Parts-feeding systems for assembly: organisation, logistics and automation

Parts-feeding systems for assembly: organisation, logistics and automation

The aim of this e-book is to play the role of a catalyst in advancing the research of part logistics in assembly systems. This is achieved by presenting novel, high-quality papers investigating part logistics and part feeding in assembly systems. These papers include innovative methodologies and tools, new real-world case studies and innovative automation technologies. The issue covers the macro, facility and micro logistics issues and factors of the problem, and enables readers to grasp the various aspects of part supply in mixed model assembly systems.

A Changeover Time Reduction through an integration of lean practices: a case study from pharmaceutical sector,A decision model for kitting and line stocking with variable operator walking distances,A genetic algorithm for supermarket location problem,A model for kitting operations planning,A simple mechanical measurement system for the posture evaluation of wing components using the PSO and ICP algorithms,Advances in assembly line parts feeding policies: a literature review,Implementation framework for a fully flexible assembly system (F-FAS),Kanban-driven parts feeding within a semi-automated O-shaped assembly line: a case study in the automotive industry,Multi-manned assembly line balancing problem with balanced load density,New Kanban model for tow-train feeding system design,Order batching and time efficiency in kit preparation,Part-feeding with supermarket in assembly systems: transportation mode selection model and multi-scenario analysis,Parts-feeding systems for assembly: macro and micro logistics,Planning models for continuous supply of parts in assembly systems,Re-balancing problem for assembly lines: new mathematical model and exact solution method,Re-layout of an assembly area: a case study at Bosch Rexroth Oil Control,Robust optimization approach to production system with failure in rework and breakdown under uncertainty: evolutionary methods

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