Research on the Optimal Energy Saving and Emission Reduction Path for Promoting High-Quality Industrial Development in Hubei Province

In December 2019, a novel coronavirus broke out in Wuhan City, Hubei Province, and, as the center of the coronavirus disease 2019 (COVID-19) epidemic, the economy and production throughout Hubei Province suffered huge temporary impacts. Based on the input–output and industrial pollution emissions data of 33 industrial industries in Hubei from 2010 to 2019, this article uses the non-parametric frontier analysis method to calculate the potential production losses and compliance costs caused by environmental regulations in Hubei's industrial sector by year and industry. Research has found that the environmental technology efficiency of the industrial sector in Hubei is showing a trend of increasing year-on-year, but the overall efficiency level is still not high, and there is great room for improvement. The calculation results with and without environmental regulatory constraints indicate that, generally, production losses and compliance costs may be encountered in the industrial sector in Hubei, and there are significant differences by industry. The potential production losses and compliance costs in pollution-intensive industries are higher than those in clean production industries. On this basis, we propose relevant policy recommendations to improve the technological efficiency of Hubei's industrial environment, in order to promote the high-quality development of Hubei's industry in the post-epidemic era.

Antecedents of digital supply chains for a circular economy: a sustainability perspective

PurposeA digital supply chain (DSC) positively enhances circular economy (CE) practices. However, what factors and conditions lead to the implementation of DSC for transitioning toward CE is not yet clear. Therefore, this study aims at identifying and subsequently analyzing the antecedents of DSC for CE.Design/methodology/approachThe study identifies major antecedents of DSC for CE to achieve sustainability objectives through literature review and expert opinions. In this study, 19 potential antecedents of DSCs for CE are established from the literature and suggestions from industry professionals. A trapezoidal fuzzy Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach is applied quantitatively to investigate the antecedents identified.FindingsConducted in the context of Indian automobile manufacturing industry, the findings of the study reflect that advanced information sharing arrangement, effective government policies for DSC and CE implementation and digitalizing the supply chains are the top three potential antecedents of DSC for a CE.Originality/valueIn the existing literature, few studies are specific to investigating the DSC and CE paradigm. The present study will help organizations develop a practical and integrated strategic approach that will foster DSC through improved knowledge of CE.

Identifying and analysis of key flexible sustainable supply chain management strategies toward overcoming the post-COVID-19 impacts

PurposeThe emerging markets are facing a lot of risks and disruptions across their supply chains (SCs) due to the deadly coronavirus disease 2019 (COVID-19) pandemic. To mitigate the significant post-COVID-19 consequences, organizations should modify their existing strategies and focus more on the key flexible sustainable SC (SSC) strategies. Still now, a limited number of studies have highlighted about the flexible strategies what firms should adopt to reduce the rampant effects in the context of emerging markets.Design/methodology/approachThis study presents an integrated approach including Delphi method, Bayesian, and the Best-Worst-Method (BWM) to identify, assess and evaluate the importance of the key flexible SSC strategies for the footwear industry in the emerging market context.FindingsThe results found the manufacturing flexibility through automation integration as the most important flexible SSC strategy to improve the flexibility and sustainability of modern SCs. Also, developing omni-channel distribution and retailing strategies and increasing the level of preparedness by using artificial intelligent are crucial strategies for overcoming the post-COVID-19 impacts.Originality/valueThe novelty of this research is that the research connects a link among flexible strategies, SCs sustainability, and the impacts of the COVID-19 pandemic. Moreover, the research proposes a novel and intelligent framework based on Delphi and Bayesian-BWM to identify and analyze the key flexible SSC strategies to build up sustainable and robust SCs which can withstand in the post-COVID-19 world.

A novel high-order multivariate Markov model for spatiotemporal analysis with application to COVID-19 outbreak.

We propose a new strategy for analyzing the evolution of random phenomena over time and space simultaneously based on the high-order multivariate Markov chains. We develop a novel Markov model of order r for m chains consisting of s possible states to gather parsimony with realism. It can capture negative and positive associations among the chains with only a reduced number of parameters, rm2s2+2, remarkably lower than msrm+1 required for the full parameterized model. Our model privileges are enhanced by a Monte Carlo simulation experiment, besides application to analyze the spatial-temporal dynamics for the risk level of a recently global pandemic (COVID-19) outbreak in world health organization (WHO) regions for predicting the risk state of epidemiological prevalence and monitoring infection control.

Mathematical optimization models for reallocating and sharing health equipment in pandemic situations.

