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To address cruise companies' revenue loss caused by over-reliance on indirect channels for ticket distribution, we design a dual-channel pricing policy that includes dynamic horizontal and vertical pricing regimes aiming to maximizing cruise companies' revenue. In the horizontal pricing regime, cruise companies and travel agencies offer uniform prices to consumers dynamically. In the vertical pricing regime, cruise companies provide dynamic commissions to travel agencies based on their sales performances. In view of the dynamic customer arrivals and uncertain demand, we model the dynamic dual-channel pricing decisions as a Markov decision process, and solve the model by means of a reinforcement learning method. Our computational results show that dual-channel pricing policies generated by our method outperform the classic Expected Marginal Seat Revenue method, which has been widely used in similar industries such as airlines for revenue management. In addition to the various features of dual-channel in ticket distribution, our model also takes into account customers' onboard consumptions, which is a critical source of revenue in cruise industry but has never been considered in the literature. As a result, our model and computational results provide insightful implications for cruise revenue management. [ABSTRACT FROM AUTHOR]

Copyright of Tourism & Management Studies is the property of Escola Superior de Gestao, Hotelaria e Turismo, Universidade do Algarve and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Characterizing the Efficiency of Static Pricing Schemes as a Function of the Supply The problem of selling a supply of k units to a stream of customers constitutes one of the cornerstones in revenue management. Static pricing schemes (that output the same price to all customers) are commonly used because of their simplicity and their many desirable properties; they are anonymous, nonadaptive, and order oblivious. Although the efficiency of those schemes should improve as the supply k increases, prior work has only focused either on algorithms that aim for a constant approximation that is independent of k or on the setting where k becomes really large. In contrast, this paper characterizes the efficiency of static pricing schemes as a function of the supply. Our approach stems from identifying a "sweet spot" between selling enough items and obtaining enough utility from customers with high valuations. Subsequent work shows that our pricing scheme is the optimal static pricing for every value of k. We study a pricing problem where a seller has k identical copies of a product, buyers arrive sequentially, and the seller prices the items aiming to maximize social welfare. When k = 1, this is the so-called prophet inequality problem for which there is a simple pricing scheme achieving a competitive ratio of 1/2. On the other end of the spectrum, as k goes to infinity, the asymptotic performance of both static and adaptive pricing is well understood. We provide a static pricing scheme for the small-supply regime: where k is small but larger than one. Prior to our work, the best competitive ratio known for this setting was the 1/2 that follows from the single-unit prophet inequality. Our pricing scheme is easy to describe as well as practical; it is anonymous, nonadaptive, and order oblivious. We pick a single price that equalizes the expected fraction of items sold and the probability that the supply does not sell out before all customers are served; this price is then offered to each customer while supply lasts. This extends an approach introduced by Samuel-Cahn for the case of k = 1. This pricing scheme achieves a competitive ratio that increases gradually with the supply. Subsequent work shows that our pricing scheme is the optimal static pricing for every value of k. Funding: This work was supported by the National Science Foundation [Grants CCF-2008006 and SHF-1704117]. T. Lykouris would like to acknowledge funding from Google Research. [ABSTRACT FROM AUTHOR]

This research focuses on the leadership management of the principal of SMP Negeri 1 Sonder. The formulation of the problem in this study is How to Understand School Principals about Leadership Management in Country 1 Sonder. How school principals in Negeri 1 Sonder implement leadership management and how school principals strive to improve leadership management. This study uses a qualitative research method that is qualitative descriptive. Observations, documents and interviews are used as data collection techniques. Based on the formulation of the problem and the results of the field study, several conclusions were drawn, including: The leader, namely the principal, understands that leadership management is a very important thing that helps him in organizing, carrying out and assuming responsibilities as a leader or principal. The principal carries out or implements his leadership by fulfilling his leadership duties even though it has not been maximized and fostering good relationships with teachers, students, and parents. The principal also strives to improve leadership management at SMP Negeri 1 Sonder, so that the principal strives for educational conditions to run well by dividing tasks, creating an organizational structure, controlling all tasks given to teachers, fostering good relationships and communication between the school, the government, parents and the community, as well as approaching teachers and students and trying to be leaders for the progress of the school. [ABSTRACT FROM AUTHOR]

This research uses descriptive methods and qualitative approaches. The purpose of this study is to know, understand, and analyze the practice of the local tax implementation process as well as the obstacles that occur and efforts made in achieving regional tax management targets at the Regional Finance and Revenue Management Agency (BPKPD) of Cirebon City. Data collection techniques are carried out by means of interviews with the field of Regional Revenue Planning, Development and Evaluation (PPEPD). By showing the results that, in practice, BPKPD has been in accordance with the implementation of tax management based on applicable regulations. However, there are still decreases in local taxes including billboard tax, street lighting tax, underground water tax, and PBB-P2. Thus, BPKPD needs to make efforts in optimizing its regional tax management. The efforts made are; Conducting socialization for taxpayers in fulfilling their obligations in paying taxes owed. In addition, conducting a supervision system (tapping box) as an effort to report taxpayers' business turnover. Efforts were made, to fulfill the utilization for the facilities and infrastructure of the City of Cirebon. [ABSTRACT FROM AUTHOR]

Keeping up with competitors' prices is one of the top operational challenges in pricing. However, competitive interactions in revenue management have not received much research attention in the past due to their complexity. We conduct a series of laboratory experiments to investigate the dynamic pricing behavior of two capacity-constrained firms under competition. Our experiments initially control for strategic interactions between the sellers and then allow for them. To gain a broader understanding, we also manipulate demand uncertainty and the expected market size. Our results confirm the dependency of dynamic pricing decisions on the competitor's behavior. We find that the theory is much more forgiving—in the sense that it predicts a lower level of competition among the sellers—than what we actually observe in the laboratory. The modeling literature indicates that the seller with the lower capacity has a competitive advantage, but our results reveal the opposite. Further, there is potential for high-capacity sellers to benefit from competition. Sellers tend to underprice (resp., overprice) their units at the beginning (resp., end) of a selling season. Also, competition lasts longer than the theory predicts, and customers benefit from the biases of the competing sellers. The higher-capacity seller following the best-response policy is not harmed due to the biases of the competitor. However, the lower-capacity seller's performance is greatly influenced by the competitor's degree of rationality. [ABSTRACT FROM AUTHOR]

