This research explores the complex dynamics of a Novel Four-Dimensional Fractional Supply Chain System (NFDFSCS) that integrates a quadratic interaction term involving the actual demand of customers and the inventory level of distributors. The introduction of the quadratic term results in significantly larger maximal Lyapunov exponents (MLE) compared to the original model, indicating increased system complexity. The existence, uniqueness, and Ulam–Hyers stability of the proposed system are verified. Additionally, we establish the global Mittag-Leffler attractive set (MLAS) and Mittag-Leffler positive invariant set (MLPIS) for the system. Numerical simulations and MATLAB phase portraits demonstrate the chaotic nature of the proposed system. Furthermore, a dynamical analysis achieves verification via the Lyapunov exponents, a bifurcation diagram, a 0–1 test, and a complexity analysis. A new numerical approximation method is proposed to solve non-linear fractional differential equations, utilizing fractional differentiation with a non-singular and non-local kernel. These numerical simulations illustrate the primary findings, showing that both external and internal factors can accelerate the process. Furthermore, a robust control scheme is designed to stabilize the system in finite time, effectively suppressing chaotic behaviors. The theoretical findings are supported by the numerical results, highlighting the effectiveness of the control strategy and its potential application in real-world supply chain management (SCM).

Supply chains comprise various interconnected components like suppliers, manufacturers, distributors, retailers, and customers, each with unique variables and interactions. Managing dynamic supply chains is highly challenging, particularly when considering various sources of risk factors. This paper extensively explores dynamical analysis and multistability analysis to understand nonlinear behaviors and pinpoint potential risks within supply chains. Different phase portraits are used to demonstrate the impact of various factors such as transportation risk, quality risk, distortion, contingency reserves, and safety stock on both customers and retailers. We introduced a sliding mode control method that computes the sliding surface and its derivative by considering the error and its derivative. The equivalent control law based on the sliding surface and its derivative is derived and validated for control purposes. Our results show that the controller SMC can significantly enhance supply chain stability and efficiency. This research provides a robust framework for understanding complex supply chain dynamics and offers practical solutions to enhance supply chain resilience and flexibility.

In this paper, we investigated a three-dimensional chaotic system that models key aspects of a monetary system, including interest rates, investment demand, and price levels. The proposed system is described by a set of autonomous quadratic ordinary differential equations. We analyze the dynamic behavior of this system through equilibrium points and their stability, Lyapunov exponents (LEs), and bifurcation diagrams. The system demonstrates a variety of behaviors, including chaotic, periodic, and equilibrium states depending on parameter values. Additionally, we explore the multistability of the system and present a radial basis function neural network (RBFNN) controller design to stabilize the chaotic behavior. The effectiveness of the controller is validated through numerical simulations, highlighting its potential applications in economic and financial modeling.

Indonesia suffers significant economic losses from floods, and state budget allocations are often inadequate. Flood bonds provide an alternative funding source, but the pricing framework is complex due to simultaneous flood and financial risk considerations. Therefore, this study aims to model flood bond prices as an alternative flood funding in Indonesia. The model is formulated using the risk-neutral-pricing measure with the stochastic assumption of the force of interest. The claim trigger is represented as maximum rainfall, which is modeled as a continuous-stochastic process with a discrete-time index. Given the varying patterns of rainy and dry seasons, we assume both durations are dynamic. Then, we provide the approximate model solution for the government to estimate bond prices quickly. This estimation shows that the bond’s trigger point is proportional to the bond prices. Additionally, bond prices are proportional to the dry season duration and inversely proportional to the rainy season duration. We also show that using a stochastic force of interest yields significant differences from a constant one except for the constant as data average. This study can help the Indonesian government price flood bonds and provide more tools for related meteorological and climatological institutions to calculate the probability of future maximum rainfall.

Housekeepers mempunyai peranan penting dalam oprasional hotel karena kebersihan dan kenyamanan merupakan daya tarik hotel, terutama pada era pandemi COVID-19. Namun, pada saat pandemi banyak industri perhotelan yang mengalami penurunan nilai Tingkat Penghunian Kamar (TPK) sehingga menyebabkan beberapa hotel terpaksa tutup termasuk di wilayah Jawa Barat. Hal tersebut mengakibatkan beberapa pegawai dirumahkan termasuk Housekeepers, maka dari itu penjadwalan harus diatur kembali agar tidak ada pengurangan pegawai. Penelitian ini dimaksudkan untuk menentukan penjadwalan Housekeepers dengan pendekatan Goal Programming di era pandemi COVID-19 studi kasus di Hotel R Kota Bandung. Perbedaan pada saat pandemi COVID-19 ini terdapat pada jumlah Housekeepers yang berbeda dikarenakan ada pengurangan pada saat pandemi, sehingga dapat menambah beban kerja bagi Housekeepers lain. Hal tersebut dibuktikan dari hasil penjadwalan menggunakan software LINGO 11.0 yang disesuaikan dengan fungsi tujuan untuk meminimumkan penyimpangan pada setiap kendala. Dari hasil yang didapatkan diperoleh perbedaan dari jumlah jam kerja serta libur bagi Housekeepers sebelum dan sesudah pandemi COVID-19 akan tetapi memiliki penyimpangan sama dengan nol.

Economic evaluation of Battery Energy Storage Systems (BESSs) integrated with solar photovoltaic (PV) systems requires the consideration of financial performance, operational behavior, battery degradation, reliability, and uncertainty. This PRISMA-based systematic review synthesizes mathematical and decision-making approaches relevant to Fuzzy Soft Set-based PV–BESS evaluation. Searches in Scopus, ScienceDirect, SpringerLink, and Taylor & Francis Online identified 789 records published between 2015 and 2026, and 74 studies were included. All studies were coded by theme, method, and economic and technical criteria, while 15 representative studies were analyzed at the equation level. Six model families were identified: techno-economic valuation, multi-objective planning, operational energy management, reliability assessment, fuzzy and multi-criteria decision-making, and degradation-aware optimization or control. Most studies appeared in 2024–2026 (57/74; 77.0%), while keyword analysis highlighted decision making and battery storage. Common formulations included NPV, NPC, LCOE, IRR, payback period, market revenue, battery capacity fade, equivalent full cycles, degradation cost, LPSP, LOLE, EENS, Monte Carlo simulation, and fuzzy ranking methods. These components remain fragmented across separate frameworks. Fuzzy Soft Set is therefore positioned as a potential parameterized decision-support layer for integrating heterogeneous outputs, subject to further theoretical development and empirical validation. This direction is relevant to Sustainable Development Goal 7 (Affordable and Clean Energy).