Endüstri Mühendisliği Bölümü Makale Koleksiyonu

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https://hdl.handle.net/20.500.12713/173

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A novel parallel heuristic method to design a sustainable medical waste management system
(Elsevier Ltd, 2024) Amirteimoori, A.; Tirkolaee, E.B.; Amirteimoori, A.; Khakbaz, A.; Simic, V.
Efficient management of waste generated in healthcare systems is crucial to minimize its environmental impact and ensure public health. Sustainable medical waste management (MWM) systems require careful network design, which can be achieved through efficient optimization techniques. This work develops a mixed-integer linear programming (MILP) to formulate the problem, a two-step MILP (TSMILP) to generate quality lower bounds, and a novel parallel heuristic algorithm to configure a sustainable waste management system including waste generation centers (WGCs), waste treatment centers (WTCs), waste recycling centers (WRCs), waste disposal centers (WDCs) and waste incineration centers (WICs). Such a hybrid methodology has not been yet offered in the literature wherein the aim is to address strategic (establishment of facilities), tactical (employment of transportation system), and operational decisions (transportation planning) optimally in large networks. As reflected in the literature, there is a huge gap in efficiency and application of combinatorial optimization, and parallel computing in sustainable MWM systems, where the suggested MILPs' solvers are not technically capable of discovering quality solutions in reasonable runtimes on large-sized instances. Thus, we suggest a novel heuristic equipped with parallel computing to share the complexity of the problem, with all the CPU cores to shorten runtime. Comparing the results generated by the parallel heuristic with those of the sequential heuristic, the MILP, and the TSMILP on three sets of benchmark instances using Nemenyi's post-hoc procedure for Friedman's test, it is inferred that the parallel heuristic is so effective in coping with the problem, and produces high-quality solutions, especially on the large-sized set. Finally, sensitivity analysis is adopted to analyze the effects of parameters on the objective values and provide useful managerial insights. © 2024 Elsevier Ltd
Towards explainable artificial intelligence through expert-augmented supervised feature selection
(Elsevier B.V., 2024) Rabiee, M.; Mirhashemi, M.; Pangburn, M.S.; Piri, S.; Delen, D.
This paper presents a comprehensive framework for expert-augmented supervised feature selection, addressing pre-processing, in-processing, and post-processing aspects of Explainable Artificial Intelligence (XAI). As part of pre-processing XAI, we introduce the Probabilistic Solution Generator through the Information Fusion (PSGIF) algorithm, leveraging ensemble techniques to enhance the exploration and exploitation capabilities of a Genetic Algorithm (GA). Balancing explainability and prediction accuracy, we formulate two multi-objective optimization models that empower expert(s) to specify a maximum acceptable sacrifice percentage. This approach enhances explainability by reducing the number of selected features and prioritizing those considered more relevant from the domain expert's perspective. This contribution aligns with in-processing XAI, incorporating expert opinions into the feature selection process as a multi-objective problem. Traditional feature selection techniques lack the capability to efficiently search the solution space considering our explainability-focused objective function. To overcome this, we leverage the Genetic Algorithm (GA), a powerful metaheuristic algorithm, optimizing its parameters through Bayesian optimization. For post-processing XAI, we present the Posterior Ensemble Algorithm (PEA), estimating the predictive power of features. PEA enables a nuanced comparison between objective and subjective importance, identifying features as underrated, overrated, or appropriately rated. We evaluate the performance of our proposed GAs on 16 publicly available datasets, focusing on prediction accuracy in a single objective setting. Moreover, we test our multi-objective model on a classification dataset to show the applicability and effectiveness of our framework. Overall, this paper provides a holistic and nuanced approach to explainable feature selection, offering decision-makers a comprehensive understanding of feature importance. © 2024 Elsevier B.V.
A two-stage risk-based framework for dynamic configuration of a renewable-based distribution system considering demand response programs and hydrogen storage systems
(Elsevier Ltd, 2024) Mojaradi, Z.; Tavakkoli-Moghaddam, R.; Bozorgi-Amiri, A.; Heydari, J.
