Periodic capacity management under a lead-time performance constraint

Abstract

In this paper, we study a production system that operates under a leadtime performance constraint which guarantees the completion of an order before a pre-determined lead-time with a certain probability. The demand arrival times and the service requirements for the orders are random. To reduce the capacity-related operational costs, the production system under study has the option to use flexible capacity. We focus on periodic capacity policies and model the production system as a queuing system that can change its capacity periodically and choose to operate in one of the two levels: a permanent capacity level and a permanent plus contingent capacity level. Contingent capacity is supplied if needed at the start of a period, and is available during that period, at a cost rate that is decreasing in period length in different functional forms. Next, we propose a search algorithm that finds the capacity levels and the switching point that minimizes the capacity-related costs for a given period length. The behaviour of the capacity-related costs changes drastically under different period lengths and cost structures. In our computational study, we observe that the periodic capacity flexibility can reduce the capacity-related operational costs significantly (up to 35%). However, in order to achieve these savings, the period length must be chosen carefully depending on ambition level and capacity-related costs. We also observe that the percentage savings are higher for more ambitious lead-time performance constraints. Moreover, we observe that the use of contingent capacity makes the total system costs more insensitive to the lead-time performance requirements.

Periodical capacity setting methods for make-to-order multi-machine production systems

“…On the one hand the due dates of the customer can be negotiated to create a more smooth capacity demand (Corti, Pozzetti, and Zorzini 2006 ; Hopp and Roof Sturgis 2000 ; Hegedus and Hopp 2001 ; Keskinocak and Tayur 2004 ). On the other hand the capacity can be adjusted according to the fluctuations of the customer demand (Bradley and Glynn 2002 ; Buyukkaramikli, Bertrand, and van Ooijen 2013 ; Kok 2000 ; Li, Hendry, and Teunter 2009 ; Mincsovics and Dellaert 2009 ; Van Mieghem and Rudi 2002 ). The methods discussed in this paper are based on capacity adjustment literature whereby a flexible capacity with upper and lower bounds is assumed.…”

“…In recent research queuing state dependent capacity adjustment models focusing on the transient behaviour of the queuing system when switching between different capacity levels are studied (Buyukkaramikli, Bertrand, and van Ooijen 2013 ; Mincsovics and Dellaert 2009 ). In Mincsovics and Dellaert ( 2009 ) a continuous setting is discussed in which an up-switching-point and a down-switching-point are identified and each switch incurs costs.…”

“…In Mincsovics and Dellaert ( 2009 ) a continuous setting is discussed in which an up-switching-point and a down-switching-point are identified and each switch incurs costs. A periodic setting with two possible capacity levels has been studied extensively in Buyukkaramikli, Bertrand, and van Ooijen ( 2013 ).…”

Integrating inventory control and capacity management at a maintenance service provider

“…Afterwards, Fu et al (2000) prove the monotonicity of the optimal control by establishing the sub-modularity of the objective function with regard to the initial queue size and the staffing level. In a single processing unit (Buyukkaramikli et al 2013a) and parallel processing unit (Make-to-order) MTO environments (Buyukkaramikli et al 2013b), savings from threshold-type, two-level periodic capacity control policies are analyzed. In both of the studies, it is assumed that capacity actions only can be taken at equidistant points in time and the systems operate under a lead time performance constraint.…”