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Cloud manufacturing (CMfg) emerges as a promising manufacturing paradigm, where service composition (SC) is a critical process concentrating on matching tasks and services. Existing studies usually ignore the dynamic nature of the CMfg environment, where task information is not always known before. Moreover, CMfg services are re-entrant, i.e. after being occupied for a period of service time, these services re-enter the CMfg platform (i.e. be available again). Re-entrant services significantly complicate CMfg platform revenue management. In this regard, we study the dynamic SC problem of CMfg (CMfg-DSC) incorporating re-entrant services within an online setting for the first time. CMfg-DSC is reformulated as an online packing problem. If a task is accepted, each requested service will be occupied until service time terminates. We propose online policies with performance guarantees, namely, static online packing policy (Static), opportunity-cost-based policy (Oppo), and dual-based policy with/without known distribution (Dual-k & Dual-u). Experiment results show that (1) Static is applicable for most cases; (2) Oppo has the potential for decent performance but at the cost of time; (3) Dual-u is reliable when only past observations are available; (4) Dual-k performs well given abundant service provision, but its performance would deteriorate if we lower the reward-cost threshold. [ABSTRACT FROM AUTHOR]

Digitalization is constantly altering company paradigms and expanding cross-border supply chain prospects. Maritime transportation plays an increasingly essential part in the global supply network. Since maritime shipping services need to exchange a huge number of papers and paperwork across numerous companies, the usage of a unified platform for inter-organisational communication and information sharing is required. To develop an integrative, adaptive, and intelligent container booking system, a multi-agent architecture is designed in this article. The proposed architecture will aid the maritime industry in establishing real-time information interchange between autonomous agents, shippers, freight forwarders, and shipping lines. The process outlined in this paper reveals how the agents communicate with one another to resolve underlying inconsistencies. With the multi-agent framework, the article also presents a container slot optimisation problem considering market segmentation, different booking periods, heterogeneous containers and port congestion scenarios. Using this model the managers can find the booking limit for each type of containers and accordingly they can accept or reject the incoming booking requests. Furthermore, a simulated case study is also provided to validate the model. [ABSTRACT FROM AUTHOR]

Blockchain (BC) applications in supply chain management (SCM) have recently received extensive attention. It is important to synthesise the extant literature on the field to identify key research themes and navigate potential future directions. This study thus develops an efficient, scalable data-driven review approach that uses text mining and Latent Dirichlet Allocation (LDA)-based topic modelling for automatic content analysis of full-text documents. Our method overcomes the drawbacks of traditional systematic literature reviews using either manual coding or bibliographic analysis for article classifications, which are highly time-consuming and biased when dealing with large amounts of text. 108 papers published between 2017 and 2022 were analysed which identified 10 key research themes, including revenue management, sustainability, traceability, manufacturing system, scheduling in cloud manufacturing, healthcare SCM, anti-counterfeit system, logistics and transportation, system architecture development, and food & agriculture SC. Five future directions are then suggested, including (1) integration of BC and other emerging technologies for global and scalable SCM, (2) crypto-X applications in SCM, (3) BC-enabled closed-loop SCM, (4) the environmental and social impacts of BC-based SCM and (5) decentralised autonomous organisations in SCM. [ABSTRACT FROM AUTHOR]

Inland waterway transportation plays a crucial role in Europe's transportation network and economy. It is an efficient and sustainable mode of transportation, with lower emissions and energy consumption than other modes of transportation, such as road and air. However, the services provided by inland waterway transport can be significantly impacted by adverse weather conditions such as heavy rain, strong winds, and flooding. These disruptions can cause delays, cancellations, or even damage to vessels or infrastructure. To improve the system reliability, we propose a set of revenue management based (demand itinerary) rerouting mechanisms for intermodal barge transportation optimisation. Revenue Management policies including several customer categories and fare differentiation are applied. Sequential accept/reject decisions are made based on a probabilistic mixed integer programming model maximising the expected revenue of a carrier. A booking framework is defined over a rolling horizon and capacity allocation/reallocation decisions are made for a set of demands including the current and relevant past and potential future transportation requests. Several (demand) rerouting mechanisms are defined and implemented on different service network configurations. The service network status is regularly updated, in particular with respect to barge capacity variations due to changing water levels. An extensive set of experiments is performed and numerical results are analysed. The study results emphasise the added value but also the need for data availability and information sharing between the different stakeholders of Inland Waterway Transportation systems. [ABSTRACT FROM AUTHOR]

It is a common practice that business and economy compartments share the seating capacity of a short or medium-haul European flight, with the middle seats in the business compartment kept vacant to enhance comfort and privacy for business passengers. The two compartments are separated by a movable curtain whose position determines the capacity allocated to each compartment. By postponing the decision on curtain position until departure, airlines gain extra flexibility in their capacity allocation. We formulate an airline's problem of dynamically determining the curtain position and the subset of fare products to offer in each compartment to maximize the combined revenue from business and economy passengers. We show that the airline should offer a business (resp., economy) fare product if and only if its fare exceeds a business (resp., economy) threshold or bid price. We then establish monotonicity properties which are useful in expediting the computation of the optimal policy. An extended model where the airline has the option to offer a free upgrade (from economy to business class) to a new arriving economy passenger is presented, and a necessary condition on when to offer upgrades is provided. Using a large-scale and real-life case study based on a proprietary dataset, we demonstrate significant revenue improvements from postponing the decision on curtain position, compared to the current practice of fixing it at the beginning of the booking horizon. Furthermore, we provide guidance for the implementation of our policy, taking into account passengers' seat selection and substitution towards lower fare classes. We also discuss the implications of our work in practice. [ABSTRACT FROM AUTHOR]

Traditional revenue management relies on long and stable historical data and predictable demand patterns. However, meeting those requirements is not always possible. Many industries face demand volatility on an ongoing basis, an example would be air cargo which has much shorter booking horizon with highly variable batch arrivals. Even for passenger airlines where revenue management (RM) is well-established, reacting to external shocks is a well-known challenge that requires user monitoring and manual intervention. Moreover, traditional RM comes with strict data requirements including historical bookings (or transactions) and pricing (or availability) even in the absence of any bookings, spanning multiple years. For companies that have not established a practice in RM, that type of extensive data is usually not available. We present a data-driven approach to RM which eliminates the need for demand forecasting and optimization techniques. We develop a methodology to generate bid prices using historical booking data only. Our approach is an ex-post greedy heuristic to estimate proxies for marginal opportunity costs as a function of remaining capacity and time-to-departure solely based on historical booking data. We utilize a neural network algorithm to project bid price estimations into the future. We conduct an extensive simulation study where we measure our methodology's performance compared to that of an optimally generated bid price using dynamic programming (DP) and compare results in terms of both revenue and load factor. We also extend our simulations to measure performance of both data-driven and DP generated bid prices under the presence of demand misspecification. Our results show that our data-driven methodology stays near a theoretical optimum (< 1% revenue gap) for a wide-range of settings, whereas DP deviates more significantly from the optimal as the magnitude of misspecification is increased. This highlights the robustness of our data-driven approach. [ABSTRACT FROM AUTHOR]

