Meteorological-Data-Based Modeling for PV Performance Optimization

Developing a sustainable and reliable photovoltaic (PV) energy system requires a comprehensive analysis of solar profiles and an accurate prediction of solar energy performance at the study site. Installing the PV modules with optimal tilt and azimuth angles has a significant impact on the total irradiance delivered to the PV modules. This paper proposes a comprehensive optimization model to integrate total irradiance models with the PV temperature model to find the optimal year-round installation parameters of PV modules. A novel integration between installation parameters and the annual average solar energy is presented, to produce the maximum energy output. The results suggest an increase in energy yields of 4% compared to the conventional scheme, where tilt angle is equal to the latitude and the PV modules are facing south. This paper uses a real-time dataset for the NEOM region in Saudi Arabia to validate the superiority of the proposed model compared to the conventional scheme, but it can be implemented as a scheme wherever real-time data are available.

Modeling SARS-CoV-2 True Infections in Catalonia through a Digital Twin

A dynamic view of the evolution of the infections of SARS-CoV-2 in Catalonia using a Digital Twin approach that forecasts the true infection curve is presented. The forecast model incorporates the vaccination process, the confinement, and the detection rate, and virtually allows to consider any nonpharmaceutical intervention, enabling to understand their effects on the disease's containment while forecasting the trend of the pandemic. A continuous validation process of the model is performed using real data and an optimization model that automatically provides information regarding the effects of the containment actions on the population. To simplify this validation process, a formal graphical language that simplifies the interaction with the different specialists and an easy modification of the model parameters are used. The Digital Twin of the pandemic in Catalonia provides a forecast of the future trend of the SARS-CoV-2 spread and information regarding the true cases and effectiveness of the NPIs to control the SARS-CoV-2 spread over the population. This approach can be applied easily to other regions and can become an excellent tool for decision-making. © 2023 The Authors. Advanced Theory and Simulations published by Wiley-VCH GmbH.

The value of probabilistic forecasting in emergency medical resource planning under uncertainty

PurposeMost epidemic transmission forecasting methods can only provide deterministic outputs. This study aims to show that probabilistic forecasting, in contrast, is suitable for stochastic demand modeling and emergency medical resource planning under uncertainty.Design/methodology/approachTwo probabilistic forecasting methods, i.e. quantile regression convolutional neural network and kernel density estimation, are combined to provide the conditional quantiles and conditional densities of infected populations. The value of probabilistic forecasting in improving decision performances and controlling decision risks is investigated by an empirical study on the emergency medical resource planning for the COVID-19 pandemic.FindingsThe managerial implications obtained from the empirical results include (1) the optimization models using the conditional quantile or the point forecasting result obtain better results than those using the conditional density;(2) for sufficient resources, decision-makers' risk preferences can be incorporated to make tradeoffs between the possible surpluses and shortages of resources in the emergency medical resource planning at different quantile levels;and (3) for scarce resources, the differences in emergency medical resource planning at different quantile levels greatly decrease or disappear because of the existing of forecasting errors and supply quantity constraints.Originality/valueVery few studies concern probabilistic epidemic transmission forecasting methods, and this is the first attempt to incorporate deep learning methods into a two-phase framework for data-driven emergency medical resource planning under uncertainty. Moreover, the findings from the empirical results are valuable to select a suitable forecasting method and design an efficient emergency medical resource plan.

