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Moadab A, Kordi G, Paydar MM, Divsalar A, Hajiaghaei-Keshteli M. Designing a sustainable-resilient-responsive supply chain network considering uncertainty in the COVID-19 era. EXPERT SYSTEMS WITH APPLICATIONS 2023; 227:120334. [PMID: 37192999 PMCID: PMC10162855 DOI: 10.1016/j.eswa.2023.120334] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/05/2023] [Revised: 04/09/2023] [Accepted: 04/29/2023] [Indexed: 05/18/2023]
Abstract
Effective supply chain management is crucial for economic growth, and sustainability is becoming a key consideration for large companies. COVID-19 has presented significant challenges to supply chains, making PCR testing a vital product during the pandemic. It detects the presence of the virus if you are infected at the time and detects fragments of the virus even after you are no longer infected. This paper proposes a multi-objective mathematical linear model to optimize a sustainable, resilient, and responsive supply chain for PCR diagnostic tests. The model aims to minimize costs, negative societal impact caused by shortages, and environmental impact, using a scenario-based approach with stochastic programming. The model is validated by investigating a real-life case study in one of Iran's high-risk supply chain areas. The proposed model is solved using the revised multi-choice goal programming method. Lastly, sensitivity analyses based on effective parameters are conducted to analyze the behavior of the developed Mixed-Integer Linear Programming. According to the results, not only is the model capable of balancing three objective functions, but it is also capable of providing resilient and responsive networks. To enhance the design of the supply chain network, this paper has considered various COVID-19 variants and their infectious rates, in contrast to prior studies that did not consider the variations in demand and societal impact exhibited by different virus variants.
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Affiliation(s)
- Amirhossein Moadab
- Department of Finance and Management Science, Carson College of Business, Washington State University, Pullman, WA, USA
| | - Ghazale Kordi
- Department of Economics and Management, University of Helsinki, Helsinki, Finland
| | - Mohammad Mahdi Paydar
- Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Ali Divsalar
- Department of Industrial Engineering, Babol Noshirvani University of Technology, Babol, Iran
| | - Mostafa Hajiaghaei-Keshteli
- Department of Industrial Engineering, School of Engineering and Science, Tecnologico de Monterrey, Puebla, Mexico
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2
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Zhang X, Fan X, Yu S, Shan A, Men R. Multi-Objective Optimization Method for Signalized Intersections in Intelligent Traffic Network. SENSORS (BASEL, SWITZERLAND) 2023; 23:6303. [PMID: 37514597 PMCID: PMC10384827 DOI: 10.3390/s23146303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/18/2023] [Revised: 07/07/2023] [Accepted: 07/09/2023] [Indexed: 07/30/2023]
Abstract
Urban intersections are one of the most common sources of traffic congestion. Especially for multiple intersections, an appropriate control method should be able to regulate the traffic flow within the control area. The intersection signal-timing problem is crucial for ensuring efficient traffic operations, with the key issues being the determination of a traffic model and the design of an optimization algorithm. So, an optimization method for signalized intersections integrating a multi-objective model and an NSGAIII-DAE algorithm is established in this paper. Firstly, the multi-objective model is constructed including the usual signal control delay and traffic capacity indices. In addition, the conflict delay caused by right-turning vehicles crossing straight-going non-motor vehicles is considered and combined with the proposed algorithm, enabling the traffic model to better balance the traffic efficiency of intersections without adding infrastructure. Secondly, to address the challenges of diversity and convergence faced by the classic NSGA-III algorithm in solving traffic models with high-dimensional search spaces, a denoising autoencoder (DAE) is adopted to learn the compact representation of the original high-dimensional search space. Some genetic operations are performed in the compressed space and then mapped back to the original search space through the DAE. As a result, an appropriate balance between the local and global searching in an iteration can be achieved. To validate the proposed method, numerical experiments were conducted using actual traffic data from intersections in Jinzhou, China. The numerical results show that the signal control delay and conflict delay are significantly reduced compared with the existing algorithm, and the optimal reduction is 33.7% and 31.3%, respectively. The capacity value obtained by the proposed method in this paper is lower than that of the compared algorithm, but it is also 11.5% higher than that of the current scheme in this case. The comparisons and discussions demonstrate the effectiveness of the proposed method designed for improving the efficiency of signalized intersections.
