The strategic shift towards satisfying customers with customised product-services has intensified over the last decade. However, a formidable challenge emerges in effectively integrating supply chain partners into this service-oriented paradigm. This complexity is compounded by a gap in the literature: a dearth of frameworks that offer a holistic view of these specialized ecosystems. To address this challenge, this paper seeks to elucidate the pivotal elements governing the management of supply chains for customised product-services and to map their interrelationships. Drawing upon an extensive literature review, we propose a novel conceptual model that overcomes the weaknesses of previous approaches by synthesizing these core elements in an integrated manner. Uniquely, the model considers the entire lifecycle of new customised product-services, framed within a collaborative vision among supply chain members. The fundamental elements affecting the management of these supply chains are systematically described. Finally, the paper concludes by proposing some lines for future research directions.
Employee promotions are essential for both organizational growth and individual career advancement, yet they often face challenges such as data imbalances and the lack of effective predictive frameworks. This study addresses these issues by applying advanced machine learning models to improve decision-making in human resource management. Using a dataset comprising 54 808 employee records, the study evaluates eight models, including Random Forest, Logistic Regression, SVM, AdaBoost, XGBoost, Gradient Boosting, Decision Tree, and Artificial Neural Networks (ANN). RF and XGBoost emerged as the most effective, with Random Forest achieving an accuracy of 96.21% and XGBoost closely following at 95.96%. Both models demonstrated strong AUC-ROC scores, highlighting their ability to handle complex data patterns. Key features influencing promotion outcomes, such as “Previous year rating” and “Average training score”, were identified as critical variables. Advanced balancing techniques such as SMOTE further improved the detection of underrepresented promoted employees, contributing to fairer evaluations. The study’s comprehensive framework, which includes detailed feature analysis and mathematical explanations, provides a practical guide for HR systems seeking to optimize promotion processes. Future research could explore hybrid deep learning models like LSTM and CNNs to enhance scalability and predictive power. Additionally, incorporating factors like employee demographics and ethical considerations would foster fairness and transparency in promotion practices, broadening the application of machine learning in HR management.
Manufacturing as a Service (MaaS) is an emerging paradigm in which a Provider exposes manufacturing capabilities such as CNC machines, additive manufacturing systems, and other production assets as on-demand services to multiple Consumers. In this setting, Provider side scheduling becomes a critical aspect, as orders arrive dynamically and must be allocated to heterogeneous resources while meeting contractual constraints. This paper proposes an implementable conceptual framework for Provider production scheduling in MaaS, formulating the problem as a Markov Decision Process and enabling Deep Reinforcement Learning based decision making. The proposed Provider Planner models resource allocation as an unrelated parallel machine scheduling problem, where decisions are taken at discrete event-driven decision epochs (e.g., order arrivals and resource releases). The framework explicitly specifies the observation space, action space, and discrete-event transition logic, incorporating practical features such as resource availability and efficiency, operating cost rates, setup states across product families, batch-size constraints, order time windows (earliest/latest start), due dates, and delay penalties. A multi-objective reward formulation is defined to jointly minimise tardiness and associated penalties, overall makespan, and total production cost computed from resource uptimes. The resulting model provides a structured basis for developing and evaluating adaptive scheduling policies for MaaS Providers under demand variability and complex operational constraints.
Efficient maritime route planning is pivotal for optimising global logistics operations in an increasingly competitive environment. This study aims to determine the optimal number of clusters among 90 georeferenced ports across six continents, minimizing distances and enhancing operational efficiency through advanced clustering techniques. Methods such as geographic k-means, Gaussian Mixture Models (GMM), and hierarchical clustering were implemented and evaluated using quality metrics like the Calinski-Harabasz, Silhouette, and Davies-Bouldin indices. The results identified 13 optimal clusters that reflect logical and geographically coherent segmentation, highlighting geographic k-means for its consistency and replicability. Additionally, integrating an intelligent routing model enabled the design of optimised routes within each cluster, reducing distances and maximizing logistical efficiency. The proposed methodology, developed in Python, demonstrates not only its applicability to maritime operations but also its potential for extrapolation to other contexts, showcasing robustness and relevance for global strategic planning. This approach represents a significant advancement in integrating clustering and optimisation techniques to enhance maritime supply chain management.
