Farhad Moeeni

A ROBUST DESIGN METHODOLOGY FOR KANBAN SYSTEM DESIGN

Many companies are interested in implementing just-in-time (JIT) manufacturing philosophies in response to increased competitive pressures on manufacturing. At the shop floor level, one application of JIT is through the introduction of Kanbans (or cards) so as to control in-process inventory. Traditionally, it has been argued that Kanban systems work well when the shop floor environment is fairly stable. In this paper, we propose and illustrate a methodology, based on the robust design concept of Taguchi, to implement Kanban systems in uncertain environments. We show how this procedure can be used to determine appropriate settings for the decision factors based on the inherent variations on the shop floor. From a managerial perspective, this procedure can be used not only for Kanban system design but also to identify shop floor factors which can be the targets of improvement efforts.

Effective Engineering Design through Simulation

Abstract This paper presents a framework for designing, analyzing and improving systems and processes via discrete event simulation. The framework incorporates a robust design philosophy into a response surface metamodeling approach, and the simulation setting provides the analyst with an increased level of control relative to industrial experimentation. System optimization and improvement efforts can be carried out efficiently and effectively, providing insights into system behavior and suggesting optimal system configurations which may yield substantial improvements over those selected using more traditional approaches. One noteworthy benefit of the simulation framework is that robust design methodologies can be applied prospectively — at the inception and conceptualization phases of an engineering design project. We illustrate the method by considering the design of a small job shop.

Stage-wise solution to a bicriteria staff-scheduling model

This paper proposes an efficient, stage-wise optimisation model for scheduling part-time staff. During stage 1, the problem is formulated as a totally unimodular integer linear programme (ILP) that produces integer solution upon solving its LP relaxation. The model produces optimum work shifts of various lengths along with the optimal number of employees needed in each shift while minimising the total labour time. In the second stage, an assignment model is used to allocate employees to various work shifts while maximising their preferences. The stage-wise model has the necessary flexibility and computational efficiency to solve many real-world business scheduling problems, as illustrated by three separate case studies.

Passive RFID-based localization, algorithms and estimation accuracy

RFID-based localization has become a major area of interest in ubiquitous computing in the last decade. RFID has been used for identifying and tracking static objects such as inventory items or mobile objects such as robots, vehicles, etc. Various range-based and triangulation methods have commonly been used with RFID systems (active or passive) for localization purposes. These methods include RSS, TOA, TDOA, etc. On the other hand, less attention has been devoted to range-free or proximity methods. Specifically, the proximity methods work well with passive RFID systems. We propose proximity—based passive RFID model that can identify the location of mobile nodes relative to existing anchor nodes, i.e. nodes with known location. The study explores various algorithms for estimating location coordinates. These algorithms are much simpler to implement than other techniques such as the tag segregation. We also discuss the resulting accuracy and precision of the proposed model, implemented in a laboratory environment.

A Refinement of the Classical Order Point Model

Factors such as demand volume and replenishment lead time that influence production and inventory control systems are random variables. Existing inventory models incorporate the parameters (e.g., mean and standard deviation) of these statistical quantities to formulate inventory policies. In practice, only sample estimates of these parameters are available. The estimates are subject to sampling variation and hence are random variables. Whereas the effect of sampling variability on estimates of parameters are in general well known in statistics literature, literature on inventory control policies has largely ignored the potential effect of sampling variation on the validity of the inventory models. This paper investigates the theoretical effect of sampling variability and develops theoretically sound inventory models that can be effectively used in different inventory policies.