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Affiliation(s)
- Xinghui Zhang
- Department of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
- College of Electronics and Information Engineering, Ankang University, Ankang 725000, China
| | - Xiumei Fan
- Department of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
| | - Shunyuan Yu
- College of Electronics and Information Engineering, Ankang University, Ankang 725000, China
| | - Axida Shan
- Department of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
- School of Information Science and Technology, Baotou Teachers' College, Baotou 014030, China
| | - Rui Men
- Department of Automation and Information Engineering, Xi'an University of Technology, Xi'an 710048, China
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3
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Kochakkashani F, Kayvanfar V, Haji A. Supply chain planning of vaccine and pharmaceutical clusters under uncertainty: The case of COVID-19. SOCIO-ECONOMIC PLANNING SCIENCES 2023; 87:101602. [PMID: 37255585 PMCID: PMC10111859 DOI: 10.1016/j.seps.2023.101602] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/24/2022] [Revised: 04/17/2023] [Accepted: 04/17/2023] [Indexed: 06/01/2023]
Abstract
As an abrupt epidemic occurs, healthcare systems are shocked by the surge in the number of susceptible patients' demands, and decision-makers mostly rely on their frame of reference for urgent decision-making. Many reports have declared the COVID-19 impediments to trading and global economic growth. This study aims to provide a mathematical model to support pharmaceutical supply chain planning during the COVID-19 epidemic. Additionally, it aims to offer new insights into hospital supply chain problems by unifying cold and non-cold chains and considering a wide range of pharmaceuticals and vaccines. This approach is unprecedented and includes an analysis of various pharmaceutical features such as temperature, shelf life, priority, and clustering. To propose a model for planning the pharmaceutical supply chains, a mixed-integer linear programming (MILP) model is used for a four-echelon supply chain design. This model aims to minimize the costs involved in the pharmaceutical supply chain by maintaining an acceptable service level. Also, this paper considers uncertainty as an intrinsic part of the problem and addresses it through the wait-and-see method. Furthermore, an unexplored unsupervised learning method in the realm of supply chain planning has been used to cluster the pharmaceuticals and the vaccines and its merits and drawbacks are proposed. A case of Tehran hospitals with real data has been used to show the model's capabilities, as well. Based on the obtained results, the proposed approach is able to reach the optimum service level in the COVID conditions while maintaining a reduced cost. The experiment illustrates that the hospitals' adjacency and emergency orders alleviated the service level significantly. The proposed MILP model has proven to be efficient in providing a practical intuition for decision-makers. The clustering technique reduced the size of the problem and the time required to solve the model considerably.
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Affiliation(s)
- Farid Kochakkashani
- Department of Electrical and Computer Engineering, George Washington University, Washington D.C, USA
| | - Vahid Kayvanfar
- Division of Engineering Management and Decision Sciences, College of Science and Engineering, Hamad Bin Khalifa University, Qatar Foundation, Doha, Qatar
| | - Alireza Haji
- Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
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4
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Mosallanezhad B, Gholian-Jouybari F, Cárdenas-Barrón LE, Hajiaghaei-Keshteli M. The IoT-enabled sustainable reverse supply chain for COVID-19 Pandemic Wastes (CPW). ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2023; 120:105903. [PMID: 36712822 PMCID: PMC9874057 DOI: 10.1016/j.engappai.2023.105903] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/16/2022] [Revised: 12/03/2022] [Accepted: 01/21/2023] [Indexed: 05/29/2023]
Abstract
Supply chains have been impacted by the COVID-19 pandemic, which is the most recent worldwide disaster. After the world health organization recognized the latest phenomena as a pandemic, nations became incapacitated to provide the required medical supplies. In the current situation, the world seeks an essential solution for COVID-19 Pandemic Wastes (CPWs) by pushing the pandemic to a stable condition. In this study, the development of a supply chain network is contrived for CPWs utilizing optimization modeling tools. Also, an IoT platform is devised to enable the proposed model to retrieve real-time data from IoT devices and set them as the model's inputs. Moreover, sustainability aspects are appended to the proposed IoT-enabled model considering its triplet pillars as objective functions. A real case of Puebla city and 15 experiments are used to validate the model. Furthermore, a combination of metaheuristic algorithms utilized to solve the model and also seven evaluation indicators endorse the selection of efficient solution approaches. The evaluation indicators are appointed as the inputs of statistical and multicriteria decision-making hybridization to prioritize the algorithms. The result of the Entropy Weights method and Combined Compromise Solution approach confirms that MOGWO has better performance for the medium-sizes, case study and an overall view. Also, NSHHO outclasses the small-size and large-size experiments.
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Affiliation(s)
- Behzad Mosallanezhad
- Department of Industrial Engineering, School of Engineering and Science, Tecnologico de Monterrey, Puebla, Mexico
| | - Fatemeh Gholian-Jouybari
- Department of Industrial Engineering, School of Engineering and Science, Tecnologico de Monterrey, Puebla, Mexico
| | | | - Mostafa Hajiaghaei-Keshteli
- Department of Industrial Engineering, School of Engineering and Science, Tecnologico de Monterrey, Puebla, Mexico
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5
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Khodoomi MR, Yaghoubi S, Seif M. Effects of COVID-19 outbreak in pricing and collaboration of a health-social dual-channel supply chain. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2023; 30:55382-55401. [PMID: 36892694 PMCID: PMC9995738 DOI: 10.1007/s11356-023-25849-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/08/2022] [Accepted: 02/06/2023] [Indexed: 06/18/2023]
Abstract
The coronavirus pandemic has affected most aspects of product supply and consumer behaviors and led to transformations in the supply chain. The COVID-19 pandemic and the requirements to reduce its prevalence have led many people to shop online and encouraged many manufacturers to sell their products online. In this study, a manufacturer, who intends to possess an online sales channel, and a retailer, who has an in-person sales channel, are considered. Then, pricing strategies and collaboration mechanisms between them in the health-social dual-channel supply chain are investigated. This study is developed in three models, including centralized, decentralized, and collaborated under Stackelberg game, whereas the optimal price of products in each channel, level of implementation of health and safety protocols in retailers, advertising level, and status of online shopping performance are obtained for improving customer trust. Moreover, the demand is represented as a function of selling prices of products in online and in-person shops, compliance level of health protocols, level of online shopping performance, and advertising in health during the COVID-19 pandemic. Although the centralized model provides more profit for the manufacturer, the collaborated model provides the highest profit for the retailer. Thus, since the supply chain profit of centralized and collaborated models is close, the collaboration model is the best option for members in this situation. Sensitivity analysis is finally performed to evaluate the impact of key parameters, and then according to obtained results, some management insights are suggested for the dual-channel supply chain during the COVID-19 pandemic.
