119 research outputs found
An Alert-Assisted Inspection Policy for a Production Process with Imperfect Condition Signals
No full textWe study the inspection scheduling decisions for a production process that goes through a hidden defective state before its failure. The production process is equipped with a predictive model, generating alert and no-alert signals. An alert signal indicates that production process is in the defective state, while a no-alert signal indicates it is in the healthy state. The signals are imperfect, meaning that an alert signal can be generated for a healthy process and a no-alert signal can be generated for a defective process. Only a costly inspection can detect the true condition. We introduce a new inspection policy, which generalizes the age-based inspection policy that performs planned inspections at predetermined intervals, by considering that an inspection can also be triggered by a certain number of alerts from the predictive model. To optimize the proposed inspection policy, a stochastic dynamic programming model is formulated with the objective of minimizing the long-run expected cost rate. The performance improvement achieved by the optimal policy is quantified by comparing it to practically relevant benchmark policies. Numerical experiments with a set of realistic problem instances show that adding alert-triggered inspections to traditional age-based inspection scheduling brings up to 44% reduction in the expected cost rate when the predictive model is sufficiently accurate. Characterizing the performance of the optimal policy at a given level of imperfectness is especially useful in practice as it allows making an assessment on how much can be invested to justify a certain level of improvement in the predictive model
Dance, Long Exposure and Drawing: An Absurd Manifesto about the Female Body
No full textThis paper summarises the evolution and production process of Kam, a long-exposure pixilation/ 2D animation film with a unique aesthetic approach that took three years to formulate and complete due to an iterative/fragmented production schedule. Kam, which means “shaman” in old Turkish, was conceived as a response to the rise of conservative and misogynist official discourse in Turkey, and it features a woman’s fierce dance. For this film, Turkish dancer Sevinc Baltali’s improvised performance was captured by the author using the technique of long-exposure photography. Condensing the motion of the dancer, the still frames created a flowing image on screen in which the dancer’s body is sometimes hardly perceivable. The dance flow was then recreated to the music of Amolvacy, an underground New York band featuring a modern interpretation of tribal music. Finally, the manifesto of the film was reinforced by adding another layer, this time of primitive drawings by the author, on top of the images, creating a more pronounced expression of the anger and the rebellious energy of the female body.
This article argues that the unique aesthetics of the film attained at the end of an iterative and fragmented production process allowed a multi-layered liminal space for meaning to emerge. By elaborating on the relationship between the aesthetic approach, the political stance and the production methodology of this film, this article aims to demonstrate how animation can create an evocative and visceral experience that highlights and communicates what Herzog (2010) defines as “ecstatic truth”
Near optimality guarantees for data-driven newsvendor with temporally dependent demand: A Monte Carlo approach
No full textDate of Conference: 8-11 Dec. 2013We consider a newsvendor problem with stationary and temporally dependent demand in the absence of complete information about the demand process. The objective is to compute a probabilistic guarantee such that the expected cost of an inventory-target estimate is arbitrarily close to the expected cost of the optimal critical-fractile solution. We do this by sampling dependent uniform random variates matching the underlying dependence structure of the demand process - rather than sampling the actual demand which requires the specification of a marginal distribution function - and by approximating a lower bound on the probability of the so-called near optimality. Our analysis sheds light on the role of temporal dependence in the resulting probabilistic guarantee, which has been only investigated for independent and identically distributed demand in the inventory management literature. © 2013 IEEE
Spare parts recommendation for corrective maintenance of capital goods considering demand dependency
No full textWe consider a maintenance service provider that services geographically dispersed customers with its local service engineers. Traditionally, when a system failure is reported, a service engineer makes a diagnostic visit to the customer's location to perform corrective maintenance. If spare parts are required, they are ordered and a second visit is scheduled at a later date to complete the corrective maintenance. In this paper, the service provider can proactively send spare parts to the customer to avoid the costly second visit. Motivated by a real-world problem in the high-tech industry, our model considers the cost of a second visit, fixed shipment costs, retrieval costs for the parts that are sent to the customer, and send-back costs for the parts that are sent but not used for corrective maintenance. The uncertainty in the set of parts required for corrective maintenance is modeled with a general distribution that can capture the dependencies between demands for different spare parts. We formulate an integer linear program to find the optimal set of spare parts that minimizes the expected total cost. We derive analytical results for the structure of the optimal policy and compare the optimal policy with two benchmark policies from practice. We observe that the policies from practice often find the optimal policy, and a new heuristic policy that exploits the structure of the optimal policy, on average, performs better than the benchmark policies.Transport and Logistic
Re-enactment simulation for buffer size optimization in semiconductor back-end production
No full textIn this work, we propose a re-enactment simulation-based optimization method to determine the minimal total buffer capacity in an assembly line required to meet a target throughput. A distinguishing feature is the use of real-time event traces, in a fast fluid flow simulation model. Employing real-time event traces avoids the necessity to make restrictive modeling assumptions. The fluid simulation is combined with a multi start search algorithm. To demonstrate its effectiveness, the method is applied to a real-world use case in lead frame based semiconductor back-end manufacturing. This use case considers an assembly line consisting of six machines, for which the proposed method determines optimal buffer size configurations within several minutes of computational time.Accepted Author ManuscriptIntensified Reaction and Separation System
Simulation-based production planning for engineer-to-order systems with random yield
