrates it by solving the Unequal Areas Facility Layout Problem (UA-FLP). A problem solution is represented by a binary tree and an objective graph. PM introduces a component model to deploy the problem logic and its solution algorithm over several interacting components. Component models isolates the objective space frdsom the search space in a black-box fashion. A novel solution algoithm is presented to demonstrate how the search process is managed and controlled while searching for optimal or near optimal solutions. The solution process and the optimization algorithm are demonstrated using the developed software framework. A set of well-known problems in the literature are reported and solved. The developed problem analysis, solution algorithm, and results demonstrate why the proposed modeling approach is promising and capable of handling more complex real-world problems. [ABSTRACT FROM AUTHOR]