<?xml version="1.0" encoding="UTF-8"?> <!DOCTYPE ArticleSet PUBLIC "-//NLM//DTD PubMed 2.7//EN" "https://dtd.nlm.nih.gov/ncbi/pubmed/in/PubMed.dtd"> <ArticleSet> <Article> <Journal> <PublisherName>Kharazmi University</PublisherName> <JournalTitle>International Journal of Supply and Operations Management</JournalTitle> <Issn>23831359</Issn> <Volume>11</Volume> <Issue>4</Issue> <PubDate PubStatus="epublish"> <Year>2024</Year> <Month>11</Month> <Day>01</Day> </PubDate> </Journal> <ArticleTitle>Multivariate Sales Forecasting Using Gated Recurrent Unit Network Model</ArticleTitle> <VernacularTitle></VernacularTitle> <FirstPage>390</FirstPage> <LastPage>416</LastPage> <ELocationID EIdType="pii">2944</ELocationID> <ELocationID EIdType="doi">10.22034/ijsom.2024.109038.2141</ELocationID> <Language>EN</Language> <AuthorList> <Author> <FirstName>W.A. Roshan S.</FirstName> <LastName>Jayasekara</LastName> <Affiliation>Department of Transport and Logistics Management, Faculty of Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka.</Affiliation> </Author> <Author> <FirstName>P. T. Ranil S.</FirstName> <LastName>Sugathadasa</LastName> <Affiliation>Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Katubedda 10400, Sri Lanka</Affiliation> </Author> <Author> <FirstName>Oshadhi</FirstName> <LastName>Herath</LastName> <Affiliation>Department of Transport and Logistics Management, Faculty of Engineering, University of Moratuwa, Katubedda 10400, Sri Lanka.</Affiliation> </Author> <Author> <FirstName>Niles</FirstName> <LastName>Perera</LastName> <Affiliation>Center for Supply Chain, Operations and Logistics Optimization, University of Moratuwa, Katubedda 10400, Sri Lanka</Affiliation> <Identifier Source="ORCID">0000-0001-6329-5967</Identifier> </Author> </AuthorList> <PublicationType>Journal Article</PublicationType> <History> <PubDate PubStatus="received"> <Year>2021</Year> <Month>04</Month> <Day>07</Day> </PubDate> </History> <Abstract>Market forecasting is an integral part of supply chain management. Machine learning models have turned a new page in predictive analysis and helped organizations achieve improved accuracy. This paper focuses on creating a Gated Recurrent Unit (GRU) model to predict sales for multiple stores as a multivariate time series. GRUs are a variation of Recurrent Neural Networks (RNNs) used to sequence modelling tasks. The dataset used to create the model contains the unit sales of 3,049 SKUs sold in 10 stores. The sales data from the 3049 SKUs were grouped into the 7 departments to use as input to the model. A Vector Autoregression (VAR) and LightGBM models were used to compare the GRU model predictions. Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) were used to compare the 2 models. The mean MAPE values for forecasts of the GRU, VAR, and LightGBM models were 13.77%, 14.87%, and 14.24% respectively, while MAE values were 68 Units, 72 Units, and 69 Units Respectively. The study reveals that the GRU model provides more accuracy for multivariate sales forecasting due to its ability to learn hidden patterns automatically and handle time mechanisms such as trends and seasonality.</Abstract> <ObjectList> <Object Type="keyword"> <Param Name="value">Keywords: Multivariate Sales Forecasting</Param> </Object> <Object Type="keyword"> <Param Name="value">Deep Learning</Param> </Object> <Object Type="keyword"> <Param Name="value">Recurrent Neural Networks (RNN)</Param> </Object> <Object Type="keyword"> <Param Name="value">Supply chains</Param> </Object> <Object Type="keyword"> <Param Name="value">Gated Recurrent Unit (GRU)</Param> </Object> </ObjectList> <ArchiveCopySource DocType="pdf">http://www.ijsom.com/article_2944_832003315c4ab2e3180f011f686c4090.pdf</ArchiveCopySource> </Article> <Article> <Journal> <PublisherName>Kharazmi University</PublisherName> <JournalTitle>International Journal of Supply and Operations Management</JournalTitle> <Issn>23831359</Issn> <Volume>11</Volume> <Issue>4</Issue> <PubDate PubStatus="epublish"> <Year>2024</Year> <Month>11</Month> <Day>01</Day> </PubDate> </Journal> <ArticleTitle>A CNN鈥揕STM Hybrid Deep Learning Model for Detergent Products Demand Forecasting: A Case Study</ArticleTitle> <VernacularTitle></VernacularTitle> <FirstPage>417</FirstPage> <LastPage>429</LastPage> <ELocationID