International Journal of Production Management and Engineering - Vol 13, No 1 (2025)

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Artículos

Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms
Exploring industry 4.0 technologies for sustainable development goals: a systematic literature review
Resilience of Spanish Firms: a comparative analysis of large and small businesses in the face of 2008 financial crisis and COVID-19
Empowering human resource management through artificial intelligence: A systematic literature review and bibliometric analysis
Decision support system for waste management: fuzzy group AHP-CoCoSo
Improving expected shortage estimations in the base-stock policy by accounting for undershoots
Developing a supply chain coordination model through a revised revenue sharing contract: Incorporating sales efforts and green quality considerations
The perspective of key stakeholders on the sustainable design of reverse logistics in e-commerce: an interpretive sensemaking review

URI permanente para esta colecciónhttps://riunet.upv.es/handle/10251/214283

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Decision support system for waste management: fuzzy group AHP-CoCoSo
(Universitat Politècnica de València, 2025-01-31) Yazdani, Morteza; Ye, Chenchui; Shaayesteh, Mayssam Tarighi; Zaraté, Pascale
[EN] Waste collection and management represents critical strategic focal points in urban development planning. The establishment and maintenance of such systems contribute significantly to policymakers' pursuit of sustainable development objectives. The efficient collection, categorization, and disposal of diverse types of waste pose formidable challenges within urban governance. This study proposes a comprehensive framework for group decision analysis employing Analytic Hierarchy Process (AHP) and Combined Compromise Solution (CoCoSo) to address the optimal site selection problem for waste disposal facilities. In order to rigorously and scientifically address collective waste management issues, this paper engages ten experts to score and evaluate criteria for waste management and alternative site locations. Innovatively integrating Fuzzy methodology with AHP-CoCoSo, the authors optimize decision-makers' preference inputs. Through Fuzzy-AHP, decision-makers' weights and criteria weights are calculated, while Fuzzy-CoCoSo is utilized to determine the final collective decision ranking. By synthesizing the ratings from the ten experts, ideal decision outcomes are obtained to aid cities in selecting the most suitable waste disposal sites. This research contributes to the advancement of urban waste management strategies, offering a systematic approach that accounts for the diverse perspectives of stakeholders and the complex dynamics inherent in waste management decision-making.
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Improving expected shortage estimations in the base-stock policy by accounting for undershoots
(Universitat Politècnica de València, 2025-01-31) Babiloni, Eugenia; Guijarro, Ester; Centro de Investigación en Gestión de Empresas (CEGEA); Departamento de Organización de Empresas; Facultad de Administración y Dirección de Empresas
[EN] Shortage events are inevitable random phenomena in inventory systems when dealing with real demand. For inventory managers, knowing the size of these shortages becomes crucial to deciding the optimal policy for the inventory system. Therefore, the Expected Shortage per Replenishment Cycle, ESPCR, is an essential indicator in inventory management for several reasons. On the one hand, it gives information about the number of units not served in each cycle, and on the other hand, it is fundamental in determining service levels or shortage costs. Therefore, ESPCR estimation for the continuous review base-stock policy (s, S) is traditionally based on the assumption that inventory levels reach exactly the reorder point, i.e., inventory levels are never below the reorder point, and undershoots are not considered. This paper shows that the traditional estimation based on neglecting undershoots is biased and proposes a new and unbiased approximation, ESPCRW. This approach is based on estimating the probability vector of the stock levels at the reorder point considering the presence of undershoots. Results of this paper show that our approach outperforms the biased nature of the traditional approximation, which may have significant practical implications for the inventory systems.
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Developing a supply chain coordination model through a revised revenue sharing contract: Incorporating sales efforts and green quality considerations
(Universitat Politècnica de València, 2025-01-31) Nakhaeinejad, Mahdi; Dehghan, Abolfazl; Vaziri, Nayereh Sadat
[EN] In response to the growing emphasis on supply chain sustainability and green business management, this study addresses the critical challenges faced by industries in balancing the costs and benefits of enhancing the green quality of products, pricing strategies, and sales efforts. As environmentally conscious consumers increasingly prefer products from sustainable supply chains, industries encounter the dilemma of investing in sustainable practices while maintaining profitability. This research identifies the need for effective decision optimization models and coordination mechanisms that incentivize supply chain members to collaborate towards sustainable production without compromising their financial goals. To this end, we propose a novel hybrid cost and revenue sharing (HCRS) contract designed to foster collaboration among supply chain partners and align their interests in promoting sustainability. By examining optimal decision-making conditions, coordination mechanisms, and profit-sharing arrangements, the study highlights how the implementation of the HCRS contract can lead to improved supply chain performance and profitability. Our findings indicate that an increase in customer sustainability consciousness (CSC) positively correlates with higher levels of product green quality and overall profit, illustrating the potential benefits of prioritizing sustainability in supply chain operations.
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The perspective of key stakeholders on the sustainable design of reverse logistics in e-commerce: an interpretive sensemaking review
(Universitat Politècnica de València, 2025-01-31) González Romero, Iria; Ortiz Bas, Ángel; Prado Prado, J. Carlos; Departamento de Organización de Empresas; Centro de Investigación en Gestión e Ingeniería de Producción; Escuela Técnica Superior de Ingeniería Industrial; Ministerio de Universidades