Wheat head identification and localization are crucial for field management and yield estimation. Traditional wheat head detection relies on labor-intensive manual counting. To address this issue, this study establishes a densely populated wheat head dataset and correspondingly proposes an efficient, rapid real-time model of CB-YOLO, which incorporates a Convolutional Block Attention Module (CBAM) mechanism (a sophisticated fusion of spatial attention mechanism and channel attention mechanism) so as to integrate channel and spatial features. Also, with an incorporated Bidirectional Feature Pyramid Network (BiFPN) to the neck, crowded wheat head detection was enhanced by establishing connections from the backbone to the bottom-up path. This enhancement surpasses the performance of the basic YOLOv5L model, achieving AP50 of 94.3% and AP50:95 of 53.9%. Among 10 classical detection models, CB-YOLO has a relatively small parameter size of 44Mb and high computing speed of 108G FLOPs. Additionally, ablation experiments demonstrate that compared to YOLOv5L without CBAM and BiFPN, the feature fusion module improves AP50 from 93.2% to 94.3%, indicating its effectiveness in enhancing detection performance. In conclusion, CB-YOLO exhibits outstanding identification performance, showing its feasibility in practical agricultural applications, and future work will focus on improving its localization ability. [ABSTRACT FROM AUTHOR]

Revenue management for passenger rail transportation has a vital role in the profitability of public transportation service providers. This study proposes an intelligent decision support system by integrating dynamic pricing, fleet management, and capacity allocation for passenger rail service providers. Travel demand and price-sale relations are quantified based on the company's historical sales data. A mixed-integer non-linear programming model is presented to maximize the company's profit considering various cost types in a multi-train multi-class multi-fare passenger rail transportation network. Due to market conditions and operational constraints, the model allocates each wagon to the network routes, trainsets, and service classes on any day of the planning horizon. Since the mathematical optimization model cannot be solved time-efficiently, a fix-and-relax heuristic algorithm is applied for large-scale problems. Various real numerical cases expose that the proposed mathematical model has a high potential to improve the total profit compared to the current sales policies of the company. [ABSTRACT FROM AUTHOR]

A field experiment was conducted during rabi season (September to January) of 2021 and 2022 at the experimental farm of Integral Institute of Agricultural Science and Technology, Integral University, Lucknow, Uttar Pradesh, India. The objective of the experiment is to find out the most efficient combination of different organic and inorganic sources of nutrients to increase the growth, yield characteristics of cauliflower. Total nine numbers of treatments along with the control treatment were assessed in Randomized Block Design with three numbers of replications. In view of, effect of different organic and inorganic sources of nutrients has observed significant variation. The result revealed that the growth parameters like plant height (5.57, 12.47, 19.77 cm), number of leaves (7.97, 9.53, 13.7), length of leaves (17.30, 24.73, 31.73 cm), width of leaves (7.17, 10.20, 15.10 cm), plant spread (285.50, 806.93, 1212.83 cm) at 20, 40 and 60 days respectively, yield parameters like weight of curd (893.73 g), yield of curd plot-1 (13.40 kg), yield of curd ha-1 (25.30 t) were observed higher in 75% N through RDF+25% N through FYM+Azotobacter @ 5 kg ha-1 (T4). INM treatments showed lesser result on growth and yield of cauliflower in T1 (control). The treatment T4 were the most beneficial treatment which may be followed for commercial cauliflower cultivation on large scale. Thus, farmers are suggested to apply 75% N through RDF+25% N through FYM+Azotobacter @ 5 kg ha-1 to increase growth and yield of cauliflower. [ABSTRACT FROM AUTHOR]

In spite of its widespread applicability in learning theory, probability, combinatorics, and in various other fields, the Mallows model has only recently been examined from revenue management perspectives. To our knowledge, the only provably-good approaches for assortment optimization under the Mallows model have recently been proposed by Désir et al. (Oper Res 69(4):1206–1227, 2021), who devised three approximation schemes that operate in very specific circumstances. Unfortunately, these algorithmic results suffer from two major limitations, either crucially relying on strong structural assumptions, or incurring running times that exponentially scale either with the ratio between the extremal prices or with the Mallows concentration parameter. The main contribution of this paper consists in devising a quasi-polynomial-time approximation scheme for the assortment optimization problem under the Mallows model in its utmost generality. In other words, for any accuracy level ϵ > 0 , our algorithm identifies an assortment whose expected revenue is within factor 1 - ϵ of optimal, without resorting to any structural or parametric assumption whatsoever. Our work sheds light on newly-gained structural insights surrounding near-optimal Mallows-based assortments and fleshes out some of their unexpected algorithmic consequences. [ABSTRACT FROM AUTHOR]

Demand uncertainty and seller competition are substantial challenges for online platforms. In "To Interfere or Not To Interfere: Information Revelation and Price-Setting Incentives in a Multiagent Learning Environment," Birge, Chen, Keskin, and Ward analyze whether and how an online platform should offer demand information or price incentives to the sellers participating on the platform. The authors show that, when facing uncertain demand, the platform could be better off by doing nothing—that is, not providing any information or incentives to the sellers. They also develop a strategic reveal-and-incentivize policy for the platform to choose when to start sharing information and offering rewards to coordinate the sellers' pricing. They prove that the strategic reveal-and-incentivize policy achieves near-optimal profit performance for the platform. We consider a platform in which multiple sellers offer their products for sale over a time horizon of T periods. Each seller sets its own price. The platform collects a fraction of the sales revenue and provides price-setting incentives to the sellers to maximize its own revenue. The demand for each seller's product is a function of all sellers' prices and some customer features. Initially, neither the platform nor the sellers know the demand function, but they can learn about it through sales observations: each seller observes its own sales, whereas the platform observes all sellers' sales as well as the customer feature information. We measure the platform's performance by comparing its expected revenue with the full-information optimal revenue, and we design policies that enable the platform to manage information revelation and price-setting incentives. Perhaps surprisingly, a simple "do-nothing" policy does not always exhibit poor revenue performance and can perform exceptionally well under certain conditions. With a more conservative policy that reveals information to make price-setting incentives more effective, the platform can always protect itself from large revenue losses caused by demand model uncertainty. We develop a strategic reveal-and-incentivize policy that combines the benefits of the aforementioned policies and thereby achieves asymptotically optimal revenue performance as T grows large. Funding: This work was supported by Duke University Fuqua School of Business, University of Chicago Booth School of Business, and CUHK Business School. Supplemental Material: The e-companion is available at https://doi.org/10.1287/opre.2023.0363. [ABSTRACT FROM AUTHOR]

