Emissions reduction strategies are essential for sustaining both economic competitiveness and environmental responsibility. This study develops an optimization model for inventory management in Bangladesh’s refrigerator retail sector, integrating profitability with sustainability objectives. The proposed model uniquely incorporates defective items, bulk purchase discounts, and multiple carbon regulation schemes, namely carbon tax (CT), cap-and-trade (CAT), cap-and-price (CAP), and carbon offset (CAO). It determines the optimal replenishment cycle and investment level in emissions control technologies that maximize annual profit under regulatory constraints. The inventory system is formulated as a constrained nonlinear optimization problem and is analytically solved using Karush-Kuhn-Tucker (KKT) conditions to obtain closed-form solutions. A customized algorithm is developed to identify the global optimum under an all-units discount (AUD) pricing scheme. A case study based on realistic parameters from the Bangladeshi refrigerator market validates the proposed model. Sensitivity analyses reveal that holding costs and transportation fuel consumption significantly influence both profit and emissions. Green investments improve performance under strict regulatory environments; however, diminishing returns indicate inherent technological limitations. Notably, a 50% reduction in holding costs increases profit by 2.72% and extends the optimal cycle by 29.42%. The findings underscore the importance of advancing green technologies and aligning inventory policies with environmental objectives.

The transition to sustainable energy is vital to curb emissions while meeting rising demand. Yet solar, wind, and hydropower are variable and stochastic, complicating reliable grid integration. This study asks a central question: how can hybrid energy storage be optimally integrated with renewables under extreme weather to improve resilience, efficiency, and sustainability? This study develop a comprehensive mathematical framework that co-optimizes battery, hydrogen, and thermal energy storage using advanced stochastic methods. Uncertainty in renewable availability, weather shocks, and demand surges is modeled with probabilistic resilience metrics derived from Generalized Extreme Value and Generalized Pareto distributions, enabling risk-aware dispatch. The framework also enforces carbon-emission limits and renewable-penetration targets aligned with current sustainability policies and market constraints. A rural-India case study evaluates performance across stress scenarios. Results show improved resource allocation, higher reliability, lower curtailment, and credible pathways toward carbon-neutral operation even during rare, high-impact events. Overall, the approach delivers robust operations under high renewable variability and provides actionable guidance for policymakers, utilities, and planners designing resilient, efficient, low-carbon power systems. Implementation details include scenario-based optimization, chance constraints for reliability, and multi-period dispatch scheduling, ensuring practical applicability and scalability for diverse geographies and grid conditions.

This study considers a stochastic inventory relocation problem for a one-way, station-based car-sharing system that utilises electric vehicles (EV), where customers' rental demands and rented vehicles' travel distances are uncertain and temporal-spatial imbalance. Workers are hired to relocate vehicles between stations. To maximise the total expected profits that can be collected by the system, a worker must determine whether to relocate an EV, which vehicle to choose, and which station to move the EV to upon their arrival at a rental station. The problem is formulated as a Markov decision process (MDP). A reinforcement learning algorithm is proposed to develop dynamic policies for the problem. The reinforcement learning algorithm uses an approximate value iteration (AVI) algorithm to overcome the computational challenges arising from the extensive state and action space. Action-space restriction and state-space aggregation schemes are developed to enhance the performance of the AVI algorithm. The effectiveness of the proposed modelling and solution methodologies is demonstrated through a comparison of the dynamic policies against benchmark solutions. Additionally, sensitivity analyses are conducted to investigate whether parameter configurations will impact the performance of the dynamic policies.

Supply chain disruptions challenge the reorganisation of the supplier selection and order allocation problems (SS&OA). Disruptions of the primary logistics service provider (PLSP) have significant effects on the customised logistics service supply chain. Therefore, establishing reactive response mitigation strategies for disruptions is of practical importance. This work develops a model that considers price discounts for SS&OA under disruptions and introduces the customer order decoupling point (CODP) to differentiate between mass service and customised service. From the perspective of reducing costs and accelerating the supply chain response process, we address disruptions by constructing different response strategy models including the remaining service capacity strategy (RSCS), the backup logistics service provider strategy (BLSP) and the combined RSCS&BLSP strategy. We then compare them with the no-option strategy (NOS). This paper provides resilient strategies that match the characteristics of disruptions and an expert toolbox that can handle disruptions in real time. Logistics managers can therefore apply an appropriate strategy on the basis of the disruption parameters, i.e., for a single disruption, they can select the BLSP or the combined RSCS&BLSP strategy, whereas for combined disruptions, the combined RSCS&BLSP strategy can be selected.

This study introduces a variant of the drone delivery problem called the heterogeneous fleet drone delivery problem with lateness considerations. The problem considers a fleet of drones that differ in speed, payload capacity, and battery endurance, with the objective of minimizing the total lateness, computed by considering each customer's scheduled delivery time for all customers. Two mixed integer linear programming formulations are proposed to find the optimal solution of the stated problem. The mathematical models vary in the number of variables and constraints, and their effectiveness is compared using test instances generated in this study. In addition to the mathematical models, a metaheuristic algorithm based on modifications to the farmland fertility algorithm (FFA) is developed. This metaheuristic incorporates two local search methods with different encoding schemes. Comparative experiments indicate that the proposed Modified-FFA outperforms both the Classical-FFA and simulated annealing in solution quality. The results also show that local search methods with different encoding schemes exhibit distinct performances, highlighting the impact of encoding choices on efficiency. Furthermore, a comprehensive sensitivity analysis is also conducted to explore the effects of drone-related parameters, such as payload and velocity, on the total weighted lateness.