The Impact of Transaction Costs on Management Decisions (on the Example of Ukrainian Companies)

The purpose of the article is to study the impact of transaction costs on managerial decision-making on the example of Ukrainian enterprises. The article notes that, despite the significant achievements of the institutional theory, there are still questions about the clarity of understanding of its key concepts and definitions, the content of transaction costs and the concept of their minimization, the expediency and efficiency of the existence of certain types of transaction costs. The subject of the study is the essence of transaction costs of the enterprise and their modern classification. The methodological basis of the study was an integrated approach to the essence and classification of transaction costs, as well as general scientific and special research methods: retrospective and systematic analysis, comparison and generalization, grouping and sampling, methods of building linear and nonlinear economic and statistical models with constraints. As noted above, the main issue of the study was the classification of transaction costs. The economic situation in Ukraine was assessed, which allowed to determine that transaction costs are quite high for enterprises due to: insufficient development of markets, unformed structure of institutions, complex and ambiguous legislation, significant tax pressure and existing facts of corruption. The authors noted that the list of components of transaction costs is constantly updated due to the complication of the socio-economic conditions for doing business associated with crisis-forming force majeure factors (in particular, the COVID-19 pandemic), which have become very significant for the world economy and have significantly changed approaches to the allocation of investment resources. As a result of the study, it is proposed to supplement the existing classification of transaction costs with costs associated with adapting to new business conditions ("adaptation cost"), which together characterize the ability of an enterprise to adapt to new conditions of functioning and development. The main conclusions of the study include the fact that, given current trends, reducing transaction costs is becoming a priority issue. At the same time, in the context of the proposed classification, a model for minimizing transaction costs was developed for the first time, which includes a modern classification of transaction costs and provides the possibility of their modeling for a more complete and logical calculation. According to the authors, the presented model will provide an opportunity to more correctly determine the effectiveness of management decisions related to investments in the core business of the enterprise. It is important to note that, given the basic provisions of institutional theory, a significant amount of transaction costs, in principle, can neutralize the investment process. Thus, the study is of both theoretical and practical importance and gives an idea of solving a number of both industrial and social problems and can become the basis for further research.

Integrated Water Resources Plan Using An Optimization Model

As part of the City of Atlanta's Department of Watershed Management (DWM) transition into a 5-year rolling Capital Improvement Program (CIP), the Atlanta Program Management Services Team (PMST) was tasked with developing the Atlanta Integrated Water Resources Plan (IWRP) to incorporate project recommendations from the City's three recently completed master plans for water, wastewater, and stormwater into an integrated CIP. This effort was especially difficult as the City's available budget for CIP projects was being significantly reduced from normal years because of the adverse revenue impacts associated with the coronavirus pandemic and the uncertain economic recovery forecasts for the 5-year rolling CIP time frame. This paper details the successful development of an optimization model designed to maximize triple bottom line (TBL) and risk reduction benefits from the universe of potential water, wastewater, and stormwater projects while meeting tight financial budget limitations. The optimization model was based on OptimizerTM software by Optimatics that uses a heuristic learning algorithm, which is an approach designed to solve multi-criteria problems in a faster and more efficient manner that favors speed of process over absolute accuracy or completeness. The model used in Atlanta was the 3-dimensional (3-D) version to accumulate as much triple bottom line per dollar (TBL/$) and risk reduction per dollar (RRB/$) as early as possible in the planning horizon while minimizing budget expenditures. Copyright © 2022 Water Environment Federation.

Location Scheme of Routine Nucleic Acid Testing Sites Based on Location-Allocation Models: A Case Study of Shenzhen City

Since late 2019, the explosive outbreak of Coronavirus Disease 19 (COVID-19) has emerged as a global threat, necessitating a worldwide overhaul of public health systems. One critical strategy to prevent virus transmission and safeguard public health, involves deploying Nucleic Acid Testing (NAT) sites. Nevertheless, determining the optimal locations for public NAT sites presents a significant challenge, due to the varying number of sites required in different regions, and the substantial influences of population, the population heterogeneity, and daily dynamics, on the effectiveness of fixed location schemes. To address this issue, this study proposes a data-driven framework based on classical location-allocation models and bi-objective optimization models. The framework optimizes the number and location of NAT sites, while balancing various cost constraints and adapting to population dynamics during different periods of the day. The bi-objective optimization process utilizes the Knee point identification (KPI) algorithm, which is computationally efficient and does not require prior knowledge. A case study conducted in Shenzhen, China, demonstrates that the proposed framework provides a broader service coverage area and better accommodates residents' demands during different periods, compared to the actual layout of NAT sites in the city. The study's findings can facilitate the rapid planning of primary healthcare facilities, and promote the development of sustainable healthy cities.

