Review of Quantitative Methods for Supply Chain Resilience Analysis

Supply chain resilience (SCR) manifests when the network is capable to withstand, adapt, and recover from disruptions to meet customer demand and ensure performance. This paper conceptualizes and comprehensively presents a systematic review of the recent literature on quantitative modeling the SCR while distinctively pertaining it to the original concept of resilience capacity. Decision-makers and researchers can benefit from our survey since it introduces a structured analysis and recommendations as to which quantitative methods can be used at different levels of capacity resilience. Finally, the gaps and limitations of existing SCR literature are identified and future research opportunities are suggested

Stochastic versus Deterministic Approach to Coordinated Supply Chain Scheduling

The purpose of this paper is to consider coordinated selection of supply portfolio and scheduling of production and distribution in supply chains under regional and local disruption risks. Unlike many papers that assume the all-or-nothing supply disruption pattern, in this paper, only the regional disruptions belong to the all-or-nothing disruption category, while for the local disruptions all disruption levels can be considered. Two biobjective decision-making models, stochastic, based on the wait-and-see approach, and deterministic, based on the expected value approach, are proposed and compared to optimize the trade-off between expected cost and expected service. The main findings indicate that the stochastic programming wait-and-see approach with its ability to handle uncertainty by probabilistic scenarios of disruption events and the much simpler expected value problem, in which the random parameters are replaced by their expected values, lead to similar expected performance of a supply chain under multilevel disruptions. However, the stochastic approach, which accounts for all potential disruption scenarios, leads to a more diversified supply portfolio that will hedge against a variety of scenarios

Distributed Predictive Control for MVDC Shipboard Power System Management

Shipboard Power System (SPS) is known as an independent controlled small electric network powered by the distributed onboard generation system. Since many electric components are tightly coupled in a small space and the system is not supported with a relatively stronger grid, SPS is more susceptible to unexpected disturbances and physical damages compared to conventional terrestrial power systems. Among different distribution configurations, power-electronic based DC distribution is considered the trending technology for the next-generation U.S. Navy fleet design to replace the conventional AC-based distribution. This research presents appropriate control management frameworks to improve the Medium-Voltage DC (MVDC) shipboard power system performance. Model Predictive Control (MPC) is an advanced model-based approach which uses the system model to predict the future output states and generates an optimal control sequence over the prediction horizon. In this research, at first, a centralized MPC is developed for a nonlinear MVDC SPS when a high-power pulsed load exists in the system. The closed-loop stability analysis is considered in the MPC optimization problem. A comparison is presented for different cases of load prediction for MPC, namely, no prediction, perfect prediction, and Autoregressive Integrated Moving Average (ARIMA) prediction. Another centralized MPC controller is also designed to address the reconfiguration problem of the MVDC system in abnormal conditions. The reconfiguration goal is to maximize the power delivered to the loads with respect to power balance, generation limits and load priorities. Moreover, a distributed control structure is proposed for a nonlinear MVDC SPS to develop a scalable power management architecture. In this framework, each subsystem is controlled by a local MPC using its state variables, parameters and interaction variables from other subsystems communicated through a coordinator. The Goal Coordination principle is used to manage interactions between subsystems. The developed distributed control structure brings out several significant advantages including less computational overhead, higher flexibility and a good error tolerance behavior as well as a good overall system performance. To demonstrate the efficiency of the proposed approach, a performance analysis is accomplished by comparing centralized and distributed control of global and partitioned MVDC models for two cases of continuous and discretized control inputs