Distribution feeder reconfiguration (DFR) in distribution systems can enhance operating conditions by reducing losses and improving voltage indices. This paper introduces a dual-stage risk-based framework that concurrently tackles day-ahead reconfiguration and microgrid scheduling within the distribution network. To control the negative aspects of uncertainties, the proposed framework integrates energy storage systems (ESS/HSS) and demand response programs (DRPs) at the network level, enhancing adaptability. The initial stage of the proposed model employs the AC power flow model, utilizing loss and voltage deviation functions as objective benchmarks for network reconfiguration. The scheduling is meticulously executed per interval, deriving optimized structures under diverse scenarios with the aid of a case reduction technique (CRT) to streamline solutions. The ultimate solution employs the grey wolf optimization (GWO) algorithm and CPLEX solver in the first and second stages respectively. Outcomes from applying this approach to the adjusted 118-bus network manifest improved operational conditions and voltage quality through reconfiguration. Impressively, the integration of ESS/HSS and DRPs yields a substantial 22.34% reduction in total operating costs, a conclusion substantiated by the numerical findings. Furthermore, by leveraging the CRT, a remarkable 56.17% reduction in problem-solution time is achieved. © 2024 Hydrogen Energy Publications LLC
Stochastic resource reallocation in two-stage production processes with undesirable outputs: An empirical study on the power industry
(Elsevier Ltd, 2024) Amirteimoori, A.; Kazemi, Matin, R.; Yadollahi, A.H.
Due to the scarcity of fossil fuels in the future, the optimal use of these products can not only increase the efficiency of power plants, but it can also be effective in reducing the production of pollutants. To deal with these situations, optimal resource allocation and reallocation was studied using the data envelopment analysis (DEA) models. The current study adopted a resource allocation model in DEA framework when undesired outputs are produced in production process. This alternative resource allocation model is, however, sensitive to uncertainty of the data. In this contribution, we, therefore, introduce a stochastic resource allocation model when there are random data and undesirable products. An applied illustrative study to the power industry consisting 21 electricity production & distribution companies for eight years (2011–2019) is performed to compare the resource reallocations and their efficiencies. The important findings are: First, if we decide to deactivate two companies, the fuel consumption, employees and net electricity generation must be reduced. These reductions will lead to a reduction in pollutants. Second, the low price of electricity in Iran leads to excessive consumption of this product, which in turn leads to the inefficiency of many companies. In order to improve the performances of the companies, the amount of sold-out electricity must significantly be increased. © 2024 Elsevier Ltd
Integrated design of a sustainable waste management system with co-modal transportation network: A robust bi-level decision support system
(Elsevier Ltd, 2024) Tirkolaee, E.B.; Simic, V.; Ghobakhloo, M.; Foroughi, B.; Asadi, S.; Iranmanesh, M.
Efficient waste management practices play a critical role in addressing the acute challenges of environmental protection, public health and resource conservation. A well-designed system guarantees that waste is efficiently collected, treated and disposed while minimizing negative impacts on ecosystems and human well-being. This work presents a robust bi-level decision support system to establish a sustainable waste management system using a co-modal transportation network to treat municipal solid waste timely and efficiently. Consequently, two integrated multi-objective mathematical models are developed to formulate the problem. Configuring the municipal solid waste network in the first level of the suggested decision support system, the transportation network is designed in the second level taking into account non-identical modes. The objectives are to minimize total cost and total emission in both levels, while maximization of total job creation is also addressed in the first level. Robust optimization method and weighted goal programming method are then utilized to accommodate the developed decision support system against uncertainty and multi-objectiveness, respectively. To validate the efficiency of these methods, they are assessed against possibilistic linear programming technique and Lp-metric approach with the help of simple additive weighting (SAW) method, respectively. Eventually, several numerical examples are generated based on the benchmarks given in the literature, which are then tackled using CPLEX solve to appraise the applicability and complexity of the developed methodology. The findings reveal the efficacy of the decision support system in terms of finding solutions in less than 448 s on average. Finally, sensitivity analyses are performed to draw out useful practical implications and decision aids. © 2024 Elsevier Ltd
Predicting Water Quality With Non-stationarity: Event-Triggered Deep Fuzzy Neural Network
(Institute of Electrical and Electronics Engineers Inc., 2024) Wang, G.; Chen, H.; Han, H.; Bi, J.; Qiao, J.; Tirkolaee, E.B.