Enhanced Feed Forward Neural Network with Adam Optimization Model (Efnnao) for Predicting the Type 2 Diabetes Using Internet of Things

The Internet of Things (IoT) is a growing technology which connects things or objects with the internet and also enables things to collect and exchange data in the network. IoT plays a vital role in all domains, especially healthcare, where IoT used for monitoring patients and taking valuable decision for a particular problem. In the current era, diabetes is a common disease among most of the people. Diabetes is associated with many life-threatening diseases such as heart attack, kidney failure, Vison loss, Covid, etc., Type 2 diabetes is a type of diabetes that usually affects the elderly. Therefore, early detection or prediction can help prevent the patient from being at risk. However, accurately analyzing the dataset collected to make the right decision is one of the biggest tasks and improving the accuracy of the prediction model is another important task. There is several analysis models are available, over the years, various Neural Network models have been used in clinical diagnosis. However, these models are still sustained a particular level of error and less accuracy in training and testing of disease diagnosis. So, this paper proposed the Enhanced Feed forwarded Neural Network with Adam Optimization model (EFNNAO) including multiple layers of network that suitable for processing IoT based dataset. The proposed model effectively structured for predicting the type 2 diabetes in IoT environment. The designed network has the ability to learn every aspect of the dataset and perform calculations efficiently by avoiding under fitting and over-fitting. Finally, the proposed model is compared with other models which are in the same aspect. The proposed EFNNAO is outperformed than other models with 92.02% accuracy. © 2022 IEEE.

An Analysis Air Traffic Prediction during a Pandemic

Air transportation during the covid-19 pandemic experienced a very drastic decline. The decrease in the number of passengers was caused by national and international restrictions. The troublesome administration makes passengers discouraged from traveling using Air transportation. Based on the National Statistics Agency, air transportation experienced a decline from early 2020 to 2021. This study focuses on air traffic predictions, namely the number of aircraft passengers during the COVID-19 pandemic at Indonesia's main airports, namely Kuala Namu, Sukarno Hatta, and Juanda airports., Ngurah Rai and Hasanuddin. The method used to predict the number of airplane passengers during a pandemic is the backpropagation algorithm using the Fletcher Reeves method. © 2022 IEEE.

Effects of Alcohol-Blended Waste Plastic Oil on Engine Performance Characteristics and Emissions of a Diesel Engine

The current study aims to investigate and compare the effects of waste plastic oil blended with n-butanol on the characteristics of diesel engines and exhaust gas emissions. Waste plastic oil produced by the pyrolysis process was blended with n-butanol at 5%, 10%, and 15% by volume. Experiments were conducted on a four-stroke, four-cylinder, water-cooled, direct injection diesel engine with a variation of five engine loads, while the engine's speed was fixed at 2500 rpm. The experimental results showed that the main hydrocarbons present in WPO were within the range of diesel fuel (C13–C18, approximately 74.39%), while its specific gravity and flash point were out of the limit prescribed by the diesel fuel specification. The addition of n-butanol to WPO was found to reduce the engine's thermal efficiency and increase HC and CO emissions, especially when the engine operated at low-load conditions. In order to find the suitable ratio of n-butanol blends when the engine operated at the tested engine load, the optimization process was carried out by considering the engine's load and ratio of the n-butanol blend as input factors and the engine's performance and emissions as output factors. It was found that the multi-objective function produced by the general regression neural network (GRNN) can be modeled as the multi-objective function with high predictive performances. The coefficient of determination (R2), mean absolute percentage error (MAPE), and root mean square error (RSME) of the optimization model proposed in the study were 0.999, 2.606%, and 0.663, respectively, when brake thermal efficiency was considered, while nitrogen oxide values were 0.998, 6.915%, and 0.600, respectively. As for the results of the optimization using NSGA-II, a single optimum value may not be attained as with the other methods, but the optimization's boundary was obtained, which was established by making a trade-off between brake thermal efficiency and nitrogen oxide emissions. According to the Pareto frontier, the engine load and ratio of the n-butanol blend that caused the trade-off between maximum brake thermal efficiency and minimum nitrogen oxides are within the approximate range of 37 N.m to 104 N.m and 9% to 14%, respectively.

