Lead-time analysis and a control policy for production lines

Abstract

We address a control problem for a production line which produces one product to stock and faces random demand. During stockouts, the system quotes a fixed response time for each arriving order, and the customers place their orders only if the response time promised meets their deadlines. Customer orders are filled on a first come, first served basis. A penalty cost is incurred whenever a customer is served later than promised. A two-parameter admission/inventory control policy is implemented that maintains a bounded backlog and a constant inventory position (total inventory minus backlog) in the system. For production lines with exponential processing times and Poisson demand, the mean profit rate of the system is computed analytically using closed queueing network formulas. For systems with general processing or interarrival time distributions, the profit rate is estimated via simulation.Simple properties are established which ensure that the profit maximizing control parameters can be determined in finite time using exhaustive search.Numerical results show that the proposed policy performs better than the make-to-order/zero-inventory and the lost-sales/make-to-stock policies. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 IntroductionProduction control involves decisions concerning when and how much to produce and when to accept incoming orders. Such decisions have significant effects on a number of operational indices such as throughput, mean inventory and backlog levels, and quality of service. These operational indices affect the mean profit rate of the system, which comprises profit from sales and the costs associated with inventory, backlog, delays in filling orders, possible loss of sales during stockout periods and so on (see, e.g., Porteus 1990, Section 3.7, p. 615). In the literature of production control there is a rich collection of policies that minimize the inventory and backlog costs.A simple and often effective inventory control policy is one that specifies a target value for the stock level, called base stock (Zipkin 2000, Buzacott andShanthikumar 1993); the manufacturing facility produces as long as the number of finished items is smaller than the base stock and idles otherwise.In order to handle incoming orders during stockout periods, many systems apply a lost sales (LS -all incoming orders rejected) or a complete backordering (CB -all incoming orders accepted) policy. When applying LS, there is no cost of delay in filling orders but the loss of sales to competition during stockouts makes it mandatory to keep a high stock level. On the contrary, facilities under CB appear to have vast backlog queues and long delays in filling orders which, apart from high backlogging costs, could also lead to potential loss of sales due to customer impatience.An admission control policy which is analogous to the base stoc...

Designing and computing optimal policies on a production model for non-instantaneous deteriorating items with shortages

“…Silver and Zufferey (2011) studied inventory control of an item with a probabilistic replenishment lead time and a known supplier shutdown period. Here we list some famous and recent research in this field: Absi et al (2011), Economopoulos and Kouikoglou (2011), Goncalves and Sousa (2011), Transchel et al (2011).…”

Bi̇r Üreti̇m Hattinin Üreti̇m Strateji̇si̇ne Karar Vermek İçi̇n Bütünleşi̇k Dematel-Anp Yaklaşimini Kullanmak

“…Senior executives place much importance on this topic, because long term production strategies play a direct role in the success of companies. (Li and Tzeng, 2009;Kılıç and Durmuşoğlu, 2015) 2 Line interruption Pull (Absi et al 2011;Kılıç and Durmuşoğlu, 2015;Wu et al, 2016) 10 Product variety Pull (Sali and Sahin, 2016;Slomp et al, 2009;Faccio, 2014) 7 Product type flexibility Pull (Kılıç and Durmuşoğlu, 2015;Slomp et al, 2009;Faccio, 2014) 18 Pricing Pull (Ozer and Uncu, 2015;Absi and Kedad-Sidhoum, 2008;Abdal, 1989;M cDaniel and M oore, 2005) 19 Technology cost Pull (Absi and Kedad-Sidhoum, 2008) 26 Product tracking Pull (Battini et al, 2013;Li and Tzeng, 2009;Bryan and Srinivasan, 2014;Holmström et al, 2010) 8 Demand flexibility Push (Giard and Jeunet, 2010;Faccio, 2014;Shao et al, 2016) 11 Inventory level Push (Li and Tzeng, 2009;Angelos and Kouikogloua, 2011;Slomp et al, 2009;Faccio, 2014) 12 Storage space Push (Kılıç and Durmuşoğlu, 2015) 13 Production capacity Push (Giard and Jeunet, 2010;Kılıç and Durmuşoğlu, 2015;Sali and Sahin, 2016;Absi and Kedad-Sidhoum, 2008) 14 Investment capacity Push (Sali and Sahin, 2016;Absi and Kedad-Sidhoum, 2008) 16 Holding cost Push (Absi and Kedad-Sidhoum, 2008) 17 Shortage cost Push (Absi and Kedad-Sidhoum, 2008) 20 Supplier produc...…”