Water quality prediction is an indispensable task in water environment and source management. The existing predictive models are mainly designed by data-driven artificial neural networks (ANNs), especially deep learning models for large-scale water quality prediction. However, the state of water environment is a dynamic process where the stationarity of water quality data suffers from time variation and human activities, which leads to a poor prediction accuracy because ANNs receive whole water quality data passively, including abnormal conditions. We consider such a tough problem in this article and propose an event-triggered deep fuzzy neural network (ET-DFNN) to pursue the better performance of water quality prediction in the complex water environment. First, a deep pretraining model is constructed to extract the effective features from raw water quality data. Second, we construct a DFNN model where the extracted effective features are considered as the input variables. Third, some events are defined to characterize the abnormal conditions of state evolution in water quality. The DFNN is trained and updated using different learning strategies only when the corresponding events are triggered, otherwise it ignores the current data sample and directly goes to the next data sample. The practical data-based experimental results show that the ET-DFNN achieves better prediction performance in accuracy and efficiency than its peers. Especially, the training efficiency of ET-DFNN is improved by 57.94% on total phosphorus prediction and 48.31% on biochemical oxygen demand prediction, respectively. IEEE
Circular closed-loop supply chain network design considering 3D printing and PET bottle waste
(Springer Science and Business Media B.V., 2024) Rajabi-Kafshgar, A.; Seyedi, I.; Tirkolaee, E.B.
One of the most critical pillars of Industry 4.0 (I4.0) is Additive Manufacturing (AM) or 3D Printing technology. This transformative technology has garnered substantial attention due to its capacity to streamline processes, save time, and enhance product quality. Simultaneously, environmental concerns are mounting, with the growing accumulation of plastic bottle waste, offering a potential source of recycled material for 3D printing. To thoroughly harness the potential of AM and address the challenge of plastic bottle waste, a robust supply chain network is essential. Such a network not only facilitates the reintegration of plastic bottle waste and 3D printing byproducts into the value chain but also delivers significant environmental, social, and economic benefits, aligning with the tenets of sustainable development and circular economy. To tackle this complex challenge, a Mixed-Integer Linear Programming (MILP) mathematical model is offered to configure a Closed-Loop Supply Chain (CLSC) network with a strong emphasis on circularity. Environmental considerations are integral, and the primary objective is to minimize the overall cost of the network. Three well-known metaheuristics of Simulated Annealing (SA), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) are employed to treat the problem which are also efficiently adjusted by the Taguchi design technique. The efficacy of our solution methods is appraised across various problem instances. The findings reveal that the developed model, in conjunction with the fine-tuned metaheuristics, successfully optimizes the configuration of the desired circular CLSC network. In conclusion, this research represents a significant step toward the establishment of a circular supply chain that combines the strengths of 3D printing technology and the repurposing of plastic bottle waste. This innovative approach holds promise for not only reducing waste and enhancing sustainability but also fostering economic and social well-being. © The Author(s) 2024.
