Probability Of Disruption Research Articles

A joint supplier selection and order allocation model with disruption risks in centralized supply chain

Abstract Supplier’s coordination plays a vital role in managing a supply chain. Organizations have learned that selecting the right suppliers and allocating right orders are the keys to success. The presence of disruption risks in the supply chain creates more complexity in the supplier selection and order allocation process. This paper presents a mixed-integer nonlinear programming model to optimize supplier selection and order allocation simultaneously in centralized supply chains considering disruption risks. In order to reduce disruption effects, two protective policies are taken into account. Protection policy for the selected suppliers and emergency inventory allocation policy for the protected suppliers are utilized in this study. The presented model proves to be non-convex, indicating that, conventional methods are not capable of solving such a complex problem. The proposed method is exemplified by numerical instances. The results indicate that with an increase in the disruption probability the factors which effect the decisions related to supplier selection and order allocation are altered. At the low disruption probability, the disruption risk plays a key role in the decisions, while at the higher disruption probability, wholesale price and disruption risk are the dominant factors to form the decisions.

Benefits and Risks of High-Intensity Blast-Furnace Operation

More intense blast-furnace operation at OAO NLMK has been made possible by improvement in quality of the coke and the iron-ore products, increase in pressure below the charge hole, and oxygen enrichment of the blast. That increases the rate of indirect wustite reduction and decreases the heat losses to the cooling water. However, high intensity also significantly increases the risks of accidents in batch admission. Besides change in quality of the batch components, the disruptions in batch descent may be due to change in the ore distribution over the furnace radius. A new diagnostic method has been developed to identify the factors responsible for such disruptions. Simulation permits analysis of the influence of change in the ore load and the size of the iron-ore products on the temperature change of the peripheral gases and the utilization of the carbon monoxide. The method is based on analysis of the total change in gas temperature and the utilization of the carbon monoxide in the peripheral region. On that basis, the factors responsible for the change in the pressure difference over the furnace height may be identified. Analysis of the dynamics of the pressure differences over the furnace height permits judgments regarding the probability of disruptions in batch delivery. The possibility of decreasing the pressure difference by redistribution of the ore over the furnace radius and decreasing the quantity of gas is assessed. Various methods of decreasing the quantity of gas are accompanied by changes in the coke consumption and furnace productivity. A mathematical model has been developed for the selection of the best combination of blast adjustments for specific conditions.

An Improved Voronoi-Diagram-Based Algorithm for Continuous Facility Location Problem under Disruptions

The classical location models implicitly assume that the facilities, once built, will always operate as planned. However, some of the facilities may become unavailable from time to time due to disruptions which highlight the urgent need to effectively manage supply chain disruptions in spite of their low probability of occurrence. Therefore, it is critical to take account of disruptions when designing a resilient supply chain network so that it performs well as a whole even after an accidental disruption. In this paper, a stylized facility location problem is considered in a continuous plane which is solved through an improved Voronoi-diagram-based algorithm under disruption risks. The research problem is to minimize the total cost in normal and failure scenarios. Furthermore, the impact of misestimating the disruption probability is also investigated. The results numerically show that although the estimated disruption probability has a significant impact on the facilities configuration, it has a minor impact on the total quantity of facilities and the expected total cost. Therefore, this paper proposes that the decision-maker should moderately overestimate disruption risk based on the “pessimistic principle”. Finally, the conclusion considers managerial insights and proposes potential areas for future research.