In this paper we provide a mathematical programming based decision tool to optimally reallocate and share equipment between different units to efficiently equip hospitals in pandemic emergency situations under lack of resources. The approach is motivated by the COVID-19 pandemic in which many Heath National Systems were not able to satisfy the demand of ventilators, sanitary individual protection equipment or different human resources. Our tool is based in two main principles: (1) Part of the stock of equipment at a unit that is not needed (in near future) could be shared to other units; and (2) extra stock to be shared among the units in a region can be efficiently distributed taking into account the demand of the units. The decisions are taken with the aim of minimizing certain measures of the non-covered demand in a region where units are structured in a given network. The mathematical programming models that we provide are stochastic and multiperiod with different robust objective functions. Since the proposed models are computationally hard to solve, we provide a divide-et-conquer math-heuristic approach. We report the results of applying our approach to the COVID-19 case in different regions of Spain, highlighting some interesting conclusions of our analysis, such as the great increase of treated patients if the proposed redistribution tool is applied.

Optimizing the COVID-19 cold chain vaccine distribution network with medical waste management: A robust optimization approach.

This paper investigates the distribution problem of the COVID-19 vaccine at the provincial level in Turkey and the management of medical waste, considering the cold chain requirements and the perishable nature of vaccines. In this context, a novel multi-period multi-objective mixed-integer linear programming model is initially presented over a 12-month planning horizon for solving the deterministic distribution problem. The model includes newly structured constraints due to the feature of COVID-19 vaccines, which must be administered in two doses at specified intervals. Then, the presented model is tested for the province of Izmir with deterministic data, and the results show that the demand can be satisfied and community immunity can be achieved in the specified planning horizon. Moreover, for the first time, a robust model is created using polyhedral uncertainty sets to manage uncertainties related to supply and demand quantities, storage capacity, and deterioration rate, and it has been analyzed under different uncertainty levels. Accordingly, as the level of uncertainty increases, the percentage of meeting the demand gradually decreases. It is observed that the biggest effect here is the uncertainty in supply, and in the worst case, approximately 30% of the demand cannot be met.

Otimização do processo de picking em supermercados: um modelo matemático para roteamento de coleta de pedidos em comércio eletrônico

O comércio eletrônico tem sido uma oportunidade para as empresas acessarem um dos canais de venda mais rentáveis, a internet. A estratégia Omnichannel se consolidou no mercado, integrando os canais físicos e online para oferecer uma experiência de compra mais completa e personalizada aos clientes. No entanto, o segmento de alimentos online não se desenvolveu tão rapidamente quanto inicialmente esperado, o que levou a um certo ceticismo em relação a esse negócio. A pandemia da COVID-19 em 2020 causou uma mudança inesperada nesse cenário, forçando os supermercados a acelerar sua digitalização para encontrar novas maneiras de atender aos clientes em suas casas. O processo de separação, empacotamento e expedição de pedidos em uma loja online é conduzido por um responsável que atribui as atividades aos separadores levando em consideração a experiência do indivíduo. Embora os separadores mais experientes usem a topologia da loja para determinar o caminho mais eficiente, frequentemente os pedidos são atribuídos a trabalhadores com tempo parcial e sem vínculo empregatício, prejudicando o desempenho. Depois de coletados os itens, os pedidos são entregues a colaboradores responsáveis pelo empacotamento e expedição, sendo que o fluxo do processo é alterado de acordo com o tipo de serviço solicitado pelo cliente. Este trabalho apresenta e discute um modelo de programação linear inteira mista para solucionar o problema de roteamento de coleta com restrições específicas de um supermercado. Os testes realizados mostraram que a utilização de uma variação do modelo matemático do Problema do Caixeiro Viajante pode ser uma ferramenta útil para a tomada de decisões gerenciais na otimização do processo de picking em supermercados. Desta forma, os gestores podem definir estratégias específicas para melhorar a eficiência das entregas, reduzindo custos e tempo de espera dos clientes.Alternate :E-commerce has been an opportunity for companies to access one of the most profitable sales channels, the internet. The Omnichannel strategy has consolidated in the market, integrating physical and online channels to offer a more complete and personalized shopping experience to customers. However, the online food segment has not developed as quickly as initially expected, leading to some skepticism about this business. The COVID-19 pandemic in 2020 caused an unexpected shift in this scenario, forcing supermarkets to accelerate their digitalization to find new ways to serve customers in their homes. The process of picking, packing, and shipping orders in an online store is conducted by a responsible person who assigns activities to pickers, taking into account the individual's experience. Although more experienced pickers use the store's topology to determine the most efficient path, orders are often assigned to part-time workers without an employment relationship, which hampers performance. After items are collected, orders are delivered to employees responsible for packing and shipping, and the process flow is changed according to the type of service requested by the customer. This paper presents and discusses an integer linear programming model to solve the collection routing problem with specific constraints of a supermarket. The tests carried out showed that the use of the mathematical model of the Traveling Salesman Problem can be a useful tool for managerial decision-making in optimizing the picking process in supermarkets. Managers can define specific strategies to improve delivery efficiency, reducing costs and waiting time for customers.

