Operational Issues Research Articles

Multi-objective optimization of district cooling systems considering cooling load characteristics

District cooling systems have been widely used for their high economic and energy performance. However, insufficient consideration of chiller configuration selection and multi-cooling scenarios causes excessive equipment installed capacity and low energy efficiency. This paper proposes a collaborative optimization method and a compatible multi-objective optimization model to address the design and operation issues affecting the economic and energy benefits of district cooling systems. Firstly, the cooling period is divided into different cooling scenarios constructed by cooling load characteristics. Subsequently, the ε-constraint-Sequential Quadratic Programming method is used to solve the multi-objective optimization models under different chiller configurations and various cooling scenarios. Finally, the Analytic Hierarchy Process-Technique for Order Preference by Similarity to Ideal Solution method is used to get the installation scheme with optimal chiller configuration and the operation strategies under various cooling scenarios. The proposed method is applied to design and optimize a district cooling system with ice thermal storage. The optimal scheme is equipped with double-duty chillers (6 × 1800 RT, 1 × 500 RT) and water-cooled chillers (1 × 1800 RT, 1 × 900 RT), considering chiller configuration selection and multi-cooling scenarios. Compared with the conventional scheme using double-duty chillers (8 × 1800 RT) and water-cooled chillers (2 × 1800 RT), the annual costs and the annual electricity consumption are reduced by 11.03% and 11.78%, respectively. The results verify the effectiveness and advantages of the optimization method.

Harnessing our greatest asset: Solving margin issues by investing in frontline leaders

The rate of change in combination with the scope and magnitude and the need for increased efficacy in healthcare operations are on the rise, especially with the advent of a plethora of patient needs in the post-COVID-19 world. Senior leaders in the field are faced with various operational and financial challenges on account of the new and rapidly rising patient requirements in the healthcare sector, and, instead of optimising existing resources to meet current challenges, they are often persuaded to just add on new programmes, leaving the existing programmes to languish. Thus, to circumvent some of the major issues in healthcare operations, a more effective strategy has to be pursued than the usual one of simply buying and implementing a new productivity system with updated targets or performance benchmarks. The solution is to assess the efficacy and profitability of existing improvement programmes and resources in the advent of a new set of requirements: deploying burst versus incremental improvement; engaging middle managers to enable their solutions; and achieving organisation speed, spread and scalability across the organisation [Caldwell, C., Cook, K., (2020), ‘Achieving speed, spread, scalability, and sustainability in health systems’, American College of Healthcare Executives-Cluster Session, Clearwater, FL, 17–20th January]. This paper describes how Dignity Health East Valley was able to optimise the use of existing performance improvement resources and deploy an accelerated improvement structure to achieve rapid gains, followed by incremental improvement to both sustain and further the gains made. The approach included mentoring middle managers to become adaptive change agents and achieve 5 per cent improvement in margins in six months. The burst method described in this paper is based on the findings from the research undertaken by top-performing hospitals that identified five essential characteristics that healthcare’s frontline leaders are expected to possess and which are a clear mark of their leadership skills and competence [Caldwell, C., Cook, K., (2020), ‘Achieving speed, spread, scalability, and sustainability in health systems’, American College of Healthcare Executives-Cluster Session, Clearwater, FL, 17–20th January]. The paper presents the background to this research as well as the structure that builds these top-performer capabilities in the front-line team in order for them to lead change and drive its implementation. The positive results that emerged from the leadership training provided to frontline leaders are substantial, including improved margin, higher patient experience and higher employee engagement.

