The material handling industry has, for many years, struggled with the problem of evaluating the reliability and availability (R&A) of complex material handling systems (MHS). Many complex systems, such as automated guided vehicle systems, automated storage and retrieval systems, and those used for order picking and sortation, are in place throughout the world, yet engineers are rarely able to adequately represent system R&A in a meaningful way. The major problem is that upon failure of individual components, these systems can often continue to operate at degraded levels of performance. In addition, this degraded performance is highly unpredictable. Currently there is no standardized method in the industry for communicating R&A characteristics of these systems. This has, at times, led to complex legal issues between customers and suppliers of these systems. This paper addresses some of these issues by proposing a "performability index" that quantifies the effect of component failure and repair on system performance. The method is based on the use of simple quadratic loss functions and discrete event simulation. An example for an actual flexible manufacturing system is presented to illustrate the approach.

This paper examines the production rate, expected buffer storage level, and part transit time for k−stage automated production flow lines with closed-loop, recirculating conveyor buffers. Analytical models are developed for the performance of the recirculating conveyor buffer. Computer simulation results are presented to validate these analytical models and to compare the operational performance of these recirculating conveyor systems with that of k−stage systems having classical static buffer storage.

MAINTAINING EFFICIENCIES IN THE OPERATIONAL PROCESSES OF CRUDE OIL GATHERING is essential to the competitiveness of every oil company and helps to ensure that consumers get the best products at the...

In a common parking lot, much of the space is devoted not to parking but to lanes in which cars travel to and from parking spaces. Lanes must not be blocked for one simple reason: a blocked car might need to leave before the car that blocks it. Self-parking and intelligent communication capabilities of autonomous vehicles introduces an opportunity to overcome this constraint, and therefore to achieve much higher storage capacity of cars. We show how to maximize the number of cars in a parking lot that assumes interfering cars could be moved out of the way by a centralized controller. We provide optimal results for small lots with a single entry point, and we offer heuristic methods for larger lots. Improvements in parking capacity of 80 percent is possible.

The proposed availability demonstration test (ADT) is an extension of the work of Usher and Taylor [Usher, J.S. and Taylor, G.D., "Availability Demonstration Testing," Qual. Reliab. Eng. Int., Vol. 22, No. 4, 2006, pp. 473-472] and provides reduced testing time. The ratio of the cumulative repair times and the cumulative failure times are then compared to an upper and lower limit. Values of the ratio above the upper limit signal a reject decision. Values below a lower limit signal an accept decision. Values in between the limits signal a repeat of the test. The advantages of the proposed test are discussed over the test plan given by Usher and Taylor. This method should be of great value to consumers and producers who want to ensure that bad systems are rejected and good systems are accepted based on system availability.

PurposeThis paper seeks to present the results of an experiment to investigate the effect of six part orientation (XY, XZ, YX, YZ, ZY, ZX) and a wide range of energy densities on ultimate tensile strength (UTS) and elongation of laser‐sintered nylon 12 (PA‐12) test specimens.Design/methodology/approachASTM Type 1 specimens were built on a DTM Sinterstation 2500+ and tensile tested on an Instron 5569 A. The resulting data were fit to non‐linear regression models based on the well‐known Weibull growth model to predict each response based on the total energy density used in each trial.FindingsThe resulting regression models provide excellent fits with low sum of squared errors and normally distributed residuals. The resulting material properties are highly affected by the energy density and the build orientation. However, once sufficient energy density is applied, properties tend to converge to consistent values. To achieve maximum UTS of approximately 52 MPa, it is recommended that values of energy density above 0.25 W‐s per mm3 be used. To achieve maximum elongation of approximately 15‐16 percent, it is recommended that values of energy density above 0.40 W‐s per mm3 be used when building parts in the XY, XZ, YX, YZ orientations. Parts built in the ZX orientation exhibit lower elongation values at or below 12 percent for even high values of energy density.Originality/valueThis paper extends previous work of Starr, Gornet and Usher on the relationship between material properties, part orientation and energy density by proposing the use of the Weibull growth model. Recommendations are provided to assist users in the selection of correct energy density to achieve desirable mechanical properties in each specified orientation.

