<?xml version="1.0" encoding="UTF-8"?> <rss xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:sy="http://purl.org/rss/1.0/modules/syndication/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"> <channel> <title>International Journal of Supply and Operations Management</title> <link>http://www.ijsom.com/</link> <description>International Journal of Supply and Operations Management</description> <atom:link href="" rel="self" type="application/rss+xml"/> <language>en</language> <sy:updatePeriod>daily</sy:updatePeriod> <sy:updateFrequency>1</sy:updateFrequency> <pubDate>Sat, 01 Feb 2025 00:00:00 +0330</pubDate> <lastBuildDate>Sat, 01 Feb 2025 00:00:00 +0330</lastBuildDate> <item> <title>Hybrid Levenberg Marquardt and Back Propagation Neural Network for House Price Prediction in Taiwan</title> <link>http://www.ijsom.com/article_2937.html</link> <description>Price prediction is an influential tool in each market to enhance economic performance. In this regard, regression methods are often used. However, with the expansion of artificial intelligence, neural networks, machine learning, and deep learning, these methods can also be used for prediction. On the other hand, pricing in the housing market is always challenging, and forecasting the house price has been one of the concerns of economic activists in this field. Accordingly, in this research, a hybrid Levenberg Marquardt (LM) and Back Propagation (BP) neural network has been developed to forecast housing prices in Taiwan. This artificial intelligence method can provide a suitable forecast for housing price trends in the future by using the information in the form of Input and Output. This method uses inputs such as inflation rate, bank interest rate, minimum wage, and gross domestic product (GDP). Moreover, the housing price index is considered as the output of the model. In order to implement the proposed method, data from Taiwan from 1998 to 2022 was used. In this regard, a percentage of this data is used as training data, and the rest is used as test data in the artificial neural network. The results show that the RMSE of the proposed method is less than classic LM and BP methods. Finally, the proposed neural network will achieve the final housing price in Taiwan from 2023 through 2027. The results show that housing prices will trend upward in this country in the next five years.</description> </item> <item> <title>Optimizing Sustainability and Risk Management in Intelligent Supply Chains: A Case Study from Thailand</title> <link>http://www.ijsom.com/article_2932.html</link> <description>In this study, an exploration is undertaken to contribute significantly to the sustainable development of intelligent supply chains by investigating the optimization of the supply chain network within the realm of risk management, with specific attention devoted to resource reliability. The formalization of risk management and its integration into intelligent supply chain optimization is evaluated through an online survey involving leaders of enterprises in Thailand. A novel mathematical model aimed at enhancing risk mitigation while considering the availability of requisite resources is introduced and subsequently optimized utilizing GAMS software. To address the multi-objective nature inherent in the proposed model, an approach grounded in fuzzy theory is applied. The research outcomes illuminate the evolving role of risk mitigation within Thai enterprises, emphasizing its responsibility to provide assurances against potential risks. Despite notable strides in risk management, persistent challenges are underscored. Furthermore, emphasis is placed on the capacity of intelligent supply chains to adapt and efficaciously address dynamic risk factors in an ever-evolving world, aligning seamlessly with the principles of sustainable development.</description> </item> <item> <title>Designing a Sustainable Model for Providing Health Services Based on the Internet of Things and Meta-Heuristic Algorithms</title> <link>http://www.ijsom.com/article_2919.html</link> <description>In this article, a health service delivery model based on the Internet of Things (IoT) under uncertainty is presented. The considered model includes a set of patients, doctors, vehicles, and services that should be provided in the shortest time and cost. The most important decisions of the network include the allocation of specialist doctors to patients, the routing of vehicles, and doctors to provide health services. The dataset of the problem has been provided to the hospital and centers using IoT tools and an integration framework has been designed for this problem. The results of solving the numerical examples show that to reduce the service delivery time and the distance traveled by vehicles, the design costs of the model should be increased. Also, the increase in the rate of uncertainty during service delivery leads to an increase in total costs in the health system. In this article, Non-Dominated Sorting Genetic Algorithm II (NSGA-II), Multi-Objective Particle Swarm Optimization (MOPSO), and Multi-objective imperialist Competitive algorithm (MOICA) were proposed to solve the model, and the results showed that the proposed methods are more efficient than the exact methods. These algorithms have achieved close to optimal results in the shortest possible time. Also, the calculation results in large numerical examples show the high efficiency of the MOICA.