We study a variation of the classical Pandora's problem in which a decision maker (DM) sequentially explores alternatives from a given set and learns their values while trying to acquire the best alternative. The variations in the model we study are (i) alternatives randomly become unavailable during exploration and (ii) the DM's ability to acquire a remaining alternative is uncertain and depends on a chosen offer price. Such acquisition uncertainties arise in many applications, including housing search, hiring problems, and e-commerce, but greatly complicate the search problem in that optimal policies retain all previously explored alternative values as part of the problem state, as opposed to only the highest explored value as in Pandora's rule. Our central insight is that despite the complexity that these acquisition uncertainties create, simple greedy policies based on static sequencing and a single threshold value enjoy strong performance guarantees. We develop such a class of policies and show how to compute them using a greedy algorithm whose worst-case run-time scales linearly (up to logarithmic factors) in the number of alternative types. We show that our policies (a) are asymptotically optimal in high multiplicity regimes with many alternatives and (b) obtain at least 1−e−1≈63.2% of the optimal value under a broad set of conditions. Extensive numerical examples support this theory: We illustrate our policies on a variation of Pandora's problem with disappearing alternatives and housing search on models calibrated on data from the online brokerage Redfin. In these examples, our policies significantly outperform policies based on Pandora's rule. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.00203. [ABSTRACT FROM AUTHOR]

This paper revisits the descending clock "reverse" auction design used in the U.S. Federal Communications Commission's 2016–2017 "incentive auction." We use extensive computational simulations to investigate the quantitative significance of various aspects of the design, leveraging a reverse auction simulator and realistic models of bidder values. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: This work was supported by the Defense Advanced Research Projects Agency [Grant FA8750-19-2-0222 CFDA\# 12.910], the Canadian Institute for Advanced Research [Canada AI Research Chair at the Alberta Machine In], the National Science Foundation [Grant 1525730], and the Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada [Discovery Grant, Discovery Grant Supplement]. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2020.02489. [ABSTRACT FROM AUTHOR]

The lack of a comprehensive perspective on municipal revenue sources within the framework of the country's macroeconomic system, alongside occasional interventions and the implementation of self-reliance and self-sufficiency policies for municipalities, has led to instability in the revenue sector of municipalities. The restriction of legal and reasonable avenues for generating municipal income has resulted in a heavy dependence on urban construction and the sale of excess building density. The continuation of this situation has created adverse effects and social, cultural, and infrastructural costs for urban management. Therefore, the present study aims to identify the factors affecting the revenues and expenses of Tabriz Municipality. The research is applied in terms of its purpose and descriptive-analytical based on a survey method using a structural modeling approach for data collection. The statistical population of the study consists of senior, middle, and operational managers, as well as experts from Tabriz Municipality, and a standard questionnaire was used to collect data. The results showed that legal solutions, the role of stakeholder groups (Ministry of Interior), the role of stakeholder groups (private sector), and the adoption of appropriate methods and provision of automated services have a positive and significant impact on the management of municipal revenues and expenses. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between project management lessons learned practices and organizational revenue. A quantitative correlational analysis of 150 project management professionals reveals a significant positive correlation between systematic lessons learned practices and financial success. These findings underscore the importance of integrating knowledge management into project frameworks to enhance both project outcomes and organizational performance. [ABSTRACT FROM AUTHOR]

Revenue-sharing agreements are very common in the live entertainment industry. Such agreements typically include a minimum revenue guarantee for the benefit of the entertainer, combined with a formula to split the revenue, in case the revenue exceeds the minimum guarantee. In essence, these are derivative contracts between the event organizer (O) and the entertainer (E). This study introduces several closed-form expressions to evaluate the risks associated with these contracts for both parties (O and E). These expressions enable the estimation of revenue for each party based on the type of agreement and the valuation of the embedded option. Notably, the expressions do not require an assumption of symmetrical information—O and E can have access to different sets of information. Additionally, the expressions are valid regardless of the risk profiles of O and E, which could be different. Finally, the usefulness of the approach is demonstrated with an example that includes a scenario analysis, that is, exploring how variations in the two key parameters defining the revenue agreement affect the revenues of both O and E. [ABSTRACT FROM AUTHOR]

The implementation of promotional pricing strategies constitutes a key component within the realm of retail revenue management. Nonetheless, the accurate prediction of sales in the presence of price discounts proves challenging due to the influence of various factors that contribute to demand uncertainty and high fluctuations. This study aims to find the most suitable prediction models for retail product unit sales while comprehensively accounting for the complex impacts of contributing factors. The dataset, sourced from a case study of a retail company, spans the temporal interval from January 2020 to December 2022. The predictive models, encompassing linear regression, random forest, XGBoost, artificial neural networks, and hybrid machine-learning models, are systematically developed. Then, the identification of the most suitable model is facilitated through the computation and comparative analysis of the Mean Absolute Percentage Error, with due consideration given to the weighting by the respective product's revenue, thereby offering a comprehensive assessment of overall performance. Additionally, different types of feature selection are experimented. Factors used in machine learning models are either using all the independent variables or using significant factors from the stepwise method, and either considering or not considering exogenous factors of other products in the same cluster grouped by category, subcategory, or K-means method. The result shows that the series hybrid model of random forest and XGBoost outperformed others. Considering factors affecting sales, it is found that the promotion period factor was the most important, followed by discount percentage and price factors. This research provides analytics framework for sales prediction for retails using promotional pricing as a key demand driver. [ABSTRACT FROM AUTHOR]

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Promotional effort is a common strategy to induce sales and broaden the market scope by enhancing the products' utility to customers. In this article, we incorporate promotional effort into the customer choice model and study the joint promotional effort and assortment optimization problems, where the customer's choice behavior follows the widely used multinomial‐logit (MNL) model. Motivated by various marketing scenarios, we introduce two distinct models that address the allocation of promotional effort: (1) differentiated promotional effort—the retailer can arbitrarily allocate promotional resources to each offered product; (2) uniform promotional effort—the retailer can determine a promotional level, and the promotional effort is equally distributed to each offered product. In the first model, the revenue‐ordered assortment strategy is optimal, and we can efficiently determine the optimal promotional effort level. In the second model, the revenue‐ordered assortment is no longer optimal. We develop a polynomial time algorithm to solve the joint optimization problem under this model. Using the algorithmic results, we conduct comparative analyses between the assortment optimization problem under the proposed models and the classic MNL model, which does not exert any promotional effort. We show that the assortment size shrinks when the retailer makes the promotional effort in the decision, which indicates that product variety and promotional effort are strategic substitutes. Moreover, the retailer and customers can be better off in the presence of promotional efforts, irrespective of the format. Additionally, we conduct extensive numerical experiments to demonstrate our analytical results and gain more managerial insights. [ABSTRACT FROM AUTHOR]