Smart Master Production Schedule for the Supply Chain: A Conceptual Framework

[EN] Risks arising from the effect of disruptions and unsustainable practices constantly push the supply chain to uncompetitive positions. A smart production planning and control process must successfully address both risks by reducing them, thereby strengthening supply chain (SC) resilience and its ability to survive in the long term. On the one hand, the antidisruptive potential and the inherent sustainability implications of the zero-defect manufacturing (ZDM) management model should be highlighted. On the other hand, the digitization and virtualization of processes by Industry 4.0 (I4.0) digital technologies, namely digital twin (DT) technology, enable new simulation and optimization methods, especially in combination with machine learning (ML) procedures. This paper reviews the state of the art and proposes a ZDM strategy-based conceptual framework that models, optimizes and simulates the master production schedule (MPS) problem to maximize service levels in SCs. This conceptual framework will serve as a starting point for developing new MPS optimization models and algorithms in supply chain 4.0 (SC4.0) environments.The research leading to these results received funding from the European Union H2020 Program with grant agreements No. 825631 "Zero-Defect Manufacturing Platform (ZDMP)" and No. 958205 "Industrial Data Services for Quality Control in Smart Manufacturing (i4Q)", and from Grant RTI2018-101344-B-I00 funded by MCIN/AEI/10.13039/501100011033 and by "ERDF A way of making Europe".Serrano-Ruiz, JC.; Mula, J.; Poler, R. (2021). Smart Master Production Schedule for the Supply Chain: A Conceptual Framework. Computers. 10(12):1-24. https://doi.org/10.3390/computers10120156124101

Evolution of supply chain ripple effect: a bibliometric and meta-analytic view of the constructs

YesIn the broad sphere of Operations Management, Supply Chain Risk Management (SCRM) is a significant area of interest for both academics and practitioners. As SCRM has transitioned from an emerging topic to a growing research area, there is a need to review existing literature in order to ascertain development in this area. There are many literature reviews on this topic, however, there is a lack of an extensive review using network analysis and meta-analysis within SCRM context including ripple effect. To address this gap, we performed a review of 2564 articles published in peer-reviewed academic journals from 1976 to December 2018. First, we apply a network analysis tool on 2564 articles and identify emerging research clusters. Second, to conduct meta-analysis, we collated empirical results from the studies identified. Of those 2564 articles, 42 studies were empirical in nature including 29 studies that used a range of different constructs with appropriate correlation values required for performing meta-analysis. Through this study, we contribute to the literature on SCRM by discussing the challenges of current research, but more importantly, by identifying and proposing five research clusters and future research directions. Finally, the paper acknowledges the theoretical contribution, the limitations of this study, and suggests further research directions

Application of a Blockchain Enabled Model in Disaster Aids Supply Network Resilience

The disaster area is a dynamic environment. The bottleneck in distributing the supplies may be from the damaged infrastructure or the unavailability of accurate information about the required amounts. The success of the disaster response network is based on collaboration, coordination, sovereignty, and equality in relief distribution. Therefore, a reliable dynamic communication system is required to facilitate the interactions, enhance the knowledge for the relief operation, prioritize, and coordinate the goods distribution. One of the promising innovative technologies is blockchain technology which enables transparent, secure, and real-time information exchange and automation through smart contracts. This study analyzes the application of blockchain technology on disaster management resilience. The influences of this most promising application on the disaster aid supply network resilience combined with the Internet of Things (IoT) and Dynamic Voltage Frequency Scaling (DVFS) algorithm are explored employing a network-based simulation. The theoretical analysis reveals an advancement in disaster-aids supply network strategies using smart contracts for collaborations. The simulation study indicates an enhance in resilience by improvement in collaboration and communication due to more time-efficient processing for disaster supply management. From the investigations, insights have been derived for researchers in the field and the managers interested in practical implementation

Advanced Modeling, Control, and Optimization Methods in Power Hybrid Systems - 2021

The climate changes that are becoming visible today are a challenge for the global research community. In this context, renewable energy sources, fuel cell systems and other energy generating sources must be optimally combined and connected to the grid system using advanced energy transaction methods. As this reprint presents the latest solutions in the implementation of fuel cell and renewable energy in mobile and stationary applications such as hybrid and microgrid power systems based on the Energy Internet, blockchain technology and smart contracts, we hope that they will be of interest to readers working in the related fields mentioned above