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Affiliation(s)
- Mohammad Reza Khodoomi
- School of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
| | - Saeed Yaghoubi
- School of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran.
| | - Marziye Seif
- School of Industrial Engineering, Iran University of Science and Technology, Narmak, Tehran, Iran
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6
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Abbasi S, Daneshmand-Mehr M, Ghane Kanafi A. Green Closed-Loop Supply Chain Network Design During the Coronavirus (COVID-19) Pandemic: a Case Study in the Iranian Automotive Industry. ENVIRONMENTAL MODELING AND ASSESSMENT 2022; 28:69-103. [PMID: 36540109 PMCID: PMC9756749 DOI: 10.1007/s10666-022-09863-0] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2021] [Accepted: 10/18/2022] [Indexed: 06/17/2023]
Abstract
This paper presents a new mathematical model of the green closed-loop supply chain network (GCLSCN) during the COVID-19 pandemic. The suggested model can explain the trade-offs between environmental (minimizing CO2 emissions) and economic (minimizing total costs) aspects during the COVID-19 outbreak. Considering the guidelines for hygiene during the outbreak helps us design a new sustainable hygiene supply chain (SC). This model is sensitive to the cost structure. The cost includes two parts: the normal cost without considering the coronavirus pandemic and the cost with considering coronavirus. The economic novelty aspect of this paper is the hygiene costs. It includes disinfection and sanitizer costs, personal protective equipment (PPE) costs, COVID-19 tests, education, medicines, vaccines, and vaccination costs. This paper presents a multi-objective mixed-integer programming (MOMIP) problem for designing a GCLSCN during the pandemic. The optimization procedure uses the scalarization approach, namely the weighted sum method (WSM). The computational optimization process is conducted through Lingo software. Due to the recency of the COVID-19 pandemic, there are still many research gaps. Our contributions to this research are as follows: (i) designed a model of the green supply chain (GSC) and showed the better trade-offs between economic and environmental aspects during the COVID-19 pandemic and lockdowns, (ii) designed the hygiene supply chain, (iii) proposed the new indicators of economic aspects during the COVID-19 outbreak, and (iv) have found the positive (reducing CO2 emissions) and negative (increase in costs) impacts of COVID-19 and lockdowns. Therefore, this study designed a new hygiene model to fill this gap for the COVID-19 condition disaster. The findings of the proposed network illustrate the SC has become greener during the COVID-19 pandemic. The total cost of the network was increased during the COVID-19 pandemic, but the lockdowns had direct positive effects on emissions and air quality.
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Affiliation(s)
- Sina Abbasi
- Department of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
| | - Maryam Daneshmand-Mehr
- Department of Industrial Engineering, Lahijan Branch, Islamic Azad University, Lahijan, Iran
| | - Armin Ghane Kanafi
- Department of Mathematics, Lahijan Branch, Islamic Azad University, Lahijan, Iran
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7
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Fallahi A, Mousavian Anaraki SA, Mokhtari H, Niaki STA. Blood plasma supply chain planning to respond COVID-19 pandemic: a case study. ENVIRONMENT, DEVELOPMENT AND SUSTAINABILITY 2022; 26:1-52. [PMID: 36530360 PMCID: PMC9734997 DOI: 10.1007/s10668-022-02793-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Accepted: 11/24/2022] [Indexed: 06/17/2023]
Abstract
The COVID-19 pandemic causes a severe threat to human lives worldwide. Convalescent plasma as supportive care for COVID-19 is critical in reducing the death rate and staying in hospitals. Designing an efficient supply chain network capable of managing convalescent plasma in this situation seems necessary. Although many researchers investigated supply chains of blood products, no research was conducted on the planning of convalescent plasma in the supply chain framework with specific features of COVID-19. This gap is covered in the current work by simultaneous regular and convalescent plasma flow in a supply chain network. Besides, due to the growing importance of environmental problems, the resulting carbon emission from transportation activities is viewed to provide a green network. In other words, this study aims to plan the integrated green supply chain network of regular and convalescent plasma in the pandemic outbreak of COVID-19 for the first time. The presented mixed-integer multi-objective optimization model determines optimal network decisions while minimizing the total cost and total carbon emission. The Epsilon constraint method is used to handle the considered objectives. The model is applied to a real case study from the capital of Iran. Sensitivity analyses are carried out, and managerial insights are drawn. Based on the obtained results, product demand impacts the objective functions significantly. Moreover, the systems' total carbon emission is highly dependent on the flow of regular plasma. The results also reveal that changing transportation emission unit causes significant variation in the total emission while the total cost remains fixed.
