Spare parts provisioning for multiple k-out-of-n:G systems

In this paper, we consider a repair shop that fixes failed components from different k-out-of-n:G systems. We assume that each system consists of the same type of component; to increase availability, a certain number of components are stocked as spare parts. We permit a shared inventory serving all systems and/or reserved inventories for each system; we call this a hybrid model. Additionally, we consider two alternative dispatching rules for the repaired component. The destination for a repaired component can be chosen either on a first-come-first-served basis or by following a static priority rule. Our analysis gives the steady-state system size distribution of the two alternative models at the repair shop. We conduct numerical examples minimizing the spare parts held while subjecting the availability of each system to exceed a targeted value. Our findings show that unless the availabilities of systems are close, the HP policy is better than the HF policy

Analysis of the finite-source multiclass priority queue with an unreliable server and setup time

In this article, we study a queueing system serving multiple classes of customers. Each class has a finite-calling population. The customers are served according to the preemptive-resume priority policy. We assume general distributions for the service times. For each priority class, we derive the steady-state system size distributions at departure/arrival and arbitrary time epochs. We introduce the residual augmented process completion times conditioned on the number of customers in the system to obtain the system time distribution. We then extend the model by assuming that the server is subject to operation-independent failures upon which a repair process with random duration starts immediately. We also demonstrate how setup times, which may be required before resuming interrupted service or picking up a new customer, can be incorporated in the model

The impact of disruption characteristics on the performance of a server

In this paper, we study a queueing system serving N customers with an unreliable server subject to disruptions even when idle. Times between server interruptions, service times, and times between customer arrivals are assumed to follow exponential distributions. The main contribution of the paper is to use general distributions for the length of server interruption periods/down times. Our numerical analysis reveals the importance of incorporating the down time distribution into the model, since their impact on customer service levels could be counterintuitive. For instance, while higher down time variability increases the mean queue length, for other service levels, can prove to be improving system performance. We also show how the process completion time approach from the literature can be extended to analyze the queueing system if the unreliable server fails only when it is serving a customer

Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources

Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci® Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset

Multilevel rationing policy for spare parts when demand is state dependent

The multilevel rationing (MR) policy is the optimal inventory control policy for single-item M / M / 1 make-to-stock queues serving different priority classes when demand rate is constant and backlogging is allowed. Make-to-repair queues serving different fleets differ from make-to-stock queues because in the setting of the former, each fleet comprises finitely many machines. This renders the characterization of the optimal control policy of the spare part inventory system difficult. In this paper, we implement the MR policy for such a repair shop/spare part inventory system. The state-dependent arrival rates of broken components at the repair shop necessitate a different queueing-based solution for applying the MR policy from that used for make-to-stock queues. We find the optimal control parameters and the cost of the MR policy; we, then compare its performance to that of the hybrid FCFS and hybrid priority policies described in the literature. We find that the MR policy performs close to the optimal policy and outperforms the hybrid policies

Temporal Segmentation of Surgical Sub-tasks through Deep Learning with Multiple Data Sources

Many tasks in robot-assisted surgeries (RAS) can be represented by finite-state machines (FSMs), where each state represents either an action (such as picking up a needle) or an observation (such as bleeding). A crucial step towards the automation of such surgical tasks is the temporal perception of the current surgical scene, which requires a real-time estimation of the states in the FSMs. The objective of this work is to estimate the current state of the surgical task based on the actions performed or events occurred as the task progresses. We propose Fusion-KVE, a unified surgical state estimation model that incorporates multiple data sources including the Kinematics, Vision, and system Events. Additionally, we examine the strengths and weaknesses of different state estimation models in segmenting states with different representative features or levels of granularity. We evaluate our model on the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), as well as a more complex dataset involving robotic intra-operative ultrasound (RIOUS) imaging, created using the da Vinci® Xi surgical system. Our model achieves a superior frame-wise state estimation accuracy up to 89.4%, which improves the state-of-the-art surgical state estimation models in both JIGSAWS suturing dataset and our RIOUS dataset

Haptic Texture Rendering and Perception Using Coil Array Magnetic Levitation Haptic Interface: Effects of Torque Feedback and Probe Type on Roughness Perception

M.S. University of Hawaii at Manoa 2016.Includes bibliographical references.A Novel maglev-based haptic platform was deployed to investigate the effects of torque feedback and stylus type on human roughness perception. For this purpose, two haptic probes, fingertip and penhandle, were 3D printed each with one and four embedded magnets respectively. Three different torque renderings namely No Torque, Slope Torque, and Stiff Torque were developed, in tendem with penetration-based force feedback to render simulated surfaces. The main difference between these conditions was the amount and type of active torque that was generated. Conventional magnitude estimation experiment for data gathering and analysis was performed. The results of the experiment showed strong effects of wavelength within all torques and probes. Participants rated surfaces rougher in the Slope Torque and with the fingertip compared to penhandle. These results revealed new means of torque-based surface generation that lead to higher roughness perception. The outcomes also highlight the importance of probe type on human roughness perception

The Benefit of Capacity Pooling for Repairable Spare Parts

Capacity pooling in production systems, in the form of production capacity or inventory pooling, has been extensively studied in the literature. While production capacity pooling has been proven to be beneficial, the impact of inventory pooling has been less significant. These results cannot be easily extended to repairable systems due to fundamental differences between repairable and production systems. For one thing, in repairable systems, the demand rate is a function of the number of operational machines, whereas it is exogenous and constant in production systems. In this Thesis, to serve different fleets of machines possibly at different locations, we study whether repair shop pooling is more cost effective than having dedicated on-site repair shops for each fleet. In the first model, we consider transportation delays and related costs, which have been traditionally ignored in the literature. We include on-site spare-part inventories that operate according to a continuous-review base-stock policy. Our numerical findings indicate that when transportation costs are reasonable, repair shop pooling is a better alternative. Next, we model a pooled repair shop that fixes failed components from different k-out-of-n:G systems. We permit a shared spare parts inventory serving all systems and/or reserved spare parts inventories for each system; we call this a hybrid model. The destination for a repaired component can be chosen either on a first-come-first-served basis or by following a static priority rule. Our findings show that both hybrid policies are more cost effective than having separate repair shops and inventories for each system. We propose implementing the multilevel rationing (MR) policy in systems with shared inventory. The MR policy prioritizes classes, and stops serving a class from inventory if the inventory level is below the inventory threshold identified for that class. When there is no inventory, the repaired component is sent to the highest priority class among those with down machines. To approximate the cost of the MR policy, we study an M/G/1//N queueing system serving multiple classes of customers with an unreliable server. Our numerical findings indicate that the MR policy performs as well as the epsilon-optimal policy and outperforms the hybrid policies.Ph