Effect of Culverts on the Thermal Regime of a Railway Embankment

A common issue for Canadian railway operators is the development of localized frost heave near railway culverts, which leads to nonuniform deformation of the track surface and degradation of the track geometry, damages the track component, and affects the quality of rides in railway operations. Frequent maintenance including placing wooden shims between rail and ties and often the reduction of train speeds are required to ensure safe operating conditions. Given the future expected climate patterns, freeze–thaw cycles are anticipated to become more frequent, and therefore, a greater understanding of their effect on the changing ground thermal regime under railway embankments is essential. This paper investigates the effects of culverts and insulation layers on changing the temperature regime of the ground during the winter–spring period through numerical modeling in TEMP/W (version 2019) two-dimensional (2D) and three-dimensional (3D) software. The model was calibrated using actual field measurements collected from an instrumented section of the track that has suffered from frost heaves at the proximity of a culvert. Using the results from 2D simulation, the effect of culverts on extending the frost zone, in both horizontal and vertical directions, was quantified. In addition, the effects of shape, installation depth, and the number of culverts on the frost penetration depth were discussed. Using TEMP/W 3D results, the most appropriate configuration to install the culverts was studied to minimize frost penetration.

A Study of International Human Rights Issues in Unmanned Aircraft Operations

With the development of technology, intelligent devices are being used more and more in every aspect of people's lives, and humans are becoming more dependent on intelligent products. Of course, not only in everyday life but also in the military, where intelligent devices play an increasingly important role. From the early days of cold warfare to the present day, the use of science and technology has greatly impacted how warfare has changed. Drones, combat robots, and cruise missiles are all the fruits of intelligent military warfare. Of these, drones are relatively widely used and play an essential role. As a new form of combat, drones have a great deal of autonomy, making them far more destructive than other forms of combat. The low cost of drones has led to the use of drones by the major powers, and the question of how to restrict the use of unmanned combat devices, especially drones, cannot be ignored. There are currently no international conventions that explicitly regulate drone use; the only ones that have any effect are the international human rights conventions. Drones pose a significant threat to human rights, particularly in terms of their indiscriminate strikes and lack of accuracy, which is one of the issues that should limit their use in warfare. The issues arising from using drones and how they threaten human rights should be the focus of attention.

Study of pipe steel resistance to deformation in laboratory conditions and on the data from industrial rolling with the use of machine learning tools

The study of resistance to deformation of various steel grades is one of the key issues for the adequate operation of automation systems, which makes it possible to obtain rolled products with the required accuracy in terms of geometric characteristics. In addition, knowledge of deformation resistance is important in the design of rolling mill equipment. In the literature, the values ​​of deformation resistance in the overwhelming majority of cases are given in the form of coefficients of various equations (for example, Hensel-Spittel). However, these formulas often have limitations in the range of technological parameters where they give an acceptable result. It should also be considered that dozens of steel grades are produced at modern rolling mills, and their chemical composition can vary over a wide range depending on the final thickness of rolled products, customer requirements, or based on economic considerations (the most advantageous alloying composition). The study of the rheological properties of such a quantity of materials in the laboratory is expensive, long-term, and labor-intensive, and the literature sources do not provide completeness of the data. The article shows that, using data from industrial rolling mills and machine learning methods, it is possible to obtain information about the rheology of the material with satisfactory accuracy, which makes it possible to avoid laboratory studies. Carrying out such studies is possible due to the high saturation of modern rolling mills with various sensors and measuring instruments. Comparison of the results from industrial data was carried out with the values ​​of the deformation resistance obtained on the Gleeble machine. Based on this comparison, the model was trained based on gradient boosting to take into account the features of the technological process in industrial production.

Revisiting the Nexus Between Sustainable Supply Chain Management and the Performance of Steel Manufacturing Organizations: A Case Study From an Emerging Economy

Notwithstanding the accretive volume of research on sustainable supply chain management (SSCM) across multiple industrial segments, limited studies have shown concrete empirical evidence of SSCM performance evaluation (SSCMPE) of steel manufacturing organizations in the context of emerging economies such as India. This research attempts to evaluate the SSCM performance of four leading India-based steel manufacturing organizations. To this end, eight critical criteria were extracted from the literature and later verified by experts. Data were collected by developing a questionnaire and conducting interviews with the selected industry personnel. Next, this research employed an integrated multicriteria decision making (MCDM) approach, in which the objective weights of criteria were determined using the entropy method, and the case organizations were prioritized using the complex proportional assessment (COPRAS) method. Results reveal that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">increase in scrap recycling</i> rate and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">reduction in resource consumption by designing products for effective utilization of resources</i> are the two most influential criteria for SSCM performance. Finally, a sensitivity analysis was performed to confirm the exactitude of the results. The distinctiveness of this research lies in considering environmental, operational, social, and economic issues in SSCMPE of the steel manufacturing organizations. The research findings may assist policymakers in formulating strategies for SSCM performance enhancement.

