Pub Date : 2023-11-08DOI: 10.1080/23302674.2023.2276414
Chenxing Li, Xianliang Shi
AbstractThis paper introduces a rapidly developing new online retail model, community group buying, and proposes a three-level agricultural logistics network optimisation model. Under the community group buysing model, it is necessary to use different types of vehicles for transportation of agricultural products with different temperature control requirements. The study establishes a multi-objective mixed integer programming model with the objectives of shortest transportation time and minimum total cost, taking into account the freshness penalty cost incurred during transport. The multi-objective problem is transformed into a single objective by normalisation and weighting methods. According to the calculation for the actual case, this paper solves the problems of community group buying grid warehouse location, multiple vehicles use strategy, loading capacity, transportation path optimisation and self-pickup station demand allocation. In addition, through sensitivity analysis, the management insights of community group buying enterprises are obtained: (1) The community group buying enterprises should use brokers of the community group buying model to enhance customer stickiness; (2) The enterprises should focus on developing business in less developed regions; (3) The enterprises need to adjust the proportion of time efficiency and logistics costs according to the real situation.KEYWORDS: Community group buyingagricultural logistics networkmulti-objective programmingmulti-commodity and multi-vehiclefreshness penalty AcknowledgementsThe authors would like to express their gratitude to the industry expert members and M Company of China, which is engaged in community group buying business, for their valuable support during the research period. The authors sincerely thank the editors and anonymous readers for providing valuable suggestions and comments to improve the quality of this paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementSome or all data or models that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by Fangshan District Bureau of Commerce of Beijing Municipality of China [Grant Number B23M100030].
{"title":"Optimisation of multi-level logistics network for agricultural products under community group buying model","authors":"Chenxing Li, Xianliang Shi","doi":"10.1080/23302674.2023.2276414","DOIUrl":"https://doi.org/10.1080/23302674.2023.2276414","url":null,"abstract":"AbstractThis paper introduces a rapidly developing new online retail model, community group buying, and proposes a three-level agricultural logistics network optimisation model. Under the community group buysing model, it is necessary to use different types of vehicles for transportation of agricultural products with different temperature control requirements. The study establishes a multi-objective mixed integer programming model with the objectives of shortest transportation time and minimum total cost, taking into account the freshness penalty cost incurred during transport. The multi-objective problem is transformed into a single objective by normalisation and weighting methods. According to the calculation for the actual case, this paper solves the problems of community group buying grid warehouse location, multiple vehicles use strategy, loading capacity, transportation path optimisation and self-pickup station demand allocation. In addition, through sensitivity analysis, the management insights of community group buying enterprises are obtained: (1) The community group buying enterprises should use brokers of the community group buying model to enhance customer stickiness; (2) The enterprises should focus on developing business in less developed regions; (3) The enterprises need to adjust the proportion of time efficiency and logistics costs according to the real situation.KEYWORDS: Community group buyingagricultural logistics networkmulti-objective programmingmulti-commodity and multi-vehiclefreshness penalty AcknowledgementsThe authors would like to express their gratitude to the industry expert members and M Company of China, which is engaged in community group buying business, for their valuable support during the research period. The authors sincerely thank the editors and anonymous readers for providing valuable suggestions and comments to improve the quality of this paper.Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementSome or all data or models that support the findings of this study are available from the corresponding author upon reasonable request.Additional informationFundingThis work was supported by Fangshan District Bureau of Commerce of Beijing Municipality of China [Grant Number B23M100030].","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"141 2","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135342639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-18DOI: 10.1080/23302674.2023.2262385
Amir Khosrojerdi, Ata Allah Taleizadeh
AbstractThe COVID-19 pandemic, with its much wider, faster prevalence compared to other pandemics, has caused widespread disruptions in supply chains that have made supply chain recovery more difficult. In this study, the impact of the disruptions brought about by this global crisis on both the supply-side and the demand-side components are evaluated simultaneously. In particular, the COVID-19 pandemic has negatively affected health commodity supply chains. Moreover, this study examines the financing problems arising from disruptions, as supply chains are bound to experience shortages of liquidity and working capital during such periods. In addition, we consider the pricing mechanism of commodities and the potential impact of innovative sales techniques. In the end, we redesign a health commodity supply chain in view of the adverse impact of the pandemic to make optimal decisions using the available financing sources to ensure higher sales and profitability in the supply chain.KEYWORDS: Supply chainpricingdisruptioninventoryhealthcare Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsAmir KhosrojerdiAmir Khosrojerdi is a highly accomplished researcher and academic with a strong background in Operations Research and Supply Chain Management. He is conducting research at University of Tehran and he has a keen interest in both theoretical and practical aspects of these fields. Amir's research interests include Operations Research, Mathematical Modelling, Multi-Criteria Decision-Making, Game Theory, and their application in various industries. He also has expertise in mixed methodological approaches, optimization, and decomposition algorithms. In terms of practical applications, Amir focuses on Healthcare and financial Supply Chain Management, Transportation Modelling, Distribution Network Design, Supply Chain Resilience, and Supplier Relationship Management. He has worked on numerous projects related to these areas and has made significant contributions to the field through his research. With several years of experience in research, and consultancy, Amir has a deep understanding of the challenges and opportunities in the field of Operations Research and Supply Chain Management. His dedication to finding innovative solutions and his passion for making a positive impact make him a valuable asset to any institution. Amir continues to actively contribute to the scientific community through his research and is committed to promoting sustainable practices in supply chain management. His expertise and practical experience make him a highly sought-after researcher and consultant in the field.Ata Allah TaleizadehProfessor Ata Allah Taleizadeh received his all B.Sc. & M.Sc. and Ph.D. degrees in industrial engineering.