</description> </item> <item> <title>Integrating Machine Learning and Text Mining to Enhance Customer Value Propositions in Hotel Supply Chain</title> <link>http://www.ijsom.com/article_2953.html</link> <description>The accelerated digital transformation in the contemporary business landscape, propelled by the Fourth Industrial Revolution, has fundamentally reshaped marketing research practices. This study leverages machine learning techniques and big data analytics to extract critical customer value propositions from extensive online reviews, aligning with predictive marketing strategies. Using a hybrid approach that combines qualitative and quantitative analyses, the research examines 8,290 customer reviews sourced from an online platform within the tourism industry. Two advanced analytical techniques were applied: clustering analysis to identify 20 distinct value components prioritized by tourists and associative rule mining to uncover seven essential patterns embedded in customer feedback. The results highlight the potential of big data and machine learning in accelerating marketing research processes, improving precision, and lowering operational costs. The findings emphasize the transformative role of digital tools in modern marketing practices, enabling businesses to enhance customer satisfaction, optimize services, and maintain competitive advantages in a data-driven economy.</description> </item> <item> <title>Optimizing Supply Chain Sustainability through AI-Driven Policies and Integrator Facility</title> <link>http://www.ijsom.com/article_2930.html</link> <description>Supply chains play a pivotal role in shaping a nation's economic landscape, making their sustainability a paramount concern. However, there is a notable lack of comprehensive policy frameworks addressing this crucial issue. This research aims to fill this gap by introducing two novel policy approaches. Our study focuses on optimizing supply chain networks through the application of AI-driven policies. We analyze the effectiveness of two specific policies: one involving subsidies for suppliers and the other entailing government intervention via an integrator facility for packaging and coordination. To assess these policies, we develop mathematical models and optimize them using the Firefly Algorithm (FA). The research outcomes distinctly reveal that subsidies confer a discernible advantage upon the first model, underscoring their role in shaping its efficacy. Intriguingly, the second model emerges as a formidable contender, particularly when untethered from the support of subsidies. This illuminates the inherent robustness of the second model's design, standing resilient even without the crutch of financial incentives. Beyond the realm of subsidies, the research imparts a profound insight into the essence of holistic policy paradigms, underpinned by AI-driven methodologies. It champions the necessity for a comprehensive approach that extends beyond mere financial aid, advocating for the installation of regulatory frameworks that galvanize publishers' accountability. This multifaceted approach ensures that the trajectory of social welfare is seamlessly woven into the very fabric of the supply chain's functioning, securing a sustainable and equitable distribution of benefits.</description> </item> <item> <title>A Multi Echelon Location-Routing-Inventory Model for a Supply Chain Network: NSGA II and Multi-Objective Whale Optimization Algorithm</title> <link>http://www.ijsom.com/article_2913.html</link> <description>In this study, we aim to explore the modeling and solution approach for a multi-objective location-routing-inventory problem. The focus is on planned transportation with the goal of minimizing total costs and reducing the maximum working hours of drivers. To achieve these objectives, we need to consider the routing of vehicles between customers and distribution centers, as well as the optimal allocation of product transfer flow between the production center and customers. Therefore, the proposed model incorporates location, routing-inventory, and allocation simultaneously. To solve the two-objective model, we employed the Epsilon-constraint method for small-sized problems. For large-sized problems, we utilized the NSGA-II and MOWOA meta-heuristic algorithms with a new chromosome. The computational results indicate that in order to reduce the maximum working hours of drivers, it is necessary to increase the number of vehicles and minimize travel distances. However, this leads to higher costs due to vehicle utilization and the need for constructing distribution centers closer to customers, which in turn increases construction costs. Finally, based on the analysis, the NSGA-II algorithm outperformed the MOWOA algorithm with a weighted value of 0.983 compared to 0.016, making it the selected algorithm.