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Affiliation(s)
- Ali Fallahi
- Department of Industrial Engineering, Sharif University of Technology, Tehran, Iran
| | | | - Hadi Mokhtari
- Department of Industrial Engineering, Faculty of Engineering, University of Kashan, Kashan, Iran
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8
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A New Wooden Supply Chain Model for Inventory Management Considering Environmental Pollution: A Genetic algorithm. FOUNDATIONS OF COMPUTING AND DECISION SCIENCES 2022. [DOI: 10.2478/fcds-2022-0021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
Abstract
Nowadays, companies need to take responsibility for addressing growing markets and the growing expectations of their customers to survive in a highly competitive context that is progressing on a daily basis. Rapid economic changes and increasing competitive pressure in global markets have led companies to pay special attention to their supply chains. As a result, in this research, a mathematical model is proposed to minimize closed loop supply chain costs taking into account environmental effects. Thus, suppliers first send wood as raw materials from forests to factories. After processing the wood and turning it into products, the factories send the wood to retailers. The retailers then send the products to the customers. Finally, customers send returned products to recovery centers. After processing the products, the recovery centers send their products to the factories. The considered innovations include: designing a supply chain of wood products regarding environmental effects, customizing the genetic solution approach to solve the proposed model 3-Considering the flow of wood products and determining the amount of raw materials and products sent and received.
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9
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Safaei S, Ghasemi P, Goodarzian F, Momenitabar M. Designing a new multi-echelon multi-period closed-loop supply chain network by forecasting demand using time series model: a genetic algorithm. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2022; 29:79754-79768. [PMID: 35244850 DOI: 10.1007/s11356-022-19341-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/03/2021] [Accepted: 02/17/2022] [Indexed: 05/20/2023]
Abstract
Demand plays a vital role in designing every closed-loop supply chain network in today's world. The flow of materials and commodities in the opposite direction of the standard supply chain is inevitable. In this way, this study addresses a new multi-echelon multi-period closed-loop supply chain network to minimize the total costs of the network. The echelons include suppliers, manufacturers, distribution centers, customers, and recycling and recovery units of components in the proposed network. Also, a Mixed Integer Linear Programming (MILP) model considering factories' vehicles and rental cars of transportation companies is formulated for the proposed problem. Moreover, for the first time, the demand for the products is estimated using an Auto-Regressive Integrated Moving Average (ARIMA) time series model to decrease the shortage that may happen in the whole supply chain network. Conversely, for solving the proposed model, the GAMS software is utilized in small and medium-size problems, and also, genetic algorithm is applied for large-size problems to obtain initial results of the model. Numerical results show that the proposed model is closer to the actual situation and could reach a reasonable solution in terms of service level, shortage, etc. Accordingly, sensitivity analysis is performed on essential parameters to show the performance of the proposed model. Finally, some potential topics are discussed for future study.
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Affiliation(s)
- Shahab Safaei
- Department of Industrial Engineering, Faculty of Industrial Engineering, Firoozkooh Branch, Islamic Azad University, Firoozkooh, Iran
| | - Peiman Ghasemi
- Department of Logistics, Tourism and Service Management, German University of Technology in Oman (GUtech), Muscat, Oman.
| | - Fariba Goodarzian
- Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, 11, 3rd Street NW, P.O. Box 2259, Auburn, WA, 98071, USA
| | - Mohsen Momenitabar
- Department of Transportation, Logistics, and Finance, North Dakota State University (NDSU), 58105-6050, Fargo, ND, USA
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Zhang W, Zeng M, Guo P, Wen K. Variable Neighborhood Search for Multi-Cycle Medical Waste Recycling Vehicle Routing Problem with Time Windows. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:12887. [PMID: 36232188 PMCID: PMC9566800 DOI: 10.3390/ijerph191912887] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 09/03/2022] [Accepted: 09/06/2022] [Indexed: 06/16/2023]
Abstract
BACKGROUND Improper disposal of urban medical waste is likely to cause a series of neglective impacts. Therefore, we have to consider how to improve the efficiency of urban medical waste recycling and lowering carbon emissions when facing disposal. METHODS This paper considers the multi-cycle medical waste recycling vehicle routing problem with time windows for preventing and reducing the risk of medical waste transportation. First, a mixed-integer linear programming model is formulated to minimize the total cost consisting of the vehicle dispatch cost and the transportation costs. In addition, an improved neighborhood search algorithm is designed for handling large-sized problems. In the algorithm, the initial solution is constructed using the Clarke-Wright algorithm in the first stage, and the variable neighborhood search algorithm with a simulated annealing strategy is introduced for exploring a better solution in the second stage. RESULTS The computational results demonstrate the performance of the suggested algorithm. In addition, the total cost of recycling in the periodic strategy is lower than with the single-cycle strategy. CONCLUSIONS The proposed model and algorithm have the management improvement value of the studied medical waste recycling vehicle routing problem.
