Jack C. Hayya

A fuzzy clustering approach to manufacturing cell formation

Cell formation, one of the most important problems faced in designing cellular manufacturing systems, is to group parts with similar geometry, function, material and process into part families and the corresponding machines into machine cells. There has been an extensive amount of work in this area and, consequently, numerous analytical approaches have been developed. One common weakness of these conventional approaches is that they implicitly assume that disjoint part families exist in the data; therefore, a part can only belong to one part family. In practice, it is clear that some parts definitely belong to certain part families, whereas there exist parts that may belong to more than one family. In this study, we propose a fuzzy c-means clustering algorithm to formulate the problem. The fuzzy approach offers a special advantage over conventional clustering. It not only reveals the specific part family that a part belongs to, but also provides the degree of membership of a part associated with each part...

Quantifying the bullwhip effect in a supply chain with stochastic lead time

Abstract In a recent paper, Dejonckheere, Disney, Lambrecht, and Towill [European Journal of Operational Research 147 (2003) 567] used control systems engineering (transfer functions, frequency response, spectral analysis) to quantify the bullwhip effect. In the present paper, we, like Chen, Ryan, Drezner, and Simchi-Levi [Management Science 46 (2000) 436], use the statistical method. But our method extends Dejonckheere et al. and Chen et al. in that we include stochastic lead time and provide expressions for quantifying the bullwhip effect, both with information sharing and without information sharing. We use iid demands in a k -stage supply chain for both. By contrast, Chen et al. provide lower bounds using autoregressive demand for information sharing and for information not sharing (with zero safety factor for stocks). Dejonckheere et al. validate Chen et al.’s results for a 2-stage supply chain without information sharing, using both autoregressive and iid normally distributed demands. We estimate the mean and variance of lead-time demand (LTD) from historical LTD data, rather than from the component period demands and lead time. Nevertheless, we also calculate the variance amplification like Chen et al., but with gamma lead times. With constant lead times, which Chen et al. used, our method yields lower variance amplification. As for the effect of information, we find that the variance increases nearly linearly in echelon stage with information sharing but exponentially in echelon stage without information sharing.

Priority dispatching with operation due dates in a job shop

Abstract This paper investigates the effects on job shop performance of setting and attempting to enforce operation due dates for jobs. It does so by comparing via simulation the performance of priority dispatching rules when employed to enforce operation due dates versus job due dates. The results indicate that operation due dates improve every conventional measure of job shop performance.

Just-In-Time purchasing: Single or multiple sourcing?

Abstract We consider two Just-In-Time (JIT) purchasing models, one utilizing a few sources, and the other using the more conventional single source. We address the issue of splitting a large order quantity into multiple deliveries, taking account of the increase in the aggregate ordering, transportation, and inspection costs. For multiple sourcing, we formulate and solve a mathematical programming problem to obtain the optimal selection of suppliers and the size of the split orders. For single sourcing, we provide a procedure that yields the optimal number of deliveries.

A Note on Trend Removal Methods: The Case of Polynomial Regression versus Variate Differencing

This paper deals with the theoretical development of some aspects of the trend removal problem. The objective is to show the difference between the two most popular trend removal methods: first differences and linear least squares regression. On the one hand, we show that if first differences are used to eliminate a linear trend, the series of residuals would be stationary but would not be white noises as they contain a first lag autocorrelation of -0.50. Furthermore, the spectral density function (SDF) of these residuals relative to that of a white noise series would be exaggerated at the high frequency portion and attenuated at the low frequency portion. On the other hand, we show that the regression residuals from the linear detrending of a random walk series would contain large positive autocorrelations in the first few lags. Relative to that of white noises, the SDF of the regression residuals would be exaggerated at the low frequency portion and attenuated at the high frequency portion.

The effect of lead-time variability: The case of independent demand

Abstract We show in this study, through analysis and examples, the impact on stockouts and stockout risk if the variability of lead time in independent demand systems is ignored. In calculating safety stocks, we recommend that the compound distribution of demand during lead time, or a good approximation to it, be used. We motivate the article by a case study on lead-time variability at the U.S. Air Force and show the impact of lead-time variability by means of numerical examples and by marginal analysis. Having established that it is essential to consider lead-time variability, we take advantage of theoretical developments and show how to calculate reorder points and safety stocks in some common situations. It is important to use the proper form for the compound distribution of demand during lead time. A normal approximation to it will often yield significant errors. This is because the true distribution is usually very much skewed to the right.

SISCO: an object-oriented supply chain simulation system

We present SISCO, the Simulator for Integrated Supply Chain Operations, an object-oriented supply chain simulation tool. SISCO advances the concept of a supply chain simulator in a number of ways. With SISCO, we introduce a fundamentally new approach to supply chain specification, storage, and model generation. The user specifies the structure and policies of a supply chain with a GUI-based application and then saves the supply chain description in the open, XML-based Supply Chain Modeling Language (SCML) format. SISCO automatically generates the simulation model when needed by mapping the contents of the SCML file to a library of supply-chain-oriented simulation classes. SISCOs object-oriented, agent-style system architecture and detailed output improve upon current supply chain simulation tools.

Joint investment in quality improvement and setup reduction

Given a limited investment, we provide a general solution procedure that examines the trade-offs and that allocates that investment optimally for quality improvement and setup reduction. The paper is a generalization and an extension of Porteus [Operations Research 34, 141–143 (1986)]. We use general, continuous functions for quality improvement and for setup reduction. We find that in the face of a budget constraint, it is usually necessary to begin with either quality improvement or setup reduction, in order to bring one of these to some threshold, before joint investment should be undertaken. The contour lines of the total cost and the locus of the optimal solutions illustrate the behavior of the optimal joint investment. We also find that there could be some backtracking when the total relevant cost function for one type of option is strictly convex and is concave for the other type, i.e. a previous threshold investment in quality improvement or setup reduction may be reduced when the budget constraint is relaxed.

A multi-formalism architecture for agent-based, order-centric supply chain simulation

Abstract Agent-based simulation models can effectively represent decentralized systems. However, many supply-chains are order-driven, and agent modeling cannot effectively represent the order life-cycle. We present a conceptual architecture that combines simulation formalisms, allowing an agent representation of the supply-chain infrastructure while enabling a process-oriented approach to representing orders. This architecture allows for a natural, realistic representation of different supply-chain constructs and subsystems while following a consistent overall viewpoint. Our approach provides for excellent representation of supply-chain operations, allows for very detailed operational data to be gathered, and provides efficient representation of concurrent supply-chain activities in a manner that avoids preemption.

Lot streaming in multistage production systems

Abstract Lot streaming is a procedure in which a production lot is split into smaller sub-lots and moved to the next processing stage so that operations at successive stages of a multistage manufacturing system can be overlapped in time. Lot streaming reduces the manufacturing lead time and thereby provides an opportunity to lower the costs of holding work-in-process inventories. In this paper, we present an economic production lot size model that minimizes the total relevant cost when lot streaming is used. Using illustrative numerical examples, we show that our model can yield significant cost economies compared to the traditional approaches.