</description> </item> <item> <title>Targeted and Personalized Online Advertising in the Age of Artificial Intelligence (AI): A Literature Review and Research Agenda</title> <link>http://www.ijsom.com/article_2948.html</link> <description>This study aims to provide a comprehensive evaluation of current machine learning (ML) algorithms employed in targeted and personalized advertising. It reveals key findings and conclusions from a wide range of sources, offering readers a concise summary. The study addresses the gap by identifying and analyzing the most significant machine learning-based targeting methods utilized in the recent studies. This helps readers understand the strengths and weaknesses of different approaches and keeps them up-to-date with the most recent advancements and best practices. Employing the PRISMA methodology, the review systematically examines existing literature on ML-driven targeted advertising. It identifies effective ML methods and strategies, presenting real-world examples to illustrate their practical implementation. Reviewing key findings from existing literature, the analysis identifies the most effective ML methods for targeted advertising. It also examines three research questions across three key dimensions: targeting, personalizing, and predicting customer preferences. This study proposes a novel theoretical framework that elucidates the application of ML in targeted advertising. Specifically, the study explores ML algorithms that enhance precision in each dimension. Key models include Long Short-Term Memory (LSTM) networks for analyzing historical customer data, Convolutional Neural Networks (CNN) for image recognition tasks, and Factorization Machines for capturing feature interactions in click-through rate (CTR) predictions. Additionally, traditional models such as logistic regression, decision trees, random forests, and support vector machines (SVM) are utilized for classification tasks, while unsupervised learning techniques like k-means clustering and hierarchical clustering facilitate user segmentation based on behavioral and demographic similarities. These models collectively enable marketers to derive actionable insights, optimize advertising content, and improve overall campaign performance. By consolidating key findings from existing literature on ML-driven targeted advertising, this study offers a valuable resource for understanding current trends and gaps. It also proposes future research directions, highlighting potential areas for further exploration, which can inspire new studies and innovations in the field.</description> </item> <item> <title>A New Multi-Objective Location Routing Problem with Hybrid Fuzzy-Stochastic Approach by Considering Capacity Restrictions: Model, Solution and Application</title> <link>http://www.ijsom.com/article_2949.html</link> <description>This study proposes a multi-objective location-routing problem considering the capacity of vehicles to decline the system's costs. The model considers probabilistic times of traveling, service, and waiting by vehicles while guaranteeing the least probability which the cumulative values of these parameters are less than a pre-determined value when minimization of this value is considered an objective function.&amp;amp;nbsp; To cope with uncertainty, fuzzy numbers for important parameters of customer demand, vehicle capacity, variable and fixed transportation costs, and depot opening costs are used. Moreover, the nonlinear constraints are linearized to reduce computational time. We also use a fuzzy ranking method to transform the presented model into an equivalent auxiliary crisp model. As the model is NP-hard, we introduce a novel Multi-Objective Imperialist Competitive Algorithm (MOICA) to address the issue. The efficacy of the presented MOICA is evaluated by comparing its performance against two well-established multi-objective metaheuristics, Pareto Archived Evolution Strategy (PAES), and Non-Dominated Sorting Genetic Algorithm-II (NSGA-II). Leveraging Response Surface Methodology (RSM), the mutation and crossover operators employed by each algorithm were meticulously tuned. Subsequently, the performance of all three algorithms was examined using four benchmark comparison metrics across a range of established benchmark examples. The results demonstrably substantiate the superiority of the proposed MOICA in achieving optimal solutions.