This paper examines how data-driven personalized decisions can be made while preserving consumer privacy. Our setting is one in which the firm chooses a personalized price based on each new customer's vector of individual features; the true set of individual demand-generating parameters is unknown to the firm and so must be estimated from historical data. We extend the existing personalized pricing framework by requiring also that the firm's pricing policy preserve consumer privacy, or (formally) that it be differentially private: an industry standard for privacy preservation. We develop privacy-preserving personalized pricing algorithms and show that they achieve near-optimal revenue by deriving theoretical (upper and lower) performance bounds. Our analyses further suggest that, if the firm possesses a sufficient amount of historical data, then it can achieve a certain level of differential privacy almost "for free." That is, the revenue loss due to privacy preservation is of smaller order than that due to estimation. We confirm our theoretical findings in a series of numerical experiments based on synthetically generated and online auto lending (CPRM-12-001) data sets. Finally, motivated by practical considerations, we also extend our algorithms and findings to a variety of alternative settings, including multiproduct pricing with substitution effect, discrete feasible price set, categorical sensitive features, and personalized assortment optimization. This paper was accepted by Vishal Gaur, operations management. Funding: R. Momot acknowledges financial support from the HEC Paris Foundation and the Agence Nationale de la Recherche (French National Research Agency) "Investissements d'Avenir" [Grant LabEx Ecodec/ANR-11-LABX-0047] during the initial stages of this project. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4925. [ABSTRACT FROM AUTHOR]

We propose how to bid in first-price auctions when a bidder knows the own value but not how others will bid. To do this, we introduce a methodology to show how to make choices in strategic settings without assuming common knowledge or equilibrium behavior. Accordingly, we first eliminate environments that are believed not to occur and then find a robust rule that performs well in the remaining environments. We test our bids using data from laboratory experiments and the field and find that our bids outperform those made by real bidders. This paper was accepted by Omar Besbes, revenue management and market analytics. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4899. [ABSTRACT FROM AUTHOR]

In various sectors, such as retail, firms encounter customers with multiunit demand and often implement nonlinear pricing to accommodate this demand structure. While effective, this pricing strategy lacks the adaptability offered by dynamic pricing, a trend gaining significance in the retail landscape due to technological advancements. Neglecting multiunit demand in dynamic pricing, however, can result in suboptimal prices and revenue losses. In response, this paper introduces multiunit dynamic pricing which integrates the strengths of both nonlinear and dynamic pricing strategies. We formulate a stage-wise optimization problem, considering customer preferences for batches of a product through a model based on random willingness-to-pay. The willingness-to-pay is influenced by a combination of the customer's attraction to and consumption of the product—both private information. The firm, functioning as a monopoly, has the ability to price-discriminate between various order sizes by quoting nonlinear batch prices. Our investigation explores three cases of observable information: attraction to the product, consumption of the product, or both. Optimality conditions are derived for all cases, establishing a closed-form expressions for two of them. Additionally, we demonstrate the preservation of desirable monotonicity in time and capacity. Leveraging this monotonicity, we showcase the dynamics of the optimal pricing policy. A simulation study underscores the potential of our approach, highlighting the value of information in supporting strategic decisions, particularly regarding investments in customer profiling and segmentation. Furthermore, we illustrate how our solutions enable firms to make informed stocking and restocking decisions, providing practical insights for firms in multiunit dynamic pricing environments. [ABSTRACT FROM AUTHOR]

This paper presents an automated approach for providing ranked lists of outliers in observed demand to support analysts in network revenue management. Such network revenue management, e.g. for railway itineraries, needs accurate demand forecasts. However, demand outliers across or in parts of a network complicate accurate demand forecasting, and the network structure makes such demand outliers hard to detect. We propose a two-step approach combining clustering with functional outlier detection to identify outlying demand from network bookings observed on the leg level. The first step clusters legs to appropriately partition and pools booking patterns. The second step identifies outliers within each cluster and uses a novel aggregation method across legs to create a ranked alert list of affected instances. Our method outperforms analyses that consider leg data without regard for network implications and offers a computationally efficient alternative to storing and analysing all data on the itinerary level, especially in highly-connected networks where most customers book multi-leg products. A simulation study demonstrates the robustness of the approach and quantifies the potential revenue benefits from adjusting demand forecasts for offer optimisation. Finally, we illustrate the applicability based on empirical data obtained from Deutsche Bahn. [ABSTRACT FROM AUTHOR]

Timely and accurate estimates of sugarcane yield provide valuable information for food management, bio-energy production, (inter)national trade, industry planning and government policy. Remote sensing and machine learning approaches can improve sugarcane yield estimation. Previous attempts have however often suffered from too few training samples due to the fact that field data collection is expensive and time-consuming. Our study demonstrates that unmanned aerial vehicle (UAV) data can be used to generate field-level yield data using only a limited number of field measurements. Plant height obtained from RGB UAV-images was used to train a model to derive intra-field yield maps based on 41 field sample plots spread over 20 sugarcane fields in the Udon Thani Province, Thailand. The yield maps were subsequently used as reference data to train another model to estimate yield from multi-spectral Sentinel-2 (S2) imagery. The integrated UAV yield and S2 data was found efficient with RMSE of 6.88 t/ha (per 10 m × 10 m pixel), for average yields of about 58 t/ha. The expansion of the sugarcane yield mapping across the entire region of 11,730 km2 was in line with the official statistical yield data and highlighted the high spatial variability of yields, both between and within fields. The presented method is a cost-effective and high-quality yield mapping approach which provides useful information for sustainable sugarcane yield management and decision-making. [ABSTRACT FROM AUTHOR]

The aim of this paper is to optimize the allocation of multiple advertisements on a Web banner, where the price of an advertisement depends on the location at the banner. This problem can be defined as a two-dimensional single orthogonal knapsack problem with a location-based pixel-price model. A formulation is proposed in which the problem is specified as a 0–1 integer programming problem. As this problem is NP-complete, we mainly focus on a heuristic approach to solve the problem. We propose two new heuristic algorithms: the reactive GRASP algorithm and the partitioning left-justified algorithm. Next to that, we present an exact algorithm that is able to solve small problem instances in a reasonable time. These newly presented algorithms are compared with respect to efficiency and effectiveness to existing algorithms that solve the problem without a location-based pixel-price model. To test the quality of the algorithms, we have executed two experiments. The results of these experiments show that overall the reactive GRASP algorithm is the most effective algorithm, whereas the greedy stripping algorithm is the most efficient. [ABSTRACT FROM AUTHOR]