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Affiliation(s)
- Wanting Zhang
- College of Management Science, Chengdu University of Technology, Chengdu 610059, China
| | - Ming Zeng
- College of Management Science, Chengdu University of Technology, Chengdu 610059, China
| | - Peng Guo
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
| | - Kun Wen
- School of Mechanical Engineering, Southwest Jiaotong University, Chengdu 610031, China
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11
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Chouhan VK, Khan SH, Hajiaghaei-Keshteli M. Hierarchical tri-level optimization model for effective use of by-products in a sugarcane supply chain network. Appl Soft Comput 2022. [DOI: 10.1016/j.asoc.2022.109468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/02/2022]
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12
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Sayarshad HR. An optimal control policy in fighting COVID-19 and infectious diseases. Appl Soft Comput 2022; 126:109289. [PMID: 35846948 PMCID: PMC9270838 DOI: 10.1016/j.asoc.2022.109289] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 05/12/2022] [Accepted: 07/04/2022] [Indexed: 11/17/2022]
Abstract
When an outbreak starts spreading, policymakers have to make decisions that affect the health of their citizens and the economy. Some might induce harsh measures, such as a lockdown. Following a long, harsh lockdown, the recession forces policymakers to rethink reopening. To provide an effective strategy, here we propose a control strategy model. Our model assesses the trade-off between social performance and limited medical resources by determining individuals' propensities. The proposed strategy also helps decision-makers to find optimal lockdown and exit strategies for each region. Moreover, the financial loss is minimized. We use the public sentiment information during the pandemic to determine the percentage of individuals with high-risk behavior and the percentage of individuals with low-risk behavior. Hence, we propose an online platform using fear-sentiment information to estimate the personal protective equipment (PPE) burn rate overtime for the entire population. In addition, a study of a COVID-19 dataset for Los Angeles County is performed to validate our model and its results. The total social cost reduces by 18% compared with a control strategy where susceptible individuals are assumed to be homogeneous. We also reduce the total social costs by 26% and 22% compared to other strategies that consider the health-care cost or the social performance cost, respectively.
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Affiliation(s)
- Hamid R Sayarshad
- School of Civil Engineering, Cornell University, Ithaca, NY 14853, USA
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13
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Supply chain management in times of crisis: a systematic review. MANAGEMENT REVIEW QUARTERLY 2022. [PMCID: PMC9362030 DOI: 10.1007/s11301-022-00272-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/18/2023]
Abstract
Complexities of crises force supply chains managers to formulate crisis-induced strategies, which contrast with the conventional strategies that give precedence to competitive priorities. Recent crises, such as the coronavirus outbreaks, large-scale product recalls, and financial crises, underscore the increasing regularity and severity of crises with imperatives for introspective and retrospective socio-economic insights on the contexts, priorities, and themes of supply chain management in times of crisis. The purpose of this article is to review the literature on supply chain management in times of crisis, systematically coalescing the related body of scholarly work; outlining current methods applied by researchers; capturing strategic priorities and themes of complexities in research studies; and highlighting potentials for future studies. Using a systematic review of 250 journal articles published between 1996 and 2021, the review finds four dimensions for restorative priorities that reflect operations strategy during crisis: (i) critical supplies with essential services, (ii) timely response with recovery, (iii) safety with security, and (iv) traceability with transparency. The review also finds that operational complexities during crises originate from network configurations and business cycle complexities, optimal selections and provisioning system complexes, and complex learning processes and demand predictions. Insights from the review aid in the proposal of build-to-cycle, organic capabilities, and operational mindfulness framings for supply chain management in times of crisis. The article concludes by recommending future research studies on supply chain upgrades, diagnosis, solidarity, mapping, temporariness, and thresholds, as well as optimal selection problems on linking crisis systems investments with liabilities and on linking crisis network allotments with cross-functionalities.
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14
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Kumar S, Jangir P, Tejani GG, Premkumar M. A Decomposition based Multi-Objective Heat Transfer Search algorithm for structure optimization. Knowl Based Syst 2022. [DOI: 10.1016/j.knosys.2022.109591] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/16/2022]
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15
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Bozkaya E, Eriskin L, Karatas M. Data analytics during pandemics: a transportation and location planning perspective. ANNALS OF OPERATIONS RESEARCH 2022; 328:1-52. [PMID: 35935742 PMCID: PMC9342597 DOI: 10.1007/s10479-022-04884-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 07/15/2022] [Indexed: 06/15/2023]
Abstract
The recent COVID-19 pandemic once again showed the value of harnessing reliable and timely data in fighting the disease. Obtained from multiple sources via different collection streams, an immense amount of data is processed to understand and predict the future state of the disease. Apart from predicting the spatio-temporal dynamics, it is used to foresee the changes in human mobility patterns and travel behaviors and understand the mobility and spread speed relationship. During this period, data-driven analytic approaches and Operations Research tools are widely used by scholars to prescribe emerging transportation and location planning problems to guide policy-makers in making effective decisions. In this study, we provide a review of studies which tackle transportation and location problems during the COVID-19 pandemic with a focus on data analytics. We discuss the major data collecting streams utilized during the pandemic era, highlight the importance of rapid and reliable data sharing, and give an overview of the challenges and limitations on the use of data.
