Random Disruptions Research Articles

Bioinspiration of Product Architecture: Trading-Off System Effectiveness for System Robustness

Abstract This work analyzes the role of bioinspired product architecture in facilitating the design of robust engineering systems. Prior works have proposed design guidelines to facilitate the implementation of bioinspired product architectures for engineered systems. This work shows that implementing a bioinspired product architecture may improve a system’s robustness to random module failures, but may degrade the system’s effectiveness in the absence of any module failure. To demonstrate such a trade-off between the robustness and the undisrupted effectiveness of a system, this study quantitatively compares biological systems to their functionally equivalent modular systems. The modular equivalents of biological systems are first derived by utilizing Functional Modeling. The application of the bioinspired product architecture guidelines is then modeled as a transition from the modular product architecture of the modular equivalents to the actual product architecture of the biological systems. The effectiveness and the robustness of the systems are analyzed after the application of each guideline by modeling the systems as multi-flow directed networks. Such an analysis is performed by introducing metrics that quantify a system’s expected effectiveness and the degradation in the system’s expected effectiveness with increasing severity of random disruptions. The findings are validated by designing and analyzing a COVID-19 breathalyzer as an engineering case study.

Data-driven Wasserstein distributionally robust mitigation and recovery against random supply chain disruption

This paper studies joint robust network design and recovery investment management in a production supply chain, considering limited historical data about disruptions and their possibilities. The supply chain is subject to uncertain disruptions that reduce production capacity at plants, and the cascading failures propagate along the supply chain network. A data-driven two-stage distributionally robust optimization model with Wasserstein ambiguity set (TWDRO) is constructed to determine the strategic location and tactical allocation decisions in the first stage as well as the operational production and inventory decisions and recovery policy by recourse in the second stage. This paper also proposes a model for depicting a recovery-fund based mitigation strategy, and the model is general in depicting the accelerated, constant-speed, and decelerated recovery processes. In addition, partial backorder policy is adopted to depict the customers’ choices upon product stockout. The TWDRO model is solved by converting it to a mixed integer linear programming model and designing a joint solution method using benders decomposition and genetic algorithm. The performance of TWDRO solutions is demonstrated through numerical experiments and a case study, benchmarking on the stochastic programming and robust optimization approaches. This paper shows the effectiveness and robustness of TWDRO and provides managerial implications and suggestions for supply chain disruption and recovery management.

Estimation Complete Combustion Coefficient in Rotary Kilns

This paper presents a model-based analysis of variability of thermodynamic and chemical parameters in a rotary kiln (RK) during thermal treatment of animal waste. The core process of chemical treatment of waste takes place in RKs; the process involves heating, gasification and partial combustion of the waste. Control over these parameters, and especially the level of complete combustion, determines the quality and efficiency of the process. In operational practice, control and analysis of the variability of process parameters is complicated by the high degree of simultaneity of individual transformations, random disruptions of the process and metrological difficulties resulting from high temperature and chemical activity of the materials being processed. The purpose of preparing the model was to obtain a tool for predicting variability of selected process parameters. By definition, model calculations assume no influence of disturbances on output values, which makes it possible to acquire accurate results that can be compared with corresponding empirically obtained data. The result of the analyses conducted is a theoretical model of the analysed process and a graphical presentation of the calculation results in the form of graphs and charts. A formula for calculating the level of complete combustion and the results of calculation of this index on the basis of empirical data from an industrial waste incineration plant are also presented herein. The presented model is a useful tool providing an insight into interdependencies between selected process parameters and facilitating design of corrective actions oriented towards process optimisation.

Optimal strategies for members in a two-echelon supply chain over a safe period under random machine hazards with backlogging

ABSTRACT This study contributes to analyzing the effect of disruption in the manufacturing process in a two-echelon supply chain involving a manufacturer and a retailer. Prior studies failed to cover the aftermath of disruption in the manufacturing process in a multi-layer supply chain. Here the manufacturing system may face random disruption after a fixed period named safe period. Due to the lengthy maintenance period of the disrupted system, shortages may hamper the smooth business for both the members of the chain. The result reveals that the manufacturer-dominated scenario favours the manufacturer, while the retailer gains in the Centralized system. Also, it is obtained that the costs of the member and chain decrease with a higher value of the safe period, and they increase when the range of the maintenance period increases. This study proposes an iterative method, depending on functional values, as a solution methodology.