</description> </item> <item> <title>The Impact of Green Logistics Management Practices on Manufacturing Firms' Sustainability Performance in Ghana and Indonesia</title> <link>http://www.ijsom.com/article_2950.html</link> <description>This study investigates the impact of four dimensions of Green Logistics Management Practices (GLMP)&amp;amp;ndash;Inbound Logistics (IL), Outbound Logistics (OL), Green Operation and Production (GOP), and Reverse Logistics (RS)&amp;amp;ndash;on economic, environmental, and social performance, which represent the sustainability dimensions in both Ghana and Indonesia. This study compares the impacts of the Green Logistics Management Practices (GLMP) in Ghana and Indonesia, considering their respective economic and environmental contexts. It aims to understand how GLMP contribute to sustainability in rapidly industrializing regions, filling a gap in the literature on the comparative effects of GLMP in different socio-economic environments. The data collection process involved the use of a plant-level survey methodology, a cross-sectional survey conducted through email, and in-person to gather information from manufacturing firms in both countries. A total of 265 manufacturing companies were randomly selected from each country for the survey, ensuring that each participant had an equal opportunity to be included, thus maintaining the sample equality between the two countries. The application of structural equation modeling demonstrates the presence of significant relationships between the dimensions of GLMP and sustainability performance in both contexts. We proposed a conceptual framework and conducted an empirical investigation into the relationship between GLMP and sustainability performance (SP) in two countries with distinct continental characteristics. We used a set of hypotheses and employed structural equation modelling (SEM) to test our proposed framework. This study provides valuable insights into the practical implications of implementing GLMPs to improve the overall performance. This study addresses a significant void in the literature by offering a comparative analysis of Ghana and Indonesia. This analysis offers distinct perspectives on the correlation between GLMPs and sustainability performance. The lack of previous comparative studies highlights the originality of this research, providing a significant contribution and establishing a groundwork for future research opportunities in various geographical contexts.</description> </item> <item> <title>Trustworthiness in Supply Chains: Leveraging Private Blockchain Solutions</title> <link>http://www.ijsom.com/article_2951.html</link> <description>This research aims to explore how private blockchain technology can enhance trust among supply chain partners. Trust plays a crucial role in building long-term relationships with customers, business partners, and other stakeholders. In this context, the sustainability and growth of a company heavily depend on its ability to maintain high levels of trust in technology, operational records, and platform reliability. The novelty of this research lies in the qualitative case study approach used to explore how collaboration and innovative solutions can improve overall performance and competitive advantage in the rapidly changing world of supply chain management. The research method employed a qualitative research approach with a case study approach. Data collection techniques involved interviews and direct observations to understand the implementation and impact of private blockchain technology in the supply chains of leading companies such as Amazon, Maersk, Microsoft, Walmart, and Alibaba. Our research shows that using blockchain in supply chains enhances transparency and builds trust among partners. It does this by offering unchangeable records that remove the necessity for third-party intermediaries, enabling business partners to engage confidently, knowing that the data exchanged is secure and accurate. Leading companies like Maersk have successfully integrated blockchain-based digital solutions such as TradeLens to improve customer trust and operational efficiency.</description> </item> <item> <title>Incorporating Sustainability in Temporary Shelter Distribution for Disaster Response by the LP-based NSGA-II</title> <link>http://www.ijsom.com/article_2952.html</link> <description>This paper introduces a comprehensive response mechanism designed to distribute temporary shelters after significant disasters effectively. The primary goal of this system is to overcome challenges posed by coordination, logistics, and resource allocation constraints to optimize relief operations following a catastrophe. The model utilizes a Linear Programming (LP) metric and a Non-dominated Sorting Genetic Algorithm (NSGA-II) as a well-known multi-objective evolutionary algorithm for advanced optimization. By leveraging these methodologies, the model validates its effectiveness while considering multiple objective functions and incorporating sustainability using a response perspective. The findings of the study verify the model&amp;amp;rsquo;s success in enhancing post-disaster shelter distribution and an overall responsive approach in various dimensional scenarios. The proposed integrated system can substantially contribute to the recovery of the impacted regions by streamlining coordination and improving the efficiency of relief operations in a more organized way. It provides valuable insights for decision-makers, practitioners, and researchers involved in disaster management. Finally, a conclusion and further research are provided.