与其他大流行相比,COVID-19大流行的范围更广、传播速度更快,造成了供应链的广泛中断,使供应链恢复更加困难。在本研究中,同时评估了全球危机对供给侧和需求侧组件所带来的中断的影响。特别是,2019冠状病毒病大流行对卫生商品供应链产生了负面影响。此外,本研究还考察了中断所产生的融资问题,因为供应链在此期间必然会出现流动性和营运资金短缺。此外,我们还考虑了商品的定价机制和创新销售技术的潜在影响。最后,考虑到大流行的不利影响,我们重新设计了卫生商品供应链,利用可用的融资来源做出最佳决策,以确保供应链中更高的销售额和盈利能力。关键词:供应链定价中断库存医疗保健数据可用性声明作者确认,支持本研究结果的数据可在文章[和/或]其补充材料中获得。披露声明作者未报告潜在的利益冲突。samir Khosrojerdi是一位非常有成就的研究人员和学者,在运筹学和供应链管理方面有很强的背景。他在德黑兰大学进行研究,他对这些领域的理论和实践方面都有浓厚的兴趣。他的研究兴趣包括运筹学、数学建模、多准则决策、博弈论及其在各行业中的应用。他在混合方法学方法、优化和分解算法方面也有专长。在实际应用方面,Amir专注于医疗保健和金融供应链管理、运输建模、分销网络设计、供应链弹性和供应商关系管理。他参与了许多与这些领域相关的项目,并通过他的研究为该领域做出了重大贡献。凭借多年的研究和咨询经验,Amir对运筹学和供应链管理领域的挑战和机遇有着深刻的理解。他致力于寻找创新的解决方案和他对产生积极影响的热情使他成为任何机构的宝贵资产。Amir通过他的研究继续积极地为科学界做出贡献,并致力于促进供应链管理的可持续实践。他的专业知识和实践经验使他成为该领域备受追捧的研究员和顾问。Ata Allah Taleizadeh教授拥有工业工程学士、硕士和博士学位。他目前是伊朗德黑兰大学工业工程正教授。他曾在著名和领先的期刊上发表过大量文章,如European Journal of Operational Research, OMEGA;国际管理科学学报,IEEE系统、人与控制论学报:系统,国际生产经济学学报,运筹学年鉴,服务科学,交通运输研究:E部分,等。他是许多国际期刊的编辑/联合编辑/编辑委员会成员,如《国际系统科学杂志》、《工业工程杂志》、《国际系统科学杂志:物流与运筹学》、《国际应用与计算杂志》、《国际医疗保健系统工程学报》。此外,他还出版了三本斯普林格在库存控制系统领域的书籍。
{"title":"Redesigning a supply chain considering financing and pricing decisions and long-term disruptions under a pandemic","authors":"Amir Khosrojerdi, Ata Allah Taleizadeh","doi":"10.1080/23302674.2023.2262385","DOIUrl":"https://doi.org/10.1080/23302674.2023.2262385","url":null,"abstract":"AbstractThe COVID-19 pandemic, with its much wider, faster prevalence compared to other pandemics, has caused widespread disruptions in supply chains that have made supply chain recovery more difficult. In this study, the impact of the disruptions brought about by this global crisis on both the supply-side and the demand-side components are evaluated simultaneously. In particular, the COVID-19 pandemic has negatively affected health commodity supply chains. Moreover, this study examines the financing problems arising from disruptions, as supply chains are bound to experience shortages of liquidity and working capital during such periods. In addition, we consider the pricing mechanism of commodities and the potential impact of innovative sales techniques. In the end, we redesign a health commodity supply chain in view of the adverse impact of the pandemic to make optimal decisions using the available financing sources to ensure higher sales and profitability in the supply chain.KEYWORDS: Supply chainpricingdisruptioninventoryhealthcare Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article [and/or] its supplementary materials.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsAmir KhosrojerdiAmir Khosrojerdi is a highly accomplished researcher and academic with a strong background in Operations Research and Supply Chain Management. He is conducting research at University of Tehran and he has a keen interest in both theoretical and practical aspects of these fields. Amir's research interests include Operations Research, Mathematical Modelling, Multi-Criteria Decision-Making, Game Theory, and their application in various industries. He also has expertise in mixed methodological approaches, optimization, and decomposition algorithms. In terms of practical applications, Amir focuses on Healthcare and financial Supply Chain Management, Transportation Modelling, Distribution Network Design, Supply Chain Resilience, and Supplier Relationship Management. He has worked on numerous projects related to these areas and has made significant contributions to the field through his research. With several years of experience in research, and consultancy, Amir has a deep understanding of the challenges and opportunities in the field of Operations Research and Supply Chain Management. His dedication to finding innovative solutions and his passion for making a positive impact make him a valuable asset to any institution. Amir continues to actively contribute to the scientific community through his research and is committed to promoting sustainable practices in supply chain management. His expertise and practical experience make him a highly sought-after researcher and consultant in the field.Ata Allah TaleizadehProfessor Ata Allah Taleizadeh received his all B.Sc. & M.Sc. and Ph.D. degrees in industrial engineering. ","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135888814","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-10-16DOI: 10.1080/23302674.2023.2269076
Boliang Lin, Zhenyu Wang
AbstractWith the accelerated changes of trade and economic structure, the fluctuation of shipment size is increasing in railway transportation. Railway companies are facing continuous challenges about how to optimise railcar transfer chain to achieve the balance between the workload of railway network and daily changing transportation demands. In this paper, elastic capacity constraints are designed to solve the number fluctuation of railcars in shipments. We define available capacity belts to describe the elastic capacities of railway network, then fuzzy theory is introduced. A membership function is designed to designate the satisfaction degree for the number of railcars, and a non-linear integer programming model is developed. We test the model with two numerical examples from the 2019 Railway Applications Section Problem Solving Competition, and a simulated annealing algorithm is employed to solve the problem. In the experiment with 16 yards, the model generated 1304 variables. Furthermore, as the scale of the railway network increases, the number of variables exhibited exponential explosive growth. In the experiment with 32 yards, the model generated 76,037 variables and determined 365 direct train services, resulting in an operating cost of 245,014,388 car-hours. The results of the experiments effectively verify the effectiveness of the model.KEYWORDS: Freight railway networkrailcar transfer chainfuzzy theorysimulated annealing algorithm Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.AcknowledgementsThe research was supported by the National Natural Science Foundation of China (U2268207).Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [grant number U2268207].Notes on contributorsBoliang LinBoliang Lin received the Ph.D. degree in transportation management engineering from Southwest Jiaotong University, Chengdu, China, in 1994. From 1995 to 1997, he was a Post-doctoral Researcher with Beijing Jiaotong University. From 1997 to 2000. He was an Associate Professor with Beijing Jiaotong University. Since 2000, he has been a Professor with the Department of Transportation Management Engineering, Beijing Jiaotong University. His research interests include railway operation management, transportation systems network design, network flow techniques, transportation and logistics, and intelligent transportation system.Zhenyu WangZhenyu Wang received the B.S. degree in transportation engineering from Lanzhou Jiaotong University, Gansu, China, in 2019. He is currently pursuing the Ph.D. degree with the Department of Transportation Management Engineering, Beijing Jiaotong University, China. His research interests include transportation optimization and intelligent transportation system.