A joint pricing and network design model for a closed-loop supply chain under disruption (glass industry)

Closed-loop supply chains have attracted more attention by researchers and practitioners due to strong government regulations, environmental issues, social responsibilities and natural resource constraints over past few years. This paper presents a mixed-integer linear programming model to design a closed-loop supply chain network and optimizing pricing policies under random disruption. Reusing the returned products is applied as a resilience strategy to cope with the waste of energy and improving supply efficiency. Moreover, it is necessary to find the optimal prices for both final and returned products. Therefore, the model is formulated based on demand function and it maximizes total supply chain’s profit. Finally, its application is explored through using the real data of an industrial company in glass industry.

Toward active disruption avoidance via real-time estimation of the safe operating region and disruption proximity in tokamaks

This paper describes a real-time capable algorithm for identifying the safe operating region around a tokamak operating point. The region is defined by a convex set of linear constraints, from which the distance of a point from a disruptive boundary can be calculated. The disruptivity of points is calculated from an empirical machine learning predictor that generates the likelihood of disruption. While the likelihood generated by such empirical models can be compared to a threshold to trigger a disruption mitigation system, the safe operating region calculation enables active optimization of the operating point to maintain a safe margin from disruptive boundaries. The proposed algorithm is tested using a random forest disruption predictor fit on data from DIII-D. The safe operating region identification algorithm is applied to historical data from DIII-D showing the evolution of disruptive boundaries and the potential impact of optimization of the operating point. Real-time relevant execution times are made possible by parallelizing many of the calculation steps and implementing the algorithm on a graphics processing unit. A real-time capable algorithm for optimizing the target operating point within the identified constraints is also proposed and simulated.

Efficient Solution Methods for a General r-Interdiction Median Problem with Fortification

This study generalizes the r-interdiction median (RIM) problem with fortification to simultaneously consider two types of risks: probabilistic exogenous disruptions and endogenous disruptions caused by intentional attacks. We develop a bilevel programming model that includes a lower-level interdiction problem and a higher-level fortification problem to hedge against such risks. We then prove that the interdiction problem is supermodular and subsequently adopt the cuts associated with supermodularity to develop an efficient cutting-plane algorithm to achieve exact solutions. For the fortification problem, we adopt the logic-based Benders decomposition (LBBD) framework to take advantage of the two-level structure and the property that a facility should not be fortified if it is not attacked at the lower level. Numerical experiments show that the cutting-plane algorithm is more efficient than benchmark methods in the literature, especially when the problem size grows. Specifically, with regard to the solution quality, LBBD outperforms the greedy algorithm in the literature with an up-to 13.2% improvement in the total cost, and it is as good as or better than the tree-search implicit enumeration method. Summary of Contribution: This paper studies an r-interdiction median problem with fortification (RIMF) in a supply chain network that simultaneously considers two types of disruption risks: random disruptions that occur probabilistically and disruptions caused by intentional attacks. The problem is to determine the allocation of limited facility fortification resources to an existing network. It is modeled as a bilevel programming model combining a defender’s problem and an attacker’s problem, which generalizes the r-interdiction median problem with probabilistic fortification. This paper is suitable for IJOC in mainly two aspects: (1) The lower-level attacker’s interdiction problem is a challenging high-degree nonlinear model. In the literature, only a total enumeration method has been applied to solve a special case of this problem. By exploring the special structural property of the problem, namely, the supermodularity of the transportation cost function, we developed an exact cutting-plane method to solve the problem to its optimality. Extensive numerical studies were conducted. Hence, this paper fits in the intersection of operations research and computing. (2) We developed an efficient logic-based Benders decomposition algorithm to solve the higher-level defender’s fortification problem. Overall, this study generalizes several important problems in the literature, such as RIM, RIMF, and RIMF with probabilistic fortification (RIMF-p).