Utilizing a dataset covering four Portuguese NUTS II regions from 2009 to 2022 and employing multiple regression analyses, this research challenges traditional tourism theories by providing empirical evidence on how various factors—average stay, overnight stays, and foreign guests—affect regional revenue in tourist accommodations. In the context of sustainable tourism development, these findings have revealed a significant negative relationship between average stay duration and revenue, indicating that longer stays may be associated with lower daily revenues. This necessitates a re-evaluation of tourist spending behavior and accommodation of pricing strategies to promote economic sustainability. This study underscores the consistently positive impact of overnight stays on revenue, reinforcing the importance of maximizing overnight stays as a strategy for sustainable economic growth within the tourism sector. Additionally, the mixed results regarding the proportion of foreign guests reveal varying regional impacts, suggesting that region-specific strategies are essential for optimizing revenue from international tourists in a sustainable manner. This research integrates these factors to offer practical insights for refining revenue strategies in tourism management, emphasizing the need for multifaceted theoretical models that account for the complexity of sustainable tourism dynamics. Future research should focus on these nuanced approaches to better understand and manage the interplay between tourism dynamics and sustainable development. [ABSTRACT FROM AUTHOR]

Ratoon rice occupies an important position in rice production owing to its time-saving, labor-saving and low-pollution planting, and increased benefits. However, the impact of tillage management on the yield in rice ratooning system has not yet been reported. Thus, field experiments were carried out to investigate the impact of seven tillage methods on the yield of ratoon rice crop in Jingzhou City in 2021–2022. The managements included winter plowing + rotary 2 times (PTw + RT2) or 3 times (PTw + RT3), spring plowing + rotary 2 times (PTs + RT2) or 3 times (PTs + RT3), no plowing + rotary 2 times (P0 + RT2) or 3 times (P0 + RT3) and no tillage (NT). PTw + RT3 had the highest total rice yield. The experimental data were collected in 2021 and 2022. In terms of main season rice yield, the order of ranking was PTw > PTs ≈ NT ≈ P0, while for ratoon rice yield, the ranking was NT > PTw ≈ PTs > P0. Generally, the root function ranked as PTw > PTs > P0 > NT. The photosynthetic capacity of the main season rice always maximized in PTw, those of the ratoon rice all maximized in NT, and those of both the main season rice and ratoon rice always minimized in P0. In the three tillage modes (PTw, PTs, P0), an additional rotary tillage did not affect the growth or yield of rice. PTw + 3RT was the highest yielding tillage management, but it is still necessary to explore other PTw + 3RT methods and more economical tillage management to increase the yield of ratoon rice. [ABSTRACT FROM AUTHOR]

Purpose – Revenue management (RM) is a significant technique to improve revenue with limited resources. With the macro environment of dramatically increasing transit capacity and rapid railway transport development in China, it is necessary to involve the theory of RM into the operation and decision of railway passenger transport. Design/methodology/approach – This paper proposes the theory and framework of generalized RM of railway passenger transport (RMRPT), and the thoughts and methods of the main techniques in RMRPT, involving demand forecasting, line planning, inventory control, pricing strategies and information systems, are all studied and elaborated. The involved methods and techniques provide a sequential process to help with the decision-making for each stage of RMRPT. The corresponding techniques are integrated into the information system to support practical businesses in railway passenger transport. Findings – The combination of the whole techniques devotes to railway benefit improvement and transit resource utilization and has been applied into the practical operation and organization of railway passenger transport. Originality/value – The development of RMRPT would provide theoretical and technical support for the improvement of service quality as well as railway benefits and efficiency.

In online platforms, the reviews posted by existing consumers are playing an increasingly important role in the purchasing decisions of potential consumers. Motivated by this observation, we study the problems faced by a platform selling a single product with no capacity constraint, where the demand is explicitly influenced by the reviews presented to the consumers. More precisely, we model a consumer's browsing of reviews for a single product as following a cascade click model, with each consumer seeing some initial number of reviews and forming a utility estimate for the product based on the reviews the consumer has read. In the first part of the paper, we consider how to rank the reviews to induce short- and long-term revenue-maximizing purchasing behaviors. In the second part, we study how to set the price of the product. We derive structural insights and bounds on both problems. We also consider the case that the parameters of the model are unknown, where we propose algorithms that learn the parameters and optimize the ranking of the reviews or the price online. We show that our algorithms have regrets O (T 2 3 ). [ABSTRACT FROM AUTHOR]

Tourism is a very complex industry and represents one of the most profitable activity in the world. A tourist product requires the execution of several multifaceted activities. Indeed, transportation, accommodation, entertainment, food, and beverages represent only some of the products/services required by a tourist. It is evident that a single enterprise cannot provide all the components of a tourist product, but several interrelated actors should collaborate. This leads to a very complex supply tourist chain that needs to be optimized. The relevant theories and methods of logistics can be used to efficiently manage all the flows that are generated in a tourist chain. The definition of appropriate policies, at the different nodes of the chain, can improve the performance of all the actors involved in tourism logistics. In this paper, we concentrate our attention on tour operators that are relevant in the touristic logistic chain since they are involved in several activities. We introduce different revenue management policies to support tour operators in the decision of accepting the most profitable tourist requests. A request consists of flights and hotel booking, characterized by a starting time of the trip and the length of stay at the destination. We allow for various combinations of flight legs and multiple categories of hotels to accommodate a variety of customer preferences and needs. A computational study is carried out by considering different scenarios, and the performance of the considered revenue management policies is analyzed in detail. [ABSTRACT FROM AUTHOR]

The purpose of the study is to develop a system of balanced scorecards for forecasting the income of hotels belonging to international chains. The subject of the study is the system processes of evaluating the effectiveness of the management analysis system, which are determined on the basis of calculating key performance indicators, and as a result of the integral indicator through the analysis and aggregation of individual functional criteria based on comparison with the reference values of the revenue management model for pricing, load planning and its redistribution between revenue centres. Methodology. The study uses the methods of theoretical and logical generalisation. The article describes a set of strategies and tactics used by international hotel chains to manage the demand for hotel services. The results of the article are to form a model for conducting a detailed operational and financial analysis of the hotel enterprise by revenue centres, which contributes to the development of a strategy. The authors have selected a set of indicators that are used to ensure a balanced approach to measuring performance through the indicator method and visual representation in a graphical representation. The paper analyses the performance of international hotel chains over twelve years in order to assess the impact of key factors on their revenues and develop forecasts; the estimated indicators were classified into seven groups: assessment of the average daily revenue per room, room cost, occupancy rate, market share, staff productivity, resource intensity and digitalisation costs. The publication uses the example of international hotel chains to clarify the content and importance of revenue forecasting in the revenue management system.

