Abstract In this paper, we present a risk evaluation and maintenance strategy optimization approach for systems with parallel identical assets subject to continuous deterioration. System performance is defined by the number of functional assets, and the penalty cost is measured by the loss of performance. To overcome the practical challenges of information sparsity, we employ a Bayesian framework to

Abstract This paper presents a framework for profit-maximizing strategic bio-energy supply chain design by taking into account variability in biomass as a response to price set as well as uncertainty in biomass yield. We present our model as a two-stage stochastic integer program for a multi-period integrated design of a network in which the here-and-now strategic decisions include biorefinery locations

Abstract In this paper, we aim to solve Bayesian Risk Optimization (BRO), which is a recently proposed framework that formulates simulation optimization under input uncertainty. In order to efficiently solve the BRO problem, we derive nested stochastic gradient estimators and propose corresponding stochastic approximation algorithms. We show that our gradient estimators are asymptotically unbiased

Abstract The flowtime network restoration problem was introduced by Averbakh and Pereira (2012) who presented a Minimum Spanning Tree (MST) heuristic, two local search procedures, and an exact branch-and-bound (B&B) algorithm. This note corrects the computational results in Averbakh and Pereira (2012).

A cyber-additive manufacturing network (CAMNet) integrates connected additive manufacturing processes with advanced data analytics as computation services to support personalized product realization. However, highly personalized product designs (e.g., geometries) in CAMNet limit the sample size for each design, which may lead to unsatisfactory accuracy for computation services, e.g., a low prediction

Abstract Due to the recent advancements in the manufacturing system, the semiconductor FABs have become larger, and thus, more overhead hoist transports (OHTs) need to be operated. In this paper, we propose a cooperative zone-based rebalancing algorithm (CZR) to allocate idle overhead hoist vehicles (OHTs) in a semiconductor FAB. The proposed model is composed of two parts: (1) a state representation

Abstract The paper deals with cycle time calculation of Automated Storage and Retrieval Systems (AS/RS). Cycle time has a high impact on the operating performance of an AS/RS, and its knowledge is essential, both at the operational and design level. The novelty of the paper concerns the peculiar kind of system that is considered, as the focus is on the Shuttle-Lift-Crane AS/RS. This solution, common

Abstract In this paper we create a novel monitoring system to detect changes within a sequence of networks. Specifically, we consider sparse, weighted, directed, and attributed networks. Our approach uses the Hurdle model to capture sparsity and explain the weights of the edges as a function of the node and edge attributes. Here, the weight of an edge represents the number of interactions between two

Abstract Chimeric antigen receptor T cell therapy has demonstrated innovative therapeutic effectiveness in fighting cancers; however, it is extremely expensive due to the intrinsic patient-to-patient variability in cell manufacturing. We propose in this work a novel calibration-free statistical framework to effectively deduce critical quality attributes under the patient-to-patient variability. Specifically

Abstract The traditional maritime logistics industry is facing an industry transformation bringing by technology development. Along with industry transformation, the maritime logistics research field is also facing new challenges and opportunities. It is found that using simulation or optimization alone to solve maritime logistics decision problems has some drawbacks. Instead, a trend of integrating

Abstract Most production environments are stochastic in nature due to the randomness inherent in the production processes. One important engineering problem commonly faced by practitioners is to determine optimal engineering tolerances to be used in production. This paper develops optimization models for determining tolerance sets to maximize the long-run average net profit on a production line with

Abstract Accelerated destructive degradation tests (ADDTs) are effective for reliability assessment of highly reliable products whose key performance characteristic has to be destructively measured. Test units in a reliability experiment typically share the same test environments, and this introduces block effects to the resulting ADDT data. Nevertheless, the block effects are seldom considered in

Abstract As an effective dimension reduction and feature extraction technique, manifold learning has been successfully applied to high-dimensional data analysis. With the rapid development of sensor technology, a large amount of high dimensional data such as image streams can be easily available. Thus, a promising application of manifold learning is in the field of sensor signal analysis, particular

Abstract This article investigates spare parts service contracts for capital goods. We consider a single-item, single-location inventory system that serves one customer with multiple machines. During the contract execution phase, the true demand rate is observed. It can differ from the estimated demand rate because of two factors: increased demand variation in finite horizon settings and a shift in

Abstract Many complex engineering devices experience multiple dependent degradation processes. For each degradation process, there may exist substantial unit-to-unit heterogeneity. In this paper, we describe the dependence structure among multiple dependent degradation processes using copulas and model unit-level heterogeneity as random effects. A two-stage estimation method is developed for statistical