{"title":"Optimising railcar transfer chain via fuzzy programming and a simulated annealing algorithm","authors":"Boliang Lin, Zhenyu Wang","doi":"10.1080/23302674.2023.2269076","DOIUrl":"https://doi.org/10.1080/23302674.2023.2269076","url":null,"abstract":"AbstractWith the accelerated changes of trade and economic structure, the fluctuation of shipment size is increasing in railway transportation. Railway companies are facing continuous challenges about how to optimise railcar transfer chain to achieve the balance between the workload of railway network and daily changing transportation demands. In this paper, elastic capacity constraints are designed to solve the number fluctuation of railcars in shipments. We define available capacity belts to describe the elastic capacities of railway network, then fuzzy theory is introduced. A membership function is designed to designate the satisfaction degree for the number of railcars, and a non-linear integer programming model is developed. We test the model with two numerical examples from the 2019 Railway Applications Section Problem Solving Competition, and a simulated annealing algorithm is employed to solve the problem. In the experiment with 16 yards, the model generated 1304 variables. Furthermore, as the scale of the railway network increases, the number of variables exhibited exponential explosive growth. In the experiment with 32 yards, the model generated 76,037 variables and determined 365 direct train services, resulting in an operating cost of 245,014,388 car-hours. The results of the experiments effectively verify the effectiveness of the model.KEYWORDS: Freight railway networkrailcar transfer chainfuzzy theorysimulated annealing algorithm Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article and its supplementary materials.AcknowledgementsThe research was supported by the National Natural Science Foundation of China (U2268207).Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingThis work was supported by the National Natural Science Foundation of China [grant number U2268207].Notes on contributorsBoliang LinBoliang Lin received the Ph.D. degree in transportation management engineering from Southwest Jiaotong University, Chengdu, China, in 1994. From 1995 to 1997, he was a Post-doctoral Researcher with Beijing Jiaotong University. From 1997 to 2000. He was an Associate Professor with Beijing Jiaotong University. Since 2000, he has been a Professor with the Department of Transportation Management Engineering, Beijing Jiaotong University. His research interests include railway operation management, transportation systems network design, network flow techniques, transportation and logistics, and intelligent transportation system.Zhenyu WangZhenyu Wang received the B.S. degree in transportation engineering from Lanzhou Jiaotong University, Gansu, China, in 2019. He is currently pursuing the Ph.D. degree with the Department of Transportation Management Engineering, Beijing Jiaotong University, China. His research interests include transportation optimization and intelligent transportation system.","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"30 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136142640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AbstractSet to begin operations in the coming years, air taxi is an emerging ride-sharing aviation service that plans to commute millions of customers in metropolitan cities every day. However, existing literature has not holistically investigated the impact of the various strategic, tactical, and operations decisions pertaining to air taxi services (ATS). This research adopts a simulation-based framework to address the capacitated infrastructure location and demand allocation problems associated with ATS. Specifically, we determine the following decisions: (i) location and size of operating facilities and charging stations (strategic), (ii) air taxi fleet required to serve the expected demand at a specified service level (strategic), (iii) threshold minimum charge required for efficient air taxi operations (tactical), and (iv) real-time allocation of customer demand (operational). The proposed approach identifies the infrastructure locations using a clustering algorithm, and subsequently addresses the capacitated location-allocation problem for ATS by employing a simulation-based model. To test the effectiveness of the proposed approach, we consider a case study of New York City. We leverage the estimated air taxi demand and evaluate the proposed model by considering the weighted sum of fleet utilization and customer waiting time.KEYWORDS: Air taxiadvanced air mobilitylocation-allocation problemfleet managementsimulation model Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data used in this study was obtained from prior literature (Rajendran & Zack, Citation2019).