Dynamic pricing is a crucial revenue management technique when product status changes over time. It can become more prominent if applied in conjunction with an inventory management strategy for deteriorating items. This paper presents a framework for dynamic pricing in a continuous time horizon for a dual-channel supply chain consisting of a manufacturer and a retailer. The manufacturer produces a deteriorating product and sells through direct and indirect retail channels, and both channels compete with each other over retail prices. The freshness of the product varies with time, and thus both channels invest in preservation technology, which further leads to carbon emissions. We derive an analytical solution to the dual-channel pricing model. Furthermore, the case of single-channel pricing has been investigated to study and compare its pricing strategy with the dual-channel pricing strategy. Lastly, a numerical illustration is presented to study the impact of key parameters on optimal decisions through sensitivity analysis. We provide managerial recommendations in various scenarios like an increase in competition, the shift of consumer preference between channels and fluctuation in demand. [ABSTRACT FROM AUTHOR]

Korean pine is one of the most important tree species in northeastern China, where Korean pine plantations produce timber and edible seeds. Often, seeds create more income than timber. Predicting the timber and cone yields of alternative management schedules of the plantations involves uncertainty because the future climatic conditions for tree growth and cone production are unknown. This study developed a simulation model that generates stochastic variation around the predictions of tree growth and cone yield models, allowing the forest manager to seek cutting schedules that maximize the expected amounts of timber or cones, or the expected economic profit, under uncertain future states of nature. Stochastic analysis also facilitates management optimizations for different risk attitudes. The differential evolution algorithm and the developed stochastic simulation model were used to optimize the management of planted Korean pine. Timber and cone yields of a management schedule were calculated under 100 different scenarios for tree growth and cone production. When the growth and cone yield scenarios were stationary (no temporal trends), the optimal management schedules were similar to those of deterministic optimization. The benefits of stochastic optimization increased when it was assumed that the tree growth scenarios may contain climate-change-induced trends. Non-stationary growth variation led to shorter optimal rotation lengths than stationary growth variation. Increasing risk tolerance shortened optimal rotations. [ABSTRACT FROM AUTHOR]

This study explores the interplay between nitrogen doses and seeding rates on wheat yield, biomass, and protein content. Utilizing tools such as the Normalized Difference Vegetation Index (NDVI), Soil Plant Analysis Development (SPAD) measurements, and canopy temperature (CT), we conducted experiments over five growing seasons. The treatments included three nitrogen levels (0, 60, 120 kg/ha) and three seeding rates (300, 400, 500 seeds/m2) in a split-plot design with 90 plots and two replications. Our results show that an intermediate nitrogen dose (60 kg/ha) combined with a moderate seed rate (400 seeds/m2) enhances wheat yield by 22.95%. Reduced nitrogen levels increased protein content, demonstrating wheat's adaptive mechanisms under nitrogen constraints. NDVI analysis highlighted significant growth during the tillering phase with high nitrogen, emphasizing early-stage nutrient management. SPAD measurements showed that early nitrogen applications boost chlorophyll content, essential for vigorous early growth, while CT data indicate that optimal nitrogen and seed rates can effectively modulate plant stress responses. As crops mature, the predictive capacity of NDVI declines, indicating the need for adjusted nitrogen strategies. Collectively, these findings advocate for refined management of nitrogen and seeding rates, integrating NDVI, SPAD, and CT assessments to enhance yields and promote sustainable agricultural practices while minimizing environmental impacts. [ABSTRACT FROM AUTHOR]

This research explores the effects of High Tide alerts on hotel prices in Venice, a city that is vulnerable to the impacts of extreme climate events due to its fragile ecosystem and a long history of floods in the city center. By analyzing and combining price data from Booking.com with publicly available information on tides and weather, this study uses regression discontinuity design to test for changes in hotel prices when tide levels reach a critical threshold. The results offer insights into the sensitivity of hotel prices to weather alerts and provide valuable information on the potential impact of climate change on Venice's tourism-driven economy, with implications for the cost–benefit analysis of activating protective barriers for lagoon protection. [ABSTRACT FROM AUTHOR]

Demand estimation is a fundamental component of revenue management systems. The demand for a product can be ascertained from the customers who purchase it. Identifying customer types in this context is a challenging endeavor, recently resolved using meta-heuristic and mathematical techniques. Meta-heuristics leverage the scarcity of data in the search space, commencing with random samples and employing the fitness function as a guide during operations. Our proposed approach generates the search space by incorporating supplementary data to identify valuable customer types. We employ a new period table with additional data to achieve this objective. Subsequently, we reduce the search space through data mining's clustering method and ultimately employ a greedy algorithm and fitness function to identify valuable customer types and construct our solution. To validate our approach, we compare our solution and the most recent research in this field, including genetic, memetic, and mathematical approaches. Compared to memetic methods, our results indicate that our solution has a smaller length, with a maximum reduction of 34%, and exhibits improvement in log value, with a maximum of 7%. [ABSTRACT FROM AUTHOR]

This article aims to identify the measures to overcome the COVID-19 crisis proposed by revenue managers during the lockdown period. The comparison of such measures to others against previous crises and their development afterwards is valuable to future decision-making processes in the hospitality industry. A survey of 322 professionals linked to revenue management was undertaken. The holistic and innovative point of view, integrating RM implementation, operations, marketing, and communication following the Flywheel Model, led us to revenue managers' viewpoints about the measures to overcome the lockdown phase of the COVID-19 crisis. It is, therefore, necessary to integrate them to improve the understanding of hospitality crisis management. [ABSTRACT FROM AUTHOR]