Managing supply chain risks and delays in construction project

PurposeThe purpose of this paper is to investigate models and methods for managing supply chain risks and delays in construction projects.Design/methodology/approachThe study mainly employs quantitative analysis in order to identify disruptions in construction supply chains. It also uses paradigms of simulation modeling, which are suitable for risk assessment and management. Both qualitative and quantitative data were collected through a literature review and details of specific construction projects, respectively. A dynamic modeling method was used, and the model was provided with an event-based simulation. Simulation modeling was used to measure the performance of the system.FindingsThe study shows the benefits of applying the dynamic modeling method to a construction project. Using event-based simulation, it was found that construction delays influence both the magnitude and the probability of disruption. This method contributes to the existing theoretical foundations of risk management practices, since it also considers the time factor. This method supplements the Monte Carlo statistical simulation method, which has no time representation. Using empirical analysis, the study proposes increasing the safety stock of construction materials at the distribution center, so as to mitigate risks in the construction supply chain.Research limitations/implicationsThe research considers a single case of a hypothetical construction project. The simulation models represent a simple supply chain with only one supplier. The calculations are based on the current economic scenario, which will of course change over time.Practical implicationsThe outcomes of the study show that the introduction of a safety stock of construction materials at the distribution center can prevent supply chain disruption. Since the consideration of risks at all stages of construction supply chain is essential to investors, entrepreneurs and regulatory bodies, the adoption of new approaches for their management during strategic planning of the investment projects is essential.Originality/valueThis dynamic modeling method is used in combination with the Monte Carlo simulation, thus, providing an explicit cause-and-effect dependency over time, as well as a distributed value of outcomes.

Retailers’ Order Strategies in Transshipments in Disruption Risks of Supply Chains

This article investigates the order problem in a two-stage supply chain consisting of retailers, primary suppliers and backup suppliers. From retailers’ perspectives, the optimal offering strategies in unidirectional transshipments are analyzed on the basis of disruption risks in supply chains. With a focus on retailers’ profits, it develops the decision model of retailers’ orders in the dual sourcing mode and in the mode of capacity options, to maximize the retailers’ expected profits. The simulation shows that retailers’ optimal order decisions are not correlated with the joint disruption probability in the two order modes without service constraints; retailers’ optimal decisions are influenced by the joint disruption probability in the two order modes with service constraints, and the impact of the joint disruption probability of the primary suppliers’ optimal order from retailers differs in the two modes. Retailers’ order decisions in respect of backup suppliers are more sensitive to the changing volumes of transshipments. The contribution of this article is the impact of main parameters on retailers’ decisions, providing some guidance for enterprises to make order decisions.

The Effect of Single Deuterium Substitutions for Protium in a DNA Molecule on the Occurrence of Open States

The data obtained by mathematical modeling of the occurrence of open states between nitrogenous bases in a D. melanogaster gene where a protium atom is replaced by deuterium are presented. It was shown that in the physiological range of deuterium concentrations substitution of deuterium for protium increases the probability of hydrogen-bond disruption between complementary bases by 0.22–0.60%. This result suggests that deuterium can decelerate the transcriptional reading of genetic information. At the same time, under conditions that weaken the hydrogen bonds between the strands of a DNA molecule, replacement of protium with deuterium facilitates the formation of open states, increasing the probability of base modification induced by damaging environmental factors.

Designing a Reliable and Congested Multi-Modal Facility Location Problem for Biofuel Supply Chain Network

This study presents a mathematical model that designs a reliable multi-modal transportation network for a biofuel supply chain system while site-dependent facility failure and congestion are taken into consideration. The proposed model locates the multi-modal facilities and biorefineries and determines the optimal production, storage, and routing plans in such a way that the overall system cost is minimized. We propose a hybrid Constraint generation-based Rolling horizon algorithm to solve this challenging NP-hard problem. The performance of this algorithm is tested in a example case study with numerical analysis showing that the hybrid algorithm can find near-optimal solutions to large-scale problem instances in a reasonable amount of time. Results indicate that the effect of congestion reduces the usage of multi-modal facilities in the biofuel supply chain network while bio-refineries and multi-modal facilities tend to move away from coastal areas when disruption probabilities are taken into consideration.

Efficient multi-unit procurement mechanism with supply disruption risk

In this paper, we study the multi-attribute multi-unit procurement mechanism design problem facing a set of potential suppliers who suffer from disruption risks. Each supplier's production cost depends on its disruption probability, and both are private information. We propose a Vickery-Clark-Groves auction with disruption risk (VCG-DR) for this problem and show that the mechanism is incentive-compatible, individual-rational and social efficient. Moreover, we compare the performance of the proposed mechanism and the popular single-attribute multi-unit forward auction (SA-MFV) with reserved attribute by numerical experiments. The results show that VCG-DR outperforms SA-MFV in both social efficiency and optimality.