EIdType="pii">2943</ELocationID> <ELocationID EIdType="doi">10.22034/ijsom.2024.109931.2752</ELocationID> <Language>EN</Language> <AuthorList> <Author> <FirstName>Imen</FirstName> <LastName>Ghazouani</LastName> <Affiliation>Industrial Engineering Department, National Engineering, School of Tunis, University of Tunis El Manar, Tunis, Tunis, Tunisia</Affiliation> </Author> <Author> <FirstName>Imen</FirstName> <LastName>Masmoudi</LastName> <Affiliation>Industrial Engineering Department, National Engineering, School of Tunis, University of Tunis El Manar, Tunis, Tunis, Tunisia</Affiliation> </Author> <Author> <FirstName>Imen</FirstName> <LastName>Mejri</LastName> <Affiliation>LR-OASIS, National Engineering School of Tunis University of Tunis El Manar, Tunis, Tunisia</Affiliation> </Author> <Author> <FirstName>SAFA BHAR</FirstName> <LastName>LAYEB</LastName> <Affiliation>Bp 37, Le Belvedere 1002 Tunis, Tunisia</Affiliation> <Identifier Source="ORCID">0000-0003-2536-7872</Identifier> </Author> </AuthorList> <PublicationType>Journal Article</PublicationType> <History> <PubDate PubStatus="received"> <Year>2023</Year> <Month>02</Month> <Day>16</Day> </PubDate> </History> <Abstract>An accurate forecast of current and future demand is an essential initial step for almost all the facets of supply chain optimization, including inventory strategy, production scheduling, distribution management, and marketing policies. Simply put, a more accurate demand prediction can lead to a more optimized supply chain process, allowing for better inventory control and higher customer satisfaction. Classical demand predictions are based principally on qualitative approaches relying on data from experts&#039; opinions; quantitative forecasts based on historical data through statistical and artificial neural network models or a mix of qualitative and quantitative techniques that is also widely used and has shown good performances. Detergents and cleaning products demand is extremely volatile and has undergone substantial change, especially during the COVID-19 health crisis. In this paper, we present a hybrid Neural Network approach for accurate demand forecasts of the detergent manufacturing industry. It mainly consists of the combination of Long Short-Term Memory (LSTM) with Convolution Neural Network (CNN) based approaches. We performed a series of experiments on real data sets and assessed the performance of the proposed CNN鈥揕STM hybrid model. Numerical results showed that the combination of LSTM layers with complementary CNN layers provides more accurate results than other state-of-the-art forecasting models.</Abstract> <ObjectList> <Object Type="keyword"> <Param Name="value">Forecasting Demand</Param> </Object> <Object Type="keyword"> <Param Name="value">Detergent Products</Param> </Object> <Object Type="keyword"> <Param Name="value">Hybrid Neural Networks Based-Model</Param> </Object> <Object Type="keyword"> <Param Name="value">Convolution Neural Networks</Param> </Object> <Object Type="keyword"> <Param Name="value">Long Short-Term Memory</Param> </Object> </ObjectList> <ArchiveCopySource DocType="pdf">http://www.ijsom.com/article_2943_569071884d949f211e15073fea345613.pdf</ArchiveCopySource> </Article> <Article> <Journal> <PublisherName>Kharazmi University</PublisherName> <JournalTitle>International Journal of Supply and Operations Management</JournalTitle> <Issn>23831359</Issn> <Volume>11</Volume> <Issue>4</Issue> <PubDate PubStatus="epublish"> <Year>2024</Year> <Month>11</Month> <Day>01</Day> </PubDate> </Journal> <ArticleTitle>Evaluate The Role of Policies in The Sustainability of the Supply Chain Through a Comprehensive Mathematical Approach</ArticleTitle> <VernacularTitle></VernacularTitle> <FirstPage>430</FirstPage> <LastPage>447</LastPage> <ELocationID EIdType="pii">2942</ELocationID> <ELocationID EIdType="doi">10.22034/ijsom.2024.110299.3027</ELocationID> <Language>EN</Language> <AuthorList> <Author> <FirstName>Abbas Jumaah</FirstName> <LastName>Al-waeli</LastName> <Affiliation>Accounting , UPSI</Affiliation> <Identifier Source="ORCID">https://orcid.org/my</Identifier> </Author> <Author> <FirstName>Raad