Experimentation is prevalent in online marketplaces and social networks to assess the effectiveness of new market intervention. To mitigate the interference among users in an experiment, a common practice is to use a cluster-based experiment, where the designer partitions the market into loosely connected clusters and assigns all users in the same cluster to the same variant (treatment or control). Given the experiment, we assume an unbiased Horvitz–Thompson estimator is used to estimate the total market effect of the treatment. We consider the optimization problem of choosing (correlated) randomized assignments of clusters to treatment and control to minimize the worst-case variance of the estimator under a constraint that the marginal assignment probability is q∈(0,1) for all clusters. This problem can be formulated as a linear program where both the number of decision variables and constraints are exponential in the number of clusters—and hence is generally computationally intractable. We develop a family of practical experiments that we refer to as independent block randomization (IBR) experiments. Such an experiment partitions clusters into blocks so that each block contains clusters of similar size. It then treats a fraction q of the clusters in each block (chosen uniformly at random) and does so independently across blocks. The optimal cluster partition can be obtained in a tractable way using dynamic programming. We show that these policies are asymptotically optimal when the number of clusters grows large and no cluster size dominates the rest. In the special case where cluster sizes take values in a finite set and the number of clusters of each size is a fixed proportion of the total number of clusters, the loss is only a constant that is independent of the number of clusters. Beyond the asymptotic regime, we show that the IBR experiment has a good approximation for any problem instance when q is not very tiny. We also examine the performance of the IBR experiments on data-driven numerical examples, including examples based on Airbnb and Facebook data. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Funding: O. Candogan acknowledges NSF [Award 2216912] for "Institute for Data, Econometrics, Algorithms and Learning." Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2021.02741. [ABSTRACT FROM AUTHOR]

Iron (Fe) biofortification of edible organs without influencing crop yield is challenging, and potential solutions are largely unknown. Recently, Yan et al. identified a key regulator NAC78 (NAM/ATAF/CUC DOMAIN TRANSCRIPTION FACTOR 78) that enriches Fe in maize kernels without compromising crop yield. This may provide new crop yield management strategies for Fe acquisition and nutritional security. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Problem definition: We study the adherence to the recommendations of a decision support system (DSS) for clearance markdowns at Zara, the Spanish fast fashion retailer. Our focus is on behavioral drivers of the decision to deviate from the recommendation, and the magnitude of the deviation when it occurs. Academic/practical relevance: A major obstacle in the implementation of prescriptive analytics is users’ lack of trust in the tool, which leads to status quo bias. Understanding the behavioral aspects of managers’ usage of these tools, as well as the specific biases that affect managers in revenue management contexts, is paramount for a successful rollout. Methodology: We use data collected by Zara during seven clearance sales campaigns to analyze the drivers of managers’ adherence to the DSS. Results: Adherence to the DSS’s recommendations was higher, and deviations were smaller, when the products were predicted to run out before the end of the campaign, consistent with the fact that inventory and sales were more salient to managers than revenue. When there was a higher number of prices to set, managers of Zara’s own stores were more likely to deviate from the DSS’s recommendations, whereas franchise managers did the opposite and showed a weak tendency to adhere more often instead. Two interventions aimed at shifting salience from inventory and sales to revenue helped increase adherence and overall revenue. Managerial implications: Our findings provide insights on how to increase voluntary adherence that can be used in any context in which a company wants an analytical tool to be adopted organically by its users. We also shed light on two common biases that can affect managers in a revenue management context, namely salience of inventory and sales, and cognitive workload. Supplemental Material: The e-companion is available at https://doi.org/10.1287/msom.2022.1166 .

Purpose: This research study aims to identify effective turnaround strategies that can resolve municipal financial distress by improving the audit outcomes of dysfunctional municipalities. Motivation: As many as 64% of South African municipalities were classified as financially distressed by the National Treasury on 7 December 2022, indicating that most South African municipalities require urgent intervention. There is a knowledge gap in the existing literature on how to resolve municipal financial distress practically. Design / Methodology / Approach: An exploratory sequential mixed methods design was utilised to identify effective turnaround strategies. An interpretative content analysis of Audit Action Plans was conducted, followed by a census of the provincial treasuries, the National Treasury and the Department of Cooperative Governance and Traditional Affairs (COGTA) in South Africa. Main findings: The main research findings indicate that turnaround strategies associated with effective and adequate internal controls, revenue management, ethics and fraud prevention can improve municipal financial viability. Practical implications/Managerial impact: The improved financial viability of municipalities will empower dysfunctional municipalities to resolve their own financial distress by implementing similar effective and practical turnaround strategies, thereby improving the quality of essential service delivery to local communities. Novelty/Contribution: Improved essential service delivery directly contributes towards local economic development and job creation. [ABSTRACT FROM AUTHOR]

Background: Organic production of cluster bean is to contribute to the enhancement of production sustainability along with ecology. Sustainability in organic farming must therefore be seen in a holistic sense, which includes ecological, economic and social aspects. A synergistic effectof bio fertilizer with crop increases the crop productivity and sustainability also. Bio fertilizers are low cost, effective and renewable sources of plant nutrients to supplement chemical fertilizers. Therfore to overcome the ill effect of chemical based farming, organic system is becoming the emerging need of the society. Methods: A field experiment was carried out in Pusa Navbahar variety of clusterbean during summer season (February to May) of 2019 at College farm, College of Horticulture, Sardarkrushinagar Dantiwada Agricultural University, Jagudan, Dist. Mehsana, Gujarat, India. Sixteen treatments having various combinations of organic sources of nutrients (FYM, vermicompost and neem cake), bio fertilizers (Rhizobium + PSB + KSM) along with RDF (20/40/0 kg/ha) as a control. They were replicated thrice in a randomized block design having a plot size of 3.0 m × 2.0 m with a spacing of 60 cm × 20 cm. Standard practices were followed during the entire course of the investigation. Result: It showed highly significant differences among different treatments for majority of the growth and yield attributing traits, but treatment T6 (75% N through FYM + Rhizobium + PSB + KSM) was rated as the best treatment for characters like days taken for germination, number of root nodules per plant, plant height at 60 DAS (cm), number of pod per cluster, number of cluster per plant, green pod yield per plant (g), green pod yield per plot (kg) and green pod yield per hectare (kg) as compared to other treatment. It can be concluded that the organic nutrient management in cluster bean with the application of 75% N through FYM + Rhizobium + PSB + KSM is beneficial for obtaining a higher yield. [ABSTRACT FROM AUTHOR]