Collaborative E-Learning Process Discovery in Multi-tenant Cloud

With the appearance of the COVID-19 pandemic, the practice of e-learning in the cloud makes it possible to: avoid the problem of overloading the institutions infrastructure resources, manage a large number of learners and improve collaboration and synchronous learning. In this paper, we propose a new e-leaning process management approach in cloud named CLP-in-Cloud (for Collaborative Learning Process in Cloud). CLP-in-Cloud is composed of two steps: i) design general, configurable and multi-tenant e-Learning Process as a Service (LPaaS) that meets different needs of institutions. ii) to fulfill the user needs, developpe a functional and non-functional awareness LPaaS discovery module. For functional needs, we adopt the algorithm A* and for non-functional needs we adopt a linear programming algorithm. Our developed system allows learners to discover and search their preferred configurable learning process in a multi-tenancy Cloud architecture. In order to help to discover interesting process, we come up with a recommendation module. Experimentations proved that our system is effective in reducing the execution time and in finding appropriate results for the user request.

A fuzzy multi-objective decision-making model for global green supplier selection and order allocation under quantity discounts

The selection of potential suppliers has recently become a big challenge for the manufacturing industries due to the rapid spread of covid-19 and the escalating frequency of natural calamities such as earthquakes and floods. When decision-makers (DMs) consider quantity discounts from multiple sources, things get much more complicated. Although previous studies have looked at selecting suitable suppliers from economic and environmental aspects, no one has considered foreign transportation risks while evaluating the textile industry's global green suppliers. In this regard, for the first time, this study combines economic and environmental factors with the foreign transportation risk criterion to develop a holistic model for global green supplier selection and order allocation (SS&OA) in the textile industry under all-unit quantity discounts. Initially, the fuzzy analytical hierarchy process (FAHP) method is used to calculate the relative weights of the criteria. Second, a multi-objective linear programming (MOLP) model is developed to reduce the total procurement cost, quality rejection rate, delivery lateness rate, greenhouse gas emissions from product procurement, and foreign transportation risks. Subsequently, the developed MOLP model is transformed into a fuzzy compromise programming (FCP) model to obtain order allocation quantities among selected suppliers with their offered quantity discount rates. A real-life case study of the Pakistani textile industry is presented to validate the proposed methodology's applicability by determining the optimal order allocation quantities among multiple suppliers based on two decision-making attitudes of DMs (neutral and risk-averse). Finally, sensitivity and comparative analyses are carried out to guarantee that the proposed technique produces accurate and optimal solutions. The final results of the proposed methodology show that it can effectively manage data uncertainties during SS&OA compared to other existing approaches. The suggested integrated methodology's outcomes can assist the supplier organization in overcoming its current shortcomings and developing a long-term relationship with the buyer organization. © 2023 Elsevier Ltd

Analyze the Effects of COVID-19 on Energy Storage Systems: A Techno-Economic Approach

During the COVID-19 pandemic, the U.S. power sector witnessed remarkable electricity demand changes in many geographical regions. These changes were evident in population-dense cities. This paper incorporates a techno-economic analysis of energy storage systems (ESSs) to investigate the pandemic's influence on ESS development. In particular, we employ a linear program-based revenue maximization model to capture the revenues of ESS from participating in the electricity market, by performing arbitrage on the energy trading, and regulation market, by providing regulation services to stabilize the grid's frequency. We consider five dominant energy storage technologies in the U.S., namely, Lithium-ion, Advanced Lead Acid, Flywheel, Vanadium Redox Flow, and Lithium-Iron Phosphate storage technologies. Extensive numerical results conducted on the case of New York City (NYC) allow us to highlight the negative impact that COVID-19 had on the NYC power sector. © 2023 IEEE.