Hierarchical Optimization for User-Satisfaction-Driven Electric Vehicles Charging Coordination in Integrated MV/LV Networks

The growing uptake of electric vehicles (EVs) will likely require management schemes to enable their connection into distribution systems. While most of the existing approaches are developed from the operator’s perspective considering EV aggregated demands at the medium-voltage (MV) level, individual users’ comfort and the particularities associated with low-voltage (LV) networks need to be considered to holistically assess the EV effects in an integrated MV/LV network. This article proposes a two-level hierarchical optimization framework for the EV charging coordination (EVCC) that maximizes users’ satisfaction, while avoiding operational grid issues in the whole distribution system. The framework is tailored for unbalanced distribution systems with high penetration of EVs and introduces a novel index to measure charging priority-based EV user satisfaction. To reduce the computational burden, the EVCC problem is disaggregated into an upper level for MV network operation, and a lower level for LV network and individual EV scheduling, using mixed-integer linear programming models. This framework is later embedded in a dynamic scheduling approach that copes with unexpected EV arrivals. Benefits (increased overall user satisfaction and reduced strain over distribution assets) are demonstrated via case studies in a 459-node three-phase network in which solutions were achieved under a 60-s threshold.

Field Programmable Gate Array-Based Execution on a Distributed Energy Resource Supported Electrical Distribution System for Enhanced Power Quality with Optimal Active Power Flow Control

This paper explains the experimental study of a distributed energy resource (DER) integrated three-phase, three-wire electrical distribution system (EDS) under power quality (PQ) and optimal active power flow control (OAPFC). In this research, a back-to-back connected two-level VSI (BTB-TVSI) based distribution static compensator (DSTATCOM) for unbalanced non-linear load is designed. The aim is to provide better OAPFC in EDS with shunt compensation to maintain a round-the-clock quality power supply for end users. The improved neural network-based adaptive least mean square (ALMS) control algorithms are employed for DSTATCOM. The ALMS control strategy is realized using the SPARTAN-6 field programmable gate array (FPGA) evaluation kit. This research involves the comparative study of a two-level voltage source inverter (VSI) and a BTB-TVSI in terms of their OAPFC capability and robustness in mitigating power quality (PQ) against source current disturbances, low power factor (PF), poor voltage regulation, unbalanced voltage at the point of common coupling (PCC), high switching stress, and issues in the EDS operation. The experimental results demonstrate that the BTB-TVSI using an ALMS controller is able to achieve better dynamic performance and lower total harmonic distortion (THD) under selected/permissible limits in comparison to a conventional VSI.

The Implementation of Digital Technology in Online Project-Based Learning during Pandemic: EFL Students’ Perspectives

Integrating technology and digital media into project-based learning (PBL) is the best strategy for addressing the challenges of the English classroom in the digital age as well as providing an online learning alternative during the Covid-19 pandemic. This is a case study that investigates the utilization of technology in PBL and the benefits and drawbacks experienced by students. To conduct the case study, open-ended questionnaires were distributed to 118 students to get a wide range of perspectives on the implementation. The findings indicated that a numerous digital technology and tools were used to succeed in PBL learning ranging from LMS, video conferencing platforms, virtual whiteboard apps, chat and messaging, messaging and collaboration apps, Web 2.0, word processing, presentation software, graphic design or drawing tools, and photo editors. They concurred that the technology-mediated PBL was beneficial because it facilitated remote learning, offered online discussion/collaboration and feedback, was accessible and flexible, encourage creativity and allowed students to explore new knowledge as well as knowledge to produce a digital product, and also enhanced technological literacy. However, there were still significant obstacles existed especially the issues of internet connectivity and technology operation. In addition, they also had difficulties in articulating ideas using the technology and managing their time to complete the project.