Sports events significantly affect the hosting destination and the interest in its visiting. Previous studies focused mainly on longitudinal events like the Olympic Games and FIFA World Cups, where the single-day events were not investigated. The paper investigates how hosting UEFA competition finals affects the local hospitality industry's performance. The study adopted the benchmarking data from Smith Travel Research (STR) and evaluated the impact on the occupancy and prices of accommodation facilities. The results show a significant pricing effect for the day of the event and the day before the finals. There was increased demand when focusing on the occupancy rate, but not as significant as for the prices. No longterm effect on the hospitality industry was identified, and, in some cases, the negative effect might be perceived after the event's termination. To benefit from the event with significant interest from football fans, hoteliers should increase the sales rates, provide the guests with a broader range of services, and cooperate with the event organisers to increase the total performance. [ABSTRACT FROM AUTHOR]

Revenue Management of Service Systems under Incomplete Information Revenue management with reusable resources finds many important applications in today's economy, such as cloud computing services, car/bicycle rental services, ride-hailing services, hotel management, project team management, and call center services. The existing literature predominantly assumes that the stochastic demand and service processes are given as an input to the models, and the pricing decisions are made with full knowledge of the distributional information. However, in practice, the decision maker may not know how demand or service rates react to price changes. Thus, the decision maker needs to learn the underlying mapping between prices and rates from past observations, while maximizing the total expected revenue on the fly. In "Online Learning and Pricing for Service Systems with Reusable Resources", H. Jia, C. Shi, and S. Shen developed a series of online learning algorithms for revenue management problems with reusable resources and showed that they admit an optimal regret bound. We consider a price-based revenue management problem with finite reusable resources over a finite time horizon T. Customers arrive following a price-dependent Poisson process, and each customer requests one unit of c homogeneous reusable resources. If there is an available unit, the customer gets served within a price-dependent exponentially distributed service time; otherwise, the customer waits in a queue until the next available unit. In this paper, we assume that the firm does not know how the arrival and service rates depend on posted prices, and thus it makes adaptive pricing decisions in each period based only on past observations to maximize the cumulative revenue. Given a discrete price set with cardinality P, we propose two online learning algorithms, termed batch upper confidence bound (BUCB) and batch Thompson sampling (BTS), and prove that the cumulative regret upper bound is O ˜ (P T) , which matches the regret lower bound. In establishing the regret, we bound the transient system performance upon price changes via a novel coupling argument, and also generalize bandits to accommodate subexponential rewards. We also extend our approach to models with balking and reneging customers and discuss a continuous price setting. Our numerical experiments demonstrate the efficacy of the proposed BUCB and BTS algorithms. Funding: This research was partially supported by an Amazon research award and the Department of Energy [Award DE-SC0018018]. [ABSTRACT FROM AUTHOR]

A Unified View of Online Matching and Resource Allocation Problems Whereas small regret online algorithms for applications as diverse as network revenue management (NRM), assemble-to-order (ATO) systems, and online stochastic bin packing (SBP) are known in the literature, the design of the existing algorithms are tailored to the specific application and often use the strategy of resolving a planning linear program. In the paper "Greedy Algorithm for Multiway Matching with Bounded Regret," Gupta proposes a unified model for studying such online matching/allocation problems. In the unified model, resources of three types—off-line (e.g., inventory in NRM), online-queueable (e.g., orders or resources in ATO systems), or online-nonqueueable (e.g., requests in NRM, items in SBP)—must be combined to create feasible configurations. Leveraging the unified framework, the author gives one simple greedy algorithm that gives small regret (bounded or logarithmic in time horizon) for these diverse applications under a mild nondegeneracy condition on the off-line planning problem. In this paper, we prove the efficacy of a simple greedy algorithm for a finite horizon online resource allocation/matching problem when the corresponding static planning linear program (SPP) exhibits a nondegeneracy condition called the general position gap (GPG). The key intuition that we formalize is that the solution of the reward-maximizing SPP is the same as a feasibility linear program restricted to the optimal basic activities, and under GPG, this solution can be tracked with bounded regret by a greedy algorithm, that is, without the commonly used technique of periodically resolving the SPP. The goal of the decision maker is to combine resources (from a finite set of resource types) into configurations (from a finite set of feasible configurations) in which each configuration is specified by the number of resources consumed of each type and a reward. The resources are further subdivided into three types—off-line (whose quantity is known and available at time 0), online-queueable (which arrive online and can be stored in a buffer), and online-nonqueueable (which arrive online and must be matched on arrival or lost). Under GPG, we prove that (i) our greedy algorithm gets bounded anytime regret of O (1 / ϵ 0) for matching reward (ϵ0 is a measure of the GPG) when no configuration contains both an online-queueable and an online-nonqueueable resource and (ii) O (log t) expected anytime regret otherwise (we also prove a matching lower bound). By considering the three types of resources, our matching framework encompasses several well-studied problems, such as dynamic multisided matching, network revenue management, online stochastic packing, and multiclass queueing systems. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2400. [ABSTRACT FROM AUTHOR]

Bounded Regret for LP-Based Revenue-Management Problems In "An Improved Analysis of LP-Based Control for Revenue Management," Chen, Li, and Ye study a class of quantity-based network revenue-management problems. The authors consider a stochastic setting where all the orders are i.i.d. sampled and the customers are of finite type. They focus on the classic LP-based adaptive algorithm and consider regret as the performance measure. They found that when the underlying LP is nondegenerate, the algorithm achieves a problem-dependent regret upper bound that is independent of the horizon/number of time periods T; when the underlying LP is degenerate, the algorithm achieves a tight regret upper bound that scales on the order of T log(T) and matches the lower bound up to a logarithmic order. In this paper, we study a class of revenue-management problems, where the decision maker aims to maximize the total revenue subject to budget constraints on multiple types of resources over a finite horizon. At each time, a new order/customer/bid is revealed with a request of some resource(s) and a reward, and the decision maker needs to either accept or reject the order. Upon the acceptance of the order, the resource request must be satisfied, and the associated revenue (reward) can be collected. We consider a stochastic setting where all the orders are independent and identically distributed-sampled—that is, the reward-request pair at each time is drawn from an unknown distribution with finite support. The formulation contains many classic applications, such as the quantity-based network revenue-management problem and the Adwords problem. We focus on the classic linear program (LP)-based adaptive algorithm and consider regret as the performance measure defined by the gap between the optimal objective value of the certainty-equivalent LP and the expected revenue obtained by the online algorithm. Our contribution is twofold: (i) When the underlying LP is nondegenerate, the algorithm achieves a problem-dependent regret upper bound that is independent of the horizon/number of time periods T; and (ii) when the underlying LP is degenerate, the algorithm achieves a tight regret upper bound that scales on the order of T log (T) and matches the lower bound up to a logarithmic order. To our knowledge, both results are new and improve the best existing bounds for the LP-based adaptive algorithm in the corresponding setting. We conclude with numerical experiments to further demonstrate our findings. Supplemental Material: The online appendix is available at https://doi.org/10.1287/opre.2022.2358. [ABSTRACT FROM AUTHOR]