Kullanılmayan ve atık ilaçların tersine lojistik faaliyetleri ile toplanmasına tüketicinin bakış açısının değerlendirilmesi
(Marmara Üniversitesi, 2022) Karadayı Usta, Saliha
İlaç endüstrisi, atık ilaçların çevre ve insan sağlığına verdiği zararların günümüzde daha sık ve net şekilde vurgulanması ile atık yönetimi konusuna gerekli önemin verilmesi konusunda çeşitli uygulamalar ve yönetmelikler sunmaktadır. Bu kapsamda kullanılmayan ve atık ilaçların uygun şekilde ve uygun zaman diliminde toplanması, bilinçli tüketicilerin bu konuda gerekli iş birliği sergilemesi önem arz etmektedir. Dolayısıyla bu araştırmanın amacı, kullanılmayan ve atık ilaçların tersine lojistik faaliyetleri ile toplanmasına tüketicinin bakış açısını anlamak, değerlendirmek ve çözüm önerileri geliştirmektir. Bu amaçla anket yoluyla veri toplanmakta, tanımlayıcı araştırma modeli kullanılmakta ve durum değerlendirmesi yapılmaktadır. Anketin ilk kısmında Aktaş ve Selvi (2019) tarafından geliştirilen Akılcı İlaç Kullanımı Farkındalığı ölçeğine başvurulmakta, takiben bu araştırmaya özgü merak edilen sorular yöneltilmektedir. Bulgular incelendiğinde tüketicinin ilaç bağışlama konusunda olumlu tavır sergilediği, ancak kullanılmayan ilacı kabul edip tüketmeye olumsuz baktığı anlaşılmaktadır. Arada kontrolü sağlayacak bir otoritenin bulunması ile güven ortamının oluşacağı düşünülmektedir. Ayrıca, ilaçların son kullanma tarihlerinin hatırlatılması için uyarı sistemlerine / hatırlatıcılara olumlu bakılmakta, özellikle telefona mesajla hatırlatma gelmesini talep etmektedir. Otomat / akıllı kutu gibi sistemi kolaylaştırıcı araçların yaratabileceği risklere önemle vurgu yapılmaktadır. Toplumun konu hakkında bilgilendirilmesi, eğitim yoluyla küçük yaşlarda farkındalık yaratılması ihtiyacının altı çizilmektedir. Çalışma çıktıları, ilaç atıklarının geri toplanmasında tüketici davranışının anlaşılmasını, uygulama aşamasına geçildiğinde ise ne tip önerileri / çözüm yolları izlemenin nasıl sonuçlar doğuracağını tahmin etme hususunda katkı sağlamaktadır. Konuyla ilgili ilaç endüstrisi yetkililerine yol gösterici çıktılar sunulmaktadır.
Sürdürülebilir moda tedarik zinciri yönetimi uygulamalarının sistematik taraması
(LODER, 2022) Karadayı Usta, Saliha
Dijitalleşmenin ve e-ticaret uygulamalarının geniş kitlelerce benimsenmesi, sosyal medyada görünür ve beğenilir olma kaygısı gibi sebepler, moda endüstrisinde neredeyse her hafta yeni koleksiyon tanıtmaya kadar giden bir hızlı üretim ve tüketim çılgınlığına sebep olmuştur. Hızlı moda akımının getirdiği en endişe verici sonuçlar arasında; kullanılan hammaddede sadece ucuzluğun dikkate alınarak sentetik kumaş kullanımının bilinçsizce artması, yine sadece ürün fiyatının düşük tutulması amacı ile tedarikçilere ödemelerin zamanında ve olması gereken miktarda yapılmaması, mevcut yerel tedarikçilerin sunduğu hizmet seviyesine ve kaliteye bakmaksızın daha ucuz mamul peşinde yurt dışı kaynaklara yönelim bulunmaktadır. Ayrıca iklim krizinin görünür hale gelmesiyle, doğal kaynakların minimum seviyede kullanımını temel alan sürdürülebilir moda hareketi büyük önem taşımaktadır. Sürdürülebilir moda kapsamında gerçekten ihtiyaç hissedildiği durumda alışveriş yapılmalı, daha dayanıklı ve kaliteli giysiler tasarlanmalı, giysiler onarılarak ürün ömrü uzatılmalı, ikinci el giyim alışverişi desteklenmeli, kullanım ömrü sona erdiğinde giysiler ipliğe veya kumaşa geri dönüştürülmeli, etik çalışma koşulları altında sürdürülebilir bir ağ kurulmalı ve bu ağ dijital kanallar üzerinden izlenmelidir. Dolayısıyla bu çalışmanın amacı, sürdürülebilir moda/tekstil/giyim tedarik zinciri literatüründe yer alan anahtar kavramları bibliyometrik analiz yoluyla belirlemek, bu kavramlar arasındaki ilişkileri ağ diyagramları ile görselleştirmek ve kavramların detaylarını vakalarla desteklemektir. Çalışmanın bulguları doğal kumaş üreticilerini, etik çalışma koşullarını sağlayan tedarikçi seçimini, tüm tedarik zinciri boyunca izlenebilirliğin sağlanmasını, yeşil lojistik uygulamalarını, bilinçli tüketici tutumlarını, ürünlerin döngüde tutularak uzun süreli kullanılmasını ve ömrünü tamamlamış ürünlerin geri dönüştürülmesini önemle vurgulamaktadır.