Modelling and Evaluating Capability of Battery Storage Systems to Provide Extreme Event Services to the DSO: Case Study of Croatia

The City of Zagreb in Croatia and its surroundings have experienced two strong earthquakes within nine months of 2020. Putting this in the context of the increased workload of healthcare facilities due to Covid-19, the distribution system operator (DSO) is encouraged to look for unconventional solutions such as integrating the battery energy storage system (BESS) to supply healthcare facilities during network fault conditions or other extreme network events. The BESS size and location are determined by optimization model, while the control system of the BESS converter, based on the virtual synchronous machine (VSM) concept, is define to test BESS ability to supply critical consumers in the off-grid mode. The models are tested and verified on several real world situations in Zagreb MV distribution network. Future developments and scenarios are also simulated to verify the robustness of the proposed investment. © 2022 IEEE.

Multi-skill training ＆ assignment model of emergency rescuers based on time satisfaction

In order to couple the training and dispatch tasks of emergency rescue, and to improve the work efficiency of emergency rescue personnel, an optimization model for the integration of training ＆ assignment of emergency rescuers was established, which took maximizing the overall time satisfaction and minimizing the total cost of training ＆ assignment as the objective function.Taking COVID-19 epidemic as the research background, the simulation and solving were conducted by using the Lingo software.The results showed that the short-term training could effectively improve the rescue capability of emergency rescuers, and meet the requirements of emergency rescue time and rescue tasks of the disaster-affected sites.

Research on decision-making optimization of emergency supply chain based on Markov chain and multi-objective model

In order to cope with the sudden disasters such as floods, COVID-19,etc.,a discrete time Markov chain and multi-objective programming model（DTMC-MOP） with the maximum supply satisfaction rate, the shortest supply time and the lowest supply cost was proposed to dynamically identify, analyze and respond to the emergency supply chain risk.The improved self-adaptive Non-dominated Sorting Genetic Algorithm-Ⅱ（NSGA-Ⅱ） was used to solve the optimization model, and the feasibility and effectiveness of the model were verified by testing and evaluation with standard test functions.Through the example analysis, the Pareto optimal front with higher precision and more uniform distribution was obtained.The results showed that the decision-maker could choose the appropriate emergency scheme based on the core objective of emergency management or different preferences.It provide a scientific method for the decision-making optimization of emergency supply chain, which has positive significance for ensuring the life safety of victims and maintaining the social harmony and stability.

Integrated Optimization of Order Allocation and Last-Mile Multi-Temperature Joint Distribution for Fresh Agriproduct Community Retail

Community retail is an important research issue in the field of fresh agriproduct e-commerce. This paper focuses on the problem of last-mile multi-temperature joint distribution (MTJD), which combines time coupling, order allocation, and vehicle scheduling. Firstly, according to the temperature of a refrigerated truck in multi-temperature zones, a split-order packing decision is proposed to integrate the different types of fresh agriproduct. Then, the order allocation strategy is incorporated into a comprehensive picking and distribution schedule, while taking into account the time-coupling of picking, distribution, and delivery time limit. To improve consumer satisfaction and reduce order fulfillment costs, an optimization model combining multi-item order allocation and vehicle scheduling is established, to determine the optimal order allocation scheme and distribution route. Finally, taking fresh agriproduct community retail in the Gulou District of Nanjing as an example, the effectiveness and feasibility of the model are illustrated. The numerical results of medium- to large-scale examples show that, compared with the variable neighborhood search algorithm (VNS) and genetic algorithm (GA), the mixed genetic algorithm (MGA) can save 29% of CPU time and 65% of iterations. This study considers the integrated optimization of multiple links, to provide scientific decision support for fresh agriproduct e-commerce enterprises.