Using Big Data by Ukrainian official statistics when martial law applies: problems and solutions

The article is focused on issues of the secure operation of official statistics in Ukraine during the application of martial law. It was found that the gaps in conventional sources of statistical data caused by the war needed to be filled with data from alternative sources, including Big Data. The level of digitalisation in Ukraine as the basis for using Big Data was analysed by the proposed indices of internetisation, social progress and digital transformation. Thanks to our research, several problems (methodological, legal, financial, and managerial) were identified as vital for statistical offices on their way to the implementation of Big Data in statistical processes. Our proposals concern tools for Big Data processing, such as Data Hypercube as a way for presenting Big Data for their visualisation, applications of Web scraping in estimating the consumer prices index, analyses of labour and real estate markets, and the applications of specialised software for the collection, processing and analysis of Big Data sets.

A Deep Learning-based Fast Fake News Detection Model for Cyber-Physical Social Services

With the prevalence of social network service in cyber-Physical space, flow of various fake news has been a rough issue for operators of social service. Although many theoretical outcomes have been produced in recent years, they are generally challenged by processing speed of semantic modeling. To solve this issue, this paper presents a deep learning-based fast fake news detection model for cyber-physical social services. Taking Chinese text as the objective, each character in Chinese text is directly adopted as the basic processing unit. Considering the fact that the news are generally short texts and can be remarkably featured by some keywords, convolution-based neural computing framework is adopted to extract feature representation for news texts. Such design is able to ensure both processing speed and detection ability in scenes of Chinese short texts. At last, some experiments are conducted for evaluation on a real-world dataset collected from a Chinese social media. The results show that the proposal possesses lower training time cost as well as higher classification accuracy compared with baseline methods.

Reinforcement Learning with Dual Safety Policies for Energy Savings in Building Energy Systems

Reinforcement learning (RL) is being gradually applied in the control of heating, ventilation and air-conditioning (HVAC) systems to learn the optimal control sequences for energy savings. However, due to the “trial and error” issue, the output sequences of RL may cause potential operational safety issues when RL is applied in real systems. To solve those problems, an RL algorithm with dual safety policies for energy savings in HVAC systems is proposed. In the proposed dual safety policies, the implicit safety policy is a part of the RL model, which integrates safety into the optimization target of RL, by adding penalties in reward for actions that exceed the safety constraints. In explicit safety policy, an online safety classifier is built to filter the actions outputted by RL; thus, only those actions that are classified as safe and have the highest benefits will be finally selected. In this way, the safety of controlled HVAC systems running with proposed RL algorithms can be effectively satisfied while reducing the energy consumptions. To verify the proposed algorithm, we implemented the control algorithm in a real existing commercial building. After a certain period of self-studying, the energy consumption of HVAC had been reduced by more than 15.02% compared to the proportional–integral–derivative (PID) control. Meanwhile, compared to the independent application of the RL algorithm without safety policy, the proportion of indoor temperature not meeting the demand is reduced by 25.06%.

Zwitterionic poly(sulfobetaine methacrylate)-based hydrogel coating for drinking water distribution systems to inhibit adhesion of waterborne bacteria.