Naser</FirstName> <LastName>Hanoon</LastName> <Affiliation>Accounting, MPU</Affiliation> <Identifier Source="ORCID">https://orcid.org/my</Identifier> </Author> <Author> <FirstName>Mustafa Razzaq</FirstName> <LastName>Flayyih</LastName> <Affiliation>Accounting, MPU</Affiliation> <Identifier Source="ORCID">https://orcid.org/00</Identifier> </Author> </AuthorList> <PublicationType>Journal Article</PublicationType> <History> <PubDate PubStatus="received"> <Year>2023</Year> <Month>12</Month> <Day>08</Day> </PubDate> </History> <Abstract>This study has two policies to support supply chains. In the first model, three types of subsidies are provided for each supplier. In the second model, the government enters the chain directly by creating an integrator facility, which is responsible for packaging and coordination between facilities, and assumes the cost of packaging, and by merging publications together in one package, reduces the number of submissions and it supports the supply chain. For each of the above cases, a mathematical model was defined and a case study was conducted for 6307 customers and 119 districts of Tehran. According to the output of the models, the profit of each supplier including subsidies was more than the second model in the first model. However, the amount of subsidies paid in the first model is much higher than in the second model. By considering this issue and eliminating subsidies from profit calculations of the two models, the second model showed a more favorable performance in equal and full welfare in all regions. However, the first model had a better performance in conditions with different welfares in various regions. According to the results, in addition to subsidies, the government must enact laws that oblige publishers to complete welfare.</Abstract> <ObjectList> <Object Type="keyword"> <Param Name="value">Keywords: Economic Savings</Param> </Object> <Object Type="keyword"> <Param Name="value">Supply Chain Management</Param> </Object> <Object Type="keyword"> <Param Name="value">Supplier</Param> </Object> <Object Type="keyword"> <Param Name="value">a Comprehensive Mathematical Approach</Param> </Object> <Object Type="keyword"> <Param Name="value">Sustainability Policies and Sustainability</Param> </Object> </ObjectList> <ArchiveCopySource DocType="pdf">http://www.ijsom.com/article_2942_10abd99222c7097beab1f19bad053414.pdf</ArchiveCopySource> </Article> <Article> <Journal> <PublisherName>Kharazmi University</PublisherName> <JournalTitle>International Journal of Supply and Operations Management</JournalTitle> <Issn>23831359</Issn> <Volume>11</Volume> <Issue>4</Issue> <PubDate PubStatus="epublish"> <Year>2024</Year> <Month>11</Month> <Day>01</Day> </PubDate> </Journal> <ArticleTitle>Impact of Work-Life Balance and Work Engagement on Innovative Work Behavior</ArticleTitle> <VernacularTitle></VernacularTitle> <FirstPage>448</FirstPage> <LastPage>461</LastPage> <ELocationID EIdType="pii">2946</ELocationID> <ELocationID EIdType="doi">10.22034/ijsom.2024.110201.2966</ELocationID> <Language>EN</Language> <AuthorList> <Author> <FirstName>Pandapotan</FirstName> <LastName>Sitompul</LastName> <Affiliation>Management Study Program, Faculty of Economics and Business, Universitas Katolik Santo Thomas</Affiliation> <Identifier Source="ORCID">0009-0002-9506-0235</Identifier> </Author> <Author> <FirstName>Djoko</FirstName> <LastName>Soelistya</LastName> <Affiliation>Department of Master of Management, Universitas Muhammadiyah Gresik</Affiliation> </Author> <Author> <FirstName>Peran</FirstName> <LastName>Simanihuruk</LastName> <Affiliation>Faculty of Economics and Business, Universitas Katolik Santo Thomas, Medan, Indonesia</Affiliation> </Author> <Author> <FirstName>Titik</FirstName> <LastName>Purwati</LastName> <Affiliation>Faculty of Social and Humanities, Universitas Insan Budi Utomo, Malang, Indonesia</Affiliation> </Author> <Author> <FirstName>Efendi</FirstName> <LastName>Efendi</LastName> <Affiliation>Faculty of Mathematics and Natural Sciences, Universitas Andalas, Padang, Indonesia</Affiliation> </Author> </AuthorList> <PublicationType>Journal Article</PublicationType> <History> <PubDate PubStatus="received"> <Year>2023</Year> <Month>10</Month> <Day>08</Day> </PubDate> </History> <Abstract>This