Due to the high uncertainty in humanitarian supply chains, utilizing pre-positioning strategies in warehouses to manage crises entails significant risks, including excess inventory and shortages. One approach to managing these risks is to use options contracts. In this study, a dual-option contract, which combines the benefits of both call and put options, is employed to manage risks in a humanitarian supply chain. In this network, in addition to the main supplier, the humanitarian organizations (HO) can also access secondary and tertiary supply sources, namely, in-kind donations and spot markets. Considering these supply sources and the HO's logistics costs including procurement, inventory and transportation, this study models the HO's procurement problem and identifies the optimal initial order, call option, and put option. After forming the centralized problem, the reservation and exercise prices of the dual-option contract are calculated to achieve coordination among supply chain members and a win-win solution. This modeling effort presents a novel application of dual option contracts within humanitarian supply chain, where the contract parameters are endogenously derived to serve as coordination mechanism, an approach not extensively explored in prior literature. Finally, sensitivity analyses are performed on key parameters using numerical examples, and several managerial insights are reported.

This paper investigates an integrated production and distribution scheduling problem in a hybrid flow shop with a batch delivery system. The goal is to schedule jobs and assign them to batches to minimize the total weighted number of tardy jobs and delivery costs, thereby improving both operational efficiency and customer satisfaction. To achieve this, a novel mixed-integer linear programming model is developed. Given its computational complexity for large instances, two metaheuristic algorithms, simulated annealing (SA) and a genetic algorithm (GA), are proposed and calibrated using the Taguchi method to enhance performance. Experimental results demonstrate that simulated annealing consistently outperforms the genetic algorithm in both solution quality and computational time. The results also demonstrate that the proposed approach reduces the overall cost of production and distribution by 10.45%.

There are many studies made to explain the relationship between the organizational factors. Burden of them, tried to mathematically model this relationship using traditional methods like PLS (Partial Least Squares), SEM (Structural Equation Modeling), RA (Regression Analysis) or Co-relation analysis. Besides, the limited explanatory variables were recently applied in such studies. Additionally, there are very rare quantitative investigations discovering the structural cause and effect interconnections among the huge number of organizational factors. This research has been designed to first distinguish the cause and effect group among the 20 organizational factors and second to weight these factors as well. DEMATEL and SWARA methodologies are the main multi criteria tools used in the present study. Results show the top 4 factors of EBLPOC(Employees' Basic Level of Perception Regarding Organizational Complexities), OCB(Organizational Citizenship Behavior), BC(Business Climate) and EJP(Employee Job Performance) hold relatively the utmost weights (closely 0.826 together). EBLPOC, EJP and BC are the cause factors. OCB is recognized as the effect factor. Furthermore, it is worthy to mention that the factor of EBLPOC (as a qualitative variable which were usually missed or ignored in previous studies), is totally ranked at first position. Finally, present study is inherently readdressing the organization's leaders, chief executives, intermediate managers and policy makers to focus on some classical foremost factors like OCB, BC and EJP (the old wounds) again.

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.

This paper presents a literature review for inventory control of deteriorating items since 2018. A classification including 18 classes and 33 subclasses is offered to categorize inventory control models, constraints, and solution methods used in previous studies. Providing standard classes in this field, such as demand, deterioration, shortages, number of warehouses, and time value of money alongside new classes, for example, the type of model costs and supply chain, inventory constraints, number of supply chain levels, time horizon, lead time, considering multi-item models, preservation technology, financial conditions, non-instantaneous deteriorating items, environmental issues, and solution methods made this classification more comprehensive. A brief history and explanation are given to understand each class better, and related articles are grouped in these classes. The research gaps and a crucial aspect that paves the way for future research are presented in each category. A broad view of the future of this topic is provided, and exciting opportunities are highlighted for researchers to contribute to this field and inspire them to explore these potential areas of research. The potential for future research in this subject is vast and promising; this article offers numerous opportunities for researchers to make significant contributions. The results show that the best ways to extend this topic are using variable deterioration rates, costs, and demand functions, considering realistic assumptions, including allowable shortages with partial backlogging, two warehouses, inflation and discounts, preservation technology, uncertain lead time, and environmental issues. Developing cyclic (if possible), multi-item, and production models with financial conditions and various inventory constraints is an excellent way to develop existing models. Finally, solving the proposed models using exact methods to find the global answer is a great effort to contribute to this field.

In today's fiercely competitive retail landscape, implementing effective pricing strategies is critical not only for boosting sales but also for securing a larger market share and ensuring long-term business sustainability. The ability to capture a greater share of the market directly influences a retailer's positioning and competitive edge, making pricing decisions pivotal. This paper introduces a hybrid optimization model that strategically combines Everyday Low Pricing (EDLP) and High-Low Pricing (HL) strategies, designed to address the intricacies of dynamic retail markets. The model is initially formulated as a nonlinear optimization problem aimed at maximizing sales to increase market share, all while maintaining profitability within a predefined threshold to ensure the retailer does not incur losses. To enhance the model's practical applicability, particularly in small-scale scenarios, the nonlinear problem is transformed into a Mixed-Integer Programming (MIP) model, facilitating its solvability. However, as retail applications scale up, the computational complexity becomes more challenging, necessitating the use of the Grey Wolf Optimization (GWO) algorithm. The GWO algorithm effectively balances computational efficiency with solution quality, making it a robust approach for large-scale problems. A significant contribution of this research is the linearization of the model under conditions where the products designated for High-Low pricing (referred to as 'Golden' products) are predetermined by the retailer. This linearization simplifies the computational process, enabling the model to scale and be applied in large retail settings. Developed in collaboration with a major Iranian supermarket chain, the model leverages real-world data to optimize discount levels and timing across various product categories. Extensive numerical experiments demonstrate the model's effectiveness in increasing sales, thereby contributing to a larger market share while ensuring that profitability remains within acceptable bounds. By providing actionable insights and strategic recommendations, this research offers a practical, scalable solution for optimizing retail pricing strategies in a data-driven and competitive environment, ultimately supporting retailers in their quest to dominate the market.