A study was carried out to ascertain the impact of planting time and weeding schedule on the yield and quality of baby corn. The trial included three planting times viz. 15 November, 15 December and 14 January and five levels of weeding regime viz. no weeding, two hand weeding (HW) at 15 days after sowing (DAS) and 30 DAS, herbicide pendimethalin @ 2.5 L ha-1 (pre-emergence) + one HW at 30 DAS, herbicide pyrazosulfuran-ethyl @ 2.0 L ha-1 (post-emergence) + one HW at 30 DAS and combined herbicide of pendimethalin + pyrazosulfuran-ethyl. The maximum cob yield with husk (11.93 t ha-1) and cob yield without husk (3.07 t ha-1) were obtained from the 15 December sowing with the application of pendimethalin followed by pyrazosulfuran-ethyl. Plants sown on 15 December with the application pyrazosulfuran-ethyl with one HW at 30 DAS gave the highest protein content in cobs (20.20%), while the leaf protein content showed the highest result (18.70%) in the plants sown on 15 December with no weeding. Therefore, it can be concluded that the baby corn might be planted on December 15 with the combined application of pendimethalin + pyrazosulfuran-ethyl herbicides and pyrazosulfuran-ethyl + one HW at 30 DAS for maximum cob yield and protein content, respectively. [ABSTRACT FROM AUTHOR]

Fusarium‐induced wilt significantly affects the cultivation and yield of pigeon peas. This warrants sustainable disease management while promoting plant growth. The present study investigated the biopotential of coinoculation of Streptomyces pseudogriseolus S‐9 and Rhizophagus irregularis for plant growth promotion and mitigation of the impact of Fusarium wilt on pigeon pea over three seasons at pot and field levels. Pigeon pea plants were subjected to Fusarium wilt stress and treated with different inoculation strategies, including single and combined applications of S. pseudogriseolus S‐9 and R. irregularis. Plant growth parameters and yields were assessed to evaluate the efficacy of the coinoculation. In the pot experiment, T‐6 treatment resulted in the longest root (62.56 ± 0.01 cm) and shoot (70.24 ± 0.01 cm) lengths compared to the application of commercial biofungicide T‐8 (Trichoderma). This treatment also significantly influenced the yield of potted plants. It resulted in the highest fresh root weight (62.27 ± 0.01 g), fresh shoot weight (70.24 ± 0.02 g), maximum root (55.25 ± 0.01 g) and shoot dry weights (52.25 ± 0.01 g). In the field experiment, pigeon pea plants treated with the bioinoculant also demonstrated a substantial increase (р ≤ 0.05) in total grain yield, the weight of 100 grains, and the number of filled grains compared to the control group in all experimental seasons. In vitro, antagonism assay of compatibility of mycorrhizae and bacteria showed good activity using powder formulation. Thus, the consortium application inspired the broad application of Streptomyces and Trichoderma as effective bioinoculants for wilt management and yield improvement in pigeon peas. [ABSTRACT FROM AUTHOR]

The growth status of winter wheat seedlings during the greening period is called the seedling situation. Timely and accurate determinations of the seedling situation type are important for subsequent field management measures and yield estimation. To solve the problems of low-efficiency artificial classification, subjective doping, inaccurate classification, and overfitting in transfer learning in classifying the seedling condition of winter wheat seedlings during the greening period, we propose an improved ConvNeXt winter wheat seedling status classification and identification network based on the pre-training–fine-tuning model addressing over-fitting in transfer learning. Based on ConvNeXt, a SETFL-ConvNeXt network (Squeeze and Excitation attention-tanh ConvNeXt using focal loss), a winter wheat seedling identification and grading network was designed by adding an improved SET attention module (Squeeze and Excitation attention-tanh) and replacing the Focal Loss function. The accuracy of the SETFL-ConvNeXt reached 96.68%. Compared with the classic ConvNeXt model, the accuracy of the Strong class, First class, and Third class increased by 1.188%, 2.199%, and 0.132%, respectively. With the model, we also compared the effects of different optimization strategies, five pre-training-fine-tuning models, and the degree of change in the pre-trained model. The accuracy of the fine-tuning models trained in the remaining layers increased by 0.19–6.19% using the last three frozen blocks, and the accuracy of the pre-trained model increased by 3.1–8.56% with the least degree of change method compared with the other methods. The SETFL-ConvNeXt network proposed in this study has high accuracy and can effectively address overfitting, providing theoretical and technical support for classifying winter wheat seedlings during the greening period. It also provides solutions and ideas for researchers who encounter overfitting. [ABSTRACT FROM AUTHOR]

Impact of integrated nutrient management on plant and soil nutrient status in sweet orange (Citrus sinensis L.) cv. Phule Mosambi was investigated for two consecutive years i.e. 2019 and 2020 at the Horticultural Research Farm, College of Horticulture, Anand Agricultural University, Anand. The experiment was laid out in completely randomized design (CRD) comprised of 11 treatments replicated thrice. Significantly higher yield was recorded with T11 [50% Recommended Dose of Nitrogen (RDN) + 20 kg farmyard manure (FYM)/tree +25% nitrogen (N) from Castor Cake + 10 ml Bio NPK Consortium + Micronutrient foliar spray (1% Grade IV)] (17.35 and 17.01 t/ha) which was at par with T10 (15.92 and 15.56 a t/ha) during 2019 and 2020, respectively. Amongst the treatments, T11 had increased the nutrient contents of soil as well as in plant and recorded maximum available nitrogen (N), available potassium (K), soil boron (B) content, plant nitrogen (N) content, plant iron (Fe) content, plant copper (Cu) content and plant boron (B) content in both the years whereas available phosphorous (P) was found higher in treatment T7 in both the years. Treatment T8 recorded highest soil copper (Cu) content in both the years. As far as plant zinc (Zn) content is concerned, it was found higher in treatment T9 in both the years. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 restrictions on restaurant consumer behavior remains largely unexplored. The current paper examined how adherence to COVID-19 restrictions impacts willingness-to-pay by restaurant consumers. We examined the results of a survey of 634 participants with choice-based conjoint analysis. The results found that employees in masks, customers in masks, and seating capacity significantly impacted willingness-to-pay by restaurant consumers. Employees in masks had the largest increase on WTP while employees not wearing masks had the largest decrease on WTP. Additionally, consumers dining behavior and changes based on COVID-19 restrictions were the most accurate predictors of their post-vaccine dining behavior. [ABSTRACT FROM AUTHOR]

A Simple Monotonic Dynamic Pricing Policy We consider a canonical revenue management setting in which a monopolist seeks to maximize expected total revenues by selling a fixed inventory of product to customers who arrive sequentially over time. It is known in the literature that a simple dynamic pricing policy can perform reasonably well in such setting. However, the literature typically assumes that price can move freely over time (i.e., it can either go up or down). In practice, firms often impose certain restrictions on price movement. In this paper, we consider the setting in which price can only move monotonically over time (either only decreasing or only increasing). We devise a simple provably near-optimal dynamic pricing policy. Aside from markdown applications, our policy can also be applied to the setting with strategic customers. We consider a canonical revenue management problem wherein a monopolist seller seeks to maximize expected total revenues from selling a fixed inventory of a product to customers who arrive sequentially over time, and the seller is restricted to implement a pricing policy that is monotonic (either nonincreasing or nondecreasing) over time. Gallego and Van Ryzin [Gallego G, Van Ryzin G (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Sci. 40(8):999–1020] show that the simplest monotonic price policy, the fixed price policy, is asymptotically optimal in the high-volume regime in which both the seller's initial inventory and the length of the selling horizon are proportionally scaled. Specifically, the revenue loss of the fixed price policy is O (k 1 / 2) , where k is the system's scaling parameter. Following the publication of Gallego and Van Ryzin [Gallego G, Van Ryzin G (1994) Optimal dynamic pricing of inventories with stochastic demand over finite horizons. Management Sci. 40(8):999–1020], several papers have attempted to improve the performance of the fixed price policy. Among them, Jasin [Jasin S (2014) Reoptimization and self-adjusting price control for network revenue management. Oper. Res. 62(5):1168–1178] develops a simple modification of the fixed price policy (that allows prices to move either up or down) with a guaranteed revenue loss of order O (ln k). In this paper, we propose a novel family of monotonic readjustment policy, which restricts the prices to only move in one direction (i.e., either up or down). We show that, if the seller updates the price for only a single time, then the revenue loss of our policy is O (k 1 / 3 (ln k) 2 α) for some α > 1 / 2. If, however, the seller updates the prices with a frequency O (ln k / ln ln k) , then the revenue loss of our policy is O ((ln k) 7 α) for some α > 1 / 2. These results show the power of dynamic pricing even in the presence of monotonic price restriction. We discuss two applications of our policy: markdown pricing and pricing in the presence of strategic customers. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2020.0774. [ABSTRACT FROM AUTHOR]