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Affiliation(s)
- Elif Bozkaya
- Department of Computer Engineering, National Defence University, Turkish Naval Academy, 34940 Tuzla, Istanbul Turkey
| | - Levent Eriskin
- Department of Industrial Engineering, National Defence University, Turkish Naval Academy, 34940 Tuzla, Istanbul Turkey
| | - Mumtaz Karatas
- Department of Industrial Engineering, National Defence University, Turkish Naval Academy, 34940 Tuzla, Istanbul Turkey
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16
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Song M, Yuan S, Bo H, Song J, Pan X, Jin K. Robust optimization model of anti-epidemic supply chain under technological innovation: learning from COVID-19. ANNALS OF OPERATIONS RESEARCH 2022; 335:1-31. [PMID: 35855699 PMCID: PMC9281244 DOI: 10.1007/s10479-022-04855-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Accepted: 06/22/2022] [Indexed: 06/15/2023]
Abstract
The anti-epidemic supply chain plays an important role in the prevention and control of the COVID-19 pandemic. Prior research has focused on studying the facility location, inventory management, and route optimization of the supply chain by using certain parameters and models. Nevertheless, uncertainty, as a vital influence factor, greatly affects the supply chain. As such, the uncertainty that comes with technological innovation has a heightened influence on the supply chain. Few studies have explicitly investigated the influence of technological innovation on the anti-epidemic supply chain under the COVID-19 pandemic. Hence, the current research aims to investigate the influences of the uncertainty caused by technological innovation on the supply chain from demand and supply, shortage penalty, and budget. This paper presents a three-level model of the anti-epidemic supply chain under technological innovation and employs an interval data robust optimization to tackle the uncertainties of the model. The findings are obtained as follows. Firstly, the shortage penalty will increase the costs of the objective function but effectively improve demand satisfaction. Secondly, if the shortage penalty is sufficiently large, the minimum demand satisfaction rate can ensure a fair distribution of materials among the affected areas. Thirdly, technological innovation can reduce costs. The technological innovation related to the transportation costs of the anti-epidemic material distribution center has a greater influence on the optimal value. Meanwhile, the technological innovation related to the transportation costs of the supplier has the least influence. Fourthly, both supply and demand uncertainty can influence costs, but demand uncertainty has a greater influence. Fifthly, the multi-scenario budgeting approach can decrease the calculation complexity. These findings provide theoretical support for anti-epidemic dispatchers to adjust the conservativeness of uncertain parameters under the influence of technological innovation.
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Affiliation(s)
- Malin Song
- Anhui University of Finance and Economics, Bengbu, China
| | - Sai Yuan
- Dalian University of Technology, Dalian, China
| | | | - Jinbo Song
- Dalian University of Technology, Dalian, China
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17
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Application of Particle Swarm Optimization for Improvement of Peel Strength in a Laminated Double-Lap Composite Joint. APPLIED SCIENCES-BASEL 2022. [DOI: 10.3390/app12146997] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
A double lap joint is commonly used in thin structures under low running loads. Peel and inter-laminar stresses are among the major limitations of this type of joint, which may cause delamination failure. These stresses should be determined for designing a stronger structure. This study presents a method based on particle swarm optimization to find the best layup for a classic double lap joint under horizontal constant tensile forces. The peel stress equation is analytically obtained and utilized as the objective function for the algorithm. The method’s accuracy is explored by assessing the algorithm’s ability. This helps to find the best arrangement with the highest strength delamination against considering four initial layups. The results show that the optimized layups, on average, can reduce peel stress by about 96%. Additionally, the effects of different parameters on joint strength are investigated.
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18
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Zhan SL, Gu X, Ye Y, Chuang YC. The Allocation Method for Personal Protective Equipment in the Emerging Infectious Disease Environment. Front Public Health 2022; 10:904569. [PMID: 35712292 PMCID: PMC9196938 DOI: 10.3389/fpubh.2022.904569] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Accepted: 04/28/2022] [Indexed: 11/15/2022] Open
Abstract
The COVID-19 pandemic gives humankind a lesson that the outbreak of an emerging infectious disease (EID) is sudden and uncertain. Accurately mastering its dynamics and putting forward an efficient and fair humanitarian logistics plan for personal protective equipment (PPE) remains difficult. This study examines the decision making for humanitarian logistics to answer the question that how to coordinate fairness and efficiency when facing supply-demand imbalance during humanitarian logistics planning in an EID environment. The main contributions include two aspects: (1) The victims' losses in terms of fairness and efficiency in receiving PPE are jointly explored by evaluating their bearing capacity evolution, and then a novel loss function is built to search for a reasonable compromise between fairness and efficiency. (2) A multi-objective optimization model is built, which is solved using the combined use of goal programming approach and improved branch and bound method. Finally, the practicability of the proposed model is tested by an EID case study. The potential advantages of the proposed model and improved approach are discussed.
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Affiliation(s)
- Sha-lei Zhan
- School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, China
| | - Xinyi Gu
- School of Management and E-Business, Zhejiang Gongshang University, Hangzhou, China
| | - Yong Ye
- Institute of Public Health & Emergency Management, Taizhou University, Taizhou, China
- Business College, Taizhou University, Taizhou, China
- *Correspondence: Yong Ye
| | - Yen-Ching Chuang
- Institute of Public Health & Emergency Management, Taizhou University, Taizhou, China
- Business College, Taizhou University, Taizhou, China
- Yen-Ching Chuang
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19
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Souri M, Chiani M, Farhangi A, Mehrabi MR, Nourouzian D, Raahemifar K, Soltani M. Anti-COVID-19 Nanomaterials: Directions to Improve Prevention, Diagnosis, and Treatment. NANOMATERIALS (BASEL, SWITZERLAND) 2022; 12:783. [PMID: 35269270 PMCID: PMC8912597 DOI: 10.3390/nano12050783] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 02/18/2022] [Accepted: 02/23/2022] [Indexed: 02/04/2023]
Abstract
Following the announcement of the outbreak of COVID-19 by the World Health Organization, unprecedented efforts were made by researchers around the world to combat the disease. So far, various methods have been developed to combat this "virus" nano enemy, in close collaboration with the clinical and scientific communities. Nanotechnology based on modifiable engineering materials and useful physicochemical properties has demonstrated several methods in the fight against SARS-CoV-2. Here, based on what has been clarified so far from the life cycle of SARS-CoV-2, through an interdisciplinary perspective based on computational science, engineering, pharmacology, medicine, biology, and virology, the role of nano-tools in the trio of prevention, diagnosis, and treatment is highlighted. The special properties of different nanomaterials have led to their widespread use in the development of personal protective equipment, anti-viral nano-coats, and disinfectants in the fight against SARS-CoV-2 out-body. The development of nano-based vaccines acts as a strong shield in-body. In addition, fast detection with high efficiency of SARS-CoV-2 by nanomaterial-based point-of-care devices is another nanotechnology capability. Finally, nanotechnology can play an effective role as an agents carrier, such as agents for blocking angiotensin-converting enzyme 2 (ACE2) receptors, gene editing agents, and therapeutic agents. As a general conclusion, it can be said that nanoparticles can be widely used in disinfection applications outside in vivo. However, in in vivo applications, although it has provided promising results, it still needs to be evaluated for possible unintended immunotoxicity. Reviews like these can be important documents for future unwanted pandemics.
