Stochastic Modeling for Automated Material Handling System Design and Control

M. Eric Johnson and Margaret L. Brandeau
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M. Eric Johnson: Owen Graduate School of Management, Vanderbilt University, Nashville, Tennessee 37203
Margaret L. Brandeau: Department of Industrial Engineering and Engineering Management, Stanford University, Stanford, California 94305-4024

Transportation Science, 1996, vol. 30, issue 4, 330-350

Abstract: Material handling systems (MHSs) are micro-transportation systems that share many of the same challenges in design and operation as their larger-scale transportation counterparts. However, because of the automation technology employed in material handling, such systems also present many new challenges. Over the past decade, progress in automation technology has led to the replacement of many non-automated MHSs with automated systems. Use of an automated MHS can lead to significantly lower overall material handling cost, and thus overall production cost. However, the installation cost of such a system can be significant, and an MHS that does not function properly can lead to significant losses in productivity and profits. Analysis of design and control problems for such systems must take into account the fact that MHSs typically operate in a stochastic environment, with stochastic demands for material handling, flexibility in vehicle routing, variable processing times and other random elements. This paper reviews research on design and control of automated MHSs, with emphasis on analytical models that incorporate stochastic elements. We focus on models of automated storage and retrieval systems and automated guided vehicle systems. In addition to evaluating the existing literature, we describe areas where further research is needed.

Date: 1996
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