Dynamic resource allocation problems arise under a variety of settings. In "Survey of Dynamic Resource-Constrained Reward Collection Problems: Unified Model and Analysis," Balseiro, Besbes, and Pizarro introduce a unifying model for a large class of dynamic optimization problems dubbed dynamic resource-constrained reward collection (DRC2) problems. Surveying the literature, they show that this class encompasses a variety of disparate and classical problems typically studied separately, such as dynamic pricing with capacity constraints, dynamic bidding with budgets, network revenue management, online matching, or order fulfillment. Furthermore, they establish that the DRC2 class is amenable to analysis by characterizing the performance of a central, certainty-equivalent heuristic. Notably, they provide a novel unifying analysis that isolates the drivers of performance, recovers as corollaries some existing specialized results, generalizes other existing results by weakening the assumptions required, and yields new results in specialized settings for which no such characterization was available. Dynamic resource allocation problems arise under a variety of settings and have been studied across disciplines such as operations research and computer science. The present paper introduces a unifying model for a very large class of dynamic optimization problems that we call dynamic resource-constrained reward collection (DRC2) problems. We show that this class encompasses a variety of disparate and classical dynamic optimization problems such as dynamic pricing with capacity constraints, dynamic bidding with budgets, network revenue management, online matching, and order fulfillment, to name a few. Furthermore, we establish that the class of DRC2 problems, although highly general, is amenable to analysis. In particular, we characterize the performance of the fluid certainty-equivalent control heuristic for this class. Notably, this very general result recovers as corollaries some existing specialized results, generalizes other existing results by weakening the assumptions required, and also yields new results in specialized settings for which no such characterization was available. As such, the DRC2 class isolates some common features of a broad class of problems and offers a new object of analysis. Funding: The work of D. Pizarro was supported by the Artificial and Natural Intelligence Toulouse Institute, which is funded by the French "Investing for the Future—PIA3" program [Grant ANR-19-P3IA-0004]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2023.2441. [ABSTRACT FROM AUTHOR]

Hiring the right person for the job is crucial for the success of any organization. In "Selection and Ordering Policies for Hiring Pipelines via Linear Programming," Epstein and Ma study several problems motivated by a firm that is carrying out a recruitment process. Restricted to a finite time budget, the firm must decide who will be interviewed out of a pool of applicants and who will receive offers among the interviewed applicants. They develop approximation algorithms with constant factor guarantees that approach optimality when the number of vacant positions grows large. The algorithms they develop are nonadaptive: they fix a subset of candidates and an order to conduct the interviews and the order remains unchanged, independent of the outcomes of other interviews. Thus, they establish bounds on the adaptivity gap: a worst-case measure of how poorly non-adaptive algorithms can perform with respect to their adaptive counterparts. Motivated by hiring pipelines, we study three selection and ordering problems in which applicants for a finite set of positions are interviewed or sent offers. There is a finite time budget for interviewing/sending offers, and every interview/offer is followed by a stochastic realization of discovering the applicant's quality or acceptance decision, leading to computationally challenging problems. In the first problem, we study sequential interviewing and show that a computationally tractable, nonadaptive policy that must make offers immediately after interviewing is near optimal, assuming offers are always accepted. We further show how to use this policy as a subroutine for obtaining a polynomial-time approximation scheme. In the second problem, we assume that applicants have already been interviewed but only accept offers with some probability; we develop a computationally tractable policy that makes offers for the different positions in parallel, which can be used even if positions are heterogeneous, and is near optimal relative to a policy that can make the same total number of offers one by one. In the third problem, we introduce a parsimonious model of overbooking where all offers are sent simultaneously, and a linear penalty is incurred for each acceptance beyond the number of positions; we provide nearly tight bounds on the performance of practically motivated value-ordered policies. All in all, our paper takes a unified approach to three different hiring problems based on linear programming. Our results in the first two problems generalize and improve the existing guarantees in the literature that were between 1/8 and 1/2 to new guarantees that are at least 1 − 1 / e ≈ 63.2 %. We also numerically compare three different settings of making offers to candidates (sequentially, in parallel, or simultaneously), providing insight into when a firm should favor each one. Supplemental Material: The online appendices are available at https://doi.org/10.1287/opre.2023.0061. [ABSTRACT FROM AUTHOR]

Optimizing Revenue in Two-Sided Online Markets through Strategic Quality Selection and Information Disclosure Ensuring profitable operations in two-sided online marketplaces demands an astute analysis of seller quality and strategic information sharing with buyers. A recent paper, titled "Quality Selection in Two-Sided Markets: A Constrained Price Discrimination Approach" by B. Light, R. Johari, and G. Weintraub delves into the nuances of this operation. The authors explore the challenge that platforms encounter in deciding which sellers to allow and how much quality information to share with buyers in order to enhance platform revenue. Utilizing two distinct two-sided market models, the paper unveils conditions under which adopting straightforward information structures, such as excluding certain sellers or not differentiating among participating sellers, proves to be a revenue-maximizing strategy. This study utilizes a constrained price discrimination problem to reveal specific strategies platforms can use to adjust information structures in diverse market scenarios, providing insights for digital platforms aiming to navigate the marketplace more effectively. Online platforms collect rich information about participants and then share some of this information back with them to improve market outcomes. In this paper, we study the following information disclosure problem in two-sided markets: if a platform wants to maximize revenue, which sellers should the platform allow to participate, and how much of its available information about participating sellers' quality should the platform share with buyers? We study this information disclosure problem in the context of two distinct two-sided market models: one in which the platform chooses prices and the sellers choose quantities (similar to ride sharing), and one in which the sellers choose prices (similar to e-commerce). Our main results provide conditions under which simple information structures commonly observed in practice, such as banning certain sellers from the platform and not distinguishing between participating sellers, maximize the platform's revenue. The platform's information disclosure problem naturally transforms into a constrained price discrimination problem in which the constraints are determined by the equilibrium outcomes of the specific two-sided market model being studied. We analyze this constrained price discrimination problem to obtain our structural results. Funding: This work was supported by the National Science Foundation [Grants 1839229 and 1931696]. [ABSTRACT FROM AUTHOR]

