Learning When Demand Is Unknown Airlines launching new routes cannot wait for years of historical data before deciding how many seats to offer at each fare or what prices to charge. They must learn when operating, often from incomplete feedback: selling 30 economy plus seats does not reveal whether demand was 30, 40, or more. In the accepted Operations Research paper “Semibandit Learning for Monotone Stochastic Optimization,” Rohan Ghuge, Zhengjia Zhuo, Arpit Agarwal, and Viswanath Nagarajan develop a general framework for such decisions. They identify a basic property, monotonicity, that allows an optimization method built for known demand distributions to become a learning algorithm for unknown distributions. The framework applies to airline and hotel revenue management, sequential posted pricing, and optimal-stopping problems. Even with partial, censored, or yes-or-no feedback, its cumulative performance gap grows essentially at the square root rate, best possible up to logarithmic factors. Firms can, therefore, improve decisions as data arrive without pausing operations for a separate data-gathering phase.
This investigation focuses on modeling the performance of an M/G/1/K fault-tolerance machining system (FTMS) that incorporates the general vacation feature under threshold N-policy, along with handling failure recovery and reboot processes. In the system design, the repairman has the flexibility to take a vacation only when no failed machines are present in the system. The repairman returns from vacation and resumes repair operations when the number of failed machines reaches the specified threshold value N. In cases where faults in the system are not successfully detected, the system reconfigures itself by initiating a reboot process. To develop the mathematical model, supplementary variables representing the remaining repair and vacation times are employed to formulate the steady-state Chapman–Kolmogorov equations. These equations are the foundation for deriving explicit formulae for the probability distributions using the Laplace-Stieltjes transform (LST) and the recursive method. The resulting system state probabilities are then utilized to establish various performance measures. The study conducts a sensitivity analysis to explore the influence of different parameters on performance measures. Furthermore, we construct the expected cost function and formulate an optimization problem to determine the minimum cost. The particle swarm optimization (PSO) is employed to solve the optimization problem, and the results are compared with those obtained by implementing a sine cosine algorithm (SCA) to find the optimum cost. The proposed model demonstrates its practical usefulness in industrial settings, especially in the electrical sector for the power supply system (PSS) and the production sector for the plastic bottle processing hub (PBPH).
Live-streaming serves as an effective means to promote the sales of fresh agricultural products. Farmers cooperate with streamers for live-streaming through the direct or resale live-streaming strategy. The third-party logistics service provider offers cold-chain services to maintain the freshness of agricultural products. Meanwhile, the government provides subsidies to farmers, streamers, or consumers. Motivated by this background, this study investigates a farmer’s choice of live-streaming strategy and its impact on other supply chain members. We find that, first, when streamer traffic is high, the farmer should adopt the resale live-streaming strategy. When streamer traffic is low, the farmer should adopt the direct live-streaming strategy if the government subsidizes farmers or consumers; if the government subsidizes streamers, the farmer’s decision on live-streaming strategy is also affected by unit subsidy. Second, the high-traffic streamer always prefers the resale live-streaming strategy, whereas the low-traffic streamer’s preference for the live-streaming strategy depends on the government subsidy policy. Finally, if streamer traffic is low (high) and subsidy is low, the government can promote the sales of fresh agricultural products, increase the farmer’s profit, and improve social welfare by subsidizing farmers (streamers); otherwise, subsidizing consumers may improve social welfare, but it is not conducive to the sales of fresh agricultural products or farmer’s profit. Furthermore, we conduct an extension study with an endogenous subsidy to verify the robustness of the main results.
The financing environment for fisheries enterprises is fraught with volatility, shaped by financial market dynamics, policy shifts, and industry-specific trends. This study addresses the critical need for a systematic and resilient approach to financing risk management in fisheries, a sector particularly vulnerable to resource depletion, policy constraints, and market fluctuations. The research identifies gaps in current enterprise risk management practices, which often fail to mitigate the complex risks faced by these enterprises, particularly in the context of global disruptions such as the U.S.-China trade war, the COVID-19 pandemic, and geopolitical conflicts. To bridge this gap, the study proposes a hybrid evaluation method, integrating the belief distribution, the generalized combination rule, the decision-making trial and evaluation laboratory, and the additive ratio assessment, grounded in the Committee of Sponsoring Organizations of the Treadway Commission framework’s eight components. The method incorporates expert opinions to assess and enhance the quality and effectiveness of risk management processes. The findings underscore the necessity of a comprehensive, resilient management framework that addresses the unique challenges of the fisheries sector. This study not only advances the theoretical understanding of enterprise risk management but also offers practical guidance for improving risk management practices, contributing to the sustainability and green transition of the marine economy.
This paper examines how decision-making sequences influence pricing and advertising strategies in a dual-brand supply chain consisting of a national brand (NB) manufacturer and a retailer offering a competing store brand (SB). The manufacturer engages in national advertising, while the retailer conducts local advertising, creating asymmetric promotional effects. We develop a game-theoretic model under a Stackelberg structure to analyze three strategic scenarios: simultaneous decision-making, advertising-first, and pricing-first sequences. The results show that the decision timing significantly influences equilibrium outcomes, profit distribution, and market competition intensity. In particular, simultaneous decision-making maximizes the manufacturer’s profit and overall system performance, whereas pricing-first strategies provide greater stability for the retailer under strong advertising spillover effects. Sensitivity analyses further demonstrate that advertising effectiveness plays a critical role in shaping optimal strategies and profit trade-offs. These findings offer both theoretical insights and practical implications for firms managing national and store brand competition under advertising interactions.