{"title":"Capacitated vertiport and charging station location-allocation problem for air taxi operations with battery and fleet dispatching considerations: a case study of New York city","authors":"Suchithra Rajendran, Akhouri Amitanand Sinha, Sharan Srinivas","doi":"10.1080/23302674.2023.2252737","DOIUrl":"https://doi.org/10.1080/23302674.2023.2252737","url":null,"abstract":"AbstractSet to begin operations in the coming years, air taxi is an emerging ride-sharing aviation service that plans to commute millions of customers in metropolitan cities every day. However, existing literature has not holistically investigated the impact of the various strategic, tactical, and operations decisions pertaining to air taxi services (ATS). This research adopts a simulation-based framework to address the capacitated infrastructure location and demand allocation problems associated with ATS. Specifically, we determine the following decisions: (i) location and size of operating facilities and charging stations (strategic), (ii) air taxi fleet required to serve the expected demand at a specified service level (strategic), (iii) threshold minimum charge required for efficient air taxi operations (tactical), and (iv) real-time allocation of customer demand (operational). The proposed approach identifies the infrastructure locations using a clustering algorithm, and subsequently addresses the capacitated location-allocation problem for ATS by employing a simulation-based model. To test the effectiveness of the proposed approach, we consider a case study of New York City. We leverage the estimated air taxi demand and evaluate the proposed model by considering the weighted sum of fleet utilization and customer waiting time.KEYWORDS: Air taxiadvanced air mobilitylocation-allocation problemfleet managementsimulation model Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementThe data used in this study was obtained from prior literature (Rajendran & Zack, Citation2019).","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135645271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-21DOI: 10.1080/23302674.2023.2259294
Brojeswar Pal, Subir Guin, Kripasindhu Chaudhuri
AbstractManufacturers are investing in the production of remanufactured products using quality controls because consumers are becoming more concerned about the environment and product-consistency. From this perspective, we examined a closed-loop supply chain with price and product quality-dependent demand by considering two different quality substitute products with two manufacturers. To examine the impact of pricing competition and product quality levels on the profitability of the supply chain members and the whole supply chain by considering the return rate of end-of-used products as a manufacturer decision variable, we have formulated a centralised and three different decentralised game models under decision power management. We provide a numerical example and analyse a few sensitivity parameters to help the described mathematical models be better understood. The result reveals that the total supply chain is profitable when all the members cooperate. The supply chain members are individually profitable when their competitor leads the market. Also, we see that the supply chain profit rises as product quality and substitutivity levels do.Keywords: Closed-loop supply chainsubstitute productsproduct quality levelremanufacturingStackelberg game AcknowledgementsThe authors would like to express their gratitude to the editors and referees for their valuable suggestions and corrections to enhance the clarity of the present article.Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingCorresponding author sincerely acknowledges the financial support given by the Council of Scientific & Industrial Research, Government of India under CSIR JRF/SRF Fellowship [file number 090025(12163)2021- EMR-I].Notes on contributorsBrojeswar PalBrojeswar Pal is an Assistant Professor in the Department of Mathematics, The University of Burdwan, West Bengal, India. He received his Ph.D. degree from the Jadavpur University, India. He has published several research papers in international journals of repute in the areas of production planning, inventory control and supply chain management.Subir GuinSubir Guin is currently working as a Junior Research Fellow at the Department of Mathematics, The University of Burdwan, India supported by Council of Scientific and Industrial Research (CSIR), New Delhi, after qualifying in the NET (National Eligibility Test). He received his B.Sc and M.Sc degree in Mathematics from the The University of Burdwan, India. He has published several papers in international journals of repute in the areas of supply chain management.Kripasindhu ChaudhuriKripasindhu Chaudhuri was a Senior Professor since 1983–2008 at the Department of Mathematics, Jadavpur University, India. He was also an UGC & AICTE emeritus fellow at Jadavpur University. He received his B.Sc deg
{"title":"Pricing and quality competition between two substitute products in a closed-loop supply chain","authors":"Brojeswar Pal, Subir Guin, Kripasindhu Chaudhuri","doi":"10.1080/23302674.2023.2259294","DOIUrl":"https://doi.org/10.1080/23302674.2023.2259294","url":null,"abstract":"AbstractManufacturers are investing in the production of remanufactured products using quality controls because consumers are becoming more concerned about the environment and product-consistency. From this perspective, we examined a closed-loop supply chain with price and product quality-dependent demand by considering two different quality substitute products with two manufacturers. To examine the impact of pricing competition and product quality levels on the profitability of the supply chain members and the whole supply chain by considering the return rate of end-of-used products as a manufacturer decision variable, we have formulated a centralised and three different decentralised game models under decision power management. We provide a numerical example and analyse a few sensitivity parameters to help the described mathematical models be better understood. The result reveals that the total supply chain is profitable when all the members cooperate. The supply chain members are individually profitable when their competitor leads the market. Also, we see that the supply chain profit rises as product quality and substitutivity levels do.Keywords: Closed-loop supply chainsubstitute productsproduct quality levelremanufacturingStackelberg game AcknowledgementsThe authors would like to express their gratitude to the editors and referees for their valuable suggestions and corrections to enhance the clarity of the present article.Data availability statementThe authors confirm that the data supporting the findings of this study are available within the article.