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Assessing the efficacy of algorithms plays a pivotal role in advancing various fields, both in theory and practice. Unlike the predictive models, due to the intricate relationship between decisions and the underlying data‐generating processes, the evaluation of decision algorithms cannot directly rely on real data. Hence, a simulator becomes indispensable for appraising decision algorithm effectiveness. In this paper, we aim to leverage assortment decisions, a widely used application in revenue management, to illustrate the utilization of a machine learning‐based simulation. The process can be summarised as: we utilize the modified Transformer‐based choice model, acting as a simulator, to generate a synthetic dataset that mimics consumer purchasing behavior. After training the MNL, DeepFM, and DeepFM‐a models, all of which can swiftly provide assortment decisions in real‐time, we utilize the simulator to evaluate the revenue generated by each assortment prescribed by different choice models. This approach mitigates the challenge of validating decision models that alter real‐world observed data. To show the benefit of such a simulation approach, we have conducted various numerical studies. These studies aim to examine the impact of outside option attractiveness, data size, the number of features, and cardinality. Admittedly, due to the close alignment between the simulator and complex consumer purchase choice datasets, some numerical observations may be challenging to explain. Nevertheless, by employing the simulator, we are able to contrast the differences between the MNL and DeepFM/DeepFM‐a models, shedding light on their respective model misspecifications. [ABSTRACT FROM AUTHOR]

With an increase in the frequency of hot-dry-windy weather and reported significant increase in winter wheat (Triticum aestivum L.) in-season average temperatures, it is important to understand how soil management affects yield stability. This study quantified the combined effects of tillage and fertilizer-N on winter wheat yield stability in a wheat–sorghum [Sorghum bicolor (L.) Moench]–fallow crop rotation. The research was conducted from 1975 to 2022 and the experimental design was a randomized complete block with a split-split-plot arrangement. Crop phases were the main plots, tillage (conventional tillage [CT], reduced tillage [RT], and no-tillage [NT]) in the sub-plot, and N application rates (0, 22, 45, and 67 kg N ha−1 or 0, 45, 90, and 134 kg N ha−1) in the sub-sub-plot. Results showed winter wheat yield under NT increased by 0.8 Mg ha−1, compared with about 1.1 Mg ha−1 for CT and RT, when environmental average increased by 1 Mg ha−1 for N rates ≤90 kg ha−1. Yields with CT and RT increased by 0.8 Mg ha−1, compared with about 1.1 Mg ha−1 rate for NT, when environmental average increased by 1 Mg ha−1 for N >90 kg ha−1. In 30%–52% of the time, CT wheat yield was greater than NT, but yield advantage with CT decreased as fertilizer-N rate increases. Yield stability of winter wheat varied by tillage and fertilizer rate. We concluded that NT wheat required greater N rates to reach the same yield potential as CT and RT, and long-term CT or RT wheat at 45 kg N ha−1 was most stable [ABSTRACT FROM AUTHOR]

Fisheries in India are a burgeoning industry with immense potential and opportunities. Ironically, because of its enormous but untapped potential, reservoir fisheries are called as "a sleeping giant". The average productivity from these reservoirs in India is about 30 kg/ha compared to the production potential of 250 kg/ha. Inadequate management leads to low rate of fish production in Indian reservoirs. A major impediment to science -based fish productivity in reservoirs is the lack of authentic data on target stocks. Inland fisheries, like those in reservoirs, are particularly affected. Investigators generally rely on simple catch statistics and hypothesis statements to assess fish production and potential yields due to inadequate resources, population data, and experts in the area. The present paper emphasizes the quintessential 44sq km huge Tehri hydro-power dam reservoir, located in the Bhagirathi and Bhilangana valleys of the lesser Himalayas in the Garhwal hills. On average the fish productivity from Tehri reservoir in the year 2021-2022 is reported as 13.75 kg/ha but, this is significantly lower than its actual productivity. Thus, the current technical paper addresses this issue by condensing and synthesizing the current status of fisheries in the Tehri Dam reservoir with past status in terms of fish species diversity. It provides baseline information on fish production and various data shortfalls in fisheries activity management. The paper also describes the ecological and economic aspects that affect fish catch to utilize the reservoir resources and mired the cold-water fishery management in the Tehri reservoir. [ABSTRACT FROM AUTHOR]

Background: Cashew being a hardy crop is able to thrive and produce yield under low nutrient management practices yet suffers low productivity as the yield potential of plant is not expressed. Persistence of low productivity of cashew in Odisha is due to inconsequence of old senile plantation of seedling origin under unfertile, marginal lands as well as no proper adaptation of scientific nutrient management practices. Methods: Present investigation was carried out at All India Coordinated Research Project on Cashew, Odisha University of Agriculture and Technology, Bhubaneswar, Odisha during 2021-2022 and 2022-23 to evaluate the effect of nutrient management practices on yield attributing traits and physico-chemical properties of cashew cv. Balabhadra in randomized block design with six treatments and four replications. Result: Treatment schedule i.e., 100% RDF+10 kg FYM/plant/year+ Foliar spray of major nutrients (3% urea+0.5% MAP+1% K2SO4)+ Foliar spray of secondary and micro-nutrients (0.5% ZnSO4 + 0.1% Solubor + 0.5% MgSO4) witnessed better performance of cashew for most of the yield attributing traits and biochemical properties of cashew under study. Pearson correlation coefficient analysis revealed positive association of yield relevant traits (P<0.001) and negative correlation for apple length and TSS of cashew apple. [ABSTRACT FROM AUTHOR]

The budget is a tool that encourages local government management to act economically, efficiently and effectively to achieve targeted goals. After the Covid-19 Pandemic, the Realization of Regional Revenue and Expenditure in Kuningan Regency every year has fluctuated, there has been a decrease and increase, especially in the period 2021 to 2023, even until there is a problem of payment delays. The government is an element in determining the direction of decision-making and determining the direction of government, so the government needs to implement good governance to create good governance, especially in the management of the APBD in order to realize the welfare of the community. The purpose of this study is to find out and analyze the effectiveness and efficiency of the Regional Revenue and Expenditure Budget of Kuningan Regency for the period of 2021 - 2023. The data was analyzed by a qualitative descriptive method with a phenomenological approach obtained values and indicators to measure the level of effectiveness and efficiency. The data sources used are primary and secondary data. The subject of this study is the measurement of regional revenue and expenditure performance, and the subject of this research is the variables for the implementation of the regional revenue and expenditure budget for 2021-2023. [ABSTRACT FROM AUTHOR]