Tidal disruption of inclined or eccentric binaries by massive black holes

Binary stars that are on close orbits around massive black holes (MBH) such as Sgr A* in the center of the Milky Way are liable to undergo tidal disruption and eject a hypervelocity star. We study the interaction between such a MBH and circular binaries for general binary orientations and penetration depths (i.e. binaries penetrate into the tidal radius around the BH). We show that for very deep penetrators, all binaries are disrupted when the binary rotation axis is roughly oriented toward the BH or it is in the opposite direction. The surviving chance becomes significant when the angle between the binary rotation axis and the BH direction is between \pi /4 and 3 \pi /4. The surviving chance is as high as $\sim$ 20$\%$ when the binary rotation axis is perpendicular to the BH direction. The angular dependence is opposite for very shallow penetrators where coplanar prograde orbits have the lowest surviving chance (or equivalently most vulnerable). We provide numerical fits to the disruption probability and energy gain at the the BH encounter as a function of the penetration depth. The latter can be simply rescaled in terms of binary masses, their initial separation and the binary-to-BH mass ratio to evaluate the ejection velocity of a binary members in various systems. We also investigate the disruption of coplanar, eccentric binaries by a MBH. It is shown that for highly eccentric binaries retrograde orbits have a significantly increased disruption probability and ejection velocities compared to the circular binaries.

Hybrid neural network for density limit disruption prediction and avoidance on J-TEXT tokamak

Increasing the plasma density is one of the key methods in achieving an efficient fusion reaction. High-density operation is one of the hot topics in tokamak plasmas. Density limit disruptions remain an important issue for safe operation. An effective density limit disruption prediction and avoidance system is the key to avoid density limit disruptions for long pulse steady state operations. An artificial neural network has been developed for the prediction of density limit disruptions on the J-TEXT tokamak. The neural network has been improved from a simple multi-layer design to a hybrid two-stage structure. The first stage is a custom network which uses time series diagnostics as inputs to predict plasma density, and the second stage is a three-layer feedforward neural network to predict the probability of density limit disruptions. It is found that hybrid neural network structure, combined with radiation profile information as an input can significantly improve the prediction performance, especially the average warning time (). In particular, the is eight times better than that in previous work (Wang et al 2016 Plasma Phys. Control. Fusion 58 055014) (from 5 ms to 40 ms). The success rate for density limit disruptive shots is above 90%, while, the false alarm rate for other shots is below 10%. Based on the density limit disruption prediction system and the real-time density feedback control system, the on-line density limit disruption avoidance system has been implemented on the J-TEXT tokamak.

Adaptive predictors based on probabilistic SVM for real time disruption mitigation on JET

Detecting disruptions with sufficient anticipation time is essential to undertake any form of remedial strategy, mitigation or avoidance. Traditional predictors based on machine learning techniques can be very performing, if properly optimised, but do not provide a natural estimate of the quality of their outputs and they typically age very quickly. In this paper a new set of tools, based on probabilistic extensions of support vector machines (SVM), are introduced and applied for the first time to JET data. The probabilistic output constitutes a natural qualification of the prediction quality and provides additional flexibility. An adaptive training strategy ‘from scratch’ has also been devised, which allows preserving the performance even when the experimental conditions change significantly. Large JET databases of disruptions, covering entire campaigns and thousands of discharges, have been analysed, both for the case of the graphite and the ITER Like Wall. Performance significantly better than any previous predictor using adaptive training has been achieved, satisfying even the requirements of the next generation of devices. The adaptive approach to the training has also provided unique information about the evolution of the operational space. The fact that the developed tools give the probability of disruption improves the interpretability of the results, provides an estimate of the predictor quality and gives new insights into the physics. Moreover, the probabilistic treatment permits to insert more easily these classifiers into general decision support and control systems.