Abstract This research examines a uniform-price auction mechanism in managing staffing for self-scheduling business such as task sourcing and work-from-home call centers. We consider two types of service providers: Type-1 agents who require advanced notice before a shift starts and Type-2 agents who are flexible enough to be scheduled on-demand. We develop an integrated framework that can jointly analyze

Multi-state series-parallel systems are widely-used for representing engineering systems. In real-life cases, engineers need to select an optimal system structure among many different multi-state series-parallel system structures. Screening of system structures is meaningful and critical. Moreover, to design a reliable structure, reliability evaluation is an indispensable part. Due to the large number

Abstract We propose optimal preventive maintenance strategies for n-component coherent systems. We assume that in the early time of the system operation all failed components are repaired, such that the state of a failed component gets back to a working state, worse than that of prior to failure. To modeling this repair action, we utilize a counting process on the interval (0,τ], known as generalized

Abstract Many cities have to cope with annual snowfall, but are struggling to manage their snow plowing activities efficiently. Despite the fact that winter road maintenance has been a popular research subject for decades, very few papers propose scalable models that can incorporate side constraints encountered in real-life applications. In this work we propose a Constraint Programming formulation

Abstract We consider strategic pricing problems in which each firm chooses between a non-cooperative (individual pricing) strategy and a cooperative (price negotiation) strategy. We first analyze a monopoly supply chain involving a supplier and a retailer, and then investigate two competing supply chains each consisting of a supplier and a retailer. We find that a proper power allocation between the

Abstract Shortening the lead time for product development (PD) provides enterprises with a competitive advantage. Given the iterative nature of PD projects, two aspects are regularly considered to shorten the PD lead time, that is, conducting faster or fewer iterations. However, executing faster iterations usually causes more iterations and vice versa. Therefore, suitable coordination between faster

Abstract The paired job scheduling problem seeks to schedule n jobs on a single machine, each job consisting of two tasks for which there is a mandatory minimum waiting time between the completion of the first task and the start of the second task. We provide complexity results for problems defined by three commonly used objective functions. We propose an integer programming formulation based on a

Sampling plans play an important role in monitoring production systems and reducing quality- and maintenance-related costs. Existing sampling plans usually focus on one assignable cause. However, multiple assignable causes may occur especially for a multistage production system, and the resulting process shift may propagate downstream. This paper addresses the problem of finding the optimal sampling

Since September 11, 2001, the United States has invested a significant amount of resources into improving aviation security operations, with the Transportation Security Administration (TSA) assuming the responsibilities for security policy-making at commercial airports. This paper reviews the literature that supports policies for risk-based passenger screening procedures and chronicles the analytical

This paper considers the preventive maintenance (PM) under a two-dimensional (2-D) warranty contract with time and usage limits. From a manufacturer’s point of view, we develop a dynamic maintenance model with a random horizon to include the impact of random and dynamic usage rates on PM decisions. The model treats the cumulative amount of usage as a state variable that provides information about the

This paper addresses an underground mine design and scheduling problem, in which ore extraction methods are determined and resulting mining activities are scheduled. The mining method influences necessary infrastructure, the activities selected, and their timing. We divide the ore body into partitions (i.e., panels), each of which is extracted using a specific method, if at all. We consider two extraction

With significant economic and environmental benefits, renewable energy is increasingly used to generate electricity. To hedge against the uncertainty due to the increasing penetration of renewable energy, an ancillary service market was introduced to maintain reliability and efficiency, in addition to day-ahead and real-time energy markets. To co-optimize these two markets, a unit commitment problem

Robust parameter design (RPD) is a quality improvement method to mitigate the effect of input noise on system output quality via adjustment of control and signal factors. This paper considers RPD with multiple functional outputs and multiple target functions based on a time-consuming nonlinear simulator, which is a challenging problem rarely studied in the literature. The Joseph-Wu formulation of multi-target

Bathtub shaped failure intensity is typical for large-scaled repairable systems with a number of different failure modes. Sometimes, repairable systems may exhibit a failure pattern different from the traditional bathtub shape due to the existence of multiple failure modes. This study proposes two superposed Poisson process models with modified bathtub intensity functions to capture this kind of failure

We consider a stochastically deteriorating system with self-announcing failures that require immediate reactive replacement. For such a system, we consider an age-replacement policy (without minimal repair) under which the system is replaced at failure (reactive replacement) or at a prescribed replacement time (preventive replacement), whichever occurs first. Motivated by factors such as decreasing