In this research, new optimization models are developed to determine the optimal preventive maintenance and replacement schedules in repairable and maintainable systems. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability over the planning horizon. Experimental results of a sensitivity analysis on the optimization models are presented and evaluated. These experiments investigate the effect of the parameters on the structure of optimal preventive maintenance and replacement schedules in multi-component systems. Two factorial design experiments based on the cost associated with maintenance and replacement activities and reliability characteristic parameters are constructed and analyzed. In addition, a comprehensive experiment is designed to analyze and compare the efficiency and accuracy of the exact and metaheuristic algorithms.

This paper presents mathematical models and a solution approach to determine the optimal preventive maintenance schedules for a repairable and maintainable series system of components with an increasing rate of occurrence of failure (ROCOF). The maintenance planning horizon has been divided into discrete and equally-sized periods and in each period, three possible actions for each component (maintain it, replace it, or do nothing) have been considered. The optimal decisions for each component in each period are investigated such that the objectives and the requirements of the system can be achieved. In particular, the cases of minimizing total cost subject to a constraint on system reliability, and maximizing system reliability subject to a budgetary constraint on overall cost have been modeled. As the optimization methodology, dynamic programming combined with branch-and-bound method is utilized and the effectiveness of the approach is presented through the use of a numerical example. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

PurposeThe purpose of this paper is to measure the effect of process conditions on mechanical properties of laser‐sintered nylon 12 (Duraform®) and to determine the range of conditions that provide consistent mechanical performance for additive manufacturing.Design/methodology/approachTensile test specimens were fabricated over a range of well‐characterized process conditions including laser power, laser speed, scan spacing, layer thickness, build orientation, and build position. Tensile modulus, yield strength, ultimate tensile strength and elongation‐at‐fracture were measured and related to process parameters.FindingsTensile properties are strongly related to the amount of energy deposited during scanning. Strength and modulus approach their maximum values as the energy deposited exceeds the amount needed to fully melt the applied powder. Elongation‐at‐fracture does not reach its maximum until higher energy‐melt ratio. Performance of blends with reused powder matches that of virgin powder when blend composition is adjusted to a standard melt‐flow index. The volumetric energy density and the energy‐melt ratio are useful for correlating mechanical properties with multiple process parameters and material thermal properties.Originality/valueThis work presents the most extensive data to date on mechanical properties of nylon 12 (Duraform®) as they relate to the full range of process parameters. These data show that mechanical performance correlates strongly with the volume energy density. In contrast to the area energy density (a.k.a. Andrews Number), this volumetric parameter includes the effect of varying layer thickness and can be related directly to the melting characteristics of the polymer material. Within the parameter range studied, this relationship allows adjustment of one scan parameter for improved speed or dimensional accuracy while ensuring good strength by an offsetting adjustment of another parameter. Such trade‐offs will be important in future manufacturing applications of the laser sintering process. Understanding the energy‐melt ratio provides insight into the relationship between scan conditions and the physics of powder melting and sintering, and offers a methodology to relate results at other bed temperatures and with other polymer powders.

In this article, a new multi-objective optimization model is developed to determine the optimal preventive maintenance and replacement schedules in a repairable and maintainable multi-component system. In this model, the planning horizon is divided into discrete and equally-sized periods in which three possible actions must be planned for each component, namely maintenance, replacement, or do nothing. The objective is to determine a plan of actions for each component in the system while minimizing the total cost and maximizing overall system reliability simultaneously over the planning horizon. Because of the complexity, combinatorial and highly nonlinear structure of the mathematical model, two metaheuristic solution methods, generational genetic algorithm, and a simulated annealing are applied to tackle the problem. The Pareto optimal solutions that provide good tradeoffs between the total cost and the overall reliability of the system can be obtained by the solution approach. Such a modeling approach should be useful for maintenance planners and engineers tasked with the problem of developing recommended maintenance plans for complex systems of components.

