The cyclical scheduling problem

Bhattacharya, Binay; Chakraborty, Soudipta; Iranmanesh, Ehsan; Krishnamurti, Ramesh

doi:10.1016/j.tcs.2014.03.034

Cited by 1 publication

(1 citation statement)

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“…Cyclic scheduling is not only applied in manufacturing systems (Bocewicz et al 2018) but is also considered to be one of the most effective approaches for managing and planning people transportation, such as urban and intercity public transport or rail and air transport (Cacchiani and Toth 2012), and has many other applications in communications (Nunes et al 2018), staff scheduling (Bhattacharya et al 2014), robotics (Elmi and Topaloglu 2017), etc. Cyclic production in manufacturing is based on a repetitive production schedule, which can provide many benefits. Indeed, synchronization between production and deliveries is in accordance with the Bjust-in-time^principle (Hall 1988;Bahroun et al 2007).…”

Section: Introductionmentioning

confidence: 99%

Determination of dynamic safety stocks for cyclic production schedules

Bahroun

Belgacem

2019

Oper Manag Res

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Safety stocks are necessary to accommodate supply or demand uncertainties. Typically, safety stocks are held constant throughout the management horizon. In this paper, we study the determination of dynamic safety stock levels under cyclic production schedules. Currently, companies often operate according to contract orders with fixed and cyclic delivery dates and varying quantities. In this context, companies have attempted to implement cyclic production modes for their cyclic delivery requirements. Indeed, cyclic production scheduling has many benefits, such as respecting just-in-time principles by synchronising production with demand and improving shop floor control. The objectives of this paper are twofold: (1) to propose a new dynamic approach for determining safety stock levels that is adapted to cyclic production and (2) to demonstrate the limitations of traditional approaches under nonstationary demands. Our dynamic approach is based on modelling a repetitive production sequence comprising many manufacturing orders. Our model estimates the dynamic safety stock levels necessary to accommodate cyclic manufacturing orders with uncertain quantities and start dates using a Monte Carlo simulation approach. For the case in which all demands follow normal probability distributions, we validated the results of our simulation model by comparing them to numerically approximated theoretical results. We then compared the proposed dynamic simulation approach with the traditional approach. The results demonstrate that the dynamic approach is more efficient in terms of simultaneously minimizing the required safety stocks and improving the service level by decreasing the probability of stock outs. Finally, we applied our simulation approach to a case in which all demands follow uniform probability distributions.

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