As Industry 4.0 continues to transform the manufacturing domain, the focus is shifting towards mass personalization of products, enabling companies to efficiently produce customized goods that meet individual customers’ unique needs and preferences. This requires manufacturing enterprises to be flexible and adaptable with their scheduling processes and manufacturing setup. Flexibility and subsequent realization of personalization of products can be realized by utilizing the notion of a Line-less Assembly System (LAS), which replaces a fixed conveyor system with a system in which the products move between machines, with products being fitted on Autonomous Mobile Robots (AMRs) to transport the products from one machine to another as per their production routing. This necessitates scheduling products as per their production routing on available AMRs to reap the benefits of LAS, which is viewed as a Job Shop Scheduling Problem (JSSP) to maximize resource utilization while adhering to constraints. The novelty of this approach is that, in addition to scheduling products, it also considers the scheduling of AMRs. A mathematical formulation to solve the deterministic JSSP is presented in the current work. The formulation is solved for various inputs using a mathematical solver. In general, JSSPs are NP-hard problems. Subsequently, a meta-heuristic-based Genetic Algorithm (GA) has been constructed to solve the JSSP. The solutions obtained through both GA and mathematical solver are compared, and it was found that GA performs well in computation and optimization efficiencies.

Recyclers can derive benefits from horizontal demand information sharing with competitors under specific conditions. However, these advantages may be compromised by the actions of remanufacturers. Information leakage occurs when a remanufacturer selectively discloses information obtained from one recycler to another. This study aims to support recyclers within the reverse supply chain in effectively engaging in horizontal information sharing while mitigating the risk of remanufacturers disclosing proprietary information to competitors, thereby preventing the dissemination of information contrary to the recyclers' intentions for sharing. The research focuses on analyzing the impact of horizontal information sharing and information leakage on the profitability of both remanufacturers and recyclers. An analytical model has been developed based on partial and asymmetric signals of customer valuation. Three scenarios are explored: no information sharing and no leakage, information sharing only, and scenarios involving both sharing and leakage. The novelty of this study lies in its examination of a demand process characterized by distributional uncertainty, which mirrors the informational challenges faced by recyclers entering new markets or expanding into new recycling categories. Recyclers operate with incomplete information and cannot determine whether they possess superior information compared to their competitors. The findings suggest that information sharing among recyclers can enhance the profits of those experiencing high demand but may adversely affect those with lower demand levels. In the absence of horizontal information sharing between recyclers, remanufacturers tend to leak information about higher-demand recyclers to others. Ultimately, managers of competing firms who face uncertainty regarding their information standing should consider sharing information to gain improved demand forecasts or, at minimum, to prevent remanufacturers from exploiting information leakage for personal gain. This refined analysis provides critical insights for stakeholders in the reverse supply chain, highlighting the complex interplay between information sharing and competitive advantage, as well as the strategic importance of managing information flow to safeguard business interests.

This study explores technology licensing in a low-carbon supply chain under cap-and-trade regulations, with an upstream firm holding partial shareholding in a downstream firm. We established a Stackelberg game to analyze four licensing strategies: free, fixed fee, royalty, and revenue-sharing. We investigate the effects of vertical shareholding and cap-and-trade regulation, as well as whether technology licensing yields a more favorable outcome compared to non-licensing and which licensing strategy proves superior. The findings reveal that when the upstream firm holds a higher share in the downstream firm, it results in increased profits for the upstream firm, the supply chain system, and consumer surplus, but decreased profit for the downstream firm. Furthermore, when carbon emission quotas are sufficiently high (low), a higher carbon trading price leads to increased (decreased) supply chain profitability, while inevitably decreasing consumer surplus. Increased carbon emission quotas consistently contribute to increased supply chain profitability, but have no impact on consumer surplus. All licensing contracts enhance the profitability of the upstream firm, the supply chain system, as well as consumer surplus, with revenue-sharing emerging as the most effective strategy. However, whether technology licensing promotes social welfare depends on factors such as the carbon emissions per unit of product and the environmental impact of each unit of carbon emission.

This paper presents a comprehensive review of the last two decades of research on Kidney Exchange Programs (KEPs), systematically categorizing and classifying key contributions to provide readers with a structured understanding of advancements in the field. The review highlights the evolution of KEP methodologies and lays the foundation for our contribution. We propose three mathematical models aimed at improving both the quantity and quality of kidney transplants. Model 1 maximizes the number of transplants by focusing on compatibility based on blood type and PRA, without additional constraints. Model 2 introduces a minimum Human Leukocyte Antigen (HLA) compatibility threshold to enhance transplant quality, though this leads to fewer matches. Model 3 extends the problem to a Multi-Agent Kidney Exchange Program (MKEP), pooling incompatible donor-recipient pairs across multiple agents, resulting in a higher number of successful transplants while ensuring fairness across agents. Sensitivity analyses demonstrate trade-offs between transplant quantity and quality, with Model 3 striking the optimal balance by leveraging multi-agent collaboration to improve both the number and quality of transplants. These findings underscore the potential benefits of more integrated kidney exchange systems.