Presence of biofilms in drinking water distribution systems (DWDS) can be a nuisance, leading to several operational and maintenance issues (i.e., increased secondary disinfectants demand, pipe damage or increased flow resistance), and so far, no single control practice was found to be sufficiently effective. Here, we propose poly (sulfobetaine methacrylate) (P(SBMA))-based hydrogel coating application as a biofilm control strategy in DWDS. The P(SBMA) coating was synthetized through photoinitiated free radical polymerization on polydimethylsiloxane with different combinations of SBMA as a monomer, and N, N'-methylenebis (acrylamide) (BIS) as a cross-linker. The most stable coating in terms of its mechanical properties was obtained using 20% SBMA with a 20:1 SBMA:BIS ratio. The coating was characterized using Scanning Electron Microscopy, Energy Dispersive X-Ray Spectroscopy, and water contact angle measurements. The anti-adhesive performance of the coating was evaluated in a parallel-plate flow chamber system against adhesion of four bacterial strains representing genera commonly identified in DWDS biofilm communities, Sphingomonas and Pseudomonas. The selected strains exhibited varying adhesion behaviors in terms of attachment density and bacteria distribution on the surface. Despite these differences, after 4h, presence of the P(SBMA)-based hydrogel coating significantly reduced the number of adhering bacteria by 97%, 94%, 98% and 99%, for Sphingomonas Sph5, Sphingomonas Sph10, Pseudomonas extremorientalis and Pseudomonas aeruginosa, respectively, compared to non-coated surfaces. These findings motivate further research into a potential application of a hydrogel anti-adhesive coating as a localized biofilm control strategy in DWDS, especially on materials known to promote excessive biofilm growth.

Deep-Learning-Based Context-Aware Multi-Level Information Fusion Systems for Indoor Mobile Robots Safe Navigation.

Hazardous object detection (escalators, stairs, glass doors, etc.) and avoidance are critical functional safety modules for autonomous mobile cleaning robots. Conventional object detectors have less accuracy for detecting low-feature hazardous objects and have miss detection, and the false classification ratio is high when the object is under occlusion. Miss detection or false classification of hazardous objects poses an operational safety issue for mobile robots. This work presents a deep-learning-based context-aware multi-level information fusion framework for autonomous mobile cleaning robots to detect and avoid hazardous objects with a higher confidence level, even if the object is under occlusion. First, the image-level-contextual-encoding module was proposed and incorporated with the Faster RCNN ResNet 50 object detector model to improve the low-featured and occluded hazardous object detection in an indoor environment. Further, a safe-distance-estimation function was proposed to avoid hazardous objects. It computes the distance of the hazardous object from the robot's position and steers the robot into a safer zone using detection results and object depth data. The proposed framework was trained with a custom image dataset using fine-tuning techniques and tested in real-time with an in-house-developed mobile cleaning robot, BELUGA. The experimental results show that the proposed algorithm detected the low-featured and occluded hazardous object with a higher confidence level than the conventional object detector and scored an average detection accuracy of 88.71%.

Managing food security using blockchain-enabled traceability system

PurposeFood insecurity is a major concern for developing economies. Operational inadequacies get introduced with unorganized interactions among stakeholders in the food supply chain, affecting social, economic, environmental and operational (SEEO) aspects of a nation. This study analyzes India's largest food safety net program, Public Distribution System (PDS) and develops a perception-based model, mapping the root causes (of food insecurity) with possible solutions. The novelty lies in leveraging blockchain in the implementation of food traceability system.Design/methodology/approachSoft system methodology (SSM) is used to identify and analyze problems in PDS, leveraging the learning and inquiry process. It relies on system thinking and action research to create a defendable and rational model, which helps in proposing recommendations for addressing the problem.FindingsBlockchain-enabled food traceability system increases transparency, thus enabling the fulfillment of basic food necessities for beneficiaries.Practical implicationsThe proposed model enables policymakers to build a profound understanding of existing operational issues and provides insightful recommendations for making informed decisions to deal with the grave issue of food insecurity.Originality/valueUnlike previous studies, this research attempts to understand operational inefficiencies during interactions among stakeholders. It proposes a perception-based conceptual model for the final implementation at the ground level. It also reveals significance of three systems: a delivery system, an enabling system empowering delivery system and a criteria system to control and monitor processes. This study thus bridges an important gap in the literature by proposing a blockchain-driven traceability system, under the control of criteria system, through the integration of system-thinking and action-research approach.