Infected individuals must be aware of disease symptoms to seek care, so outreach and education programs are critical to disease control. However, public health organizations often only have limited resources for outreach and must carefully design campaigns to maximize effectiveness, potentially leveraging word-of-mouth information spread. We show how classic epidemiological models can be reformulated

Abstract We model, analyze and study the effects of considering condition-based replacement of parts within an integrated Service Parts Logistics (SPL) system, where geographically dispersed customers’ products are serviced with new parts from network facilities. Conventional SPL models consider replacing the parts upon failure. This is true even for the latest models in which facility locations and

This paper examines a fundamental lot-sizing problem which arises in the context of a make-to-order remanufacturing environment. The problem setting is characterized by a stochastic used-item return process along with a stochastic remanufactured-item demand process faced by a remanufacturer. We explicitly take into account for all relevant costs, including the fixed costs (associated with remanufacturing

Abstract We investigate the impact of effective lifetime of items in an age-based control policy for perishable inventories, a so-called (Q, r, T) policy, with positive lead time and fixed lifetime. The exact analysis of this control policy in the presence of a service level constraint is available in the literature under the restriction that the aging process of a batch begins when it is unpacked

Due to the increasing presence of profile data in manufacturing, profile monitoring has become one of the most popular research directions in statistical process control. The core of profile monitoring is how to model the profile data. Most of the current methods deal with univariate profile modeling where only within-profile correlation is considered. In this article, a linear mixed-effect model framework

We consider the problem of estimating the resulting utilization and cycle times in manufacturing settings that are subject to significant capacity losses due to setups when switching between different product or part types. In particular, we develop queuing approximations for a multi-item server with sequence-dependent setups operating under four distinct setup rules that we have determined to be common

Capacity planning models for tactical to operational decisions in manufacturing systems require a performance evaluation component that relates demand processes with production resources and system state. Steady-state queueing models are widely used for such performance evaluations. However, these models typically assume stationary demand processes. With shorter new product development and life cycles

One-shot units are produced in batches and stored in dormant or standby environment until retrieved or activated to perform their functions when needed. In this paper, we propose hybrid reliability testing approaches to utilize the advantages of non-destructive test (NDT) and destructive test (DT) for one-shot units’ reliability metrics assessment. Specifically, we design a sequence of optimal hybrid

Applications of cloud computing are increasing as companies shift from on-premise IT environments to public, private, or hybrid clouds. Consequently, cloud providers use capacity planning to maintain the capacity of computing resources (instances) required to meet the dynamic nature of computing demand (queries). However, there is a trade-off between deploying too many costly instances, and deploying

In this study, we are primarily motivated by the research problem of recognizing heterogeneous customer behavior towards waiting for order fulfillment under the threshold rationing policy (also known as the critical level policy), and aim to find its effect on system stock levels and performance measures. We assume a continuous review one-for-one ordering policy with generally distributed lead times

Additive manufacturing, especially those based on fused filament fabrication mechanism, has low productivity. One solution is to adopt a collaborative additive manufacturing system that employs multiple printers/extruders working simultaneously to improve productivity by reducing the process makespan. However, very limited research is available to address a grand challenge in the co-scheduling of printing

It is commonly observed in food industry, battery production, automotive paint shop, and semiconductor manufacturing that intermediate product’s residence time in the buffer within a production line is controlled by a time window to guarantee product quality. There is typically a minimum time limit reflected by part’s travel time or process requirement. Meanwhile, these intermediate parts are prevented

The advances in sensing technology have generated multi-modality datasets with complementary information in various domains. In health care, it is common to acquire images of different types/modalities for the same patient to facilitate clinical decision making. We propose a clustering method called hierarchically-structured Factor Mixture Model (hierFMM) that enables cluster discovery from multi-modality

Supervisory control and data acquisition (SCADA) systems are commonly used in critical infrastructures. However, these systems are typically vulnerable to cyberattacks. Among the different types of cyberattacks, the covert attack is one of the hardest to detect – it is undetectable when the system is operating under normal conditions. In this paper, we develop a data-driven detection framework that

Recent advances in information and communication technology are enabling availability to event sequence data from equipment fleets comprising potentially a large number of similar units. The data from a specific unit may be related to multiple types of events, such as occurrence of different types of failures, and are recorded as part of the unit’s service history. In this paper, we present a novel

In many emerging economies, callers may abandon ambulance requests due to a combination of operational (small fleet size), infrastructural (long travel times) and behavioral factors (low trust in the ambulance system). As a result, ambulance capacity, which is already scarce, is wasted in serving calls that are likely to be abandoned later. In this paper, we investigate the design of an ambulance system

Multimodality datasets are becoming increasingly common in various domains to provide complementary information for predictive analytics. One significant challenge in fusing multimodality data is that the multiple modalities are not universally available for all samples due to cost and accessibility constraints. This results in a unique data structure called Incomplete Multimodality Dataset (IMD).

