|کد مقاله||کد نشریه||سال انتشار||مقاله انگلیسی||ترجمه فارسی||نسخه تمام متن|
|1698061||1519300||2016||4 صفحه PDF||سفارش دهید||دانلود رایگان|
Any assembly line planning (ALP) problem requires the knowledge of multitude of planners and is therefore the interface between the product-oriented and process-oriented planning process. The assembly line planning process (i.e., assembly task definition, time analysis, product-oriented assembly task sequencing, assembly line balancing and assembly line sequencing and scheduling) can be associated as the gateway between the fuzzy front end and the fuzzy back end of the innovation process. As ALP can be solved using solution search approaches, the formalization of the required information is the key to be able find at least a feasible, preferably a satisfying at most optimal solution. The input to solve the ALP are optimization objective(s), a precedence graph and further restrictions to consider. As decision making problems are widely researched, the collaboration for the precedence graph and the restriction modeling is regarded for the ALP.As the Collaborative Precedence and Constraint Modeling (CPCM) has also to meet the requirements of the solution method. In this case genetic algorithms (GA) is used for finding solution to the problem. The used algorithm for solution search is fast and state of the art, but also requires uniform input data. Hence, only an acyclic precedence and constraint digraph is computable in an adequate way.As soon multiple planners work jointly together within a virtual environment, only locking or conflict resolution approaches are offered to generate a computable precedence and constraint model for the solving an ALP using GA. As locking is a practical way from the technical point of view, it does not meet the requirements of the planners to model relevant precedence and constraint relations of the regarded elements (tasks, stations, resources and line segments). As a result a recommender system for transforming a cyclic digraph to an acyclic digraph and promoting solution improvements are presented based on network structure metrics of precedence, constraint and solution graph.
Journal: Procedia CIRP - Volume 52, 2016, Pages 130–133