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationFundingCorresponding author sincerely acknowledges the financial support given by the Council of Scientific & Industrial Research, Government of India under CSIR JRF/SRF Fellowship [file number 090025(12163)2021- EMR-I].Notes on contributorsBrojeswar PalBrojeswar Pal is an Assistant Professor in the Department of Mathematics, The University of Burdwan, West Bengal, India. He received his Ph.D. degree from the Jadavpur University, India. He has published several research papers in international journals of repute in the areas of production planning, inventory control and supply chain management.Subir GuinSubir Guin is currently working as a Junior Research Fellow at the Department of Mathematics, The University of Burdwan, India supported by Council of Scientific and Industrial Research (CSIR), New Delhi, after qualifying in the NET (National Eligibility Test). He received his B.Sc and M.Sc degree in Mathematics from the The University of Burdwan, India. He has published several papers in international journals of repute in the areas of supply chain management.Kripasindhu ChaudhuriKripasindhu Chaudhuri was a Senior Professor since 1983–2008 at the Department of Mathematics, Jadavpur University, India. He was also an UGC & AICTE emeritus fellow at Jadavpur University. He received his B.Sc deg","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136237688","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
AbstractCoordinating various activities among company members facing real-life uncertainties or disruptions is a great issue of concern in today’s business world. With this background, a multi-stage decentralised supply chain (SC) network is studied in this paper, where demand uncertainties are considered in each stage of the chain. We consider a serial SC network with single independent entities (manufacturer – distributor – retailer) in each level under restricted information sharing characteristics. The increased variability of uncertain demand through upward sections of the chain is studied. A two-phase planning model is proposed to coordinate the independent members with random customer demand. We develop a scenario-based stochastic optimisation approach where a probability is assigned for each scenario. A rolling horizon-based dynamic updating approach is proposed to update the model results for the current period as uncertainties are revealed. We develop a rule-based solution heuristic and conduct numerical analyses to validate the model. Our results are compared with two approaches – deterministic with mean demand and centralised structure with multiple scenarios. The comparative result shows that our model provides better feasible results with fewer shortage costs. Also, sensitivity analyses are performed on important parameters to observe their effect on the model.KEYWORDS: Decentralised supply chainDemand uncertaintyHeuristicScenario-based analysis Data availability statementAll data are included inside the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsMarjia HaqueMarjia Haque is a Casual Academic in the School of Engineering and IT (SEIT) at UNSW Canberra, Australia. Her research interests include supply chain management, operations management, applied operations research and decision analytics.Sanjoy Kumar PaulSanjoy Kumar Paul is an Associate Professor at the UTS Business School, University of Technology Sydney, Sydney, Australia. His research interests include sustainable and resilient supply chains, applied operations research, modelling and simulation, and intelligent decision-making.Ruhul SarkerRuhul Sarker is a Professor in the School of Engineering and IT (SEIT) at UNSW Canberra, Australia. His broad research interests include decision analytics, CI / evolutionary computation, operations research, and applied optimisation.Daryl EssamDaryl Essam a Senior Lecturer in the School of Engineering and IT (SEIT) at UNSW Canberra, Australia. His research interests include genetic algorithms, with a focus on both evolutionary optimisation and large-scale problems.
{"title":"A novel heuristic approach for planning decentralised supply chain under uncertainties","authors":"Marjia Haque, Sanjoy Kumar Paul, Ruhul Sarker, Daryl Essam","doi":"10.1080/23302674.2023.2258778","DOIUrl":"https://doi.org/10.1080/23302674.2023.2258778","url":null,"abstract":"AbstractCoordinating various activities among company members facing real-life uncertainties or disruptions is a great issue of concern in today’s business world. With this background, a multi-stage decentralised supply chain (SC) network is studied in this paper, where demand uncertainties are considered in each stage of the chain. We consider a serial SC network with single independent entities (manufacturer – distributor – retailer) in each level under restricted information sharing characteristics. The increased variability of uncertain demand through upward sections of the chain is studied. A two-phase planning model is proposed to coordinate the independent members with random customer demand. We develop a scenario-based stochastic optimisation approach where a probability is assigned for each scenario. A rolling horizon-based dynamic updating approach is proposed to update the model results for the current period as uncertainties are revealed. We develop a rule-based solution heuristic and conduct numerical analyses to validate the model. Our results are compared with two approaches – deterministic with mean demand and centralised structure with multiple scenarios. The comparative result shows that our model provides better feasible results with fewer shortage costs. Also, sensitivity analyses are performed on important parameters to observe their effect on the model.KEYWORDS: Decentralised supply chainDemand uncertaintyHeuristicScenario-based analysis Data availability statementAll data are included inside the manuscript.Disclosure statementNo potential conflict of interest was reported by the author(s).Additional informationNotes on contributorsMarjia HaqueMarjia Haque is a Casual Academic in the School of Engineering and IT (SEIT) at UNSW Canberra, Australia. Her research interests include supply chain management, operations management, applied operations research and decision analytics.Sanjoy Kumar PaulSanjoy Kumar Paul is an Associate Professor at the UTS Business School, University of Technology Sydney, Sydney, Australia. His research interests include sustainable and resilient supply chains, applied operations research, modelling and simulation, and intelligent decision-making.Ruhul SarkerRuhul Sarker is a Professor in the School of Engineering and IT (SEIT) at UNSW Canberra, Australia. His broad research interests include decision analytics, CI / evolutionary computation, operations research, and applied optimisation.Daryl EssamDaryl Essam a Senior Lecturer in the School of Engineering and IT (SEIT) at UNSW Canberra, Australia. His research interests include genetic algorithms, with a focus on both evolutionary optimisation and large-scale problems.","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135063523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1080/23302674.2023.2259293