research explores the influence of work-life balance and work participation on innovative work behavior at PT Astra Agro Lestari Tbk. Using a quantitative approach with multiple linear regression analysis, this research involved 98 employees as research samples. The results of the normality test show that the data is usually distributed, while the linearity test indicates a linear relationship between work-life balance, work participation, and innovative work behavior. The regression analysis results show that work-life balance and work participation have a positive and significant effect on innovative work behavior, with an R虏 value of 0.291. This means that the two independent variables explain 29.1% of the variability in innovative work behavior. This study emphasizes the importance of work-life balance and work participation in driving innovation in the workplace. Companies can increase employees &#039; innovative capabilities by balancing work and personal life and employee engagement. These findings provide insight for the management of PT Astra Agro Lestari Tbk to develop policies that support work-life balance and active participation as strategies to increase innovative behavior and organizational competitiveness.</Abstract> <ObjectList> <Object Type="keyword"> <Param Name="value">Innovative Way of Working</Param> </Object> <Object Type="keyword"> <Param Name="value">Work-Life Balance</Param> </Object> <Object Type="keyword"> <Param Name="value">Commitment to Work</Param> </Object> </ObjectList> <ArchiveCopySource DocType="pdf">http://www.ijsom.com/article_2946_6224276deaf6b0fc5f90f0eb10241bda.pdf</ArchiveCopySource> </Article> <Article> <Journal> <PublisherName>Kharazmi University</PublisherName> <JournalTitle>International Journal of Supply and Operations Management</JournalTitle> <Issn>23831359</Issn> <Volume>11</Volume> <Issue>4</Issue> <PubDate PubStatus="epublish"> <Year>2024</Year> <Month>11</Month> <Day>01</Day> </PubDate> </Journal> <ArticleTitle>Assessment of a Hybrid Machine Learning Algorithm in Healthcare Management for Predicting Diabetes Disease</ArticleTitle> <VernacularTitle></VernacularTitle> <FirstPage>462</FirstPage> <LastPage>482</LastPage> <ELocationID EIdType="pii">2945</ELocationID> <ELocationID EIdType="doi">10.22034/ijsom.2024.110346.3067</ELocationID> <Language>EN</Language> <AuthorList> <Author> <FirstName>Azin</FirstName> <LastName>Nodoust</LastName> <Affiliation>Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran</Affiliation> </Author> <Author> <FirstName>Ali</FirstName> <LastName>Rajabzadeh Ghatari</LastName> <Affiliation>Department of Industrial Management, Faculty of Management and Economics, Tarbiat Modares University, Tehran, Iran</Affiliation> </Author> </AuthorList> <PublicationType>Journal Article</PublicationType> <History> <PubDate PubStatus="received"> <Year>2024</Year> <Month>02</Month> <Day>05</Day> </PubDate> </History> <Abstract>Diabetes Mellitus is one of the most chronic diseases in all over the world. Every year, many people die due to this disease in all countries. Therefore, identifying early detection methods for this disease can reduce its mortality. Today, many diseases can be diagnosed and prevented from progressing by using data mining techniques and machine learning algorithms. In this paper, diabetes prediction has been aimed by comparing the efficiency of several classical machine-learning techniques. For this reason, for the sake of diabetes prediction algorithms such as Na茂ve Bayes, Logistic Regression (LR), Multi-Layer Perceptron (MLP), Sequential Minimal Optimization (SMO), J48, Random Forest (RF), Regression Tree (RT) algorithms and a new hybrid algorithm based on Multi-Verse Optimizer (MVO) and Multi-Layer Perceptron (MLP) algorithms are employed for this evaluation based on Accuracy (ACC) Indicator and Area under Curve (AUC) criteria. Numerous and diverse methods and algorithms have been used to predict diabetes. Each of these algorithms has been effective in predicting diabetes with a different level of accuracy. Our goal in this research is to introduce a new combined algorithm that has the highest level of accuracy in predicting diabetes compared to the old frequent