A Bayesian Sequential Single Machine Scheduling Problem to Minimize the Expected Weighted Sum of Flowtimes of Jobs with Exponential Processing Times

Toshio Hamada and Kevin D. Glazebrook
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Toshio Hamada: Himeji College of Hyogo, Himeji, Japan
Kevin D. Glazebrook: University of Newcastle Upon Tyne, Newcastle Upon Tyne, United Kingdom

Operations Research, 1993, vol. 41, issue 5, 924-934

Abstract: In this paper, we consider a scheduling problem in which m classes, J 1 , J 2 , …, J m , of independent jobs with ready time 0 are to be processed by a single machine. The number of jobs of class J i is n i and the processing times of these n i jobs are independent and identically distributed exponentially distributed with unknown parameter θ i , which has a conjugate gamma prior. The objective is to minimize the expected (weighted) sum of flowtimes of all the jobs, where R i is the weight for a job of class J i . The problem is formulated as a dynamic program and optimal strategies are derived.

Keywords: dynamic programming/optimal control: Bayesian; dynamic allocation index; production/scheduling: sequencing; stochastic; single machine (search for similar items in EconPapers)
Date: 1993
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