Masoume Gholizade, Mohammad Rahmanimanesh, Hadi Soltanizadeh, Shib Sankar Sana
AbstractMulti-criteria sorting problems have gained significant importance across various fields. However, when experts express their opinions using linguistic terms and quantitative evaluation criteria are not feasible, the fuzzy set theory proves to be a suitable framework. Moreover, in such scenarios, the use of hesitant fuzzy sets becomes necessary because each criterion might have multiple values compared with other alternatives. This paper introduces a novel method called Hesitant Triangular Fuzzy Sorting (HTFFS), which is a variation of the fuzzy FlowSort method based on the preference ranking organisation method for enrichment evaluation (PROMETHEE). The HTFFS method incorporates the theory of fuzzy sets and establishes theoretical foundations for calculating the degree of superiority of each alternative over others. This calculation uses mathematical operators specifically designed for uncertain fuzzy sets and uncertain triangular fuzzy numbers. To demonstrate the effectiveness and practicality of the proposed HTFFS method, two numerical examples are presented. The results obtained from these examples showcase the applicability and validity of the HTFFS method in handling multi-criteria sorting problems.KEYWORDS: Hesitant fuzzy setsmulti-criteria problemsranking methodsFlowSort method Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementAll data used to justify the proposed model are given in the manuscript.Additional informationNotes on contributorsMasoume GholizadeMasoume Gholizade is a researcher who received her Bachelor of Science degree in Hardware Computer Engineering and her Master of Science degree in Artificial Intelligence from Guilan University and Safahn University of Iran in 2012 and 2019, respectively. She is currently pursuing her Ph.D. with the Electrical and Computer Engineering faculty at Semnan University, Semnan, Iran. Her research focuses on machine learning and computer vision, with a specific interest in fuzzy system, transfer learning and image processing. Her Google Scholar address is: https://scholar.google.com/citations?hl=en&user=LZGQzaEAAAAJ&view_op=list_works&sortby=pubdate.Mohammad RahmanimaneshMohammad Rahmanimanesh received his Master of Science and Ph.D. in Computer Engineering both from the Tarbiat Modares University, Tehran, Iran, and a Bachelor of Science in Computer Engineering from the Sharif University of Technology, Tehran, Iran. He is currently an Assistant Professor at Semnan University, Semnan, Iran. He is a member of IEEE and his research interests include network security, fuzzy systems, soft computing, and data mining. His Google Scholar address is: https://scholar.google.com/citations?user=i-hR4XIAAAAJ&hl=en&oi=ao.Hadi SoltanizadehHadi Soltanizadeh received a Bachelor of Science in Electrical Engineering from Babol University, Babol, Iran, in 1999. Master of Science degree in Electrical Engineering from Iran University of Science an
{"title":"Hesitant triangular fuzzy FlowSort method: the multi-criteria decision-making problems","authors":"Masoume Gholizade, Mohammad Rahmanimanesh, Hadi Soltanizadeh, Shib Sankar Sana","doi":"10.1080/23302674.2023.2259293","DOIUrl":"https://doi.org/10.1080/23302674.2023.2259293","url":null,"abstract":"AbstractMulti-criteria sorting problems have gained significant importance across various fields. However, when experts express their opinions using linguistic terms and quantitative evaluation criteria are not feasible, the fuzzy set theory proves to be a suitable framework. Moreover, in such scenarios, the use of hesitant fuzzy sets becomes necessary because each criterion might have multiple values compared with other alternatives. This paper introduces a novel method called Hesitant Triangular Fuzzy Sorting (HTFFS), which is a variation of the fuzzy FlowSort method based on the preference ranking organisation method for enrichment evaluation (PROMETHEE). The HTFFS method incorporates the theory of fuzzy sets and establishes theoretical foundations for calculating the degree of superiority of each alternative over others. This calculation uses mathematical operators specifically designed for uncertain fuzzy sets and uncertain triangular fuzzy numbers. To demonstrate the effectiveness and practicality of the proposed HTFFS method, two numerical examples are presented. The results obtained from these examples showcase the applicability and validity of the HTFFS method in handling multi-criteria sorting problems.KEYWORDS: Hesitant fuzzy setsmulti-criteria problemsranking methodsFlowSort method Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementAll data used to justify the proposed model are given in the manuscript.Additional informationNotes on contributorsMasoume GholizadeMasoume Gholizade is a researcher who received her Bachelor of Science degree in Hardware Computer Engineering and her Master of Science degree in Artificial Intelligence from Guilan University and Safahn University of Iran in 2012 and 2019, respectively. She is currently pursuing her Ph.D. with the Electrical and Computer Engineering faculty at Semnan University, Semnan, Iran. Her research focuses on machine learning and computer vision, with a specific interest in fuzzy system, transfer learning and image processing. Her Google Scholar address is: https://scholar.google.com/citations?hl=en&user=LZGQzaEAAAAJ&view_op=list_works&sortby=pubdate.Mohammad RahmanimaneshMohammad Rahmanimanesh received his Master of Science and Ph.D. in Computer Engineering both from the Tarbiat Modares University, Tehran, Iran, and a Bachelor of Science in Computer Engineering from the Sharif University of Technology, Tehran, Iran. He is currently an Assistant Professor at Semnan University, Semnan, Iran. He is a member of IEEE and his research interests include network security, fuzzy systems, soft computing, and data mining. His Google Scholar address is: https://scholar.google.com/citations?user=i-hR4XIAAAAJ&hl=en&oi=ao.Hadi SoltanizadehHadi Soltanizadeh received a Bachelor of Science in Electrical Engineering from Babol University, Babol, Iran, in 1999. Master of Science degree in Electrical Engineering from Iran University of Science an","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"2022 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135107876","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-19DOI: 10.1080/23302674.2023.2252325
Karthick B., Uthayakumar R.