This study proposes a novel multi-period, multi-product, and multi-echelon closed-loop supply chain (CLSC) model that simultaneously addresses economic and environmental objectives under dynamic demand and return conditions. Formulated as a mixed-integer linear programming (MILP) model, the framework incorporates key operational decisions including facility location, production planning, inventory control, recovery, and disposal while integrating sustainability factors such as the use of clean technologies and environmentally friendly materials. A weighted sum approach is applied to generate Pareto-optimal solutions, enabling decision-makers to explore trade-offs between cost minimization and environmental performance. The model is validated through a numerical example, and a detailed sensitivity analysis is conducted to assess the impact of critical parameters on supply chain behavior. The results reveal that while sustainability initiatives may increase operational costs, strategic planning and capacity optimization can achieve effective cost-environment trade-offs. The proposed model offers a comprehensive and practical decision-support tool for designing efficient and sustainable CLSC systems, contributing to both academic research and real-world supply chain practice.
The Capacitated Lot-Sizing and Scheduling Problem (CLSSP) integrates production planning and scheduling decisions under capacity constraints. It combines multiple complex subproblems, including lot-sizing, production assignment, sequencing, and timing (often subject to dependent setup times), as well as inventory management across periods. Although several mathematical programming models have been proposed to address this problem, the literature lacks a structured framework to systematically characterise and compare these formulations. To fill this gap, this paper presents a novel framework specifically designed for characterising mathematical programming models for the CLSSP. The framework consists of seven dimensions, each developed through a structured methodology combining a PRISMA-based systematic literature review of related works, an in-depth analysis of selected papers. It was validated both by applying it to existing optimisation models in the literature and through a real-world industrial case study. The validation results demonstrate the framework’s applicability in literature reviews, as it captured all modelled characteristics from the analysed selected studies, enabling the identification of emerging trends and research gaps in CLSSP modelling. As for its application in industry, the target users of the framework are described and its use is illustrated through a industrial case study based on a real problem. Therefore, the proposed framework offers three main functionalities: i) providing a structured basis to characterise the CLSSP, ii) enabling systematic analysis and comparison of existing mathematical programming models for the CLSSP, and iii) supporting the design of new optimisation models to effectively address the CLSSP in real-world industrial settings.
The search for integrated solutions in the machining industry, particularly in job shop production scheduling and maintenance planning, is crucial. This development aims to simultaneously inform production scheduling decisions and maintenance planning, thereby minimizing makespan and operational costs. The limited time for decision-making on an industrial scale presents a challenge in providing fast and optimal decisions. Therefore, optimization is carried out using an approximation approach: NSGA-II and MOALNS. A multi-objective optimization model has been successfully developed and can provide optimal results for the integrated production and maintenance scheduling. The mathematical model demonstrated robustness in various solved cases. NSGA-II reduced operational costs by up to 28% but increased makespan by 4%. In comparison, MOALNS reduced the makespan by up to 4% but only reduced operational costs by up to 16%. Finally, NSGA II consistently provides better performance results than MOALNS. With significant reductions in operational costs, the industry can save significantly on total operational costs, such as machine maintenance, labor costs, and maintaining output quality, from existing operations and can continue to serve customers more sustainably. NSGA-II is superior in covering the objective function space, achieving solution quality close to the Pareto front, and maintaining a consistent distribution of solutions.
No-idle flow shop scheduling is a critical challenge in manufacturing, where minimising overall tardiness directly impacts efficiency and customer satisfaction. This study introduces a novel hybrid algorithm, EDD-NEH-GA (ENG), which integrates Earliest Due Date (EDD) and Nawaz-Enscore-Ham (NEH) heuristics with a Genetic Algorithm (GA) to balance global exploration and local optimisation. The objective is to overcome premature convergence and achieve superior tardiness reduction. Computational experiments on Taillard’s benchmark instances demonstrate ENG’s effectiveness compared to the Mixed Integer Linear Programming (MILP) based approach by Balogh. Across all tested cases, ENG updated 93% of previously best-known solutions, achieving an average tardiness reduction of 18–52%. These results confirm ENG as a robust and efficient solution for complex no-idle flow shop environments, offering significant gains in scheduling performance and operational productivity. After performing statistical analysis it is noted that ENG outperforms. ENG significantly improved scheduling performance, averaging 8430 units of reduction in overall tardiness. According to the standard error of the difference (SE = 1859), this improvement appears to be constant across cases.