How to front the physical distancing requirement within workforce scheduling: empirical investigation through an optimization model

This work is focused on workforce scheduling for assembly lines with the additional constraint of workforce distancing. The aim is to warrant the necessary safety and health requirements due to COVID-19. The research stems within an industrial case in which a methodology has been developed with the objectives of i) developing a constraint optimization model considering the social distancing of workers as part of the workforce scheduling requirements and ii) investigating how the workforce distancing can affect certain production performances. Through an empirical investigation the impact of distancing on workforce allocation is appraised in terms of daily production capacity. Then, different distancing thresholds are assessed to seek the optimal balance among production performances and safety requirements. The research resulted in a tool able to adapt the scheduling sequence to those health/safety situations where the production manager needs to minimize losses in terms of production capacity, warranting the safest working conditions. © 2022 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.

Delivery pattern planning in retailing with transport and warehouse workload balancing

Goods from warehouses must be scheduled in advance, prepared, routed, and delivered to shops. At least three systems directly interact within such a process: warehouse workforce scheduling, delivery scheduling, and routing system. Ideally, the whole problem with the preceding inventory management (restocking) would be solved in one optimization pass. In order to make the problem simpler, we first decompose the total problem by isolating the delivery scheduling. Then we connect the optimization model to the rest of the system by workload balancing goal that is a surrogate of coordination and criterion for the system robustness. This paper presents the practical application of top-down discrete optimization that streamlines operations and enables better reactivity to changes in circumstances. We search for repetitive weekly delivery patterns that balance the daily warehouse and transportation utilization in the absence of capacity constraints. Delivery patterns are optimized for the quality criteria regarding specific store-warehouse pair types, with a special focus on fresh food delivery that aims at reducing inventory write-offs due to aging. The previous setup included semi-manual scheduling based on templates, historical prototypes, and domain knowledge. We have found that the system augmented with the new automated delivery scheduling system brings an improvement of 3% in the performance measure as well as speed in adjusting to the changes, such was the case with changes in policies during COVID-19 lockdowns.

Optimal Emergency Ordering Policy for Inventory with a Random-Ending-Time Supply Disruption

This paper considers the emergency ordering strategy for the classical economic ordering quantity inventory system with a supply disruption. For situations where the ending time of supply disruption is stochastic and the purchase price increases over time during that period, we develop an emergency ordering optimization model based on maximizing retailer’s profits. Through modeling analysis in various situations, the closed-form solution of the model is obtained, and the optimal emergency ordering strategy is provided for retailers. Numerical experiments verify the effectiveness of the model and the influence of related parameters on the optimal ordering strategy.

Increasing Forklift Time Utilization in a Food Equipment Manufacturing Plant with a Kaizen Event

The COVID-19 pandemic impacted several services organizations such as hotels and restaurants. Some studies suggest that 50% of restaurants closed during quarantining days will not reopen in the future, producing a domino effect with their suppliers. Therefore, to survey in this crisis, organizations focused their efforts on reducing waste and/or repurposing their manufacturing operations by introducing new products. The purposes of this paper are (1) to mention an overall strategy followed by food equipment manufacturing plant to address the COVID-19 pandemic challenge;and (2) to describe one of the five Kaizen events conducted to address COVID-19 challenges. This Kaizen focused on increasing forklifts utilization rate from 44% to 80%. The authors followed three steps to achieve this aim: understanding company background, conducting a literature review, and elaborating on a Kaizen event. Practitioners in manufacturing and services organizations could extract several highlights to help them to sort operations problems during crisis time. However, future research still needs to understand the socio-economic impact that COVID-19 pandemic around the world. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