AbstractThis paper investigates a two-echelon disrupted supply chain model that includes energy consumption and carbon emissions. During the global crisis caused by the previous pandemic, demand for essential goods increased, and as a consequence, businesses struggled to produce and ship goods to buyers. In this situation, it is crucial to shorten the lead time in order to deliver the goods to the buyer as soon as possible. Based on this, this paper analyses lead time into three components, namely: set-up time, transport time and production time. Additionally, Vendor Managed Inventory-Consignment Stock policy is adopted to increase business connectivity between supply chain players and reduce inventory costs. In such a case, this work addresses the ambiguity using an intuitionistic fuzzy number for unexpected demand. Therefore, the key objective of this work is to obtain the minimum total cost of a disrupted supply chain with respect to three different optimization techniques under triangular intuitionistic fuzzy demand. So far, no such inventory model has been developed with the aim of reducing set-up and transportation time in an intuitionistic fuzzy environment. Also, numerical experiments and sensitivity analysis are performed to test the performance of the proposed model. Finally, administrative insights and conclusions are presented.Keywords: Consignment stockuncertain demandsetup timetransportation timecarbon emission Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article as no new data were created or analysed in this study.Additional informationNotes on contributorsKarthick B.B. Karthick holds the position of Assistant Professor in the Department of Mathematics at M. Kumarasamy College of Engineering, Karur. In the year 2023, he received his PhD degree in Mathematics from The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India. His research interests notably encompass operations research, delving into the intricacies of optimizing complex systems; inventory control, a critical aspect of efficient resource management; fuzzy optimization, a field dealing with uncertainty and imprecision in decision-making; and supply chain management, a pivotal area ensuring seamless product distribution and availability.Uthayakumar R.R. Uthayakumar is currently a Professor and Head in the Department of Mathematics at The Gandhigram Rural Institute (Deemed to be University) located in Gandhigram, Tamil Nadu, India. In the past, he held the position of Senior Research Fellow in the National Board for Higher Mathematics (NBHM) and worked on a Department of Atomic Energy (DAE) Project at The Gandhigram Rural Institute in 1994. This project focused on the research area of “Study on Convergence of Optimization Problems.” In 2000, he successfully obtained his PhD degree. He has authored around 220 articles that have been publishe
{"title":"Effectiveness of lead-time management in a sustainable supply chain under intuitionistic fuzzy environment: analytical and metaheuristic optimisation approach","authors":"Karthick B., Uthayakumar R.","doi":"10.1080/23302674.2023.2252325","DOIUrl":"https://doi.org/10.1080/23302674.2023.2252325","url":null,"abstract":"AbstractThis paper investigates a two-echelon disrupted supply chain model that includes energy consumption and carbon emissions. During the global crisis caused by the previous pandemic, demand for essential goods increased, and as a consequence, businesses struggled to produce and ship goods to buyers. In this situation, it is crucial to shorten the lead time in order to deliver the goods to the buyer as soon as possible. Based on this, this paper analyses lead time into three components, namely: set-up time, transport time and production time. Additionally, Vendor Managed Inventory-Consignment Stock policy is adopted to increase business connectivity between supply chain players and reduce inventory costs. In such a case, this work addresses the ambiguity using an intuitionistic fuzzy number for unexpected demand. Therefore, the key objective of this work is to obtain the minimum total cost of a disrupted supply chain with respect to three different optimization techniques under triangular intuitionistic fuzzy demand. So far, no such inventory model has been developed with the aim of reducing set-up and transportation time in an intuitionistic fuzzy environment. Also, numerical experiments and sensitivity analysis are performed to test the performance of the proposed model. Finally, administrative insights and conclusions are presented.Keywords: Consignment stockuncertain demandsetup timetransportation timecarbon emission Disclosure statementNo potential conflict of interest was reported by the author(s).Data availability statementData sharing is not applicable to this article as no new data were created or analysed in this study.Additional informationNotes on contributorsKarthick B.B. Karthick holds the position of Assistant Professor in the Department of Mathematics at M. Kumarasamy College of Engineering, Karur. In the year 2023, he received his PhD degree in Mathematics from The Gandhigram Rural Institute (Deemed to be University), Gandhigram, Tamil Nadu, India. His research interests notably encompass operations research, delving into the intricacies of optimizing complex systems; inventory control, a critical aspect of efficient resource management; fuzzy optimization, a field dealing with uncertainty and imprecision in decision-making; and supply chain management, a pivotal area ensuring seamless product distribution and availability.Uthayakumar R.R. Uthayakumar is currently a Professor and Head in the Department of Mathematics at The Gandhigram Rural Institute (Deemed to be University) located in Gandhigram, Tamil Nadu, India. In the past, he held the position of Senior Research Fellow in the National Board for Higher Mathematics (NBHM) and worked on a Department of Atomic Energy (DAE) Project at The Gandhigram Rural Institute in 1994. This project focused on the research area of “Study on Convergence of Optimization Problems.” In 2000, he successfully obtained his PhD degree. He has authored around 220 articles that have been publishe","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"469 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135107658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-13DOI: 10.1080/23302674.2023.2253147
Reza Shahabi-Shahmiri, Thomas S. Kyriakidis, Mohammad Ghasemi, Seyed-Ali Mirnezami, Seyedali Mirjalili