No full textWe consider an engineer-to-order production system with unknown yield. We model the yield as a random variable which represents the percentage output obtained from one unit of production quantity. We develop a beta-regression model in which the mean value of the yield depends on the unique attributes of the engineer-to-order product. Assuming that the beta-regression parameters are unknown by the decision maker, we investigate the problem of identifying the optimal production quantity. Adopting a Bayesian approach for modeling the uncertainty in the beta-regression parameters, we use simulation to approximate the posterior distributions of these parameters. We further quantify the increase in the expected cost due to the so-called input uncertainty as a function of the size of the historical data set, the product attributes, and economic parameters. We also introduce a sampling-based algorithm that reduces the average increase in the expected cost due to input uncertainty
Alendronate treatment in children with osteogenesis imperfecta
No full textBackground: Recent studies reported beneficial effect of cyclical intravenous administration of pamidronate in children and adolescents With osteogenesis imperfecta (OI). However, this treatment requires frequent hospital admissions and is relatively expensive. Alendronate is an oral bisphosphonate effectively used in adults with osteoporosis. Experience with alendronate treatment in children with OI is limited Aims: To report our experience with alendronate in children with OI. Methods: 12 children with 01 (7 with type 1, 4 with type III and I with type IV; 7 boys, 5 girls) aged 1.8 to 15.4 years (7.9 +/- 4.4 yrs),were included in this retrospective study. The patients were treated with alendronate in a dose of 5-10 mg/day along with calcium (500 mg/day) and vitamin D (400-1000 IU/day) supplements for 19.8 +/- 11.3 months (range: 7-46 months). Serum calcium (Ca), phosphorus (P), alkaline phosphatase (ALP), osteocalcin (OC), pyrilinks-D and urinary Ca/Cr ratio were studied 3 monthly and bone mineral density (BMD) by DXA on 612 monthly basis. Results: Fracture rate of the patients significantly decreased after treatment (1.2 +/- 1.5 vs. 0.16 +/- 0.32 peryear, P<0.05). Treatment improved bone density in each individual case. Z-scores of lumbar DXA (L2-L4) significantly increased during treatment (-4.60 +/- 1.30 vs -2.47 +/- 1.52, P< 0.05). Urinary pyrilinks-D decreased with treatment (90.8 +/- 136.3 vs. 35.1 +/- 29.9, P < 0. 05). Serum Ca, P, ALP, OC and urinary Ca/Cr did not change significantly during treatment. Conclusion: We conclude that alendronate is effective, safe and practical alternative to intravenous bisphosphonates in treatment of children with OI
How good must failure predictions be to make local spare parts stock superfluous?
No full textThanks to Industry 4.0 technologies, predictive algorithms can provide advance demand information on spare parts demand. Understanding how the goodness of predictions affects on-hand inventory and costs is important for decision makers before integrating these models into existing systems. We consider a spare parts inventory problem for multiple technical systems that are supported by one local stockpoint. Each system has a single critical component that is subject to random failures. Signals are generated to predict component failures. The signal that corresponds to a failure is generated a certain amount of time before the failure, referred to as the demand lead time. However, not every signal results in a failure and some failures are undetected. A component is replaced from the stock when a failure occurs. In case of stock-outs, an emergency shipment takes place. We formulate a discrete-time Markov decision process model to optimize the replenishment decisions with the objective of minimizing the long-run average cost per period. We investigate the effect of precision (i.e., the fraction of true signals among all signals) and sensitivity (i.e., the fraction of detected failures among all failures) of the predictions and the demand lead time on the costs, order-up-to levels, average on-hand inventory and emergency shipments under the optimal policy. In the worst case, the precision, sensitivity or demand lead time is zero. We show analytically that the optimal policy and optimal costs only depend on the sensitivity and the demand lead time through their product. In numerical experiments, we observe a Pareto principle for the reduction of costs in precision (e.g., a 30% perfectness in precision brings a 70% reduction in optimal cost compared to the worst case) and an inverse Pareto principle in the product of sensitivity and demand lead time (e.g., 70% perfectness in the sensitivity or demand lead time only brings 30% reduction in optimal cost compared to the worst case). Finally, we observe that the local spare parts stock only becomes superfluous when the signals are really close to perfect.Transport and Logistic
Scheduling a Real-World Photolithography Area With Constraint Programming
No full textThis paper studies the problem of scheduling machines in the photolithography area of a semiconductor manufacturing facility. The scheduling problem is characterized as an unrelated parallel machine scheduling problem with machine eligibilities, sequence- and machine-dependent setup times, auxiliary resources and transfer times for the auxiliary resources. Each job requires two auxiliary resources: a reticle and a pod. Reticles are handled in pods and a pod contains multiple reticles. Both reticles and pods are used on multiple machines and a transfer time is required if transferred from one machine to another. A novel constraint programming (CP) approach is proposed and is benchmarked against a mixed-integer programming (MIP) method. The results of the study, consisting of a real-world case study at a global semiconductor manufacturer, demonstrate that the CP approach significantly outperforms the MIP method and produces high-quality solutions for multiple real-world instances, although optimality cannot be guaranteed
Input uncertainty in stochastic simulations in the presence of dependent discrete input variables
No full textThis paper considers stochastic simulations with correlated input random variables having NORmal-To-Anything (NORTA) distributions. We assume that the simulation analyst does not know the marginal distribution functions and the base correlation matrix of the NORTA distribution but has access to a finite amount of input data for statistical inference. We propose a Bayesian procedure that decouples the input model estimation into two stages and overcomes the problem of inconsistently estimating the base correlation matrix of the NORTA distribution in the presence of discrete input variables. It further allows us to estimate the variability of the simulation output data that are attributable to the input uncertainty due to not knowing the NORTA distribution. Using this input uncertainty estimate, we introduce a simple yet effective method to obtain input uncertainty adjusted credible intervals. We illustrate our method in an assemble-to-order production system with a correlated demand arrival process
- …