Humans and large aquatic predators compete for fish and negative interactions are widely reported as 'human–wildlife conflicts'. When aquatic predators are perceived to damage fisheries or fishing equipment, lethal control can occur. The perceptions and reality of damage are infrequently compared, but this relationship is key to determining how negative outcomes can be mitigated. We examine coexistence between people and six large aquatic piscivores (two caiman, two dolphins, two otters) in Amazonian Peru. We determine the extent of damage to fishing equipment caused by each species and compare this to the amount of damage perceived by fishers. Giant otter populations have recently recovered in some areas, so we expected different perceptions relating to experiences with otters. We trained fishers to complete fishing registers (n = 278, 1173 h of fishing) to record damage to nets by wildlife. We interviewed 302 people from three sites to determine perceptions of damage by predators, and attitudes towards giant otters. Rates of damage to nets reflected the presence and populations of different aquatic predators at each site, but when present, dolphins and caimans damaged nets more than otters, which rarely damaged nets. People living where giant otters had recently recovered perceived higher relative levels of damage to nets by them and had more negative attitudes about them, compared to people from areas where giant otters had been present for longer, aquatic predators were more abundant, and community resource management was longer‐established. Better knowledge and more experience with a species may lead to more accurate perceptions of damage and increased tolerance. Where humans and animals compete for natural resources, conflict mitigation rarely includes better resource management. If tolerance of predators is greater where predators are common, and resources have not been overexploited, resource management may yield greater gains for stakeholders than other commonly prescribed forms of mitigation. [ABSTRACT FROM AUTHOR]

The risk of revenue instability is a concern for any nonprofit. Existing research leads to the well-known strategy of equalizing revenues across sources to reduce revenue volatility. This study offers several expansions to this strategy. First, rather than focusing solely on deviations from mean revenue, I incorporate extreme revenue loss, or downside risk, as it threatens organizational survival and occurs more frequently than expected. Second, while revenue equalization is indifferent to the type of revenue source, I incorporate portfolio diversification that seeks a negative correlation between sources to avoid simultaneous losses. Using 2008 to 2012 financial data, results of fixed-effects regression suggest that portfolio diversification can reduce revenue volatility and downside risk. Moreover, the relationships between financial flexibility, growth potential, and revenue risk are more nuanced compared to existing research focused solely on deviation risk. The results can help nonprofits consider incorporating downside risk in revenue portfolio management to enhance financial security. [ABSTRACT FROM AUTHOR]

Subscription models where consumers pay fixed (monthly) fees for access to services are becoming increasingly popular across a wider set of service offerings as firms look for ways to stabilize revenues. Lodging subscription services are natural and flexible extensions of timeshare models whereby consumers pay monthly subscriptions fees to get access to a wide variety of stay options. The value of the subscription service may be difficult for consumers to evaluate and hence limit adoption. This study demonstrates that service providers can assist consumers in maximizing use of their subscription through addition of simple sort functionality or via more robust optimization approaches. Using synthetic data as well as data from a lodging subscription provider, we develop approaches for consumers to maximize their use of the subscription service - we compare and contrast optimal approaches to readily deployed heuristic approaches. [ABSTRACT FROM AUTHOR]

Strategic levers play a fundamental role in revenue management (RM). Earlier research has established price, time, and space as the three levers businesses can wield to optimize performance, but a synthesis of all three is missing. This article presents a three-dimensional model of revenue management levers that categorizes industries in eight octants, visualized as the cube of RM levers. The cube sharpens RM theory and helps companies to identify new opportunities for revenue optimization through comparison with other businesses within their octant. Similarly, it facilitates evaluating possibilities of moving to another octant. Finally, the cube can also assist businesses new to RM to apply strategic levers, address RM collaboration challenges in tourism destinations, and contribute to education in the field. [ABSTRACT FROM AUTHOR]

This paper studies the opaque selling strategy for a parallel-flight airline based on a newsvendor model with stochastic demand. The optimal pricing decision and capacity allocation policy are obtained and analyzed subject to necessary assumptions. There is a relationship between the optimal allocated capacities of these flights, and the airline can adjust its capacity allocation decisions according to this relationship. In addition, the airline is suggested to allocate more capacity for opaque seats if the demand variance is high or the difference in consumer's preferences between flights is small; meanwhile, a lower price for opaque seats will be provided when the variance is high or the difference is large. Numerical experiments are presented to show the effectiveness of the opaque selling strategy, and the results indicate that this strategy brings a 1.09% revenue increment on average compared to the conventional strategy. [ABSTRACT FROM AUTHOR]

Traditional revenue management systems are built under the assumption of independent demand per fare. The fare adjustment theory is a methodology to adjust fares that allows for the continued use of optimization algorithms and seat inventory control methods, even with the shift toward dependent demand. Since accurate demand forecasts are a key input to this methodology, it is reasonable to assume that for a scenario with uncertainties it may deliver suboptimal performance. Particularly, during and after COVID-19, airlines faced striking challenges in demand forecasting. This study demonstrates, firstly, the theoretical dominance of the fare adjustment theory under perfect conditions. Secondly, it lacks robustness to forecast errors. A Monte Carlo simulation replicating a revenue management system under mild assumptions indicates that a forecast error of ± 20 % can potentially prompt a necessity to adjust the margin employed in the fare adjustment theory by - 10 % . Moreover, a tree-based machine learning model highlights the forecast error as the predominant factor, with bias playing an even more pivotal role than variance. An out-of-sample study indicates that the predictive model steadily outperforms the fare adjustment theory. [ABSTRACT FROM AUTHOR]

Booking control focuses on the problem of deciding whether to accept or reject bookings to maximize revenue while considering limited capacity. For freight applications, computing the cost of fulfilling requests requires solving an operational decision-making problem which often corresponds to a mixed-integer linear program. We propose a two-phase learning-based approach that first learns to predict the objective of the operational problem, then leverages the prediction within reinforcement learning algorithms to compute the policies. The method is general and applies to different problems faced in practice. We show strong performance on two booking control problems in the literature: distributional logistics and airline cargo management. [ABSTRACT FROM AUTHOR]

Setting the right fares is a key lever for increasing operating profitability in the airline industry. It is crucial to design fares that are both appealing to passengers and contribute to an increase in airline revenue. Although airline revenue management techniques have evolved to capture customer-choice behavior, the pricing and allocation decisions continue to be taken independently. However, since they are linked, a single optimization model can address this shortcoming. This paper presents a joint optimization model (JOM) that considers product prices and their allocation quantities as decision variables. A sequential optimization technique that divides the model into two decision problems is adopted to cope with JOM's complexity. The problem is divided into a master problem and a sub-problem, wherein product price changes are made in the master problem and, with these fixed prices, optimization is performed in the sub-problem. The sub-problem is solved by simplifying the non-linear JOM into a linear programming problem. The direction of product price changes in the master problem is identified using price elasticity of demand. A heuristic based on this concept is proposed and tested. The Elasticity-integrated Pricing and Allocation Heuristic (EPAH) is observed to produce a consistent increase in existing revenues. [ABSTRACT FROM AUTHOR]