The forecasting of demand or cancellations is highly important for efficient revenue management in the hotel industry. Previous studies have mainly focused on the accuracy of the prediction of reservation number or cancellation rate on a specific accommodation or hotel chain; therefore, the application of the prediction to different accommodations or under the behavioral change of customers in response to natural or human events is difficult without the re-estimation of the prediction model. Information of the customer behavioral trend on the accommodation reservations is necessary for the construction of a general forecasting model. In this study, we focus on one of the general trends of customer behavior, that is, the reservation timing and the time changes of the cancellation probability using the big data of the reservation records provided by an online trip agency in Japan. We showed that the reservation timing and cancellation probability can be decomposed by five and six exponential functions of the days until the stay and the days from the reservations. We also showed that the significant factors influencing the time changing patterns are the guest numbers per room for both reservation and cancellation, composition of guests in terms of the number and gender of guests, and the stay length for reservation. These findings imply that the customer behavior during accommodation reservation could be categorized into multiple motivational factors toward reservations or cancellations. Our results contribute to the construction of a general forecasting model on the accommodation reservations. [ABSTRACT FROM AUTHOR]

Problem definition: We study a robust screening problem where a seller attempts to sell a product to a buyer knowing only the moment and support information of the buyer's valuation distribution. The objective is to maximize the competitive ratio relative to an optimal hindsight policy equipped with full valuation information. Methodology/results: We formulate the robust screening problem as a linear programming problem, which can be solved efficiently if the support of the buyer's valuation is finite. When the support of the buyer's valuation is continuous and the seller knows the mean and the upper and lower bounds of the support for the buyer's valuation, we show that the optimal payment is a piecewise polynomial function of the valuation with a degree of at most two. Moreover, we derive the closed-form competitive ratio corresponding to the optimal mechanism. The optimal mechanism can be implemented by a randomized pricing mechanism whose price density function is a piecewise inverse function adjusted by a constant. When the mean and variance are known to the seller, we propose a feasible piecewise polynomial approximation of the optimal payment function with a degree of at most three. We also demonstrate that the optimal competitive ratio exhibits a logarithmic decay with respect to the coefficient of variation of the buyer's valuation distribution. Managerial implications: Our general framework provides an approach to investigating the value of moment information in the robust screening problem. We establish that even a loose upper bound of support or a large variance can guarantee a good competitive ratio. Funding: The research of S. Wang is partially supported by the National Natural Science Foundation of China [Grant 72394395]. The research of S. Liu is partly supported by the National Natural Science Foundation of China [Grant NSFC-72072117]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0072. [ABSTRACT FROM AUTHOR]

Problem definition: Production systems deteriorate stochastically due to use and may eventually break down, resulting in high maintenance costs at scheduled maintenance moments. This deterioration behavior is affected by the system's production rate. Although producing at a higher rate generates more revenue, the system may also deteriorate faster. Production should thus be controlled dynamically to tradeoff deterioration and revenue accumulation in between maintenance moments. We study systems for which the relation between production and deterioration is known and the same for each system and systems for which this relation differs from system to system and needs to be learned on-the-fly. The decision problem is to find the optimal production policy given planned maintenance moments (operational) and the optimal interval length between such maintenance moments (tactical). Methodology/results: For systems with a known production-deterioration relation, we cast the operational decision problem as a continuous time Markov decision process and prove that the optimal policy has intuitive monotonic properties. We also present sufficient conditions for the optimality of bang-bang policies, and we partially characterize the structure of the optimal interval length, thereby enabling efficient joint optimization of the operational and tactical decision problem. For systems that exhibit variability in their production-deterioration relations, we propose a Bayesian procedure to learn the unknown deterioration rate under any production policy. Numerical studies indicate that on average across a wide range of settings (i) condition-based production increases profits by 50% compared with static production, (ii) integrating condition-based production and maintenance decisions increases profits by 21% compared with the state-of-the-art sequential approach, and (iii) our Bayesian approach performs close, especially in the bang-bang regime, to an Oracle policy that knows each system's production-deterioration relation. Managerial implications: Production should be adjusted dynamically based on real-time condition monitoring and the tactical maintenance planning should anticipate and integrate these operational decisions. Our proposed framework assists managers to do so optimally. Funding: This work was supported by the Nederlandse Organisatie voor Wetenschappelijk Onderzoek [Grant 439.17.708]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2022.0473. [ABSTRACT FROM AUTHOR]

We study how to optimally match agents in a dynamic matching market with heterogeneous match cardinalities and values. A network topology determines the feasible matches in the market. In general, a fundamental tradeoff exists between short-term value—which calls for performing matches frequently—and long-term value—which calls, sometimes, for delaying match decisions in order to perform better matches. We find that in networks that satisfy a general position condition, the tension between short- and long-term value is limited, and a simple periodic clearing policy (nearly) maximizes the total match value simultaneously at all times. Central to our results is the general position gap ϵ; a proxy for capacity slack in the market. With the exception of trivial cases, no policy can achieve an all-time regret that is smaller, in terms of order, than ϵ−1. We achieve this lower bound with a policy, which periodically resolves a natural matching integer linear program, provided that the delay between resolving periods is of the order of ϵ−1. Examples illustrate the necessity of some delay to alleviate the tension between short- and long-term value. This paper was accepted by David Simchi-Levi, revenue management and market analytics. Funding: This work was supported by the National Science Foundation [Grant CMM-2010940] and the U.S. Department of Defense [Grant STTR A18B-T007]. [ABSTRACT FROM AUTHOR]