algorithms so that it can help people in the timely treatment of this disease. In the structure of the MLP algorithm, the backpropagation algorithm is used for training. This article uses the MVO algorithm to train the MLP instead of the backpropagation algorithm, which built the hybrid algorithm called MVO-MLP. The accuracy results and the area under the ROC diagram Indicated that the proposed hybrid algorithm increases the accuracy by 107% compared to the MLP algorithm with the default structure. The outcomes of the accuracy of the new model are also higher than other algorithms used in this article</Abstract> <ObjectList> <Object Type="keyword"> <Param Name="value">Keywords: Diabetes Mellitus</Param> </Object> <Object Type="keyword"> <Param Name="value">Machine Learning Algorithm</Param> </Object> <Object Type="keyword"> <Param Name="value">Data mining</Param> </Object> <Object Type="keyword"> <Param Name="value">Accuracy</Param> </Object> <Object Type="keyword"> <Param Name="value">Area under Curve</Param> </Object> <Object Type="keyword"> <Param Name="value">Multi-Verse Optimizer</Param> </Object> <Object Type="keyword"> <Param Name="value">Multi-Layer Perceptron</Param> </Object> </ObjectList> <ArchiveCopySource DocType="pdf">http://www.ijsom.com/article_2945_74b94fa84c5fa3710592d5934571a596.pdf</ArchiveCopySource> </Article> <Article> <Journal> <PublisherName>Kharazmi University</PublisherName> <JournalTitle>International Journal of Supply and Operations Management</JournalTitle> <Issn>23831359</Issn> <Volume>11</Volume> <Issue>4</Issue> <PubDate PubStatus="epublish"> <Year>2024</Year> <Month>11</Month> <Day>01</Day> </PubDate> </Journal> <ArticleTitle>A Review Study on Advancements in Reverse Supply Chain Management for Industrial Waste Management Process</ArticleTitle> <VernacularTitle></VernacularTitle> <FirstPage>483</FirstPage> <LastPage>501</LastPage> <ELocationID EIdType="pii">2947</ELocationID> <ELocationID EIdType="doi">10.22034/ijsom.2024.110472.3161</ELocationID> <Language>EN</Language> <AuthorList> <Author> <FirstName>Sunil</FirstName> <LastName>Kumar K</LastName> <Affiliation>Noorul Islam Centre for Higher Education</Affiliation> </Author> <Author> <FirstName>N.</FirstName> <LastName>Ramasamy</LastName> <Affiliation>Noorul Islam Centre for Higher Education Kumaracoil, Thuckalay, Kanyakumari District, 629180</Affiliation> </Author> <Author> <FirstName>M. Dev</FirstName> <LastName>Anand</LastName> <Affiliation>Noorul Islam Centre for Higher Education Kumaracoil, Thuckalay, Kanyakumari District, 629180</Affiliation> </Author> </AuthorList> <PublicationType>Journal Article</PublicationType> <History> <PubDate PubStatus="received"> <Year>2024</Year> <Month>06</Month> <Day>14</Day> </PubDate> </History> <Abstract>Supply chain management (SCM) is the active management of supply chain activities to optimize customer value and establish a sustainable competitive advantage. It demonstrates that supply chain management firms are actively working to establish and run source chains as profitably and well as they can. The activities that comprise the supply chain include the development of products, procurement, manufacturing, shipping, and the data systems wanted to oversee these processes. This review presents information. SCM was introduced in the opening paragraph of this page, which was followed by information about the participants and the procedures used by different businesses. It employs sustainable supply chain management (SSCM), which offers five-dimensional sustainable approaches, and presents GSCM in this study. The needs in the industrial industry&#039;s supply chain are continuously presented. Accordingly, the waste material management process is presented for different manufacturing industries, however, it consists of waste management methods like disposal method, landfill, and incineration. Additionally, this study presents detailed information about reverse Supply Chain Management (RSCM), their concept and process are explained in this review. The review describes the state of the art in survey technology, the methodology of implementation, the definition and motivation of the research topic, current trends and advancements, and the goal of the study. Reviewers came to the conclusion that RSCM controls the waste product well as a result. Utilizing the components and materials of returned goods to cut down on raw material usage and expenses is the focus of supply management in reverse logistics (RL). In future, reverse logistic SCM is introduced in the automobile industry to efficiently manage automobile waste.