Impact of information sharing in hierarchical decision-making framework in manufacturing supply chains

This paper presents a comprehensive framework for the analysis of the impact of information sharing in hierarchical decision-making in manufacturing supply chains. In this framework, the process plan selection and real-time resource allocation problems are formulated as hierarchical optimization problems, where problems at each level in the hierarchy are solved by separate multi-objective genetic algorithms. The considered multi-objective genetic algorithms generate near optimal solutions for NP-hard problems with less computational complexity. In this work, a four-level hierarchical decision structure is considered, where the decision levels are defined as enterprise level, shop level, cell level, and equipment level. Using this framework, the sources of information affecting the achievement of best possible decisions are then identified at each of these levels, and the extent of their effects from sharing them are analyzed in terms of the axis, degree and the content of information. The generality and validity of the proposed approach have been successfully tested for diverse manufacturing systems generated from a designed experiment.