Origin of Relaxor Behavior in Barium‐Titanate‐Based Lead‐Free Perovskites

AbstractIt is well known that disordered relaxor ferroelectrics exhibit local polar correlations. The origin of localized fields that disrupt long‐range polar order for different substitution types, however, is unclear. Currently, it is known that substituents of the same valence as Ti4+ at the B‐site of barium titanate lattice produce random disruption of TiOTi chains that induces relaxor behavior. On the other hand, investigating lattice disruption and relaxor behavior resulting from substituents of different valence at the B‐site is more complex due to the simultaneous occurrence of charge imbalances and displacements of the substituent cation. The existence of an effective charge mediated mechanism for relaxor behavior appearing at low (<10%) substituent contents in heterovalent modified barium titanate ceramics is presented in this work. These results will add credits to the current understanding of relaxor behavior in chemically modified ferroelectric materials and also acknowledge the critical role of defects (such as cation vacancies) in lattice disruption, paving the way for chemistry‐based materials design in the field of dielectric and energy storage applications.

A Study of Link Disruption on BP From Spatial and Temporal Perspectives in Deep-Space Vehicle Communications

Developed as the main protocol of delay/disruption-tolerant networking (DTN) for space, bundle protocol (BP) is intended to establish an overlay network for reliable data delivery in a heterogeneous networking environment. The effect of a link disruption on reliable data transmission of BP is currently under study. In this paper, a study of the effect of link disruption on BP's reliable data delivery in deep-space vehicle communications, from spatial and temporal perspectives, is presented employing both analytical and experimental methods. The effect is modeled in various transmission cases by considering the physical distance the bundle has traversed when the link disruption starts and ends, the duration of link disruption in time, and the physical span of the link disruption. The models are built to quantify the number of transmission attempts that fail due to link disruption, the total number of transmission attempts, bundle delivery time, and transmission goodput performance for successful bundle delivery in presence of a link disruption. The models are validated by realistic bundle delivery over a PC-based experimental testbed. The validation indicates that the analytical models predict the transmission performance of BP in presence of random link disruption in various aspects of successful bundle delivery.

A time-dependent location-routing problem of hazardous material transportation with edge unavailability and time window

The transportation of hazardous materials (hazmat) is an inseparable component of any industrial society, where truck transportation is the most applied transportation solution for hazmat due to its versatility and flexibility, especially for short-distance and direct shipments. This paper explores the application of robust optimization in hazmat location-routing problems with edge unavailability, time-dependent parameters, and delivery time window. To be specific, random disruptions are formulated as a scenario-based robust optimization model, which is integrated with a vehicle routing problem with time windows, and then solved by an augmented epsilon constraint method. In applying the robust optimization, variabilities in cost and risk functions are introduced as critical indicators for designing robust and reliable transportation plans. In the end, the model is applied to a real-world hazmat transportation network, from which practical insights are derived. The resulting assessments shed light on the trade-off between hazmat risk versus cost, and introduce several useful indicators to facilitate the carrier in making appropriate decisions.

Mean throughput rate in restart-upon-failure systems subject to random disruptions

We model a manufacturing or service system processing a fixed number of tasks subject to random disruptions generated by a renewal process, such that a task has to start over from scratch if it is interrupted. We study the impact of an increase in disruption rate on the mean throughput of the system, using tools from stochastic ordering. We show that a system that is stochastically better than another does not necessarily have a higher mean throughput - the deciding factor is the hazard rate of the underlying disruption process. We prove that a system that is better than another in the sense of hazard rate ordering of time between disruptions is guaranteed to enjoy a higher average throughput.

Optimizing Disruption Tolerance for Rail Transit Networks Under Uncertainty

In this paper, we develop a distributionally robust optimization model for the design of rail transit tactical planning strategies and disruption tolerance enhancement under downtime uncertainty. First, a novel performance function evaluating the rail transit disruption tolerance is proposed. Specifically, the performance function maximizes the worst-case expected downtime that can be tolerated by rail transit networks over a family of probability distributions of random disruption events given a threshold commuter outflow. This tolerance function is then applied to an optimization problem for the planning design of platform downtime protection and bus-bridging services given budget constraints. In particular, our implementation of platform downtime protection strategy relaxes standard assumptions of robust protection made in network fortification and interdiction literature. The resulting optimization problem can be regarded as a special variation of a two-stage distributionally robust optimization model. In order to achieve computational tractability, optimality conditions of the model are identified. This allows us to obtain a linear mixed-integer reformulation that can be solved efficiently by solvers like CPLEX. Finally, we show some insightful results based on the core part of Singapore Mass Rapid Transit Network.