The nitrogen (N) requirement for barley cultivation can vary from 90 to 130 kg ha−1 cycle−1, representing 30% of production costs. Furthermore, obtaining mineral N is difficult due to constant conflicts involving producing countries. Excess N will increase the cost of production and contaminate the soil and groundwater, unbalancing the fauna, flora, and microbiology of the environment. The objective of this research was to evaluate the management of inoculation and N fertilization with A. brasilense on the development and productivity of barley. Field experiment was carried out for two years. A randomized block design was used in a 5 (Factor A) × 2 (Factor D) factorial arrangement with four replications. In which the "factor A" included five doses of A. brasilense, whereas the "factor D" was evaluated N management (with or without N management). The inoculant used had approximately of 2.0 × 108 viable cells mL−1 (Ab-V5 and Ab-V6 strains). Innoculation was carried out on the seeds 1 hour before sowing, in order to homogenize the inoculant in the seeds, to guarantee homogeneity of the distribution of microorganisms on the seeds. The best dose of A. brasilense was 12.5 mL kg−1 of seed, which provided an increase in plant height, number of ears, mass and grain productivity. The use of A. brasilense showed significant improvements in management with or without the use of mineral N. However, better results are obtained when the association between N management and the use of inoculant is made. Thus, when using up to 22.5 kg of N, the best results were observed. [ABSTRACT FROM AUTHOR]

Background: Field experiment was conducted at Anbil Dharmalingam Agricultural College and Research Institute, Thiruchirappalli, Tamil Nadu during the rabi season of 2022-23 to evaluate the effect of spacing and weed management practices of yield parameters and yield of direct seeded rice (DSR) under sodic soil using three levels of spacing and seven weed management strategies. Methods: The main plots were spacing of 20 × 15 cm, 20 × 20 cm and 25 × 25 cm. Sub-plot consisted of weed management practices in DSR viz., pyrazosulfuron ethyl 20 g ha-1 at 3 DAS + bispyribac sodium 25 g ha-1 at 20 DAS, pyrazosulfuron ethyl 20 g ha-1 at 3 DAS + cono weeding at 20 DAS + hand weeding (HW) at 40 DAS, pyrazosulfuron ethyl 20 g ha-1 at 3 DAS + cono weeding at 20 DAS and 40 DAS, pyrazosulfuron ethyl 20 g ha-1 at 3 DAS + bispyribac sodium at 25 g ha-1 at 20 DAS + cono weeding at 40 DAS, pyrazosulfuron ethyl 20 g ha-1 at 3 DAS + bispyribac sodium at 25 g ha-1 at 20 DAS + HW at 40 DAS, hand weeding at 20 and 40 DAS and unweeded control. The field experiment was laid out in split plot design with three replications. Result: The results indicated that lower total weed density (40.39 no. m-2), dry weight of weeds (85.52 g m-2) and higher WCE (77.5%) were registered under 20 × 15 cm which was on par with spacing of 20 × 20 cm. The higher number of productive tillers m-2 (389), number of grains panicle-1 (192), number filled grains panicle-1 (187), grain yield (4546 kg ha-1) and straw yield (6714 kg ha-1) were observed under spacing of 20 × 20 cm. Application of pyrazosulfuron ethyl 20 g ha-1 at 3 DAS + bispyribac sodium 25 g ha-1 at 20 DAS + cono weeding at 40 DAS possessed the lowest total weed density (7.67 no. m-2), dry weight of weeds (12.63 g m-2), higher WCE (97.0) at 45 DAS and significantly improved the yield parameters and yields compared to unweeded control. The highest grain and straw yields were recorded from interaction between 20 × 20 cm and pyrazosulfuron ethyl 20 g ha-1 at 3 DAS + bispyribac sodium 25 g ha-1 at 20 DAS + cono weeding at 40 DAS. [ABSTRACT FROM AUTHOR]

The increasing cost of fossil-based energy sources has driven research in bio-based alternatives, such as perennial grasses for feedstock. The mid-Atlantic receives appreciable summer rainfall that may support a two-cut-per-year−1 harvest. At Virginia State University, a study on annual forage sorghum and two one-year stand perennials, miscanthus, and selected switchgrass ecotypes was carried out. The experimental design was a split-plot with harvest systems and feedstock grass species randomly assigned to the main and sub-plots, respectively. Only perennial grasses were assigned to the two-cut-per-year−1 system. The first cut occurred in early summer, and the second and single cut occurred after the frost-kill. Under the two-cut system, in 2022, the first-cut dry matter (DM) yield ranged from 8.9 Mg ha−1 in Blackwell to 14.7 Mg ha−1 in BoMaster. Additionally, except for BoMaster, the regrowth DM yields were within 10% of the first-cut DM yield. Under the one-cut system, the yield ranged from 10.8 Mg ha−1 in Blackwell to 23.2 Mg ha−1 in sorghum. Under the two-cut system, in 2023, miscanthus produced the greatest first-cut DM yield of 18.4 Mg ha−1, while other perennials averaged 10.1 Mg ha−1. Compared to the first cut, the hot and dry summer significantly reduced regrowth for all feedstock species, with the miscanthus DM yield dropping by 64%. While forage attributes differ among feedstock species, in general, both the first cut and regrowth showed greater crude protein and mineral elements, as well as lower ADF contents compared to a single cut following a killing freeze. Sorghum had better forage quality for the one-cut-per-year−1 feedstock material, and, along with the first cut and regrowth, it may have the potential for use as forage for maintenance energy in animal systems. For perennials, the two-cut-per-year−1 system removed the greatest quantities of nutrients during both years, with the first-cut harvest contributing about 65% of all removed N and K. Sorghum removed the greatest quantities of nutrients compared to the perennial under a one-cut-per-year−1 system. Therefore, while a two-cut-per-year−1 system can result in the greatest DM yields for dual-purpose use, its adoption calls for a critical analysis of economic benefits that considers feedstock bioenergy processing approaches, stand persistence, and fertilizer management strategies to address potential soil fertility depletion due to mineral element mining. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of predicting the passenger load factor (PLF) in air transportation to optimize capacity management and revenue maximization. Leveraging historical reservation data from 19 Turkish Airlines market routes and sample flights, we propose a novel approach combining deep assessment methodology (DAM) with fractional calculus theory. By modeling the relationship between PLF and the number of days remaining until a flight, our method yields minimal errors compared to traditional techniques. Through a continuous curve constructed using the least-squares approach, we enable the anticipation of future flight values. Our analysis demonstrates that the DAM model with a first-order derivative outperforms linear techniques and the Fractional Model-3 in both modeling capabilities and prediction accuracy. The proposed approach offers a data-driven solution for efficiently managing air transport capacity, with implications for revenue optimization. Specifically, our modeling findings indicate that the DAM wd model improves prediction accuracy by approximately 0.67 times compared to the DAM model, surpassing the fractional model and regression analysis. For the DAM wd modeling method, the lowest average mean absolute percentage error (AMAPE) value achieved is 0.571, showcasing its effectiveness in forecasting flight outcomes. [ABSTRACT FROM AUTHOR]