Numerical Study of the Influence of the Thermal Gas Expansion on the Boundary Layer Flame Flashback in Channels with Different Wall Thermal Conditions

In recent years, boundary layer flame flashback (BLF) has re-emerged as a technological and operational issue due to the more widespread use of alternative fuels as a part of a global effort to promote carbon neutrality. While much understanding has been achieved in experiments and simulations of BLF in the past decades, the theoretical modeling of BLF still largely relies on the progress made as early as the 1940s, when the critical gradient model (CGM) for the laminar flame flashback was proposed by Lewis and von Elbe. The CGM does not account for the modification of the upstream flow by the flame, which has been recently shown to play a role in BLF. The aim of the present work is to gain additional insight into the effects of thermal gas expansion and confinement on the flame-flow interaction in laminar BLF. Two-dimensional simulations of the confined laminar BLF in a channel are performed in this work. The parametric study focuses on the channel width, the thermal gas expansion coefficient, and the heat losses to the wall. This study evaluates the influence of these factors on the critical condition for the flame flashback. By varying the channel width, it is demonstrated that at the critical condition, the incoming flow in narrow channels is modified globally by the thermal gas expansion, while in wider channels, the flow modification by the flame tends to be more local. In narrow channels, a non-monotonic dependence of the critical-condition centerline velocity on the channel width has been identified. The variation of the heat loss to the wall confirms that the wall’s thermal conditions can significantly alter the flashback limit, with the flashback propensity being larger when the thermal resistance of the wall is high. To assess the general applicability of the CGM, the flame consumption speed and the flow velocity near the wall are quantified. The results confirm that the assumption of flame having no influence on the upstream flow, employed in the CGM, is not fulfilled under confinement for a realistic thermal gas expansion. This results in a general disagreement between the simulations and the CGM, which implies that the thermal expansion effects should be accounted for when considering the confined boundary layer flashback limits. It is shown that the critical velocity gradient increases with the gas expansion coefficient for the given channel width and wall thermal condition.

Operational Issues of Contemporary Distribution Systems: A Review on Recent and Emerging Concerns

Distribution systems in traditional power systems (PS) constituted of passive elements and the distribution issues were then limited to voltage and thermal constraints, harmonics, overloading and unbalanced loading, reactive power compensation issues, faults and transients, loss minimization and frequency stability problems, to name a few. Contemporary distribution systems are becoming active distributed networks (ADNs) that integrate a substantially increasing amount of distributed energy resources (DERs). DERS include distributed generation (DG) sources, energy storage resources and demand side management (DSM) options. Despite their evidenced great benefits, the large-scale deployment and integration of DERs remain a challenge as they subsequently lead to the network operational and efficiency issues, hampering PS network reliability and stability. This paper carries out a comprehensive literature survey based on the last decade’s research on operational challenges reported and focusing on dispatchable and non-dispatchable DGs grid integration, on various demand response (DR) mechanisms and, on battery energy storage system (BESS) charging and discharging challenges, with the aim to pave the way to developing suitable optimization techniques that will solve the coordination of multiple renewable sources, storage systems and DRs to minimize distribution systems’ operational issues and thus improve stability and reliability. This paper’s findings assist the researchers in the field to conduct further research and to help PS planners and operators decide on appropriate relevant technologies that address challenges inherent to DG grid integration.

Quick coherence technique facilitating commercial pilots’ psychophysiological resilience to the impact of COVID-19