Minimizing Energy Usage and Makespan of Elevator Operation in Rush Hour Using Multi-Objective Variable Neighborhood Strategy Adaptive Search with a Mobile Application

The purpose of this study is to address two major issues: (1) the spread of epidemics such as COVID-19 due to long waiting times caused by a large number of waiting for customers, and (2) excessive energy consumption resulting from the elevator patterns used by various customers. The first issue is addressed through the development of a mobile application, while the second issue is tackled by implementing two strategies: (1) determining optimal stopping strategies for elevators based on registered passengers and (2) assigning passengers to elevators in a way that minimizes the number of floors the elevators need to stop at. The mobile application serves as an input parameter for the optimization toolbox, which employs the exact method and multi-objective variable neighborhood strategy adaptive search (M-VaNSAS) to find the optimal plan for passenger assignment and elevator scheduling. The proposed method, which adopts an even-odd floor strategy, outperforms the currently practiced procedure and leads to a 42.44% reduction in waiting time and a 29.61% reduction in energy consumption. Computational results confirmed the effectiveness of the proposed approach.

Optimization Models for Medical Procedures Relocation.

As a side-effect of the Covid-19 pandemic, significant decreases in medical procedures for noncommunicable diseases have been observed. This calls for a decision support assisting in the analysis of opportunities to relocate procedures among hospitals in an efficient or, preferably, optimal manner. In the current paper we formulate corresponding decision problems and develop linear (mixed integer) programming models for them. Since solving mixed integer programming problems is NP-complete, we verify experimentally their usefulness using real-world data about urological procedures. We show that even for large models, with millions of variables, the problems' instances are solved in perfectly acceptable time.

MAXIMUM n-TIMES COVERAGE FOR VACCINE DESIGN

We introduce the maximum n-times coverage problem that selects k overlays to maximize the summed coverage of weighted elements, where each element must be covered at least n times. We also define the min-cost n-times coverage problem where the objective is to select the minimum set of overlays such that the sum of the weights of elements that are covered at least n times is at least τ. Maximum n-times coverage is a generalization of the multi-set multi-cover problem, is NP-complete, and is not submodular. We introduce two new practical solutions for n-times coverage based on integer linear programming and sequential greedy optimization. We show that maximum n-times coverage is a natural way to frame peptide vaccine design, and find that it produces a pan-strain COVID-19 vaccine design that is superior to 29 other published designs in predicted population coverage and the expected number of peptides displayed by each individual's HLA molecules. © 2022 ICLR 2022 - 10th International Conference on Learning Representationss. All rights reserved.

Cost Accounting in Multiple-Source and Multiple-Sink Logistics Management From Network Flow Perspectives

Transportation and logistics costs are becoming a large portion of the operating expenses for many businesses. Recently, supply chain disruptions caused by the COVID-19 pandemic and inflation crisis have brought challenges, especially, to many small- and medium-sized companies. Not only are companies struggling with logistics costs, but logistics bottlenecks are often preventing businesses from growing, expanding, and obtaining additional market shares. According to both academics and practitioners, there must be more literature and studies to address these logistics management challenges from cost accounting perspectives. This study focuses on multiple-source and multiple-sink scenarios, in which products are delivered from various production units to various stores. Optimized solutions to these cases may suggest optimal logistics strategies in terms of the minimized costs, as well as provide insights for later profitability analysis through common cost allocations and segment income statement reports. This paper can contribute to the practical examples in logistics management for businesses and is an addition to the current literature on cost accounting issues.

UNIFORM INFERENCE IN A GENERALIZED INTERVAL ARITHMETIC CENTER AND RANGE LINEAR MODEL

Via generalized interval arithmetic, we propose a Generalized Interval Arithmetic Center and Range (GIA-CR) model for random intervals, where parameters in the model satisfy linear inequality constraints. We construct a constrained estimator of the parameter vector and develop asymptotically uniformly valid tests for linear equality constraints on the parameters in the model. We conduct a simulation study to examine the finite sample performance of our estimator and tests. Furthermore, we propose a coefficient of determination for the GIA-CR model. As a separate contribution, we establish the asymptotic distribution of the constrained estimator in Blanco-Fernández (2015, Multiple Set Arithmetic-Based Linear Regression Models for Interval-Valued Variables) in which the parameters satisfy an increasing number of random inequality constraints.

An Algorithm of Nonparametric Quantile Regression.

