Determining cycle time for a multi-product FPR model with rework and an improved delivery policy by alternative approach

The present study determines the common cycle time for a multi-product finite production rate (FPR) model with rework and an improved delivery policy [1] by an alternative approach. Conventional method to the multi-product FPR problem employs the differential calculus to first prove convexity of the system cost function, then to derive the optimal common production cycle time that minimizes the long-run average system cost per unit time; whereas the proposed approach obtains the optimal cycle time without the need to reference the differential calculus. Such a simplified method may help those practitioners who have insufficient knowledge of calculus to effectively manage the real-life multi-product FPR problem

Priority allocation decisions in large scale MTO/MTS multi-product manufacturing systems : Technical report

In this paper, the authors consider a single stage multi-product manufacturing facility producing a large number of end-products for delivery within a service constraint for the customer lead-time. The manufacturing facility is modeled as a multi-product, multi-priority queuing system. In order to reduce inventory costs, an e±cient priority allocation between items consists in producing some items according to a Make-To-Stock (MTS) policy and others according to a Make-To-Order (MTO)policy epending on their features (costs, required lead-time, demand rates). The authors propose a general optimization procedure that gives a near-optimal °ow control (MTO or MTS) to associate with each product and the corresponding near-optimal priority strategy. We illustrate e±ciency of our procedure via several examples and by a numerical analysis. In addition, we show numerically that a small number of priority classes is su±cient to obtain near-optimal performances.Make-to-Stock (MTS); Make-to-Order (MTO); Priority allocation; Scheduling rule; Heterogeneous multi-product queuing system

Branching-type polling systems with large setups

The present paper considers the class of polling systems that allow a multi-type branching process interpretation. This class contains the classical exhaustive and gated policies as special cases. We present an exact asymptotic analysis of the delay distribution in such systems, when the setup times tend to infinity. The motivation to study these setup time asymptotics in polling systems is based on the specific application area of base-stock policies in inventory control. Our analysis provides new and more general insights into the behavior of polling systems with large setup times. © 2009 The Author(s)

Setting safety stocks for stable rotation cycle schedules

The article of record as published may be found at http://dx.doi.org/10.1016/j.ijpe.2014.05.020In the process industries, specialized equipment and production processes often necessitate the manufacture of products in a pre-determined sequence to minimize change over time and to simplify scheduling complexity; these types of schedules are referred to as pure rotation schedules, or product wheels, where the circumference of the wheel is the production cycle length. In these industries change over times between the production of individual products can consume considerable time as well as raw materials and it is therefore often desirable to stabilize the production cycles in order to minimize unplanned change overs as well as quote accurate lead times to customers. Materials requirements planning (MRP) systems are often used to plan and coordinate production and supply resources with demand in these environments. Central to the effectiveness of the MRP system is the dependability of the lead time parameters. In this paper, we introduce an optimization model to determine safety stock levels that minimize long run expected costs where as table, cyclic schedule is used. Our model may be used strategically to assess inventory investment requirements as a function of capacity investment, product mix, production technology, demand volatility, and customer service levels. It may be used tactically to optimize item-level planning parameters such as lot size, safety stock and lead time in an MRP system and to support sales and operations planning(S&OP) processes where knowing the future costs associated with current decisions is highly desirable

Iterative approximation of k-limited polling systems

The present paper deals with the problem of calculating queue length distributions in a polling model with (exhaustive) k-limited service under the assumption of general arrival, service and setup distributions. The interest for this model is fueled by an application in the field of logistics. Knowledge of the queue length distributions is needed to operate the system properly. The multi-queue polling system is decomposed into single-queue vacation systems with k-limited service and state-dependent vacations, for which the vacation distributions are computed in an iterative approximate manner. These vacation models are analyzed via matrix-analytic techniques. The accuracy of the approximation scheme is verified by means of an extensive simulation study. The developed approximation turns out be accurate, robust and computationally efficient

Production and inventory control in complex production systems using approximate dynamic programming.

Production systems focus not only on providing enough product to supply the market, but also on delivering the right product at the right price, while lowering the cost during the production process. The dynamics and uncertainties of modern production systems and the requirements of fast response often make its design and operation very complex. Thus, analytical models, such as those involving the use of dynamic programming, may fail to generate an optimal control policy for modern production systems. Modern production systems are often in possession of the features that allow them to produce various types of product through multiple working stations interacting with each other. The production process is usually divided into several stages, thus a number of intermediate components (WIP) are made to stock and wait to be handled by the next production stage. In particular, development of an efficient production and inventory control policy for such production systems is difficult, since the uncertain demand, system dynamics and large changeover times at the work stations cause significant problems. Also, due to the large state and action space, the controlling problems of modern production systems often suffer from the curse of dimensionality