This study investigates the effect of quick coherence technique (QCT) on commercial pilots’ resilience to the unprecedented impact of a pandemic. Eighteen commercial pilots voluntarily participated in a 2-day training course on QCT followed by 2 months of self-regulated QCT practicing during controlled rest in the flight deck and day-to day life. There are subjective and objective assessments to evaluate the effects of QCT on commercial pilots’ psychophysiological resilience. Results demonstrated that QCT training can significantly increase pilots’ psychophysiological resilience thereby improving their mental/physical health, cognitive functions, emotional stability and wellness on both subjective (PSS & AWSA) and objective measures (coherence scores). Moreover, pilots who continued practicing self-regulated QCT gained the maximum benefits. Current research has identified great potential to enhance pilots’ mental/physical health via QCT training. Operators can develop peer support programs for pilots to increase resilience and maintain mental and physical health using the QCT technique. Practitioner summary: QCT breathing has been proven to increase commercial pilots’ resilience by moderating psychophysiological coherence, strengthening mental/physical capacity and sustaining positive emotions to deal with the challenges both on the flight deck and in everyday life. HIGHLIGHTS Pilots have suffered from the impact of the Covid-19 pandemic across many factors including social, economic, mental, physical, emotional, and operational issues Biofeedback training can increase commercial pilots’ resilience by moderating psychophysiological coherence, strengthening mental and physical capacity Self-regulated practicing QCT to form a habitual behaviour is required to sustain the maximum benefits either in the flight or day-to-day life QCT is an effective intervention for aviation authorities and airline operators to develop peer support programs to increase pilots’ fatigue resilience

Nanogel-based coating as an alternative strategy for biofilm control in drinking water distribution systems

Biofilm formation and detachment in drinking water distribution systems (DWDS) can lead to several operational issues. Here, an alternative biofilm control strategy of limiting bacterial adhesion by application of a poly(N-isopropylmethacrylamide)-based nanogel coating on DWDS pipe walls was investigated. The nanogel coatings were successfully deposited on surfaces of four polymeric pipe materials commonly applied in DWDS construction. Nanogel-coated and non-coated pipe materials were characterized in terms of their surface hydrophilicity and roughness. Four DWDS relevant bacterial strains, representing Sphingomonas and Pseudomonas, were used to evaluate the anti-adhesive performance of the coating in 4 h adhesion and 24 h biofilm assays. The presence of the nanogel coating resulted in adhesion reduction up to 97%, and biofilm reduction up to 98%, compared to non-coated surfaces. These promising results motivate further investigation of nanogel coatings as a strategy for biofilm prevention in DWDS.

Integrated optimization of demand-driven timetable, train formation plan and rolling stock circulation with variable running times and dwell times

Timetable, train formation plan and rolling stock circulation are the key operational issues for efficient railway operation. The three issues are optimized sequentially based on the order of the operational plan. Sequential optimization manner cannot balance the costs of the three stages effectively and could create an infeasible solution for a later stage when the resources are limited. In addition, because of the computational complexity, multiple variable elements, such as the variable running times, variable dwell times and the coupling/decoupling operations, of a transportation system are ignored. This paper focuses on optimizing timetable, train formation plan and rolling stock circulation simultaneously to minimize costs and meet passenger demand. The key to solving the problem is to determine operation times (i.e., arrival times, departure times, running times and dwell times), the formation type of each train service and rolling stock connections (including the turnaround operations and the coupling/decoupling operations) between these train services. Considering the passenger costs and operator costs, a multi-objective mixed-integer nonlinear programming (MINLP) model is proposed to minimize the total passenger waiting time (TWT), the number of rolling stocks (NR), the number of formations (NF) and the number of coupling/decoupling operations (NC) based on a time–space network. The multi-objective MINLP model is further reformulated into a single-objective mixed-integer linear programming (MILP) model by logical linearizing, piecewise linearizing and fuzzy programming. A numerical example is used in which the exact solution of MINLP obtained by the BARON solver is compared with the approximate solution of MILP obtained by the CPLEX solver to verify the effectiveness of the piecewise linearization. Finally, the model is tested on a real-world case study of the Jinshan Line in Shanghai. The CPLEX solver can efficiently produce the approximate solution within a given computation time in acceptable gaps. The results demonstrate that the integrated model can reduce the number of rolling stocks and improve the utilization rate of rolling stocks. Furthermore, the integrated model can effectively resolve the shortage of rolling stocks when only one depot has rolling stocks or the number of available rolling stocks is limited. In addition, considering the multiple variable elements have a significant effect on the improvement of all the objectives and reduce both the passenger costs and the operator costs.