Extreme events, such as earthquakes, tsunamis, and market crashes, can have substantial impact on social and ecological systems. Quantile regression can be used for predicting these extreme events, making it an important problem that has applications in many fields. Estimating high conditional quantiles is a difficult problem. Regular linear quantile regression uses an L 1 loss function [Koenker in Quantile regression, Cambridge University Press, Cambridge, 2005], and the optimal solution of linear programming for estimating coefficients of regression. A problem with linear quantile regression is that the estimated curves for different quantiles can cross, a result that is logically inconsistent. To overcome the curves crossing problem, and to improve high quantile estimation in the nonlinear case, this paper proposes a nonparametric quantile regression method to estimate high conditional quantiles. A three-step computational algorithm is given, and the asymptotic properties of the proposed estimator are derived. Monte Carlo simulations show that the proposed method is more efficient than linear quantile regression method. Furthermore, this paper investigates COVID-19 and blood pressure real-world examples of extreme events by using the proposed method.

Cost minimizing planning of container inspection and repair in multiple facilities.

A problem of optimal mid-term or long-term planning of inspection and repair of freight containers in multiple facilities is introduced and investigated. The containers are of different types and quality levels, which define their repair costs and workforce requirements. The objective function includes the total holding, inspection, repair, transportation and rejection costs. We propose a deterministic, time-dependent, integer linear min-cost multi-commodity network-flow formulation. The problem is shown to be polynomially solvable if there is a single facility, a single time period and all the containers are repairable and have to be repaired. It is shown to be NP-hard for three important special cases. The computational results of our experiments on randomly generated instances based on real data show that instances of sizes 3 facilities, 4 container types and up to 9 container quality levels can be solved with CPLEX in 5 minutes on a conventional PC, even for 30 periods, with an optimality gap of less than 3%. This is sufficient for medium-term or weekly planning or for short-term recovery planning. However, there are instances of the same magnitude, but with 360 periods of a considerably longer planning horizon, for which an optimality gap of 28% remained even after 10 hours of CPLEX computation.

Safe distancing in the time of COVID-19.

The spread of viruses such as SARS-CoV-2 brought new challenges to our society, including a stronger focus on safety across all businesses. Many countries have imposed a minimum social distance among people in order to ensure their safety. This brings new challenges to many customer-related businesses, such as restaurants, offices, theaters, etc., on how to locate their facilities (tables, seats etc.) under distancing constraints. We propose a parallel between this problem and that of locating wind turbines in an offshore area. The discovery of this parallel allows us to apply Mathematical Optimization algorithms originally designed for wind farms, to produce optimized facility layouts that minimize the overall risk of infection among customers. In this way we can investigate the structure of the safest layouts, with some surprising outcomes. A lesson learned is that, in the safest layouts, the facilities are not equally distanced (as it is typically believed) but tend to concentrate on the border of the available area-a policy that significantly reduces the overall risk of contagion.

A multi-objective optimization modelling for design and planning a robust closed-loop supply chain network under supplying disruption due to crises

Global crises such as COVID-19 pandemic and the Russian-Ukrainian war pose many challenges for closed-loop supply chain networks (CLSCN) due to the lack of supplies of raw materials and returned products. Therefore, this research focused on developing a multi-objective MILP mathematical model for the design and planning of CLSCN to help overcome these challenges considering the uncertainty in both the supplying capacity of the raw materials and the return rate of the used products.The developed models aim to maximize total profit, minimize total cost, and maximize overall cus-tomer service level (OCSL) using the e-lexicographic procedure.The effect of variation in both the supply capacity and return rate of the used products on the design and performance of the CLSCN have been studied. It is recommended to optimize the profit then the total cost with a maximum allowable deviation of 5%, and finally optimize the OCSL.(c) 2022 THE AUTHORS. Published by Elsevier BV on behalf of Faculty of Engineering, Ain Shams Uni-versity. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/ by-nc-nd/4.0/).

Comparative analysis of first and second stage data envelopment approaches: Performance of marine capture fisheries in the European Union

I explored harvest productivity and economic efficiency of marine fisheries across European Union member states using comparative first and second‐stage data envelopment analyses, linear programming, and econometric models, based on a panel data set of technical, social, and economic data between 2008 and 2020 when the first implications of the global Covid‐19 outbreak began in the European Union. During the period, harvest productivity increased for 52 percent of the 21 member states between 2008 and 2020, with an average economic efficiency of 0.76. The economic efficiency and harvest productivity of European Union member states' fisheries fluctuated, with noticeable declines throughout the study period. Gross domestic product per capita, population size, and aquaculture production were related to performance metrics. The results are aimed to guide European Union fisheries managers to better understand how improvements in harvest productivity and economically efficient performance are achieved without constant reliance on subsidization, over‐allocation, and overexploitation. [ FROM AUTHOR]