Developing responsive dynamic marketing strategies can be challenging in the absence of complete customer information, such as share of wallet, limiting the ability of the firm to target promotions and other marketing efforts with a view to optimizing customer lifetime value (CLV). Furthermore, much of the existing research on CLV treats customers as receivers, rather than co-creators, of services. We address these two key challenges by developing a reinforcement learning (RL)-based promotion optimization model to determine which promotion strategies are most suitable for targeting different customer groups. Specifically, using feedback derived from customers’ real-time transactional responses to promotional campaigns, we present an RL algorithm that (a) continually refines the estimated effectiveness of promotions, aligning them to customers’ preferences to maximize their CLV, and that (b) supports value co-creation by involving customers as active participants to enhance their service experience. We demonstrate the effectiveness of the model through simulation scenarios within the context of a ferry travel agency, providing evidence of its real-world potential.

Accurate streamflow modeling in data-scarce catchments remains a significant challenge due to the limited availability of historical records. Transfer Learning (TL), increasingly applied in hydrology, leverages knowledge from data-rich catchments (sources) to enhance predictions in data-scarce catchments (targets), providing new possibilities of hydrological predictions. Most existing TL approaches pre-train models on large-scale meteoro-hydrological datasets and show good generalizability across multiple target catchments. However, for a specific target catchment, it remains unclear which source catchments contribute most effectively to the accurate prediction. Including many irrelevant sources may even degrade model performance. In this study, we investigated how source catchment selection affects TL performance by employing similarity-guided strategies based on three key factors, i.e., spatial distance, physical attributes, and flow regime characteristics. Using the CAMELS-GB dataset, we conducted comparative experiments by pre-training the networks with different ranked groups of the source catchments and fine-tuning them on three target catchments representing distinct hydrological environments. The results showed that carefully selected small subsets (fewer than 40, or even as few as 10) of highly similar catchments can achieve comparable or better TL performance than using all 668 available source catchments. All three target catchments yielded better NSE results from source catchments with closer spatial proximity and more consistent flow regimes. The TL performance of physical attribute similarity-based selection varied depending on the attribute combinations, with those related to land cover, climate, and soil properties leading to superior performance. These findings highlight the importance of similarity-guided source selection in hydrological TL. In addition, they demonstrate ways to reduce computational costs while improving modeling accuracy in data-scarce regions.

Resource flexibility and dynamic pricing are effective strategies in mitigating uncertainties in production systems but have yet to be explored in relation to the improvement of field operations services. We investigate the value of dynamic pricing and flexible allocation of resources in the field service operations of a regulated monopoly providing two services: installations (paid-for) and maintenance (free). We study the conditions under which the company can improve service quality and the profitability of field services by introducing dynamic pricing for installations and the joint management of the resources allocated to paid-for (with a relatively stationary demand) and free (with seasonal demand) services when there is an interaction between quality constraints (lead time) and the flexibility of resources (overtime workers at extra cost). We formalize this problem as a contextual multi-armed bandit problem to make pricing decisions for the installation services. A bandit algorithm can find the near-optimal policy for joint management of the two services independently of the shape of the unobservable demand function. The results show that (i) dynamic pricing and resource management increase profitability; (ii) regulation of the service window is needed to maintain quality; (iii) under certain conditions, dynamic pricing of installation services can decrease the maintenance lead time; (iv) underestimation of demand is more detrimental to profit contribution than overestimation.

Leaky barriers mimic the natural accumulation of large wood in watercourses to effectively slow and store runoff and flow. Their role in flood management has attracted increasing attention due to their potential to reduce downstream risk. Numerous field studies have demonstrated the effectiveness of leaky barriers in retaining flood water in upstream catchment. However, their hydraulic behaviour remains poorly quantified due to limited empirical data and the modelling challenges. This review systematically investigates and synthesises research conducted over the past five years on the hydraulic behaviour and numerical representation of leaky barriers, while also drawing on earlier relevant studies to provide broader context. Additionally, it summarizes key hydraulic parameters, empirical equations, and modelling approaches that are used to characterise these structures. Furthermore, this review highlights the challenges of modelling individual leaky barriers in the field, which complicate their structural design and implementation. Future research should investigate the long-term performance of leaky barriers and explore optimal placement strategies to enhance flood mitigation within a catchment.

Companies are under increasing legal and societal pressure to reduce CO₂ emissions from their delivery vehicles, while maximizing profit remains their prime objective. We study a problem where a company sends engineers with vehicles to customer sites to provide services. Customers request the service at their preferred time windows through a website or by calling a call center, and the company needs to allocate these service tasks to time windows and decide on how to schedule these tasks among its vehicles. We propose an approach to this problem that applies low-emission vehicle-scheduling techniques with dynamic pricing to reduce CO₂ emissions and maximize profit. When a customer requests a service with a preferred time window, the company will provide the customer with different service time window options and their corresponding prices. Incentives are included in the prices to influence the customers to reduce CO₂ emissions. Our approach solves the problem in two phases: the first phase solves time-dependent vehicle scheduling models with the objective of minimizing CO₂ emissions, and the second phase solves a dynamic pricing model to maximize profit. Results show that our approach significantly reduces CO₂ emissions and increases profits. A GenAI-based interpretation tool is used to translate the optimization outputs into actionable guidance for planners.