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Global supply chains are increasingly exposed to operational and disruption risks that threaten their business continuity. This paper presents a novel two-stage scenario-based mixed stochastic-possibilistic programming model for integrated production and distribution planning problem in a two-echelon supply chain over a midterm horizon under risk. Operational risks are handled by introducing imprecise (i.e. possibilistic) parameters while disruption risks are accounted for through stochastic disruption scenarios. The proposed model accounts for the risk mitigation options and recovery of lost capacities in an integrated manner. In the first stage, the structure of the chain and proactive risk mitigation decisions are determined, while the second stage specifies the recovery plan of lost capacities in addition to production and distribution plans. Considering extra capacities in the production facilities, backup routes for transportation links and pre-positioning of emergency inventory in distribution centres are considered as feasible options to improve the resilience level of the supply chain. We propose a new indicator for optimising the resilience level of the chain based on restoration of lost capacities. For the sake of robustness, the expected worst case of the second stage's objective function is considered by utilising the conditional value at risk (CVaR) measure. The validation and applicability of the proposed model are examined through several numerical experiments. [ABSTRACT FROM AUTHOR]

In this research work, sandwich composite panels made by fiber metal laminate (FML) as the facesheets and polymer foams as the core material are investigated in tensile and bending loads. To change or enhance the behaviour of sandwich panels in tensile and bending loads, shape memory alloy wires with pseudoelastic behaviour are also embedded in between FML layers in facesheets. The shape memory wires are also pre-strained in the FML facesheets of sandwich panels. To study the tensile and flexural properties of sandwich panels with smart FML facesheets three types of sandwich panels are considered and made including panels without shape memory alloy wire, panels with shape memory wires with 0% tensile pre-strain, and panels with shape memory wires with 5% tensile pre-strain for the same cross section. By placing SMA wires in the FML, the strength and stiffness of the smart sandwich specimens are increased significantly in tensile and bending loads. However, the effect of prestraining the SMA wires is more predominant on stiffness of the specimens. The tensile and flexural toughness or energy absorption is much higher in case of the specimen with 5% pre-strained SMA wires. At the expenses of adding the SMA wires in the sandwich structures, the densities of various specimens are changed by nearly 1% to 5% for various specimens, but a significant increase in mechanical properties such as the strength and particularly the stiffness and toughness were achieved by the present lightweight smart sandwich structures. [ABSTRACT FROM AUTHOR]

Objective: Today, extensive political, economic, social, and environmental challenges have made designing the gas supply chain network one of the biggest concerns of governments, local states, and global companies. Due to the development of global regulations about environmental concerns, important issues such as sustainability and resilience are needed to be considered in building up supply chain networks. The purpose of this study is to present a mathematical model of a three-echelon gasoline supply chain network, as well as to consider sustainability and resilience approaches. Methods: This is a fundamental and applied study. The mathematical model developed in this research is a two-stage scenario-based multi-objective stochastic one that considers the risks of chain disruption in the form of stochastic scenarios. The disruption considered in this study included supply disruption due to disruption of refinery production capacity, reduction of gasoline imports due to political pressures, disruption of storage facilities, and a demand surge in some customer zones. In order to find robust solutions against scenarios, the Aghezzaf robust optimization method was used, and to find efficient solutions. The Torabi-Hosseini approach was applied to the multi-objective model. Results: Some of the most important findings of the present study were the quantification of sustainability measures, including the cost of network establishment, environmental effects of CO2 emissions due to the gasoline production and transmission in the network, and the social effects of the network development on the job opportunities, while improving the economic conditions of local areas. The development of a quantitative approach to optimizing various dimensions of the network resilience, including design quality besides the proactive-reactive capabilities against these disturbances were the other finding of this study. Proactive capabilities encompass the establishment of backup storage facilities in critical nodes of the chain and devising backup links for transporting gasoline from backup refineries to the disrupted facilities. In addition, fortification of critical facilities to be operable in the face of disruptions was another proactive option considered in the proposed mathematical model. Reactive capabilities included planning the recovery of disrupted storage tanks and gasoline pipelines. Conclusion: The proposed model, that quantitatively optimizes all three aspects of sustainability, i.e., economic, social, and environmental, in the gasoline supply chain network, strengthens the network resilience against disruption. Besides, the applicability and efficiency of the proposed approach were shown through a real case study of the gasoline supply chain network design problem in the Khorasan Razavi province of Iran. The obtained results showed that the cost reduction in the whole network along with sustainability and resilience achievements were made in comparison with the current condition of the gasoline supply chain in Khorasan Razavi. [ABSTRACT FROM AUTHOR]

