Modelling,measurement and evaluation of sequencing flexibility in manufacturing systems

Sequencing flexibility refers to the possibility of interchanging the order in which required manufacturing operations are performed. In this paper, we address several issues related to the modelling, measurement and performance evaluation of this flexibility in manufacturing systems. In particular, we introduce several representation and measurement schemes for sequencing flexibility and discuss the usefulness and limitations each. We then propose a new performance-based approach for quantifying the value of flexibility. To this effect, we study the relationship between flexibility and system performance under a variety of design assumptions and operating conditions. These relationships are used to identify key characteristics of a measure of flexibility that is reflective of system performance. This measure is to allow system designers and managers to predict performance based on existing levels of flexibility and/or determine the amount of flexibility required to achieve a certain level of performance. This measure is shown to be easier to calculate than traditional performance measures and to be possible to use as a substitute for these measures in estimating the effect of flexibility on performance.     

Workload allocation in multi-product, multi-facility production systems with setup times

In this article, we model the problem of assigning work to M heterogeneous servers (machines), which arises from exogenous demands for N products, in the presence of nonzero setup times. We seek a workload allocation which minimizes the total expected Work-in-Progress (WIP) inventory. Demands are assumed to arrive according to independent Poisson processes, but the setup and the processing times can have arbitrary distributions. Whenever a machine produces more than one product type, production batch sizes are determined by a group scheduling policy; which is also known as the cyclic-exhaustive polling policy. We formulate the workload allocation problem as a nonlinear optimization problem and then provide several insights gleaned from first order necessary conditions, from numerical examples, and from a close examination of the objective function. For example, we show that increasing either the load or the number of products assigned to a machine, or both, does not necessarily increase its contribution to total WIP. These insights are then used to devise a heuristic workload allocation as well as a lower bound. The heuristic allocation is further refined using a nonlinear optimization algorithm.     

Scheduling policies,batch sizes,and manufacturing lead times

In this paper we examine the impact of scheduling policies on batch sizing decisions in a multi-item production system. We also investigate the joint effect of scheduling policies and batch sizing decisions on production lead times. In particular we compare the performance of a first-come-first-served (FCFS) policy with that of a group scheduling (GS) policy and study the effect of each on the optimal batch size. We show that a GS policy can lead to significant performance gains, as measured by reduced lead times and higher production rates, relative to the FCFS policy, and characterize conditions under which these gains are realized. We also study the impact of the GS policy on other system operating parameters. In particular we find that using a GS policy eliminates the need for batching, preserves system capacity despite the presence of setups, and accommodates higher product mix variety. These results are shown to be very different from those obtained for the FCFS policy and to have important implications for the management and control of multi-item production facilities.