EXPLORING QUANTUM COMPUTING POTENTIALS IN SOLVING A COMBINATORIAL OPTIMIZATION PROBLEM TO MINIMIZE EXPOSURE TO COVID-19 DURING A CITY JOURNEY

COVID-19 is an airborne virus that can be spread directly or indirectly from one person to another. Spreading the virus strongly depends on the location and time and hence, a Spatio-temporal event. Moreover, traffic congestion will increase the spread of the virus not only because of the vicinity but also because of increased temperature and humidity in these spaces for a short or long time. This paper introduces a vehicle routing optimization model to reduce COVID-19 exposure risk during a city journey by solving it as a quadratic unconstrained binary optimization problem on a quantum annealing computer. Indeed, the objective of the COVID-19 prevention optimization problem is to minimize the risk of exposure for a given set of road users between origins and destinations. Microsoft Taxi data from the city of Beijing have been used to simulate road users’ movement. The problem has been run onto three different solvers. One of the solvers is executed on classical computers, and two other solvers are executed on hybrid quantum solvers. Hybrid solvers return the solution within less than 0.03 seconds on quantum processing unit time. However, the results will be returned at least 5 seconds after the execution in the classical solver. It is worth mentioning that, as there is no direct access to the quantum computers, it is hard to compare the results on the same scale as the queries will go on a queue in D-wave quantum computers. Applying the proposed model on the trajectory data shows a better distribution of the vehicles on the road network.

Optimization and Machine Learning Applied to Last-Mile Logistics: A Review

The growth in e-commerce that our society has faced in recent years is changing the view companies have on last-mile logistics, due to its increasing impact on the whole supply chain. New technologies are raising users’ expectations with the need to develop customized delivery experiences;moreover, increasing pressure on supply chains has also created additional challenges for suppliers. At the same time, this phenomenon generates an increase in the impact on the liveability of our cities, due to traffic congestion, the occupation of public spaces, and the environmental and acoustic pollution linked to urban logistics. In this context, the optimization of last-mile deliveries is an imperative not only for companies with parcels that need to be delivered in the urban areas, but also for public administrations that want to guarantee a good quality of life for citizens. In recent years, many scholars have focused on the study of logistics optimization techniques and, in particular, the last mile. In addition to traditional optimization techniques, linked to the disciplines of operations research, the recent advances in the use of sensors and IoT, and the consequent large amount of data that derives from it, are pushing towards a greater use of big data and analytics techniques—such as machine learning and artificial intelligence—which are also in this sector. Based on this premise, the aim of this work is to provide an overview of the most recent literature advances related to last-mile delivery optimization techniques;this is to be used as a baseline for scholars who intend to explore new approaches and techniques in the study of last-mile logistics optimization. A bibliometric analysis and a critical review were conducted in order to highlight the main studied problems, the algorithms used, and the case studies. The results from the analysis allow the studies to be clustered into traditional optimization models, machine learning approaches, and mixed methods. The main research gaps and limitations of the current literature are assessed in order to identify unaddressed challenges and provide research suggestions for future approaches.

Methods in Constrained Community Detection: An Integer Optimization Model and Heuristic Approach for Cohort Creation

As a result of the COVID-19 pandemic, many organizations and schools have switched to a virtual environ-ment. Recently, as vaccines have become more readily available, organizations and educational institutions have started shifting from virtual environments to physical office spaces and schools. For the highest level of safety and caution with respect to the containment of COVID-19, the shift to in-person interaction requires a thoughtful approach. With the help of an Integer Programming (IP) Optimization model, it is possible to formulate the objective function and constraints to determine a safe way of returning to the office through cohort development. In addition to our IP formulation, we developed a heuristic approximation method. Starting with an initial contact matrix, these methods aim to reduce additional contacts introduced by subgraphs representing the cohorts. These formulations can be generalized to other applications that benefit from constrained community detection. © 2021 IEEE.