Strategic actions of urban development to define the intervention policies of slums

The problem of slums is one of the most imperative problems experienced by many developing countries as well as some developed countries. The random gatherings shape real disruption in the economic and social development plans of the concerned countries. The importance of the research lies in conducting a field study for Ezbet El-Shahatin area in Mansoura city, given that it is the largest unplanned informal area in Dakahlia Governorate in terms of area. Geographic information systems were used to make enter data to form a coordinated database. In addition, policy intervention matrix in slums was used to make the appropriate decision regarding the development of the area. The study concludes to specifying several recommendations to deal with slums and set a model for integrated urban development in low-income areas that can be repeated in other similar locations.

An optimization approach for sustainable and resilient supply chain design with regional considerations

Regional and geographical differences between facilities is of paramount importance in supply chain design. However, the impact of the regions' performance on supply chain design decisions remains a relatively under-researched subject in the literature. This paper presents a hybrid methodology for designing a sustainable supply chain that is resilient to random disruptions. We propose a multi-period multi-objective optimization model that utilizes a k-means clustering method to evaluate the regions' sustainability performance. The model aims to determine sourcing and network design decisions as well as resilience strategies. To manage the operational risks associated with the supply chain, we employ a new robustness measure that eliminates the need to estimate the probability distribution of random parameters. Finally, a Benders decomposition algorithm is developed to solve the model. Practical insights are drawn from an actual case study of a downstream petrochemical industry in Iran.

Analysis of an inventory system with emergency ordering option at the time of supply disruption

This paper studies a continuous-review stochastic inventory problem for a firm facing random demand and random supply disruptions. The supplier experiences operational (on) and disrupted (off) periods with exponentially distributed durations. The firm adopts an order-up-to level policy during the on period and additionally can release an emergency order based on the inventory level just before disruption. This inventory policy is described by a continuous-time Markov chain model. We analyze the model for two different lead time scenarios and suggest solution approaches yielding the optimal policy parameters. In a numerical study, we explore the value of exercising such a policy and show that an emergency ordering opportunity at the disruption time brings substantial cost savings in cases with high lost sales cost, long off period, and low percentage of supplier’s availability.

A framework to managing disruption risk in rail-truck intermodal transportation networks

Rail-truck intermodal transportation plays a vital role in freight transportation in North America, and hence a crucial issue is to ensure continuity and minimize the adverse impacts from disruption. We propose a framework based on optimization and regression analysis for recovery from random disruptions of rail intermodal terminals. Two mixed-integer programming models are developed to simulate the normal and post-disruption operations, and the outputs are used in a predictive analytics model to identify the determinants of terminal criticality. The proposed framework is applied to a case study built using the realistic infrastructure of a rail-truck intermodal network in the United States. The resulting analyses underscore the importance of implementing the mitigation strategy at a marginal increase in pre-disruption cost, which in turn not only improves network resiliency but also results in significant cost savings by not using the more expensive recovery strategies. We further show that intermodal train service design resulting in a more balanced distribution of freight in the network could also help reduce terminal criticality, and that it makes sense to have backup rental capacities for some terminals.

Hybrid Retransmissions of Bundle Protocol for Reliable Deep-Space Vehicle Communications in Presence of Link Disruption

Targeted as the main protocol of delay/disruption-tolerant networking (DTN) for space, bundle protocol (BP) is intended to establish an overlay network for reliable file delivery in a heterogeneous networking environment. Reliable data transmission mechanisms of BP are presently under development. In this paper, a new transmission approach is proposed for BP for highly reliable data delivery in deep-space vehicle communications in presence of an unpredictable or random link disruption. The main idea of the proposed approach is to use a hybrid of proactive retransmission and reactive retransmission mechanisms during file transfer. Analytical models are built in various transmission cases to study the effect of link disruption on reliable transmission of BP, with focus on the number of failed transmission attempts, file delivery time and goodput performance normalized with respect to the total amount of data sent. The models are validated by the file transfer experiment over a PC-based testbed. A performance comparison between the proposed approach and the existing transmission approach with respect to transmission reliability is also presented.