Technical challenges of blockchain technology for sustainable manufacturing paradigm in Industry 4.0 era using a fuzzy decision support system
(Elsevier Ltd, 2023) Su, Dan; Zhang, Lijun; Peng, Hua; Saeidi, Parvaneh; Tirkolaee, Erfan Babaee
Since 2008, many academics have increasingly paid attention to blockchain technology from different perspectives. In general, researchers desire to achieve global blockchain systems within a sustainable manufacturing domain; however, a number of technical challenges have come to exist in the recent decade, for instance, consensus algorithms and computing paradigms that can meet the privacy protection requirements of manufacturing systems. Therefore, an integrated decision-making framework called Pythagorean fuzzy-entropy-rank sum-Combined Compromise Solution (PF-entropy-RS-CoCoSo) is developed in this study, including two main phases. In the first phase, the PF-entropy-RS method is applied to obtain the subjective and objective weights of criteria to evaluate the technical challenges of transforming blockchain technology for a sustainable manufacturing paradigm in the Industry 4.0 era. The PF-CoCoSo model is then utilized in the second phase to assess the preferences of organizations over different technical challenges of the blockchain technology transformation for the sustainable manufacturing paradigm in the Industry 4.0 era. An empirical case study is taken to assess the main technical challenges of blockchain technology transformation for the sustainable manufacturing paradigm. Furthermore, a comparison analysis and a sensitivity investigation are made to demonstrate the superiority of the developed framework. © 2023 Elsevier Inc.
Breast tumor localization and segmentation using machine learning techniques: overview of datasets, findings, and methods
(Elsevier, 2023) Ranjbarzadeh, Ramin; Dorosti, Shadi; Jafarzadeh Ghoushchi, Saeid; Caputo, Annalina; Tirkolaee, Erfan Babaee; Ali, Sadia Samar; Arshadi, Zahra; Bendechache, Malika
The Global Cancer Statistics 2020 reported breast cancer (BC) as the most common diagnosis of cancer type. Therefore, early detection of such type of cancer would reduce the risk of death from it. Breast imaging techniques are one of the most frequently used techniques to detect the position of cancerous cells or suspicious lesions. Computer-aided diagnosis (CAD) is a particular generation of computer systems that assist experts in detecting medical image abnormalities. In the last decades, CAD has applied deep learning (DL) and machine learning approaches to perform complex medical tasks in the computer vision area and improve the ability to make decisions for doctors and radiologists. The most popular and widely used technique of image processing in CAD systems is segmentation which consists of extracting the region of interest (ROI) through various techniques. This research provides a detailed description of the main categories of segmentation procedures which are classified into three classes: supervised, unsupervised, and DL. The main aim of this work is to provide an overview of each of these techniques and discuss their pros and cons. This will help researchers better understand these techniques and assist them in choosing the appropriate method for a given use case. © 2022 Elsevier Ltd
Intuitionistic fuzzy power aczel-alsina model for prioritization of sustainable transportation sharing practices
(Elsevier, 2022) Senapati, Tapan; Simic, Vladimir; Saha, Abhijit; Dobrodolac, Momcilo; Rong, Yuan; Tirkolaee, Erfan Babaee