AbstractThe presented study proposes a novel bi-objective mixed integer linear programming (MILP) framework for the multi-mode resource-constrained project scheduling problem (MRCPSP) with pre-emptive and non-preemptive activities splitting under uncertain conditions. Minimising the project makespan and resource costs are the considered objectives. Renewable and non-renewable resources along with different modes are taken into account for activities implementation. Additionally, some activities can be crashed by consuming additional renewable and non-renewable resources. Model uncertainty is efficiently addressed by utilising a fuzzy chance constrained programming (CPP) method as well as extending two robust possibilistic programming models. The capability of the presented mathematical framework is validated using problem instances from PSPLIB (j10, j14, j20, and j30) and MMLIB (MM50 and MM100). Finally, a detailed computational comparison is presented to assess the performance of the two robust possibilistic programming models.KEYWORDS: MRCPSPactivity preemptiontime crashingrobust chance constrained programmingtime–cost trade-off AcknowledgmentsThe authors would like to thank the Editor-in-Chief, Associate Editor, and anonymous reviewers for their valuable comments on this presentation for remarkable improvement. The authors would also like to express their gratitude to Ms. Fateme Nazeri and Ms. Fateme Zarei for their provision of data, as well as Dr. Hasan Shirzadi for the final validation of the obtained results.Disclosure statementNo potential conflict of interest was reported by the authors.Compliance with ethical standardsAvailability of data and material: All data generated or analysed during this research are included in this published article.Code availability: Not applicableFunding: Not applicableConsent to participate: Not applicableConsent for publication: Not applicableEthics approval: The authors certify that they have no affiliation with or involvement with human participants or animals performed by any of the authors in any organisation or entity with any financial or non-financial interest in the subject matter or materials discussed in this paper.Authors’ contributions: All authors contributed to all parts of this research including Conceptualisation; Formal analysis; Resources; Methodology; Supervision; Data collection and investigation; Software; Validation; and Writing – review & editing.Notes1 Total number of variables.Additional informationNotes on contributorsReza Shahabi-ShahmiriReza Shahabi-Shahmiri received an M.Sc. in systems optimisation from the University of Tehran. His master's thesis was about scheduling and routing of heterogeneous vehicles in multiple cross-docks. His current research interests and areas include developing novel mathematical optimisation models in project scheduling, supply chain management, cross-docking systems and routing and scheduling problems. He published some ISI papers in well-known jou
他对自然启发的人工智能技术的贡献获得了国际认可,发表了500多篇论文,被引用超过8万次,h指数为85。自2019年以来,他一直名列高引用研究人员的前1%,科学网(Web of Science)将他评为世界上最具影响力的研究人员之一。在2022年和2023年,《澳大利亚人报》将他评为人工智能领域的全球领导者,以及进化计算和模糊系统领域的国家领导者。他是IEEE的高级成员,并在几个顶级人工智能期刊担任编辑职位,包括人工智能的工程应用,应用软计算,神经计算,工程软件进展,生物和医学计算机,医疗保健分析,应用智能和决策分析。
{"title":"A robust chance-constrained programming approach for a bi-objective pre-emptive multi-mode resource-constrained project scheduling problem with time crashing","authors":"Reza Shahabi-Shahmiri, Thomas S. Kyriakidis, Mohammad Ghasemi, Seyed-Ali Mirnezami, Seyedali Mirjalili","doi":"10.1080/23302674.2023.2253147","DOIUrl":"https://doi.org/10.1080/23302674.2023.2253147","url":null,"abstract":"AbstractThe presented study proposes a novel bi-objective mixed integer linear programming (MILP) framework for the multi-mode resource-constrained project scheduling problem (MRCPSP) with pre-emptive and non-preemptive activities splitting under uncertain conditions. Minimising the project makespan and resource costs are the considered objectives. Renewable and non-renewable resources along with different modes are taken into account for activities implementation. Additionally, some activities can be crashed by consuming additional renewable and non-renewable resources. Model uncertainty is efficiently addressed by utilising a fuzzy chance constrained programming (CPP) method as well as extending two robust possibilistic programming models. The capability of the presented mathematical framework is validated using problem instances from PSPLIB (j10, j14, j20, and j30) and MMLIB (MM50 and MM100). Finally, a detailed computational comparison is presented to assess the performance of the two robust possibilistic programming models.KEYWORDS: MRCPSPactivity preemptiontime crashingrobust chance constrained programmingtime–cost trade-off AcknowledgmentsThe authors would like to thank the Editor-in-Chief, Associate Editor, and anonymous reviewers for their valuable comments on this presentation for remarkable improvement. The authors would also like to express their gratitude to Ms. Fateme Nazeri and Ms. Fateme Zarei for their provision of data, as well as Dr. Hasan Shirzadi for the final validation of the obtained results.Disclosure statementNo potential conflict of interest was reported by the authors.Compliance with ethical standardsAvailability of data and material: All data generated or analysed during this research are included in this published article.Code availability: Not applicableFunding: Not applicableConsent to participate: Not applicableConsent for publication: Not applicableEthics approval: The authors certify that they have no affiliation with or involvement with human participants or animals performed by any of the authors in any organisation or entity with any financial or non-financial interest in the subject matter or materials discussed in this paper.Authors’ contributions: All authors contributed to all parts of this research including Conceptualisation; Formal analysis; Resources; Methodology; Supervision; Data collection and investigation; Software; Validation; and Writing – review & editing.Notes1 Total number of variables.Additional informationNotes on contributorsReza Shahabi-ShahmiriReza Shahabi-Shahmiri received an M.Sc. in systems optimisation from the University of Tehran. His master's thesis was about scheduling and routing of heterogeneous vehicles in multiple cross-docks. His current research interests and areas include developing novel mathematical optimisation models in project scheduling, supply chain management, cross-docking systems and routing and scheduling problems. He published some ISI papers in well-known jou","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"68 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135783519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-09-06DOI: 10.1080/23302674.2023.2252321
Qin Li, Yu Wang, Yu Xiong, Shu Zhang, Yu Zhou
{"title":"Machine learning-based optimisation in a two-echelon logistics network for the dry port operation in China","authors":"Qin Li, Yu Wang, Yu Xiong, Shu Zhang, Yu Zhou","doi":"10.1080/23302674.2023.2252321","DOIUrl":"https://doi.org/10.1080/23302674.2023.2252321","url":null,"abstract":"","PeriodicalId":46346,"journal":{"name":"International Journal of Systems Science-Operations & Logistics","volume":"101 1","pages":""},"PeriodicalIF":4.2,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85807185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}