TY - JOUR
T1 - QMCD approach for perishability models
T2 - The (S, s) control policy with lead time
AU - Barron, Yonit
AU - Baron, Opher
N1 - Publisher Copyright:
© 2020, Copyright © 2020 “IISE”.
PY - 2020/2/1
Y1 - 2020/2/1
N2 - We consider cost minimization for an (S, s) continuous-review perishable inventory system with random lead times and times to perishability, and a state-dependent Poisson demand. We derive the stationary distributions for the inventory level using the Queueing and Markov Chain Decomposition (QMCD) methodology. Applying QMCD, we develop an intuitive approach to characterizing the distribution of the residual time for the next event in different states of the system. We provide comprehensive analysis of two main models. The first model assumes a general random lifetime and an exponential distributed lead time. The second model assumes an exponential distributed lifetime and a general lead time. Each model is analyzed under both backordering and lost sales assumptions. We consider a fixed cost for each order, a purchase cost, a holding cost, a cost for perished items, and a penalty cost in the case of shortage. Numerical examples are provided and show that variability of lead time is more costly than that of perishability time. Therefore, after reducing lead time and increasing perishability time, managers should focus on reducing variability of lead time.
AB - We consider cost minimization for an (S, s) continuous-review perishable inventory system with random lead times and times to perishability, and a state-dependent Poisson demand. We derive the stationary distributions for the inventory level using the Queueing and Markov Chain Decomposition (QMCD) methodology. Applying QMCD, we develop an intuitive approach to characterizing the distribution of the residual time for the next event in different states of the system. We provide comprehensive analysis of two main models. The first model assumes a general random lifetime and an exponential distributed lead time. The second model assumes an exponential distributed lifetime and a general lead time. Each model is analyzed under both backordering and lost sales assumptions. We consider a fixed cost for each order, a purchase cost, a holding cost, a cost for perished items, and a penalty cost in the case of shortage. Numerical examples are provided and show that variability of lead time is more costly than that of perishability time. Therefore, after reducing lead time and increasing perishability time, managers should focus on reducing variability of lead time.
KW - (S, s) policy
KW - inventory/production
KW - perishability
KW - stochastic processes
UR - http://www.scopus.com/inward/record.url?scp=85068780033&partnerID=8YFLogxK
U2 - 10.1080/24725854.2019.1614697
DO - 10.1080/24725854.2019.1614697
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AN - SCOPUS:85068780033
SN - 2472-5854
VL - 52
SP - 133
EP - 150
JO - IISE Transactions
JF - IISE Transactions
IS - 2
ER -