Traffic congestion and environmental pollution generated by transportation activities significantly endanger the sustainable development of cities. This study presents a new strategy for implementing the concept of shared mobility, where postal operators use their widespread networks of units and serve as service providers. To enhance its real-world implementation, four sustainable transportation sharing practices are elaborated. The key question is how to identify the most sustainable alternative that should be offered by service providers. To resolve this challenge, this paper develops an advanced decision support model based on Aczel-Alsina aggregation operators and power operators within the intuitionistic fuzzy (IF) environment. The criteria weights are determined through the Shannon entropy-based power weighted method. Aczel-Alsina operations for IF numbers are proposed to aggregate the decision information. In light of these operational laws, two IF Aczel-Alsina aggregation operators and their enviable characteristics are provided. These advanced aggregation operators are used to formulate the IF power Aczel-Alsina model. The case study of the city of Novi Sad illustrates its applicability. According to the research findings, it is recommended that the public postal operator invests in a sharing e-bicycle fleet. The comparative investigation demonstrates the superiority of the developed decision support model. Its major strengths are simple calculation and fast information processing. © 2022 Elsevier Ltd
A tree augmented naive bayes-based methodology for classifying cryptocurrency trends
(ELSEVIER, 2023) Dağ, Ali; Dağ, Aslı Z.; Asilkalkan, Abdullah; Şimşek, Serhat; Delen, Dursun
As the popularity of blockchain technology and investor confidence in Bitcoin (BTC) increased in recent years, many individuals started making BTC and other cryptocurrency investments, in expectation of high returns. However, as recent market movements have shown, the lack of regulation and oversight makes it difficult to guard against high volatility and potentially significant losses in this sector. In this study, we propose a datadriven Tree Augmented Naive (TAN) Bayes methodology that can be used for identifying the most important factors (as well as their conditional, interdependent relationships) influencing BTC price movements. As the model is parsimonious without sacrificing accuracy, sensitivity, and specificity-as evident from the average accuracy value-the proposed methodology can be used in practice for making short-term investment decisions.
A sustainable-circular citrus closed-loop supply chain configuration: pareto-based algorithms
(ELSEVIER, 2023) Goodarzian,Fariba; Ghasemi,Peiman; Gonzalez,Ernesto DR. Santibanez; Tirkolaee, Erfan Babaee
Configuration of sustainable supply chains for agricultural products has been a well-known research field recently which is continuing to evolve and grow. It is a complex network design problem, and despite the abundant literature in the field, there are still few models offered to integrate social impacts and environmental effects to support network design decision-making to support the configuration of the citrus supply chain. In this work, the citrus supply chain design problem is investigated by integrating the production, distribution, inventory control, recycling and locational decisions in which the triple bottom lines of sustainability, as well as circularity strategy, are addressed. Accordingly, a novel multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to formulate a multi-period multi-echelon problem to design the sustainable citrus Closed-Loop Supply Chain (CLSC) network. To solve the developed model, the ?-constraint approach is employed in small-sized problems. Furthermore, Strength Pareto Evolutionary Algorithm II (SPEA-II) and Pareto Envelope-based Selection Algorithm II (PESA-II) algorithms are used in medium- and large-sized problems. Taguchi design technique is then utilized to adjust the parameters of the algorithms efficiently. Three well-known assessment metrics and convergence analysis are regarded to test the efficiency of the suggested algorithms. The numerical results demonstrate that the SPEA-II algorithm has a superior efficiency over PESA-II. Moreover, to validate the applicability of the developed methodology, a real case study in Mazandaran/Iran is investigated with the help of a set of sensitivity analyses.