Abstract We study the optimal pricing problem for a tandem queueing system with an arbitrary number of stations, finite buffers, and blocking. The problem is formulated using a Markov decision process model with the objective to maximize the long-run expected time-average revenue or gain of the service provider. Our interest lies in comparing the performances of static and dynamic pricing policies

Many products degrade over time and their degradation processes could be affected by instantaneous shocks during field usage. Instantaneous shocks can cause incremental increases to the degradation signals through shock damages, and can also increase the degradation rates of products. In practice, some kinds of products can recover fully or partially from shock damages in a certain period of time.

In urban and rural hospitals, congested Emergency Departments (EDs) are filled with patients boarding in the ED awaiting admission to inpatient wards. We study this problem beyond the walls of the ED, examining the multi-departmental process managed by specialists. In rural hospitals, an Internal Medicine Specialist (Internist) commonly serves simultaneously as both the Intensive Care Unit (ICU) physician

Multi-stream degradation signals have been widely used to predict the residual useful lifetime of partially degraded systems. To achieve this goal, most of the existing prognostics models assume that degradation signals are complete, i.e., they are observed continuously and frequently at regular time grids. In reality, however, degradation signals are often (highly) incomplete, i.e., containing missing

New container terminals are embracing robotized transport vehicles such as lift-automated guided vehicles (LAGVs) and automated guided vehicles (AGVs) to enhance the terminal throughput capacity. While LAGVs have a high container handling time, they require less coordination with other terminal equipment in comparison to AGVs. In contrast, AGVs are hard-coupled resources, require less container handling

In a multistage manufacturing process (MMP), multiple types of sensors are deployed to collect intermediate product quality measurements after each stage of manufacturing. This study aims at modeling the relationship between these quality outputs of mixed profiles and sparse effective process inputs. We propose an analytical framework based on four process characteristics: (1) every input only affects

Machine faults and systematic failures are resulted from manufacturing process deteriorations. With early recognition of patterns closely related to process deteriorations, e.g., trends, preventative maintenance can be conducted to avoid severe loss of productivity. Change-point detection identifies the time when abnormal patterns occur, thus are ideal for this purpose. However, trend detection is

The ordered block model (OBM) is a special form of directed graphical models and is widely used in various fields. In this paper, we focus on learning of structures of OBM based on prior knowledge obtained from historical data. The proposed learning method is applied to a multistage car body assembly process to validate the learning efficiency. In this approach, Bayesian score is used to learn the

This research develops an efficient ranking and selection procedure of selecting the best design for terminating simulation under sequential sampling. This approach enables us to obtain an accurate estimate of the mean performance at a particular point using regression in the case of terminating simulation. The sequential sampling constraint is imposed to fully utilize the information along the simulation

Additive Manufacturing (AM), specifically Fused Filament Fabrication (FFF) is revolutionizing the production of many products. FFF is one of the most popular AM processes because it is inexpensive, requires little maintenance, and has high material utilization. Unfortunately, long cycle times are a significant drawback that prevents FFF from being more widely implemented, especially for large-scale

A flow picking system in which the existing picking list is updated in real time has been considered as an effective solution for e-commerce warehouses to increase order fulfillment efficiency. The pivotal issues of performance analysis of flow picking systems, and comparison between batch picking systems and flow picking systems are of great concern, both for academics and practitioners of warehouse

Significant research and practice efforts have been devoted to flexible manufacturing systems. Many of them focus on performance analysis, production and inventory control, planning, and scheduling. Steady state analysis is prevalent in these studies. The transient behavior of flexible lines is less investigated. However, the dynamic changes in customer demands and the uncertain nature in production

To perfect a service, service providers must understand the fundamental emotional effects that a service may invoke. Kansei Engineering (KE) has been recently adapted to service industries to realize the relationships between service design elements and customers’ emotional perceptions. However, effective service design based on KE is still seriously challenged by the uncertainty and behavioral biases