Impact of optimal power generation scheduling for operating cleaner hybrid power systems with energy storage

De-carbonization of the electric power system, reduction in greenhouse gas emissions and the enhancement of environmental security have emphasized the need for the optimal utilization of renewable/cleaner energy in power system operation. However, unpredictability and intermittency are the major barriers that limit the penetration of renewable power generation (RPG). In this context, large-scale energy storage (LSES) facilities may prove to be an effective means for mitigating the long-term volatility and vulnerability of RPG and subsequently reduce the power dependence on fossil fuel-based generation systems. Thus, the possibility of increasing the penetration of cleaner power may be achieved by simultaneously implementing LSES and an intelligent scheduling strategy so that the hybrid power system may be operated with lower power loss, more cost-effective operation, and an enhanced voltage security. Previous researchers in multiple works have demonstrated the operational analysis and optimization of hybrid configurations with and without storage in stand-alone, islanded and remote operations. In contrast, attempts have been made in this work to incorporate, analyse and validate the impact of LSES facilities (ie, pumped hydroelectric storage (PHS) and compressed air energy storage (CAES)) with benchmark power grid configuration (ie, New-England-39 bus).In this work, a comparative analysis among different hybrid configurations, particularly wind-solar-thermal, wind-solar-hydro-thermal, wind-solar-hydro-thermal-PHS and wind-solar-hydro-thermal-CAES is conducted within a multi-objective optimization environment. The proposed design problem is analysed with the inclusion of real-time constrictions in the form of stochastic variation in renewable power output and random disruptions. The modified bacteria foraging algorithm is used to evaluate the optimum generation schedule for which the operational objectives will be achieved for the design problem. Further, using fuzzy membership function, the trade-off between conflicting objectives is portrayed in Pareto optimal domain. Among different hybrid configurations considered, the HPS which incorporates the CAES system exhibits the most superior performance.

Feedback Control of Cluster Tools: Stability Against Random Time Disruptions

In this research, we examine <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">feedback control</i> -based cluster tool scheduling methods to maintain <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">consistent</i> wafer sojourn times when a tool is subject to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">random disruptive events</i> . In our previous work, we proposed a feedback control design that regulates wafer sojourn times not to exceed the upper limits on wafer delays in a deterministic processing environment. Although such a feedback controller may ensure that wafer delay upper limits are always satisfied, it does not necessarily guarantee that the tool always restores its initial tool state after the occurrence of time disruptive events. This article thus further examines under which conditions a feedback controller enforces the wafer sojourn times to be <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stabilized</i> in a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">stochastic</i> processing environment with unexpected random time disruptions. <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Note to Practitioners</i> —In semiconductor manufacturing, excessive wafer sojourn times at wafer fabrication tools increase the risk of wafer quality failures. In particular, the wafer quality fluctuates when the sojourn times are inconsistent over different wafers. Therefore, in cluster tools, wafer sojourn times are often strictly regulated to be minimized or to remain constant with an objective of reducing the risk of wafer quality degradation. In this research, we examine a cluster tool scheduling framework that enables to maintain stable tool operations even when a tool is randomly disrupted by unexpected exceptional events.

Improving supply system reliability against random disruptions: Strategic protection investment

Supply chains, as vital systems to the well-being of countries and economies, require systematic approaches to reduce their vulnerability. In this paper, we propose a non linear optimisation model to determine an effective distribution of protective resources among facilities in service and supply systems so as to reduce the probability of failure to which facilities are exposed in case of external disruptions. The failure probability of protected assets depends on the level of protection investments and the ultimate goal is to minimise the expected facility-customer transport or travel costs to provide goods and services. A linear version of the model is obtained by exploiting a specialised network flow structure. Furthermore, an efficient GRASP solution algorithm is developed to benchmark the linearised model and resolve numerical difficulties. The applicability of the proposed model is demonstrated using the Toronto hospital network. Protection measures within this context correspond to capacity expansion investments and reduce the likelihood that hospitals are unable to satisfy patient demand during periods of high hospitalisation (e.g. during a pandemic). Managerial insights on the protection resource distribution are discussed and a comparison between probabilistic and worst-case disruptions is provided.