A mathematical model to investigate the interactive effects of important economic factors on the behaviors of retailers
(SPRINGER, 2022) Khakbaz, Amir; Tirkolaee, Erfan Babaee
Cost management is a key step to the success of any logistics system and supply chain management. Inventory costs are an important part of logistics costs which are highly affected by economic factors such as demand growth rate (DGR), interest rate (ir), and inflation rate (e). Analyzing the interactive effects of these economic factors plays a key role in preventing failures of logistics systems This study aims to develop a novel mathematical model and investigate the interactive effects of these factors on the behavior of retailers in Iran. To the best of our knowledge, this is the first time that the sale price is defined as a function of time and inflation rate where the demand rate is built up with a linear function of time. Different scenarios and sub-scenarios are then taken into consideration based on different combinations of factors and assumptions. As the main findings of the study, it is revealed that if e < 18% or ir > 40.52%, holding costs are much higher than buying costs, and retailers are reluctant to invest in inventories. Given that DGR is independent of the inflation rate, and also if e > 20.45% or ir < 31.9%, then DGR fluctuations have no impact on the total cost. Hence, in this case, buying costs are much higher than holding costs, and retailers are eager to invest in inventories instead of bank deposits. Furthermore, it is concluded that decision-makers can use the interest rate as leverage to set the probability of shortages and hoardings. Finally, some useful future research directions are discussed based on the main limitations of the study.
Optimal matchday schedule for Turkish professional soccer league using nonlinear binary integer programming
(RAMAZAN YAMAN, 2022) Göçgün, Yasin; Bakır, Niyazi Onur
Sports scheduling problems are interesting optimization problems that require the decision of who play with whom, where and when to play. In this work, we study the sports scheduling problem faced by the Turkish Football Federation. Given the schedule of games for each round of the season, the problem is to determine the match days with the goal of having a fair schedule for each team. The criteria we employ to establish this fairness are achieving an equal distri-bution of match days between the teams throughout the season and the ideal assignment of games to different days in each round of the tournament. The problem is formulated as a nonlinear binary integer program and is solved op-timally for each week. Our results indicate that significant improvements over the existing schedule can be achieved if the optimal solution is implemented.
Identifying and prioritizing resilient health system units to tackle the COVID-19 pandemic
(Elsevier Ltd., 2022) Adabavazeh, Nazila; Nikbakht, Mehrdad; Tirkolaee, Erfan Babaee
Since human health greatly depends on a healthy and risk-free social environment, it is very important to have a concept to focus on improving epidemiology capacity and potential along with economic perspectives as a very influential factor in the future of societies. Through responsible behavior during an epidemic crisis, the health system units can be utilized as a suitable platform for sustainable development. This study employs the Best-Worst Method (BWM) in order to develop a system for identifying and ranking health system units with understanding the nature of the epidemic to help the World Health Organization (WHO) in recognizing the capabilities of resilient health system units. The purpose of this study is to identify and prioritize the resilient health system units for dealing with Coronavirus. The statistical population includes 215 health system units in the world and the opinions of twenty medical experts are also utilized as an informative sample to localize the conceptual model of the study and answer the research questionnaires. The resilient health system units of the world are identified and prioritized based on the statistics of “Total Cases”, “Total Recovered”, “Total Deaths”, “Active Cases”, “Serious”, “Total Tests” and “Day of Infection”. The present descriptive cross-sectional study is conducted on Worldometer data of COVID-19 during the period of 17 July 2020 at 8:33 GMT. According to the results, the factors of “Total Cases”, “Total Deaths”, “Serious”, “Active Cases”, “Total Recovered”, “Total Tests” and “Day of Infection” are among the most effective ones, respectively, in order to have a successful and optimal performance during a crisis. The attention of health system units to the identified important factors can improve the performance of epidemiology system. The WHO should pay more attention to low-resilience health system units in terms of promoting the health culture in crisis management of common viruses. Considering the importance of providing health services as well as their significant effect on the efficiency of the world health system, especially in critical situations, resilience analysis with the possibility of comparison and ranking can be an important step to continuously improve the performance of health system units.