[EN] The uncontrolled rise of returns in e-commerce has become a challenge for supply chains and key stakeholders (e-retailers, customers, transport companies and institutions). Literature, however, has not yet offered a systematic understanding of this subject. In this context, this article aims to better comprehend sustainable reverse logistics by defining the elements involved and the stakeholders' perspective. To do so, a systematic literature review is carried out through a sensemaking interpretative review, defining seven elements of sustainable reverse logistics (return policy, return location, demand and return location density, validation point, return destination, transportation strategy and information management). Furthermore, the perspective of each stakeholder is presented. In this sense, e-retailers, as ultimate decision-makers, are involved in all seven elements. When this process is outsourced, transport companies also have an impact on all relevant elements. Customers have an impact on almost all elements, as they demand a high level of service. Institutions, as regulators seeking more sustainable cities, apply measures that influence almost all relevant elements. This analysis also highlights the importance of collaboration between stakeholders, as well as specific gaps in the literature. Finally, this research provides a suitable tool for managers to establish sustainable reverse logistics that meets the preferences of each stakeholder.
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Empowering human resource management through artificial intelligence: A systematic literature review and bibliometric analysis
(Universitat Politècnica de València, 2025-01-31) Benabou, Adil; Touhami, Fatima
[EN] Drawing on a systematic literature review and bibliometric analysis, this article examines the burgeoning field of Artificial Intelligence (AI) integration into Human Resource Management (HRM) practises. By evaluating 77 selected articles from two extensive databases, Scopus and Web of Science, this study illuminates the dynamic intersection of AI technologies and HRM, encapsulating the profound implications for organisational and individual aspects of HR practises. This analysis delineates three primary thematic areas: AI's transformative role in HRM, the emerging paradigm of human-AI collaboration, and the nuanced challenges and opportunities presented by AI in HR practises. This research contributes to the academic discourse by mapping the current state of AI applications in HRM, identifying gaps and proposing directions for future research, emphasising the need for ethical frameworks and the strategic integration of AI to enhance HR practises. Through this scholarly endeavour, we aim to offer a comprehensive overview that aids practitioners and researchers in navigating the complexities of AI's role in reshaping HRM towards more efficient, ethical, and innovative practises.
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Resilience of Spanish Firms: a comparative analysis of large and small businesses in the face of 2008 financial crisis and COVID-19
(Universitat Politècnica de València, 2025-01-31) Sevilla, Jose; Ruiz-Martin, Cristina; Nebro, Juan Jose; Lopez-Paredes, Adolfo
[EN] The global impact of the COVID-19 crisis has revealed divergent outcomes for businesses, with large corporations thriving while small companies facing challenges. Analyzing the Spanish market, which relies heavily on tourism and lacks large international companies, challenges conventional analyses. Despite these anomalies, business closures align with global trends. Applying the dynamic organizational resilience model Absorption, Adaptation, and Learning we propose that large Spanish companies, having weathered the 2008 financial crisis, were better equipped for COVID-19. This article investigates whether deploying organizational resilience justifies the Spanish market's response, exploring the influence of company size and crisis type on survival capacity between 2007 and 2023.
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Exploring industry 4.0 technologies for sustainable development goals: a systematic literature review
(Universitat Politècnica de València, 2025-01-31) Kumar, Pramod; Bhamu, Jaiprakash; Bhadu, Jagdish; Saraswat, Praveen
[EN] In recent years, Industry 4.0 (I4.0) has emerged as a most significant concept in global manufacturing. Empirical research in the field of I4.0 or Smart Manufacturing (SM) is dominating continuously with great emphasis on conceptual models and case studies. Literature reviews of I4.0 or SM are less portrayed as compared to other manufacturing theories. In the present study, a categorized review of the published literature on I4.0 and SM is lucubrated and precisely visualized. The study examines the research trends in Industry 4.0 (I4.0) from 2011 to 2022, focusing on various parameters like definitions, research methods, journals, and authors to support sustainable development. It analyzed 121 articles, categorizing them by enablers, barriers, and sustainability aspects of I4.0. China, the USA, India, and Germany lead in publishing on I4.0. While conceptual models dominate, there s a growing focus on case studies and mathematical modeling. The study highlights challenges small industries face in adopting I4.0 due to resource constraints and emphasizes the need for government and expert support. It also shows that integrating Lean and Six Sigma with I4.0 can improve efficiency. However, the research lacks clarity on I4.0 s long-term impacts, and few studies explore non-manufacturing sectors. The study concludes by identifying opportunities for further research, particularly in mathematical modeling, supply chain management, and linking sustainability with I4.0 technologies.
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Optimizing cut order planning: A comparative study of heuristics, metaheuristics, and MILP algorithms
(Universitat Politècnica de València, 2025-01-31) Al-Mahmud, Sharif; Cano, Jose Alejandro; Campo, Emiro Antonio; Weyers, Stephan
[EN] Cut Order Planning (COP) optimizes production costs in the apparel industry by efficiently cutting fabric for garments. This complex process involves challenging decision-making due to order specifications and production constraints. This article introduces novel approaches to the COP problem using heuristics, metaheuristic algorithms, and commercial solvers. Two different solution approaches are proposed and tested through experimentation and analysis, demonstrating their effectiveness in real-world scenarios. The first approach uses conventional metaheuristic algorithms, while the second transforms the nonlinear COP mathematical model into a Mixed Integer Linear Programming (MILP) problem and uses commercial solvers for solution. Modifications to existing heuristics, combined with tournament selection in genetic algorithms (GA), improve solution quality and efficiency. Comparative analysis shows that Particle Swarm Optimization (PSO) outperforms GA, especially for small and medium-sized problems. Cost and runtime evaluations confirm the efficiency and practical applicability of the proposed algorithms, with commercial solvers, delivering superior solutions in shorter computation times. This study suggests the use of solvers for the COP problem, especially for smaller orders, and reserves PSO and GA for larger orders where commercial solvers may not provide a solution.

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