Heterocyclic compounds represent essential structural motifs in pharmaceuticals, agrochemicals, natural products, and functional materials. Among various synthetic approaches, organoboron compounds, particularly boronic acids and related organoboron reagents, have emerged as versatile building blocks owing to their high chemical stability, low toxicity, and excellent functional-group tolerance. In combination with transition metal catalysts, these reagents enable the efficient construction of diverse heterocyclic frameworks through C–C and C–heteroatom bond-forming transformations. This review summarizes recent advances in transition metal-catalyzed heterocycle synthesis using boronic acid-based reagents, with particular emphasis on catalytic systems, reaction mechanisms, and synthetic applications. Emerging sustainable strategies, including photocatalytic, electrochemical, and heterogeneous catalytic approaches, are also discussed. Finally, the current challenges and future perspectives of boronic acid-enabled transition metal catalysis for heterocyclic synthesis are highlighted.

Fourier Transform Infrared (FT-IR) spectroscopy is a cornerstone for verifying drug loading in nanocarriers, polymeric matrices, and formulated granules. However, researchers frequently encounter the disappearance of characteristic drug peaks in the composite spectra, often leading to misinterpretations regarding loading success. This paper critically examines the physicochemical mechanisms behind this phenomenon, including signal masking by dominant substrate bands, crystalline-to-amorphous transitions, and deep encapsulation beyond the penetration depth of ATR-FT-IR. We propose a paradigm shift in spectral interpretation: the absence of discrete drug peaks does not imply loading failure; rather, subtle alterations in the substrate's spectral signature such as shifts, broadening, or intensity variations might serve as proxies for successful functionalization.

The COVID-19 pandemic highlighted humanity's vulnerability to emerging infectious diseases and the critical need for proactive strategies. While COVID-19's immediate crisis recedes, it's crucial not to underestimate other threats like Hantavirus, which, despite lower transmissibility, carries high fatality rates and lacks specific treatments or vaccines. This paper argues that the lessons and technological advancements from the COVID-19 response offer a vital blueprint for Hantavirus mitigation. We advocate for a rapid, multi-faceted approach, mirroring COVID-19's urgency. This includes implementing personal protective measures (e.g., masks, gloves) proven effective in reducing viral transmission. Crucially, we propose leveraging the significant advancements in Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) that matured during the pandemic. These technologies can revolutionize Hantavirus drug discovery by accelerating drug repurposing from existing libraries, executing rapid virtual screening and molecular docking of vast chemical databases to identify inhibitors, and informing de novo drug design. This accelerated process demands a 'Human-in-the-Loop' (HITL) paradigm. While AI/ML/DL offer unparalleled speed in hypothesis generation, human expertise from virologists, clinicians, and public health officials remains indispensable for validating models, interpreting complex biological data, guiding experimental design, and translating insights into effective interventions. By synergizing advanced computational power with invaluable human intuition, we can proactively develop robust therapeutic and preventive strategies for Hantavirus, shifting from reactive crisis management to a data-driven defense against future viral threats.

The stochastic contextual bandit problem, recognized for its effectiveness in navigating the classic exploration-exploitation dilemma through ongoing player-environment interactions, has found broad applications across various industries. This utility largely stems from the algorithms’ ability to accurately forecast reward functions and maintain an optimal balance between exploration and exploitation, contingent upon the precise selection and calibration of hyperparameters. However, the inherently dynamic and real-time nature of bandit environments significantly complicates hyperparameter tuning, rendering traditional offline methods inadequate. While specialized methods have been developed to overcome these challenges, they often face three primary issues: difficulty in adaptively learning hyperparameters in ever-changing environments, inability to simultaneously optimize multiple hyperparameters for complex models, and inefficiencies in data utilization and knowledge transfer from analogous tasks. To tackle these hurdles, this paper introduces an innovative transfer learning-based approach designed to harness past task knowledge for accelerated optimization and dynamically optimize multiple hyperparameters, making it well-suited for fluctuating environments. The method employs a dual Gaussian meta-model strategy—one for transfer learning and the other for assessing hyperparameters’ performance within the current task —enabling it to leverage insights from previous tasks while quickly adapting to new environmental changes. Furthermore, the framework’s meta-model-centric architecture enables simultaneous optimization of multiple hyperparameters. Experimental evaluations demonstrate that this approach markedly outperforms competing methods in scenarios with perturbations and exhibits superior performance in 70% of stationary cases while matching performance in the remaining 30%. This superiority in performance, coupled with its computational efficiency on par with existing alternatives, positions it as a superior and practical solution for optimizing hyperparameters in contextual bandit settings.