Thomas L. Landers

Intelligent tracking in manufacturing

Dynamic scheduling is an important new innovation in manufacturing and supply chain management. However, the success of dynamic scheduling will depend on real-time information. This paper describes intelligent tracking technologies that provide real-time information throughout the supply chain to support keywords a logistics planning and execution.

THE VIRTUAL WAREHOUSING CONCEPT

The virtual warehouse (VW) is a state of real-time global visibility for logistics assets such as inventory and vehicles. The efficiency of the VW is comparable to that achieved in a local warehouse facility. We assess the enabling technologies, develop a conceptual framework, and identify the essential decision-support modules of a virtual warehouse. A case study for field repair service illustrates potential benefits of perfect information, in terms of reduced transportation, labor costs and service times. The case study pertains to field logistics support for business telephone systems. The productivity and utilization of highly skilled field technicians are improved substantially. A field scrap policy supported by VW reduces the volume of returns to the repair depot by over 20%.

A heuristic algorithm for minimizing total production time for a sequence of jobs on a surface mount placement machine

In the high-mix, low-volume environment faced by many electronics manufacturers, setup engineering techniques are becoming increasingly imporlant for competitiveness. This is especially true in printed wiring board (PWB) assembly where assignment of components to machine slots is a very time-consuming task. In this paper we develop the intelligent slot-assignment (ISA) algorithm to sequence a group of PWB assembly jobs on one placement machine with the objective of minimizing the total production time. The algorithm consists of two stages: the assignment stage and the reassignment stage. In the first stage, new parts are assigned on the machine with the objective of minimizing setup time; while in the second stage, parts are reassigned to different locations on the machine to minimize the runtime. We also demonstrate that the algorithm tends to produce job sequences which are close to the optimum.

An assignment algorithm for dynamic picking systems

In a dynamic environment, such as high-technology industry, a forward picking area requires an intelligent approach to ongoing rewarehousing (reassignment of stock items to locations). The items go...

Assessment of repairable-system reliability using proportional intensity models: a review

This paper provides an overview of methods, and surveys the literature on engineering applications of proportional intensity (PI) models with explanatory variables (covariates), for repairable systems reliability assessment. The semi-parametric PI method relaxes the assumption of an underlying distribution, and is potentially useful in engineering practice, where the underlying information for a failure process is usually not available. PI semi-parametric models initially proposed for clinical studies in medical applications include PWP (Prentice, Williams, and Peterson), AG (Andersen, and Gill), and WLW (Wei, Lin, and Weissfeld). Abundant funding received in medical research has advanced PI models to become well developed, and widely referenced in the biostatistics field. This paper reviews both the available methods for repairable-system reliability assessment, and the published engineering application case studies. An engineering application example that applies PI model to a maintainability process used in US Army M1A2 Arams Main Battle Tank is presented.

A reliability simulation approach for use in the design process

A simulation model for use in the engineering design process is reported. The simulation model facilities reliability modeling by design engineers and reliability analysts early in the design process. The model applies to preliminary feasibility and design tradeoff studies. The authors describe the model, focus on applications to mission reliability analysis, and give a case study for the voice-communication system of the F-16 fighter aircraft. >

A discretizing approach for stress/strength analysis

This paper implements and evaluates a discretizing approach for estimating the reliability of systems for which complex functions define strength or stress and where the derivation of reliability exceed analytic techniques. The discretizing approach predicts system reliability with reasonably high accuracy. Specifically, there is little difference in the accuracy of predictions for three engineering problems when compared to simulation results. The reliability predictions are near the 95% confidence intervals of the simulation results and are best in the high reliability and low reliability regions. The small errors observed are attributed to the estimation errors of the discretizing approach. The mid-range reliability values (e.g. 50% reliability) are not generally of interest in engineering applications, and even for these value, the errors are small. There is little improvement in increasing the number of points in the pmf from 3 to 6. Due to this small difference, 3 discretizing points are recommended for reliability predictions when computational ease is of concern and limited to 4 points when more accurate reliability predictions are required. This paper models three systems and evaluates the robustness (departures from assumed distributions) of the discretizing approach. The discretizing approach is not too sensitive to departures from the assumed distribution of the underlying random variables regions are accurately estimated.

Semi-parametric proportional intensity models robustness for right-censored recurrent failure data

This paper reports the robustness of the four proportional intensity (PI) models: Prentice–Williams–Peterson-gap time (PWP-GT), PWP-total time (PWP-TT), Andersen–Gill (AG), and Wei–Lin–Weissfeld (WLW), for right-censored recurrent failure event data. The results are beneficial to practitioners in anticipating the more favorable engineering application domains and selecting appropriate PI models. The PWP-GT and AG prove to be models of choice over ranges of sample sizes, shape parameters, and censoring severity. At the smaller sample size (U=60), where there are 30 per class for a two-level covariate, the PWP-GT proves to perform well for moderate right-censoring (Pc≤0.8), where 80% of the units have some censoring, and moderately decreasing, constant, and moderately increasing rates of occurrence of failures (power-law NHPP shape parameter in the range of 0.8≤δ≤1.8). For the large sample size (U=180), the PWP-GT performs well for severe right-censoring (0.8<Pc≤1.0), where 100% of the units have some censoring, and moderately decreasing, constant, and moderately increasing rates of occurrence of failures (power-law NHPP shape parameter in the range of 0.8≤δ≤2.0). The AG model proves to outperform the PWP-TT and WLW for stationary processes (HPP) across a wide range of right-censorship (0.0≤Pc≤1.0) and for sample sizes of 60 or more.

Robustness of a semi-parametric proportional intensity model

The Prentice, Williams, Peterson (PWP) semiparametric model for the failure processes of repairable systems involves regression on explanatory variables across strata defined by the failure-event count. A heuristic method is developed for assessing the robustness of the PWP model, where the true underlying process is nonhomogeneous Poisson with power-law intensity function. The PWP model performed well for large samples and increasing rates of occurrence of failures and poorly for small samples and decreasing rates of occurrence of failures. >

Semi-parametric PWP model robustness for log-linear increasing rates of occurrence of failures

Abstract Repairable systems have reliability (failure) and maintainability (restoration) processes that tend to improve or deteriorate over time depending on life-cycle phase. External variables (covariates) can explain differences in event rates and thus provide valuable information for engineering analysis and design. In some cases, the processes may be modeled by a parametric non-homogeneous Poisson process (NHPP) with proportional intensity function, incorporating the covariates. However, the true underlying process may not be known, in which case a distribution-free or semi-parametric model may be very useful. The Prentice, Williams and Peterson (PWP) family of proportional intensity models has been proposed for application to repairable systems. This paper reports results of a study on the robustness of one PWP reliability model over early failure history. The assessment of robustness was based on the semi-parametric PWP models ability to predict the successive times of occurrence of events when the underlying process actually is parametric (specifically a NHPP having log-linear proportional intensity function with one covariate). A parametric method was also used to obtain maximum likelihood estimates of the log-linear parameters, for purposes of validation and as a reference for comparison. The PWP method provided accurate estimates of the time to next event for NHPP log-linear processes with moderately increasing rates of occurrence of events. Potential engineering applications to repairable systems, with increasing rates of event occurrence, include reliability (failure) processes in the wear-out phase and maintainability (restoration) processes in the learning phase. A real example of a maintainability (restoration) process (log-linear NHPP with two explanatory covariates) for US Army M1A2 Abrams Main Battle Tank serves to demonstrate the engineering relevance of the methods evaluated in this research.