Performance evaluation of a multi-product system under CONWIP control

Abstract

Analytical models of multi-product manufacturing systems operating under CONWIP control are composed of closed queuing networks with synchronization stations. Under general assumptions, these queuing networks are hard to analyze exactly and therefore approximation methods must be used for performance evaluation. This research proposes a new approach based on parametric decomposition. Two-moment approximations are used to estimate the performance measures at individual stations. Subsequently, the traffic process parameters at the different stations are linked using stochastic transformation equations. The resulting set of non-linear equations is solved using an iterative algorithm to obtain estimates of key performance measures such as throughput, and mean queue lengths. Numerical studies indicate that the proposed method is computationally efficient and yields fairly accurate results when compared to simulation.

A simulation study of CONWIP assembly with multi-loop in mass production, multi-products and low volume and OKP environments

“…Satyam and Krishnamurthy (2008) analysed multi-product manufacturing systems operating under CONWIP control. Multi-product manufacturing systems are composed of closed queuing networks with synchronisation stations.…”

A comparison of HK-CONWIP and BK-CONWIP control strategies in a multi-product manufacturing system

“…Some of these studies proposed techniques for solving scheduling issues that arise when production authorisation cards for two or more product types are waiting in a queue and a decision is required to ascertain which product-type should be released first [22][23][24][25]. Additionally, a number of studies [26,27] developed mathematical or simulation models for optimisation of production authorisation card in order to minimise the inventory, production and shortage costs in a multi-product system, while certain studies [28][29][30][31] evaluated the effect of the WIP cap of CONWIP in multi-product manufacturing systems. To address the issue of selection of an appropriate pull control strategy, researchers compare and rank the performance of various pull control strategies [32][33][34][35][36][37].…”

Evaluation of multi-product lean manufacturing systems with setup and erratic demand

“…These studies assumed that research results on single-product lean manufacturing environments are scalable to multi-product lean manufacturing environments. This assumption is not reliable because a pure implementation of a PPCS in a multi-product environment requires maintaining semi-finished parts of each of the products distributed throughout the system, which proliferates WIP inventory (Olaitan & Geraghty, 2013;Onyeocha, 2012Onyeocha, , 2014Onyeocha, Khoury, & Geraghty, 2013, 2015Satyam & Krishnamurthy, 2008).…”

“…Duenyas, 1994;El-Khouly, El-Kilany, & El-Sayed, 2009;Prakash & Chin, 2014;Ryan, Baynat, & Choobineh, 2000;Ryan & Vorasayan, 2005;Satyam & Krishnamurthy, 2008; investigated the behaviour and the effect of the work-in-process inventory limit of CONWIP). The assumptions that production authorisation cards are the same in single and multi-product lean manufacturing environments were prevalent in these studies, resulting in the dedication of production authorisation cards to a part-type in these studies.…”