Strategies for a Centralized Single Product Multiclass M/G/1 Make-to-Stock Queue

Make-to-stock queues are typically investigated in the M/M/1 settings. For centralized single-item systems with backlogs, the multilevel rationing (MR) policy is established as optimal and the strict priority (SP) policy is a practical compromise, balancing cost and ease of implementation. However, the optimal policy is unknown when service time is general, i.e., for M/G/1 queues. Dynamic programming, the tool commonly used to investigate the MR policy in make-to-stock queues, is less practical when service time is general. In this paper we focus on customer composition: the proportion of customers of each class to the total number of customers in the queue. We do so because the number of customers in M/G/1 queues is invariant for any nonidling and nonanticipating policy. To characterize customer composition, we consider a series of two-priority M/G/1 queues where the first service time in each busy period is different from standard service times, i.e., this first service time is exceptional. We characterize the required exceptional first service times and the exact solution of such queues. From our results, we derive the optimal cost and control for the MR and SP policies for M/G/1 make-to-stock queues.

[1]  Uri Yechiali,et al.  Stationary remaining service time conditional on queue length , 2007, Oper. Res. Lett..

[2]  Yoav Kerner The Conditional Distribution of the Residual Service Time in the M n /G/1 Queue , 2008 .

[3]  G. F. Newell,et al.  A relation between stationary queue and waiting time distributions , 1971, Journal of Applied Probability.

[4]  Tayfur Altiok,et al.  Performance analysis of manufacturing systems , 1996 .

[5]  Nima Sanajian,et al.  The impact of production time variability on make-to-stock queue performance , 2009, Eur. J. Oper. Res..

[6]  Lawrence M. Wein,et al.  Scheduling a Make-To-Stock Queue: Index Policies and Hedging Points , 1996, Oper. Res..

[7]  Stephen C. Graves,et al.  A Single-Product Inventory Model for Multiple Demand Classes , 2005, Manag. Sci..

[8]  Albert Y. Ha Inventory rationing in a make-to-stock production system with several demand classes and lost sales , 1997 .

[9]  Yves Dallery,et al.  Optimal Stock Allocation for a Capacitated Supply System , 2002, Manag. Sci..

[10]  Opher Baron,et al.  Regulated Random Walks and the LCFS Backlog Probability: Analysis and Application , 2008, Oper. Res..

[11]  Jean-Philippe Gayon,et al.  Stock rationing in an M/E r /1 multi-class make-to-stock queue with backorders , 2009 .

[12]  Carl M. Harris,et al.  Fundamentals of queueing theory , 1975 .

[13]  Miklós Telek,et al.  Introduction to Queueing Systems , 2019, Introduction to Queueing Systems with Telecommunication Applications.

[14]  N E Manos,et al.  Stochastic Models , 1960, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

[15]  Albert Y. Ha Stock-rationing policy for a make-to-stock production system with two priority classes and backordering , 1997 .

[16]  Fikri Karaesmen,et al.  Stock Rationing in an M / E k / 1 Make-to-Stock Queue with Backorders , 2005 .

[17]  Dimitris Bertsimas,et al.  The Distributional Little's Law and Its Applications , 1995, Oper. Res..

[18]  Richard S. Varga,et al.  Proof of Theorem 5 , 1983 .

[19]  William L. Maxwell,et al.  Theory of scheduling , 1967 .

[20]  Yves Dallery,et al.  Assessing the Benefits of Different Stock-Allocation Policies for a Make-to-Stock Production System , 2001, Manuf. Serv. Oper. Manag..