PurposeThis paper seeks to develop and present a new mathematical formulation to determine the optimal preventive maintenance and replacement schedule of a system.Design/methodology/approachThe paper divides the maintenance‐planning horizon into discrete and equally‐sized intervals and in each period decide on one of three possible actions: maintain the system, replace the system, or do nothing. Each decision carries a specific cost and affects the failure pattern of the system. The paper models the cases of minimizing total cost subject to a constraint on system reliability, and maximizing the system reliability subject to a budgetary constraint on total cost. The paper presents a new mathematical function to model an improvement factor based on the ratio of maintenance and repair costs, and show how it outperforms fixed improvement factor models by analyzing the effectiveness in terms of cost and reliability of the system.FindingsOptimal decisions in each period over a planning horizon are sought such that the objectives and the requirements of the system can be achieved.Practical implicationsThe developed mathematical models for this improvement factor can be used in theoretical and practical situations.Originality/valueThe presented models are effective decision tools that find the optimal solution of the preventive maintenance and replacement scheduling problem.

Masked system life test data arises when the exact component which causes the system failure is unknown. Instead, it is assumed that there are two observable quantities for each system on the life test. These quantities are the system life time, and the set of components that contains the component leading to the system failure. The component leading to the system failure may be either completely unknown (general masking), isolated to a subset of system components (partial masking), or exactly known (no masking). In the dependent masked system. life test data, it is assumed that the probability of masking may depend on the true cause of system failure. Masking is usually due to limited resources for diagnosing the cause of system failures, as well as the modular nature of the system. In this paper, we present point, and interval maximum likelihood, and Bayes estimators for the reliability measures of the individual components in a multi-component system in the presence of dependent masked system life test data. The life time distributions of the system components are assumed to be geometric with different parameters. Simulation study will be given in order to 1) compare the two procedures used to derive the estimators for the reliability measures of system components, 2) study the influence of the masking level on the accuracy of the estimators obtained, and 3) study the influence of the masking probability ratio on the accuracy of the estimators obtained.

PurposeThe purpose of this paper is to examine whether or not driver life, carrier performance, and customer service can be improved as a result of the use of a technique called yard‐stacking in the truckload trucking industry. The technique seeks to find ways to provide level freight availability during normal weekly cycles in an effort to seek improvement relative to all constituencies.Design/methodology/approachSimulation is used to examine the use of yard‐stacking on Fridays to provide additional freight on weekends, which is generally much less available than on weekdays. In this technique, before being dispatched on Friday for a long‐haul, a driver initially picks up a load to make a short “dray” move from the customer site to the carrier's closest terminal yard. During the weekend, another driver picks up the drayed load. In this research, we evaluate the potential of weekend yard‐stacking under a variety of scenarios.FindingsThe paper shows that a carrier's adaptation of weekend freight leveling can be beneficial to both trucking companies and their customers, while remaining relatively neutral to drivers.Research limitations/implicationsCarriers may be able to utilize Friday yard‐stacking to improve their cost efficiency, driver satisfaction and customer performance.Originality/valueThis research extends the knowledge base of truckload freight imbalance problems. It was industrially motivated by J.B. Hunt Transport, Inc., one of the world's largest truckload carriers, who provided freight data and conceptual guidance.

This paper presents a methodology for production planning within facilities involved in the remanufacture of products. Remanufacturing refers to the process of accepting inoperable units, salvaging good and repairable components from those units, and then re-assembling good units to be re-issued into service. These types of facilities are common, yet many suffer from the unpredictability of good and repairable component yields, as well as processing time variation. These problems combine to make it extremely difficult to predict whether overall production output will be sufficient to meet demand. Low yields of key components can lead to shortages which require the facility to purchase new components for legacy systems, often with long lead times, thus causing overall delays. The approach developed here is a probabilistic form of standard material requirements planning (MRP), which considers variable yield rates of good, bad, and repairable components that are harvested from incoming units, and probabilistic processing times and yields at each stage of the remanufacturing process. The approach provides estimates of the expected number of remanufactured units to be completed in each future period. In addition, we propose a procedure for generating a component purchase schedule to avoid shortages in periods with a low probability of meeting demand. The proposed methodology is applied to an antenna remanufacturing process at the Naval Surface Warfare Center (NSWC). In this case study the proposed methodology identifies a potential shortage of a key component and suggests a corrective action to avoid significant delay in the delivery of remanufactured units.