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Affiliation(s)
- Mohammad Souri
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran; (M.S.); (M.C.); (A.F.)
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
| | - Mohsen Chiani
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran; (M.S.); (M.C.); (A.F.)
| | - Ali Farhangi
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran; (M.S.); (M.C.); (A.F.)
| | - Mohammad Reza Mehrabi
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran; (M.S.); (M.C.); (A.F.)
| | - Dariush Nourouzian
- Department of NanoBiotechnology, Pasteur Institute of Iran, Tehran 13169-43551, Iran; (M.S.); (M.C.); (A.F.)
| | - Kaamran Raahemifar
- Data Science and Artificial Intelligence Program, College of Information Sciences and Technology (IST), Penn State University, State College, PA 16801, USA;
- Department of Chemical Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
- School of Optometry and Vision Science, Faculty of Science, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada
| | - M. Soltani
- Department of Mechanical Engineering, K. N. Toosi University of Technology, Tehran 19967-15433, Iran
- Department of Electrical and Computer Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Centre for Biotechnology and Bioengineering (CBB), University of Waterloo, Waterloo, ON N2L 3G1, Canada
- Advanced Bioengineering Initiative Center, Multidisciplinary International Complex, K. N. Toosi University of Technology, Tehran 14176-14411, Iran
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20
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Impact of Socioeconomic Environment on Home Social Care Service Demand and Dependent Users. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:ijerph19042053. [PMID: 35206244 PMCID: PMC8872414 DOI: 10.3390/ijerph19042053] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 02/09/2022] [Accepted: 02/09/2022] [Indexed: 02/01/2023]
Abstract
An aging population and rising life expectancy lead to an increased demand for social services to care for dependent users, among other factors. In Barcelona, home social care (HSC) services are a key agent in meeting this demand. However, demand is not evenly distributed among neighborhoods, and we hypothesized that this can be explained by the user’s social environment. In this work, we describe the user’s environment at a macroscopic level by the socioeconomic features of the neighborhood. This research aimed to gain a deeper understanding of the dependent user’s socioeconomic environment and service needs. We applied descriptive analytics techniques to explore possible patterns linking HSC demand and other features. These methods include principal components analysis (PCA) and hierarchical clustering. The main analysis was made from the obtained boxplots, after these techniques were applied. We found that economic and disability factors, through users’ mean net rent and degree of disability features, are related to the demand for home social care services. This relation is even clearer for the home-based social care services. These findings can be useful to distribute the services among areas by considering more features than the volume of users/population. Moreover, it can become helpful in future steps to develop a management tool to optimize HSC scheduling and staff assignment to improve the cost and quality of service. For future research, we believe that additional and more precise characteristics could provide deeper insights into HSC service demand.
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21
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Chouhan VK, Khan SH, Hajiaghaei-Keshteli M. Sustainable planning and decision-making model for sugarcane mills considering environmental issues. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2022; 303:114252. [PMID: 34894493 DOI: 10.1016/j.jenvman.2021.114252] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2021] [Revised: 11/06/2021] [Accepted: 12/05/2021] [Indexed: 06/14/2023]
Abstract
Many companies and organizations are pursuing "carbon footprint" projects to estimate their own contribution due to growing concerns about global climate change and carbon emissions. Measures such as carbon taxes are the most powerful means of dealing with the threats of climate change. In recent years, researchers have shown a particular interest in modelling supply chain networks under this scheme. Disorganized disposal of by-products from sugarcane mills is the inspiration of this research. In order to connect the problem with the real world, the proposed sustainable sugarcane supply chain network considers carbon taxes on the emission from industries and during transportation of goods. The presented mixed-integer linear programming modelling is a location-allocation problem and, due to the inherent complexity, it is considered a Non-Polynomial hard (NP-hard) problem. To deal with the model, three superior metaheuristics Genetic Algorithm (GA), Simulated Annealing (SA), Social Engineering Optimizer (SEO) and hybrid methods based on these metaheuristics, namely, Genetic-Simulated Annealing (GASA) and Genetic-Social Engineering Optimizer (GASEO), are employed. The control parameters of the algorithms are tuned using the Taguchi approach. Subsequently, one-way ANOVA is used to elucidate the performance of the proposed algorithms, which compliments the performance of the proposed GASEO.