In this study mechanical properties and buckling behavior of laminated cylindrical composite shells embedded with shape memory alloys (SMAs) were investigated. Samples were fabricated by filament winding process. Stacking sequences were ±55°/SMA/±55°. SMAs with shape memory effect used in the form of wire were placed between plies 2 and 3. Moreover, the effect of zero and 5% tensile prestrain and number of SMA wires were studied by embedding zero, four, and six wires in the composite. The results showed that the critical buckling load increased by using four SMA wires and decreased due to increasing the number of SMAs up to six. [ABSTRACT FROM AUTHOR]

In this research, a Mach reflection and its effect on explosive free forming of confined cylindrical shells are studied numerically. This shells were manufactured from extruded 6063-T5 Aluminum alloy. The diameter of shell was 1.5 times larger than its length. Its ends were sealed with rigid sheets. The simulation of formation of Mach reflection and plastic response of shell were done with Autodyne hydrocode and coupled Lagrangian-Eulrian spatial discretization. Formation of Mach reflection occurred on end plates. It is observed that the generated pressure in an area that is affected by Mach stem is higher than elsewhere. This phenomena causes rupture in boundaries area of shell to plate connections, before forming process. The maximum of transverse deformation that obtained from this study compared with experimental results which done in explosion mechanic laboratory in K. N. Toosi university of technology. The experimental and numerical results shows more than 93% agreement. Meanwhile, because of blast waves reflection and interaction of waves, coupled Lagrangian-Eulrian method is suitable method for investigation of internal explosion problems. In addition failure modes was simulated with finite element software Abaqus and good agreement was found between the results. [ABSTRACT FROM AUTHOR]

Modelling maintenance activities could be a complex and non-linear system which consists of different parameters. This article presents an integrated fuzzy simulation–fuzzy data envelopment analysis (FSFDEA) to cope with a special case of ‘maintenance activity planning’ problem. First, the maintenance activities are simulated by means of Awesim®. Due to the ambiguity associated with the time between failures, fuzzy sets theory is incorporated into the simulation network. Different distribution functions for production, failure and maintenance times are estimated using historical data. Then, outputs are computed by carrying out the simulation for different scenarios which are combinations of periodic maintenances. Several outputs including machines and operators’ availability, reliability, efficiency and queue length are computed. Data envelopment analysis (DEA) method is used to select the preferred policy for this multi-criteria problem. In order to handle fuzzy outputs of the simulation, fuzzy DEA model is applied. To show the applicability of the proposed FSFDEA algorithm for planning maintenance activities, the data for a series production line is used and different scenarios are investigated. The proposed approach of this study would help managers to identify the preferred strategy considering and investigating various parameters and scenarios. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has had a significant impact on hospitals and healthcare systems around the world. The cost of business disruption combined with lingering COVID-19 costs has placed many public hospitals on a course to insolvency. To quickly return to financial stability, hospitals should implement efficiency measure. An average technical efficiency (ATE) model made up of data envelopment analysis (DEA) and stochastic frontier analysis (SFA) for assessing efficiency in public hospitals during and after the COVID-19 pandemic is offered. The DEA method is a non-parametric method that requires no information other than the input and output quantities. SFA is a parametric method that considers stochastic noise in data and allows statistical testing of hypotheses about production structure and degree of inefficiency. The rationale for using these two competing approaches is to balance each method's strengths, weaknesses and introduce a novel integrated approach. To show the applicability and efficacy of the proposed hybrid VRS-CRS-SFA (VCS) model, a case study is presented. [ABSTRACT FROM AUTHOR]