The rapid advancement in IoT-based industrial systems emphasizes maximizing important quality parameters like reliability, latency, and throughput. While previous researches focus on individual factors, an integrated approach is required to assess the influence of Six Sigma on the quality of service (QoS) in IoT environments. The present study leverages Six Sigma's DMAIC process to drive significant improvements in the quality of service for IoT systems, explicitly targeting critical sectors such as manufacturing, logistics, and energy. This research used a quasi-experimental design involving 30 IoT systems before and after implementing Six Sigma. Based on the Stratified Random Sampling method, basic information on QoS issues, was captured in milliseconds, percentage, and Mbps, respectively. Qos results have shown significant improvement of 22% in latency, 15% in system reliability, and 25% in throughput rates. The practical implications of this research are significant, involving greater productivity, rationalization of costs, and enhanced competitive advantage for industries dependent on IoT networks, thus successfully demonstrating the value of Six Sigma in managing industrial systems.

ABSTRACT Previous studies have established the critical role of indium in flat panel displays (FPD) due to its low melting point, superconductivity, and transparency. However, these studies have often overlooked the complex interdependencies between indium and FPD supply networks and the dynamic risk propagation mechanisms that emerge from such interconnections. To the best of our knowledge, this study is the first to integrate an interdependent network modeling approach to simultaneously analyze the structural and dynamic vulnerabilities in the interdependent indium and FPD supply networks. From a static perspective, strong interdependence exists between the two networks. Key indium nodes are located in Hunan, Guangdong, and Zhejiang; FPD nodes are located in Guangdong, Zhejiang, Shanghai, and Jiangsu. Trade dependencies reveal critical regional interconnections contributing to systemic risk. From a dynamic perspective, we simulate four disruption scenarios: supply-side shocks, demand-side crises, synergistic supply-demand failures, and cooperation breakdowns. Risk sources show distinct regional patterns, and strengthening key nodes nonlinearly improves overall resilience. The interdependent networks resist demand disruptions better than supply disruptions, while cooperation risks pose intermediate vulnerability. These findings offer data-driven insights for pinpointing high-risk entities, prioritizing interventions, and designing early warning systems to bolster the resilience of global supply networks.

In real-world multi-attribute decision-making (MADM), understanding the inter-relationships and hierarchical structures among factors is paramount. Once established, the focus shifts to optimizing the goal attribute by addressing these factors effectively. Some practical decision domains, such as the healthcare system, education system, business sectors, etc. need continuous evaluation and modification of the optimal strategies to achieve the best results. This paper proposes a multistep self-regularized strategy for assigning effort to the factors in long-term continuous decision-making problems. In successive time steps, the strategy considers the higher-level factors for the effort allocation on a reducing-level basis. The strategy also uses the concept of feedback from upper-level factors to the lower-level factors in the underlying hierarchy. Two self-regularization parameters are used to trade-off between different heuristics associated with the factors for the effort assignment. The strategy is analyzed in a case study involving administrative factors in high schools that contribute to improving students' performance. A comparative analysis is performed between the proposed strategy and existing single-step strategies. The highest effort propagation achieved in the strategy is approximately 21.7143% when the feedback is used, whereas it is 14.7535% when the feedback is not considered. This underscores the importance of incorporating feedback in any continuous and long-term decision-making process.

ABSTRACT Effective flood disaster management requires strategic coordination of volunteer resources to ensure a timely, efficient, and adaptive response in large-scale, unpredictable scenarios. This study proposes an Adaptive Two-Tier Optimization (ATTO) model, integrating ArcGIS spatial analysis with an enhanced Multi-Strategy Grey Wolf Optimization Algorithm (MSGWOA) for volunteer scheduling. The model optimizes volunteer and disaster victim satisfaction at the upper level while minimizing response times and resource allocation costs at the lower level. MSGWOA employs hybrid metaheuristic strategies and adaptive local search, improving efficiency in handling disaster uncertainties. Empirical validation in real flood scenarios in Guangzhou confirms the model's computational efficiency and practical applicability. Results show stable response times for real-time decision-making and cost-effectiveness while maintaining high volunteer (82.1%–92.5%) and disaster victim satisfaction (78.5%–89.7%). Comparative analysis against Grey Wolf Optimization (GWO) and other benchmarks demonstrates MSGWOA's superior efficiency, adaptability, and resource allocation. This study presents a scalable, data-driven decision-support framework for real-time volunteer scheduling. By integrating spatial intelligence with advanced optimization techniques, the model enhances disaster response operations, ensuring an adaptive, cost-efficient, and fair allocation of resources for rapid intervention in flood-affected regions.

Blood is collected from donors through blood donation centres and mobile blood donation vehicles, converted into blood products and delivered to hospitals and large health centres. Within the scope of this study, we aim to increase the amount of blood collected from donors through the correct determination of the location of blood supply chain components, optimal routing of mobile components, deliver the required blood and blood products to those in need at a higher rate, and reduce total operational costs while doing so. In this context, a mixed integer mathematical model is proposed, and two heuristic solution methods are developed for large-sized examples. The proposed model and heuristic solution methods are applied to the current Turkish Red Crescent, and the two heuristics developed respectively give 19% and 21% more cost effective solutions compared to the current situation. In addition, the amount of blood donations collected with the proposed methods has increased by 6.76% compared to the current situation, and it has been possible to respond to the currently unresponsive blood and blood product demands (4% of the current demand) with the proposed methods.