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

The increased complexity of manufacturing systems makes the acquisition of the system performance estimate a black-box procedure (e.g., simulation tools). The efficiency of most black-box optimization algorithms is affected significantly by initial designs (populations). In most population initializers, points are spread out to explore the entire domain, e.g., space-filling designs. Some population

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

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

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

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

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, existing

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

Workforce allocation and configuration decisions at airport security checkpoints (e.g., number of lanes open) are usually based on passenger volume forecasts. The accuracy of such forecasts is critical for the smooth functioning of security checkpoints where unexpected surges in passenger volumes are handled proactively. In this article, we present a forecasting model that combines flight schedules

The actual failure times of individual components are usually unavailable in many applications. Instead, only aggregate failure-time data are collected by actual users, due to technical and/or economic reasons. When dealing with such data for reliability estimation, practitioners often face the challenges of selecting the underlying failure-time distributions and the corresponding statistical inference

Condition-Based Maintenance (CBM) is an effective maintenance strategy to improve system performance while lowering operating and maintenance costs. Real-world systems typically consist of a large number of components with various interactions among components. However, existing studies on CBM mainly focus on single-component systems. Multi-component CBM, which joins the components’ stochastic degradation

We study the problem of scheduling in manufacturing environments which are dynamically reconfigurable for supporting highly flexible individual operation compositions of the jobs. We show that such production environments yield the simultaneous process design and operation sequencing with dynamically changing hybrid structural-logical constraints. We conceptualize a model to schedule jobs in such environments

Modern manufacturing systems typically involve multiple production stages, the real-time status of which can be tracked continuously using sensor networks that generate a large number of profiles associated with all process variables at all stages. The analysis of the collective behavior of the multistage multivariate profile data is essential for understanding the variance patterns of the entire manufacturing

Shape accuracy control is one of the quality issues of greatest concern in Additive Manufacturing (AM). An efficient approach to improving the shape accuracy of a fabricated product is to compensate the fabrication errors of AM systems by modifying the input shape defined by a digital design model. In contrast with mass production, AM processes typically fabricate customized products with extremely

Abstract We consider randomly failing high-precision machine tools in a discrete manufacturing setting. Before a tool fails, it goes through a defective phase where it can continue processing new products. However, the products processed by a defective tool do not necessarily generate the same reward obtained from the ones processed by a normal tool. The defective phase of the tool is not visible and

A case-picking operation with Multiple In-The-Aisle Pick Positions (MIAPP) is modeled as an M/G/1/N queueing system. Cases are picked manually from pallets located on the bottom level of storage racks. An aisle-captive Narrow-Aisle Lift Truck (NALT) travels rectilinearly to replenish the floor level of the rack by retrieving a pallet load from an upper level of the rack. From a queueing perspective

Failure Modes and Effects Analysis (FMEA) is a very useful reliability-management instrument for detecting and mitigating risks in various fields. The linguistic assessment approach has recently been widely used in FMEA. Words mean different things to different people, so FMEA members may present Personalized Individual Semantics (PIS) in their linguistic assessment information. This article presents

This research addresses the problem of dividing a group of people into a collection of teams that need to be “balanced” across a variety of different attributes. This type of problem arises, for example, in an academic setting where it is necessary to partition students into a number of balanced study teams and also in a youth camp in which children need to be formed into sports teams that are competitive

The layout of a retail store directly affects the products to which shoppers are exposed, and ultimately their purchase. Optimizing a layout to increase product visibility can directly benefit both the retailer (via increased revenue) and the shopper (via increased satisfaction). We introduce the Rack Orientation and Curvature Problem, which determines the orientation and curvature of each rack in

During humanitarian relief operations, designated facilities are established to assist the affected population and distribute relief goods. In settings where the authorities manage the operations, they instruct the population regarding which facility they should visit. However, in times of crises and uncertainty, these instructions are often not followed. In this work, we investigate how the authorities

Mass casualty incidents often cause a shortage of resources for emergency medical services such as ambulances and emergency departments. These resources must be effectively managed to save as many lives as possible. Critical decisions in operating emergency medical service systems include the prioritization of patients for ambulance transport and the selection of destination hospitals. We develop a

Emergency disaster managers are concerned with responding to disasters in a timely and efficient manner. We are concerned with determining the location and amount of disaster relief supplies to be prepositioned in anticipation of disasters. These supplies are stocked when the locations of affected areas and the amount of relief items needed are uncertain. Furthermore, a proportion of the prepositioned

The increasing availability of data has popularized risk estimation models in many industries, especially healthcare. However, properly utilizing these models for accurate diagnosis decisions remains challenging. Our research aims to determine when a risk estimation model provides sufficient evidence to make a positive or negative diagnosis, or if the model is inconclusive. We formulate the Two-Threshold

Several studies have shown that biomass cofiring is a viable short-term option for coal-fired power plants to reduce their emissions if supported by appropriate tax incentives. These results suggest a unique opportunity for governments to design monetary incentives such as tax credits that lead to reduction in greenhouse gas emissions in biomass-rich regions. Therefore, a natural question is: What

Standby is used extensively in mission-critical and safety-critical systems to improve reliability and availability. The dormant period during standby may introduce additional failure modes to the standby components. This is commonly observed in many real systems, yet it has been overlooked in existing research. This study is motivated by a two-motor standby system used in a power plant, in which periodic

Production planners in the non-ferrous metal industry face an inherent combinatorial complexity of the metal production process within a fast-changing market environment. Herein, we study the benefit of an integrated optimization-based planning approach. We present the first value-based optimization approach for operational planning in the non-ferrous metal industry that yields high economic and technical

Estimating a batch of parameter vectors of a nonlinear model is considered, where there exists a model interpreting the independent and the dependent variables, and the parameter vectors of the model are assumed to be sampled from a multivariate normal distribution. The mean vector and the covariance matrix of the parameter distribution can be assumed and such a parameter distribution is referred to

We address the design optimization problem for a mine in which the ore is concentrated in a system of long, thin veins and for which the so-called top-down open-stope mining method is customarily used. In such a mine, a large volume of earth below the surface is envisioned for extraction and is conceptually divided into three-dimensional rectangular blocks on each of several layers. The mine design