Amidst rivalry and intricate value networks the relationship between Supply Chain Management (SCM) and Total Quality Management (TQM) has emerged as a fundamental element of contemporary operational strategies and is essential for securing long-term competitive benefits. This bibliometric analysis methodically charts the framework and research directions within this crucial overlap. Drawing on a collection of 371 publications from the Scopus database, this study offers an in-depth summary of the discipline's progression from 1994, to 2025. The study indicates a developed research area that has seen a significant rise in scholarly attention with the number of publications increasing more than threefold since 2017. Major contributions are regionally clustered with the USA, India and China standing out as the leading contributors. A thematic keyword map uncovers the fields framework: 'Total Quality Management' and 'Supply Chain Management' serve as central driving themes propelling the research. These are underpinned by fundamental themes, like 'sustainability'. The analysis also indicates an evolution in terminology, with older concepts like 'just in time' now appearing as declining themes, superseded by more integrated frameworks. This survey serves as a valuable resource for researchers and practitioners by providing a data-driven landscape of the field's foundational pillars, dominant topics, and future research trajectories.

Deferred payment and revenue-sharing contracts are significantly important for promoting the collaboration and the management of retail export supply chains for cross-border e-commerce. This research addresses the real-world challenges faced by managers in this domain by using a joint optimization model to investigate the best ordering and pricing tactics within cross-border e-commerce retail export supply chains, particularly taking into account export tax rebates and import tariffs. Our findings reveal that while revenue-sharing contracts and deferred payment mechanisms can significantly enhance supply chain profitability, their effectiveness is contingent on variables such as export rebate rates, tariffs, and tariff transfer factors. The practical implications of this study suggest that business administrators should carefully assess these factors when designing contracts to ensure robust supply chain coordination. When traditional contract mechanisms fail, hybrid approaches combining revenue-sharing and deferred payment can offer superior outcomes, thus providing a strategic advantage in volatile markets. These insights are crucial for managers seeking to navigate the complexities of international trade and optimize their supply chain performance.

This research paper investigates the integration of Explainable Artificial Intelligence (XAI) into Predictive Maintenance (PdM) systems, aiming to enhance transparency, interpretability, and reliability in industrial applications. The primary contribution is the introduction of the Explainability Parameters (XPA) framework, which offers a structured methodology for evaluating and applying XAI in PdM. The study systematically reviews recent advancements and challenges in the literature, categorising explanations into pre-modelling, in-modelling, and post-modelling processes. It presents and analyses significant case studies across various industrial sectors to illustrate the practical implications and hurdles of XAI methodologies. Key findings indicate that while XAI significantly improves the effectiveness and trustworthiness of PdM by clarifying model predictions, its implementation is hindered by the complexity of industrial data and the absence of standardised evaluation methods. The XPA framework addresses these challenges by providing tailored metrics for specific applications and advocating for a multi-phase approach to convert technical outputs into actionable maintenance recommendations. The originality of this paper lies in its comprehensive review and the establishment of rigorous standards for assessing XAI methodologies, thereby bridging the gap between theoretical frameworks and practical applications. By promoting adaptable XAI frameworks that cater to real-world industrial needs, this study fosters trust in automated decision-making processes. It enhances the overall understanding of XAI's role in PdM.

This study focuses on the flexible job shop scheduling problem with sequence-dependent setup times (FJSP-SDST), and the goal is minimizing the makespan. To solve FJSP-SDST, first, we develop a constraint programming (CP) model to obtain optimal solutions. Due to the NP-hardness of FJSP-SDST, a CP assisted meta-heuristic algorithm (C-VNS-CP) is designed to make use of the advantages of both CP model and cooperative variable neighborhood search (C-VNS). The C-VNS-CP algorithm consists of two stages. The first stage involves C-VNS, for which eight neighborhood structures are defined. In the second stage, CP is used to further optimize the good solution obtained from C-VNS. In order to prove the efficiency of the C-VNS algorithm, CP model, and C-VNS-CP algorithm, experiments of 20 instances are conducted.

The paper explores how social influence and consumer engagement affect the purchase intention of complementary and alternative medicine. The study aimed to investigate consumers of alternative medicine within Indonesia. The study provides practical insights into enhancing marketing strategies and strengthening consumer engagement with complementary and alternative medicine (CAM) by understanding these factors. A quantitative approach was used, involving a sample of 580 complementary and alternative medicine consumers in Indonesia. Participants were selected using purposive sampling criteria, which included individuals aged 17 years and older who had consumed CAM at least twice in the month. Data analysis was conducted using SmartPLS4 as the analytical tool. These insights offer a deeper understanding of how consumers behave in relation to alternative medicine, which can assist healthcare professionals in formulating more effective, targeted approaches to encourage the adoption of alternative medicinal practices.

Many uncertainties arise during the manufacturing process, such as changes in the working environment, traffic transportation delays, machine breakdowns, and worker performance instabilities. These factors can cause job processing times and ready times to change. In this study, we address a scheduling model for a single machine where both job release dates and processing times are scenario dependent. The objective is to minimize the total completion time across the worst-case scenarios. Even without the uncertainty factor, this problem is NP-hard. To solve it, we derive several properties and a lower bound used in a branch-and-bound method to find an optimal solution. We propose nine heuristics based on a linear combination of scenario-dependent processing times and release times for approximate solutions. Additionally, we offer an iterated greedy population-based algorithm that efficiently solves this problem by taking advantage of the diversity of solutions. We evaluate the performance of the proposed nine heuristics and the iterated greedy population-based algorithm.