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Affiliation(s)
- Vivek Kumar Chouhan
- Department of Mechanical Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, 600127, Tamil Nadu, India.
| | - Shahul Hamid Khan
- Department of Mechanical Engineering, Indian Institute of Information Technology, Design and Manufacturing, Kancheepuram, Chennai, 600127, Tamil Nadu, India.
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22
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Tirkolaee EB, Goli A, Ghasemi P, Goodarzian F. Designing a sustainable closed-loop supply chain network of face masks during the COVID-19 pandemic: Pareto-based algorithms. JOURNAL OF CLEANER PRODUCTION 2022; 333:130056. [PMID: 34924699 PMCID: PMC8671674 DOI: 10.1016/j.jclepro.2021.130056] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/27/2021] [Accepted: 12/09/2021] [Indexed: 05/20/2023]
Abstract
This study develops a novel mathematical model to design a sustainable mask Closed-Loop Supply Chain Network (CLSCN) during the COVID-19 outbreak for the first time. A multi-objective Mixed-Integer Linear Programming (MILP) model is proposed to address the locational, supply, production, distribution, collection, quarantine, recycling, reuse, and disposal decisions within a multi-period multi-echelon multi-product supply chain. Additionally, sustainable development is studied in terms of minimizing the total cost, total pollution and total human risk at the same time. Since the CLSCN design is an NP-hard problem, Multi-Objective Grey Wolf Optimization (MOGWO) algorithm and Non-Dominated Sorting Genetic Algorithm II (NSGA-II) are implemented to solve the proposed model and to find Pareto optimal solutions. Since Meta-heuristic algorithms are sensitive to their input parameters, the Taguchi design method is applied to tune and control the parameters. Then, a comparison is performed using four assessment metrics including Max-Spread, Spread of Non-Dominance Solution (SNS), Number of Pareto Solutions (NPS), and Mean Ideal Distance (MID). Additionally, a statistical test is employed to evaluate the quality of the obtained Pareto frontier by the presented algorithms. The obtained results reveal that the MOGWO algorithm is more reliable to tackle the problem such that it is about 25% superior to NSGA-II in terms of the dispersion of Pareto solutions and about 2% superior in terms of the solution quality. To validate the proposed mathematical model and testing its applicability, a real case study in Tehran/Iran is investigated as well as a set of sensitivity analyses on important parameters. Finally, the practical implications are discussed and useful managerial insights are given.
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Affiliation(s)
| | - Alireza Goli
- Department of Industrial Engineering and Future Studies, Faculty of Engineering, University of Isfahan, Isfahan, Iran
| | - Peiman Ghasemi
- Department of Logistics, Tourism and Service Management, German University of Technology in Oman (GUtech), Muscat, Oman
| | - Fariba Goodarzian
- Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Washington, USA
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23
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Tlili T, Masri H, Krichen S. Towards an efficient collection and transport of COVID-19 diagnostic specimens using genetic-based algorithms. Appl Soft Comput 2021; 116:108264. [PMID: 34903957 PMCID: PMC8656180 DOI: 10.1016/j.asoc.2021.108264] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2020] [Revised: 11/01/2021] [Accepted: 11/27/2021] [Indexed: 02/02/2023]
Abstract
The speed by which the COVID-19 pandemic spread throughout the world makes the emergency services unprepared to answer all the patients’ requests. The Tunisian ministry of health established a protocol planning the sample collection from the patients at their location. A triage score is first assigned to each patient according to the symptoms he is showing, and his health conditions. Then, given the limited number of the available ambulances in each area, the location of the patients and the capacity of the nearby hospitals for receiving the testing samples, an ambulance scheduling and routing plan needs to be established so that specimens can be transferred to hospitals in short time. In this paper, we propose to model this problem as a Multi-Origin–Destination Team Orienteering Problem (MODTOP). The objective is to find the optimal one day tour plan for the available ambulances that maximizes the collected scores of visited patients while respecting duration and capacity constraints. To solve this NP-hard problem, two highly effective approaches are proposed which are Hybrid Genetic Algorithm (HGA) and Memetic Algorithm (MA). The HGA combines (i) a k-means construction method for initial population generation and (ii) a one point crossover operator for solution recombination. The MA is an improvement of HGA that integrates an effective local search based on three different neighborhood structures. Computational experiments, supported by a statistical analysis on benchmark data sets, illustrate the efficiency of the proposed approaches. HGA and MA reached the best known solutions in 54.7% and 73.5% of instances, respectively. Likewise, MA reached a relative error of 0.0675% and performed better than four existing approaches. Real-case instances derived from the city of Tunis were also solved and compared with the results of an exact solver Cplex to validate the effectiveness of our algorithm.
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Affiliation(s)
- Takwa Tlili
- LARODEC Laboratory, Institut Supérieur de Gestion de Tunis, Université de Tunis, 41 Rue de la liberté, Le Bardo 2000, Tunisia
| | - Hela Masri
- LARODEC Laboratory, Institut Supérieur de Gestion de Tunis, Université de Tunis, 41 Rue de la liberté, Le Bardo 2000, Tunisia
| | - Saoussen Krichen
- LARODEC Laboratory, Institut Supérieur de Gestion de Tunis, Université de Tunis, 41 Rue de la liberté, Le Bardo 2000, Tunisia
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