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

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

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).

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 their part

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

Abstract We investigate the impact of the 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 the service level constraint is provided by Tekin et al. (2001) under the restriction that the aging process of a batch begins when it is

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

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 in maximizing

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

We develop and study a block-type three-dimensional (3D) printer that can assemble toy blocks based on 3D CAD models. Our system automatically converts a 3D CAD model into a block model consisting of toy blocks in basic shapes. Next, it automatically generates a feasible assembly plan for the block model. An industrial robot then assembles the block sculpture layer-by-layer, from bottom to top, using

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

In disruptive innovation, a new entrant firm with fewer resources challenges the established incumbent firms. The new entrant firm offers an innovative product that is considered superior in new features appealing to a group of new customers, but inferior along the traditional performance attributes valued by mainstream customers. Previous research primarily focuses on the product strategies that could

Critical infrastructure from a cross-section of sectors has become increasingly reliant on cyber systems and cyberinfrastructure. Increasing risks to these cyber components, including cyber-physical systems, have highlighted the importance of cybersecurity in protecting critical infrastructure. The need to cost-effectively improve cyberinfrastructure security has made this topic suitable for optimization

We study the problem of finding optimal routes and schedules for multiple vehicles traveling in a network. Vehicles may have different origins and destinations, and must coordinate their trajectories to keep a minimum distance from each other at any time. We determine a route and a schedule for each vehicle, which possibly requires vehicles to wait at some nodes. Vehicles are heterogeneous in terms

Most previous studies concerning process flexibility designs have focused on expected sales and demand uncertainty. In this paper, we examine the worst-case performance of flexibility designs in the case of demand and supply uncertainties, where the latter can be in the form of either plant or arc disruptions. We define the Plant Cover Index under Disruptions (PCID) as the minimum required plants’

Abstract Fill-and-finish is among the most commonly outsourced operations in biopharmaceutical manufacturing and involves several challenges. For example, fill-operations have a random production yield, as biopharmaceutical drugs might lose their quality or stability during these operations. In addition, biopharmaceuticals are fragile molecules that need specialized equipment with limited capacity

Industrial Symbiosis or By-Product Synergy is defined as a resource-sharing strategy that engages traditionally separate industries in a collective approach that involves a physical exchange of materials, water, energy, and by-products. Inspired by a real-world example of a paper–sugar symbiotic complex, we study the impact of competition on a firm’s willingness to implement an industrial symbiotic

Biofuel, an efficient alternative to fossil fuels, has gained considerable attention as a potential source to satisfy energy demands. Biomass collection and distribution typically incur a significant portion of the biofuel production cost. Thus, it is imperative to design a biofuel supply chain network that not only aims to minimize the delivery cost, but also incorporates biomass quality properties

Degradation branching is a common phenomenon in many real-life applications. The degradation of a location not only increases with time, but also propagates to other locations in the same system. While the degradation of an individual location has been studied extensively, research on degradation branching is sparse. In this paper, we develop a general stochastic degradation branching model that characterizes

We study statistical calibration, i.e., adjusting features of a computational model that are not observable or controllable in its associated physical system. We focus on functional calibration, which arises in many manufacturing processes where the unobservable features, called calibration variables, are a function of the input variables. A major challenge in many applications is that computational

Extended warranties have become a rich source of profits for manufacturers. In the current marketplace, two-dimensional extended warranties are often sold to all customers with a fixed coverage region and the same price. However, customers are heterogeneous, as they have different use rates and failure history, which significantly affects the cost of the extended warranty. In this study, we first propose

Deep learning has emerged as a powerful tool to model complicated relationships between inputs and outputs in various fields including degradation modeling and prognostics. Existing deep learning-based prognostic approaches are often used in a black-box manner and provide only point estimations of remaining useful life. However, accurate interval estimations of the remaining useful life are crucial

Information visualization significantly enhances human perception by graphically representing complex data sets. The variety of visualization designs makes it challenging to efficiently evaluate all possible designs catering to users’ preferences and characteristics. Most existing evaluation methods perform user studies to obtain multivariate qualitative responses from users via questionnaires and

We formulate a novel class of online matching problems with learning. In these problems, randomly arriving customers must be matched to perishable resources so as to maximize a total expected reward. The matching accounts for variations in rewards among different customer–resource pairings. It also accounts for the perishability of the resources. Our work is motivated by a healthcare application, but

Abstract Networks have been widely employed to represent interactive relationships among individual units in complex systems such as the Internet of Things. Assignable causes in systems can lead to abrupt increased or decreased frequency of communications within the corresponding network, which allows us to detect such assignable causes by monitoring the communication level of the network. However

Government plays a vital role in improving community resilience against natural disasters. Due to the limited relief capacity of a government, it is desirable to develop a risk-sharing mechanism involving both private sector providers (e.g., insurers, for-profit disaster agencies, and firms that provide resources for risk mitigation and recovery) and the public. In this article, we take catastrophe