Markov-modulated analysis of a spare parts system with random lead times and disruption risks

Spare parts supply chains are highly dependent on the dynamics of their installed bases. A decreasing number of capital products in use increases the nonstationary supply-side risk especially towards the end-of-life of capital products. This supply-side risk appears to present itself through varying lead times coupled with supply disruptions. To model the nonstationary supply-side risk, we consider an exogenous Markov chain that modulates random lead times and disruption probabilities. Assuming that order crossovers do not occur, we prove the optimality of a state-dependent base stock policy. Later, we conduct an impact study to understand the value of considering stochastic lead times and supply disruption risk in spare parts inventory control. Our results indicate that the coupled effect of random lead times and disruptions can be larger than the summation of individual effects even for moderate lead time variances. Also, the effect of nonstationarity on total cost can be as large as the summation of all risk factors combined. In addition to this managerial insight we present a procedure for supply risk mitigation based on an empirical model and our mathematical model. Experiments on a real business case indicate that the procedure is capable of reducing costs while making the inventory system more prepared for disruptions.

Monte‐Carlo based approach to consider the cost of voltage dip and long duration interruption in optimal planning of SDGs

Voltage dip and long duration interruption (LDI) are among the most costly power quality phenomena. In this study, a Monte-Carlo based approach is proposed to consider the cost of sensitive loads disruption caused by dip and LDI in the optimal planning of synchronous distributed generations (SDGs). The idea is to link between trip probability due to dip and LDI and their yearly costs by employing Monte-Carlo simulation and acquiring their total costs during the planning horizon. The addition of disruption cost along with the traditional planning objectives like network upgrade cost and loss cost allows utilities to include the customer's perception during planning. A formula for the probability of disruption due to dip is derived and a modified Monte-Carlo approach is proposed. The methodology is illustrated on the distribution level of the IEEE 30-bus system and the optimisation problem is solved by particle swarm optimisation algorithm. The results demonstrate that the sensitive loads performance is improved from the dip standpoint in the presence of SDGs. However, the LDI cost is either not affected or aggravated by the presence of SDGs depending on the protection model. The total disruption cost is decreased.

Using backup supply with responsive pricing to mitigate disruption risk for a risk-averse firm

We consider a risk-averse firm’s sourcing problem with two suppliers: a dedicated one and a backup one. The dedicated supplier charges a lower wholesale price, but faces potential disruption risk. The backup supplier is assumed to be perfectly reliable, but charges a higher wholesale price. To mitigate the disruption risk, the firm uses a joint backup supply and responsive pricing strategy. We consider three common backup strategies between the firm and the backup supplier: advance purchase, reservation and contingency purchase. We derive under what conditions each strategy could be optimal. The results show that the thresholds that determine the optimal backup supply strategy are affected by the risk aversion level. When the risk aversion level is not extremely high, the firm should choose among the three backup suppliers by considering the disruption probability and the reservation fee. Firms with a higher risk aversion level tend to rely more on ex-ante preventive efforts (i.e. reservation or advance purchase strategies). When the risk aversion level is extremely high, the firm never considers the contingency purchase strategy, even for a low-probability disruption event. Additionally, market conditions yield non-negligible influences on the firm’s strategic choices due to the existence of risk aversion.

Robust reliable humanitarian relief network design: an integration of shelter and supply facility location

The human societies are threatened by natural disasters. Thus, preparedness and response planning is necessary to eliminate or mitigate their negative effects. Relief network design plays an important role in the efficient response to the affected people. This paper addresses the problem of relief logistics network design under interval uncertainty and the risk of facility disruption. A mixed-integer linear programming model is proposed (1) to consider distribution center (DC) disruption (2) to support the disrupted DC by backup plan (3) to take in to the account both supply and evacuation issues (4) and finally, to mitigate disruption impact by investment. Moreover, robust optimization methodology is applied to hedge against uncertain environments. We conduct computational experiments by using generated instances and a real-world case to perform sensitivity analysis and provide managerial insights. The results show that the total cost of relief network increases by increasing the conservatism level. Moreover, the result show that the total cost of the network can be decrease by reducing the interval of uncertain parameters. As a result, providing more information and better estimation about uncertain parameters can reduce network costs. Disruption probability effect is also investigated and the result indicates that the network tries to establish more reliable facilities as the disruption probability increases. To demonstrate superiority of reliable network described in this paper over the classic network, a Monte Carlo procedure is used to compare two networks and results confirmed superiority of reliable network.