</Abstract> <ObjectList> <Object Type="keyword"> <Param Name="value">Keywords: Supply Chain Management</Param> </Object> <Object Type="keyword"> <Param Name="value">Manufacturing Industry</Param> </Object> <Object Type="keyword"> <Param Name="value">Consumer</Param> </Object> <Object Type="keyword"> <Param Name="value">Distributer</Param> </Object> <Object Type="keyword"> <Param Name="value">Green supply chain</Param> </Object> <Object Type="keyword"> <Param Name="value">Sustainable supply chain</Param> </Object> <Object Type="keyword"> <Param Name="value">Reverse Supply Chain</Param> </Object> </ObjectList> <ArchiveCopySource DocType="pdf">http://www.ijsom.com/article_2947_3c84470df2aca0e4295795e10279b30a.pdf</ArchiveCopySource> </Article> <Article> <Journal> <PublisherName>Kharazmi University</PublisherName> <JournalTitle>International Journal of Supply and Operations Management</JournalTitle> <Issn>23831359</Issn> <Volume>11</Volume> <Issue>4</Issue> <PubDate PubStatus="epublish"> <Year>2024</Year> <Month>11</Month> <Day>01</Day> </PubDate> </Journal> <ArticleTitle>Green Ports Assessment Model regarding Uncertainty by Best-Worst and Hesitant Fuzzy VIKOR Methods: Iranian Ports</ArticleTitle> <VernacularTitle></VernacularTitle> <FirstPage>502</FirstPage> <LastPage>516</LastPage> <ELocationID EIdType="pii">2909</ELocationID> <ELocationID EIdType="doi">10.22034/ijsom.2023.109553.2477</ELocationID> <Language>EN</Language> <AuthorList> <Author> <FirstName>Niloofar</FirstName> <LastName>Vahabzadeh Najafi</LastName> <Affiliation>industrial Engineering Department, Faculty of Engineering, Kharazmi University, Tehran, Iran</Affiliation> </Author> <Author> <FirstName>Alireza</FirstName> <LastName>Arshadi Khamseh</LastName> <Affiliation>Department of Industrial Engineering, Kharazmi University,Tehran,Iran</Affiliation> </Author> </AuthorList> <PublicationType>Journal Article</PublicationType> <History> <PubDate PubStatus="received"> <Year>2022</Year> <Month>02</Month> <Day>22</Day> </PubDate> </History> <Abstract>These days environmental protection has crucial importance in different scopes of scientific research due to the environmental climate change, and popular concern about the future of the world. Ports and maritime transportation also play a noteworthy role in sustainability owing to the fact they are known as crucial and significant economic hubs all around the world. Hence, here environmental factors associated with the ports have been illustrated and according to the experts 鈥榓ttitudes by using the Best-Worst method (BWM) to find less incompatibility, the criteria鈥檚 weight would have been calculated. Emissions of pollutants into the waters, environmental pollution, and General waste handling are the best, and Technology and Education, Hazardous waste handling, and Port staff training are the worst criteria respectively. Afterward, owing to the ambiguity in experts 鈥榓ttitudes, the combination of the VIKOR decision-making method and the hesitant fuzzy has been utilized in order to compare alternatives through uncertainty in data and to decrease related errors. Finally, the proposed assessment model has been examined with sensitivity analysis in a real case of Iranian Ports.</Abstract> <ObjectList> <Object Type="keyword"> <Param Name="value">Green Ports Assessment</Param> </Object> <Object Type="keyword"> <Param Name="value">BWM</Param> </Object> <Object Type="keyword"> <Param Name="value">Hesitant Fuzzy Set</Param> </Object> <Object Type="keyword"> <Param Name="value">VIKOR Method</Param> </Object> <Object Type="keyword"> <Param Name="value">Uncertainty</Param> </Object> <Object Type="keyword"> <Param Name="value">Iran Ports</Param> </Object> </ObjectList> <ArchiveCopySource DocType="pdf">http://www.ijsom.com/article_2909_998fb8bb6c9e8c6ab43cc97dd33fef05.pdf</ArchiveCopySource> </Article> </ArticleSet>