</description> </item> <item> <title>A Goal Programming Based Bi-Stage Network Design for COVID-19 Immunization Waste Management</title> <link>http://www.ijsom.com/article_2954.html</link> <description>The recent global efforts to control the spread of highly contagious COVID-19 pandemic have been successful, largely due to extensive vaccination campaigns. However, these campaigns have generated an enormous amount of infectious medical waste. This paper presents a weighted goal programming-based optimization model for managing medical waste generated from COVID-19 vaccination efforts. The model proposes an efficient system by integrating decisions of locating treatment centers and the routing of generated waste to these centers and eventually to disposal sites, with a focus on cost reduction, risk mitigation for the environment and the nearby population. The objectives include minimizing the setup and transportation costs, reducing risks to the population, limiting the number of installed units, and ensuring environmental sustainability of disposal sites. A set of randomly selected test instances is used to test the model's effectiveness. The results indicate that the compromised solution provides both cost benefits and reduced risk to the population. Specifically, the cost objective was compromised by only 5.98% and the risk objective by 1.54%, while the environmental sustainability objective was fully achieved.&amp;amp;nbsp; This approach effectively supports strategic choices in recycling healthcare waste generated from COVID-19 immunization. The study is expected to aid municipal managers and decision-makers of healthcare facilities in managing vaccination related waste more efficiently.</description> </item> <item> <title>Designing A Sustainable Closed Loop Supply Chain Network under Uncertainty: A Robust Possibilistic Programming Approach</title> <link>http://www.ijsom.com/article_2955.html</link> <description>This paper studies a comprehensive multi-objective closed loop supply chain network design problem by considering economic performance, environmental impacts, and social responsibilities as the most important concerns of a supply chain&amp;amp;rsquo;s stakeholders. Due to the unavailability of historical data, all uncertain parameters are represented as fuzzy numbers based on the subjective knowledge of experts. A novel multi-objective mixed integer programming model is developed to formulate the problem. Furthermore, a robust possibilistic counterpart model is derived to generate robust solutions under epistemic uncertainty of parameters. Because of the multi-objective nature of the problem an NSGA-II algorithm is designed to yield Pareto-optimal solutions. A case study in the automotive industry is provided to validate the developed model and its solution method. Finally, several sensitivity analyses are carried out to determine the impact of critical parameters.</description> </item> <item> <title>Understanding the Nexus of Marketing and Artificial Intelligence (AI): Customer Experience is of the Essence</title> <link>http://www.ijsom.com/article_2956.html</link> <description>Artificial intelligence (AI) is assuming an increasingly pivotal role in marketing, as evidenced by its extensive implementation across a diverse array of sectors. Review studies are indispensable across all scientific disciplines, particularly within emerging fields, as they provide scholars and practitioners with insights into the current state of knowledge and prospective avenues for development. In this context, the primary objective of the present study is to examine the social and conceptual framework underpinning the application of artificial intelligence in marketing. To achieve this goal, all bibliographic data up to 2022 were retrieved and analyzed using VOSviewer software. This analysis encompasses descriptive statistics, keyword co-occurrence analysis and co-authorship analysis. The descriptive analysis identifies the most highly cited papers, authors, countries, universities, and journals within the field. The co-authorship analysis reveals the social structure, emphasizing collaboration patterns among researchers. Additionally, the keyword co-occurrence analysis provides insights into the conceptual framework of the field, particularly by highlighting recent research topics and their temporal trends. The findings indicate that AI has become an essential and important tool for businesses to identify and understand customer behavioral patterns and needs, particularly throughout the customer journey and in enhancing customer experiences. These technologies not only support businesses in optimizing their strategies but also assist customers in their decision-making processes.</description> </item> </channel> </rss>