Multiple sourcing and order allocation problem under supplier disruption risk and quantity discount

In this paper, we developed two stochastic mixed integer linear programs. The objective is to determine the optimal order quantities for each supplier in order to maximise the expected net profit (ENP) under disruption risk. In fact, to model this situation, overall combination of disruption probability under different settings is calculated first, and then the expected profit function for neutral risk setting is developed. Afterward, this model is extended to minimise the operational loss to model the risk averse behaviour. Discount on total quantity and on business volume are considered. The two models are illustrated through a numerical study and sensitivity analysis. The result shows that the expected profit and expected losses are very influenced by failure probability and discount levels. In fact, in presence of disruption risk, it is better to allocate order quantity from supplier who offers large discount level.

Strategic cooperation with a backup supplier for the mitigation of supply disruptions

We study optimal sourcing decisions for a firm with a dedicated supplier and a backup supplier. The dedicated supplier charges a lower wholesale price but faces a potential disruption risk. The backup supplier is assumed to be perfectly reliable but charges a higher wholesale price. The primary question we address is how the firm should cooperate with the backup supplier to hedge against the disruption risk. We consider three common cooperation options: advance purchase, reservation and contingency purchase. Our basic results show that the firm should choose advance purchase strategy if the disruption probability is high, while contingency purchase strategy benefits the firm more if the disruption probability is low. Under an intermediate disruption probability, the firm should choose reservation strategy only if the reservation fee is sufficiently low. Then, we explore the optimal backup strategy under partial disruption risk. The results show that the advance purchase and the reservation strategy should be adopted more widely when the dedicated supplier guarantees a relatively high yield rate after disruption.

Male-free environment prevents pregnancy disruption in domestic horse mares mated away of home

In a previous study (Bartoš et al., 2011) we have reported on the effects of the male social environment on the maintenance or disruption of pregnancy in domestic mares mated away from home. In this follow-up study we compare the effects of returning to a home environment including a male (or males) to which the returning mare had, or was denied, access or did not have access, with that of returning to a home environment containing only other females. As in the previous study, we used data collected from private horse owners in the Czech Republic. The probability of disruption of pregnancy was lowest when the mare was sharing an enclosure only with other mares. Incidence of pregnancy failure was highest when the mare was in one enclosure, while the male or males stayed in an adjacent enclosure and intermediate where females returned to a home enclosure and shared it with the home male(s). In addition, the probability of pregnancy disruption decreased with increasing number of foals delivered in the mare’s previous reproductive history.

Assessing the impact of earthquake scenarios in transportation networks: the Portuguese mining factory case study

This study presents an open-source framework for the evaluation of the consequences of seismic events on transportation systems, and their impact on the surrounding industry. When applied to the specific case of a given company or organization, the framework allows the estimation of expected losses due to the disruption of specific transportation routes. The methodology was applied to a case study of a Portuguese mining factory whose production and exportation rely on the accessibility to strategic regions in the country using the highway and railway networks. Several methodological issues (e.g., spatial correlation in the ground motion, correlation in the damage) are explored within a sensitivity analysis to identify which features can impact seismic performance indicators (collapse and disruption probabilities; repair and disruption time) of specific routes.

Managing global sourcing with disruption risks in an assemble-to-order system

We consider an assemble-to-order (ATO) system that purchases components from both overseas and local suppliers. The overseas suppliers may be disrupted, while the local suppliers are stable. The objective is to study how the ATO system should utilize a flexible local sourcing policy to mitigate overseas disruption risks. We derive optimal sourcing decisions which are determined by local suppliers’ cost ratios between pre-purchasing and replenishing and overseas suppliers’ disruption probabilities and total procurement cost. Furthermore, we discuss the balance between cost and responsiveness, explore the value of the flexible local sourcing policy and propose a simple and effective heuristic policy.