Modelling joint deterioration in roller compacted concrete pavement
(Springer, 2022) Mohammed, Haneen; Abed, Ahmed; Thom, Nick
Joints in Roller Compacted Concrete (RCC) pavements are used to distribute traffic loading between adjacent slabs by friction. The Load Transfer Stiffness (LTS) of the joints has critical effects on RCC pavement performance near the joints. Research has shown that LTS can deteriorate over time due to traffic loading or environmental conditions. This study investigates the deterioration of LTS of RCC pavement joints and its effect on the fatigue cracking performance near the joints. To achieve that, first, an innovative experimental programme was designed to measure LTS as a function of number of load repetition, joint width, and RCC mix properties using a cyclic shear test setup. Second, a mathematical model was derived to predict LTS deterioration in joints. This model was validated against the experimental data. Lastly, an RCC pavement design model was developed using the LTS deterioration model. To demonstrate the application of the developed solution, a hypothetical RCC pavement structure consisting of four slabs was considered. The analysis results show that LTS has inverse relationship and direct impact of fatigue life of RCC. In particular, the results demonstrate that fatigue damage over an analysis period of 20 years is negligible if LTS is assumed constant, which is unrealistic, but it can reach 40% if LTS deterioration is considered in the analysis. Accordingly, this study recommends considering the deterioration of RCC joint LTS when design that kind of pavement structures. © 2022, The Author(s), under exclusive licence to Chinese Society of Pavement Engineering.
A hybrid biobjective markov chain based optimization model for sustainable aggregate production planning
(IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC, 2022) Tirkolaee, Erfan Babaee; Aydın, Nadi Serhan; Mahdavi, Iraj
This research addresses the sustainable aggregate production planning problem by considering the outsourcing option and workforce skill levels as well as taking a Markov process approach for the inventory level. For this purpose, a hybrid biobjective mixed-integer nonlinear programming model featuring a continuous-time Markov chain to accommodate the inventory decision process is developed. The proposed Markov chain approach efficiently describes system dynamics modeling of the production system through a stochastic process. The objective functions are to minimize total cost and total environmental pollution at the same time. To validate the applicability of the methodology and to evaluate the model complexity, three numerical examples are generated based on one of the previous studies in the literature. It is demonstrated that the suggested methodology is able to come up with the final feasible solution based on optimal inventory decisions in less than 65 s. Finally, a number of sensitivity analyses are presented to study the behavior of the objectives under real-world instability and discuss the practical implications and managerial insights. As one of the main findings, it is revealed that the objective functions have no sensitivity to some change intervals of the parameters, which can be analyzed more earnestly by the management in case of the resource allocation process.
A socio-economic optimization model for blood supply chain network design during the COVID-19 pandemic: an interactive possibilistic programming approach for a real case study
(Elsevier Ltd., 2022) Tirkolaee, Erfan Babaee; Golpîra, Hêris; Javanmardan, Ahvan; Maihami, Reza
In uncertain circumstances like the COVID-19 pandemic, designing an efficient Blood Supply Chain Network (BSCN) is crucial. This study tries to optimally configure a multi-echelon BSCN under uncertainty of demand, capacity, and blood disposal rates. The supply chain comprises blood donors, collection facilities, blood banks, regional hospitals, and consumption points. A novel bi-objective Mixed-Integer Linear Programming (MILP) model is suggested to formulate the problem which aims to minimize network costs and maximize job opportunities while considering the adverse effects of the pandemic. Interactive possibilistic programming is then utilized to optimally treat the problem with respect to the special conditions of the pandemic. In contrast to previous studies, we incorporated socio-economic factors and COVID-19 impact into the BSCN design. To validate the developed methodology, a real case study of a Blood Supply Chain (BSC) is analyzed, along with sensitivity analyses of the main parameters. According to the obtained results, the suggested approach can simultaneously handle the bi-objectiveness and uncertainty of the model while finding the optimal number of facilities to satisfy the uncertain demand, blood flow between supply chain echelons, network cost, and the number of jobs created.

Güncel Gönderiler