Supply chain management (SCM) using artificial intelligence (AI) transforms business practices by encouraging sustainability. Gaining insight into AI's role in improving supply chain effectiveness and lowering environmental impact is essential as demand for sustainable practices rises. This study aims to investigate how AI contributes to sustainability in SCM and determine the primary challenges and opportunities associated with implementing AI. The study aims to provide an extensive review of AI's potential to assist sustainable and green supply chain practices. This standard and strategic literature review was conducted employing the Scopus database. The five-stage methodology was adopted in the review process, which includes pilot search, locating studies, study selection, synthesis analysis, and reporting. The choice of 82 relevant studies on AI and sustainable SCM was made during the review after the exclusion of irrelevant articles. The review emphasises AI's significant role in enhancing sustainability in SCM by reducing environmental impact, improving resource efficiency, and promoting green practices. However, the study also highlights the identification of challenges such as integration complexity, implementation cost, and technological limitations and future agenda.
The last mile is a critical segment in logistics, significantly impacting the efficiency and profitability of delivery operations. However, managing courier workflows remains a challenge, especially given the variations in work patterns and external factors such as route deviations or traffic conditions. This study analyzes the workflows of couriers, and provides insights into couriers’ individual efficiency and the underlying factors influencing its performance. Using real-world data from a Spanish logistics company, the study measures the impact of work shifts, workload density, and contextual elements on courier efficiency. The research seeks to serve as a tool to identify potential bottlenecks, optimize task allocation, and improve overall service efficiency.
This study explores the integration of Artificial Intelligence (AI) into the Reverse Logistics Process (RLP) to enhance decision-making, operational efficiency, and resource recovery. Through a systematic literature review, the applications of AI across critical RLP stages (planning, execution and control) are identified. Key findings highlight the transformative role of AI in optimizing network design, improving product collection and inspection, and supporting decision-making for the disposition of recovered items. AI technologies and functionalities such as machine learning, predictive analytics, and decision-support systems demonstrate significant potential for automating complex processes, reducing operational costs, and improving logistical precision. However, barriers to AI adoption in the RLP include high implementation costs, organisational resistance, lack of specialist personnel, and limited technological infrastructure. The review also identifies enablers such as advancements in the Internet of Things, blockchain, and big data analytics, which facilitate AI adoption and integration in RLP. The study concludes that AI is essential for establishing resilient and adaptive RL systems and offers substantial opportunities to address uncertainty while improving efficiency and decision-making. Future research should focus on overcoming adoption barriers, advancing predictive models, and integrating AI with other Industry 4.0 technologies to enhance RLP outcomes. This review fills a significant gap in the literature by offering a structured analytical framework that categorises AI applications based on decision types and RLP stages, providing an integrated perspective not previously addressed in the field.
The continuous growth in the volume of goods transported by sea has led to a significant increase in the number of vessels and containers arriving at ports, requiring more efficient management of vessel loading and unloading operations. These operations involve three key problems: berth allocation, quay crane assignment, and quay crane scheduling. In response to these challenges, numerous mathematical programming models have been proposed, and various literature reviews analysing these models have emerged in recent years. However, the abundance of such reviews highlights the need for a systematic organisation of available information to consolidate the state of the art in this field. To this end, this paper conducts a systematic literature review, following PRISMA guidelines, based on publications retrieved from Scopus and Web of Science. This study focuses specifically on existing state-of-the-art studies that analyse mathematical models for berth allocation, quay crane assignment, and/or quay crane scheduling. The objectives of this work are threefold: (i) to assess the scope of previous literature reviews, including their purpose, the problems addressed, and their temporal coverage; (ii) to identify and group the dimensions they use to categorize the problems and the models designed to address them; and (iii) to compile the future research directions proposed in these reviews and highlight those that remain relevant. The findings provide a comprehensive overview of trends, highlight research gaps, and propose dimensional groups to consider when developing more holistic decision support frameworks for port operations.
Cold storage is crucial for ensuring food security and optimizing supply chain efficiency, particularly in tropical regions with seasonal demand fluctuations. Existing optimization models often focus solely on economic and environmental aspects, neglecting a holistic sustainability approach and the uncertainty of seasonal demand fluctuations. This study develops a multi-objective non-linear programming (MO-NLP) model that optimizes economic profit, carbon emissions, and food security, incorporating sustainability weights (λ1, λ2, λ3) to accommodate different priorities. The model considers stochastic demand and land area, budget, and electricity constraints. The results show that the model effectively balances profitability, environmental impact, and food security. In the standard scenario, the optimal cold storage area is 256 m², resulting in a profit of USD 742,368, carbon costs of USD 122,579, and a probability of 0.9962 for food security. Sensitivity analysis indicates that increasing land availability improves performance but eventually reaches a saturation point, while seasonal demand fluctuations have a significant impact on decisions. A higher food security weight (λ3) stabilizes supply but reduces profitability, whereas profit dominance (λ1) boosts income but compromises environmental and social aspects. This study provides an optimization-based tool for industry stakeholders and policymakers. Integrating sustainability into a single framework provides an adaptive and efficient approach to cold storage planning in the face of economic and environmental uncertainties.
The textile industry is among the most polluting sectors globally, primarily due to the rise of fast fashion and limited recycling practices. Addressing this challenge requires transformative approaches, among which Industry 4.0 technologies stand out as promising tools. This paper presents the findings of a qualitative study based on 16 in-depth interviews with key stakeholders in the Spanish textile sector. The research explores the potential of these technologies to enhance sustainability across various stages of the textile product life cycle. The results indicate that Industry 4.0 technologies can significantly contribute to improving environmental, economic, and social sustainability in the sector. However, further research is needed to identify the most impactful applications and to support their adoption, particularly among small and medium-sized enterprises.
This study evaluates the effectiveness of World Class Manufacturing (WCM) implementation in optimizing operational performance within the fibreglass insulation manufacturing sector. The study employed a systematic approach utilising Value Stream Mapping (VSM) and Overall Equipment Effectiveness (OEE) as the primary evaluation tools. Data collection was conducted from 2023 to 2024, focusing on performance, availability, and quality metrics. Results show a significant improvement in OEE from 81.3% to 87.3%, validating the effectiveness of WCM in optimizing operations. The implementation fostered a culture of operational excellence, as evidenced by improved process understanding and increased employee engagement in improvement initiatives. Pareto analysis revealed several inefficiencies, enabling targeted interventions in line with WCM’s “zero waste” philosophy. The methodology was found to be adaptable to existing production systems, suggesting its viability for manufacturing environments in emerging economies. This study contributes to the literature by providing empirical evidence of the effectiveness of WCM in driving systematic improvement and organizational change, particularly in industrial development in emerging economies. The findings support the role of WCM in establishing sustainable operational excellence frameworks capable of meeting global competitive challenges.
Considering environmental, economic, and social sustainability in production planning has become a priority research topic to achieve significant improvements in production systems. For this reason, the objective of this study is to analyze the impact, the approach, and the potential benefits of sustainability in production planning. Using systematic literature review as a base methodology and supported by a content analysis, patterns, trends, and relationships between different elements, such as keywords, authors, and thematic areas, were identified. The impact of sustainability on production planning shows that (i) the environmental dimension seeks as a priority the reduction of greenhouse gases; (ii) the economic dimension the reduction of costs; and (iii) the social dimension focuses more on the generation of employment, however, a balance between these three dimensions is not observed. In the reviewed scientific papers, the most commonly used approach is the combination of economic and environmental dimensions, seeking to implement strategies that allow increasing profits without compromising the environment. Finally, it is evident that by adopting a sustainable approach, corporate performance can be improved, which allows to respond to customer demands, and comply with regulatory pressures.
Disasters are events that disturb and threaten people’s lives caused by nature or/and non-natural factors as well as human factors that cause casualties and economic losses. Since the 1950s, the number and scale of major natural disasters such as earthquakes, tsunamis, cyclones, floods, volcanoes, etc. have grown exponentially (Hu & Dong, 2019). The polemic of uneven assistance and delays in the event of a natural disaster is the most common thing that occurs during a natural disaster. Location Routing Problem is a continuation of the classical routing problem that combines strategic and operational decisions with the facility location problem and the vehicle routing problem. This study aims to determine the location of the distribution center construction and the optimal route using the NSGA II methods with the objective function of minimizing total costs and minimization of maximum travelling time for the distribution of humanitarian aid in the 2018 natural disaster in Sigi Regency. Optimization is designed into two scenarios, namely the construction of two distribution centers and three distribution centers. The result show that construction of two distribution centers can be designed at locations D3 and D4 with a total cost of IDR406 280 000 and a maximum service time of 12.002 hours, while the construction of three distribution centers can be done at locations DC1, DC4 and DC 7 or with a total cost of IDR605 363 000 and a maximum service time of 7.4253 hours.This research develops a new mathematical model that optimizes humanitarian aid distribution by balancing the setup cost of distribution centers and minimizing travel time. It utilizes the NSGA-II algorithm for multi-objective optimization and the Displaced Ideal Solution (DIS) method to select the best solution from the Pareto front.
Waste collection represents critical strategic focal point 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, the authors optimize decision-makers' preference inputs. Through our proposed method, 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 study advances the 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.
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.
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.