Rail ticket allocation is a typical technique of revenue management that the operators determine the quantity of each train tickets allocated to each OD (Origin-Destination) pair. For increasing ticket allocation scheme flexibility and selling more tickets under stochastic demand, rail operators propose OD-shared tickets that are shared by all OD passengers. For avoiding OD-shared tickets are cut off by short-distance OD at early stage which causes long-distance passengers fail to purchase tickets, we integrate OD-shared ticket sales strategy into ticket allocation problem to further utilize OD-shared tickets. We formulate the model to jointly optimize both OD ticket and OD-shared ticket scheme and OD-shared ticket sales strategy. We simulate passenger purchasing process to calculate the ticket sold quantity and nest it in PSO algorithm to solve the model. Numerical experiment results show that considering OD-shared ticket sales strategy effectively improves OD-shared ticket and total ticket revenue by 9.55% and 2.26% respectively. [ABSTRACT FROM AUTHOR]

The study analyzed how elements of crop management can contribute to increasing the yield of the winter rape crop. Field research took place in the area of Satu Mare locality, Arad County, Romania, in the period 2022-2023. The field experiments were organized in farm conditions, with accessible agricultural technologies. Two soil working systems (Sws) were considered: a classic system of soil preparation by disc tillage (SwsA), and a system of soil preparation by direct seeding (SwsB). Four treatments (T) of nitrogen and sulfur fertilization (T1, T3 and T4) were applied to the background of each system, compared to a classic fertilization (T2). In the case of soil tillage SwsA system, rape yield average was Y(AvgSwsA) = 3,537.50 kg ha-1 (T4 treatment generated increase yield, ΔY = 380.50 kg ha-1). In the case of soil tillage SwsB system, rape yield average was Y(AvgSwsB) = 3,795.00 kg ha-1 (T4 treatment generated positive growth, ΔY = 1,007.00 kg ha-1). At the experiment level, the average yield was Y(AvgSwsA&B) = 3,666.25 kg ha-1. A significant yield increase was recorded only in the case of the T4 treatment, with ΔY(SwsB-T4) = 1,135.75 kg ha-1. Multiparameter analysis (PCA, CA) facilitated distribution diagrams and classification of variants, under conditions of statistical safety (Coph.corr = 0.936). PC1 explained 40.35% of variance and PC2 explained 29.297% of variance. Mathematical models described the variation of yield (Y) in relation to biometric parameters of the plants (p<0.001, R² = 0.873 to R² = 0.989). [ABSTRACT FROM AUTHOR]

Studying an integrated management of the risks that young entrepreneurs face in trying to establish and maintain a lavender crop has become of real interest, as the competition has become fiercer. The present article focuses on some of the results obtained from our own research, carried out in Cornatelu commune, Dambovita county, in a family holding of 7,500 sqm, in the period 2017-2019. The total area was divided into three equal parts, 2,500 sqm each, the first being classically (control), the second intensively, and the third ecologically managed. During the three years, all the technological stages were followed, making correlations between the applied agrotechnics, the health status of the lavender crop, the yields obtained and the ways of its valorization for each of the tested variants. Results of the research refer both to the risks arising during the vegetation period, as well as to those from the time of harvesting, processing, packaging and delivery of the products for commercialization. The average of the testing period indicated that the classic system brings yield increases of 5-9%, the quality being also superior. [ABSTRACT FROM AUTHOR]

The Hassan II dam, located in the eastern part of Morocco in the Upper Moulouya, serves as the primary water supply system for drinking water and irrigation of agricultural lands in the surrounding areas. Yet, considering the imminent challenge posed by climate change, this hydraulic infrastructure faces environmental sustainability challenge due to sedimentation caused by soil degradation in its upstream entering. Therefore, monitoring soil loss, identifying the sediment origins with the vulnerable areas under high risk of erosion have become critical for decision-makers to choose wisely effective conservation strategies that promote sustainable land management, water mitigation and provide protection to Hassan II dam from siltation and improve its lifespan. This study evaluates the spatial distribution of soil loss and sediment yield in the Upper Moulouya watershed using the Revised Universal Soil Loss Equation (RUSLE) combined with a geographic information system (GIS). By integrating the main factors controlling soil erosion, such as topography, vegetation cover, soil proprieties, and climate, the maps of soil erosion and sediment yield were achieved. The results showed that the mean annual soil loss was 10.82 t/ha/y with an annual erosion amount of 3.46 million tons, highlighting a hazardous rate of erosion potential that could rapidly silt the Hassan II dam. In total, 45.14% of the watershed surface experienced soil erosion less than 5 t/ha/y, while 21.38% endured high to severe risk of soil loss. Areas characterized by steeper slopes and limited vegetation cover experienced elevated levels of soil degradation. The sediment yield using the delivery ratio was identified to be 5.56 t/ha/y, which was lower than the bathymetric value of 12.32 t/ha/y. The difference of 7 t/ha/y between these values may be attributed to the fact that a part of these sediments was not retained in the Upper basin but instead continued downstream to the hydraulic infrastructure of the Hassan II dam. The results undeniably assisted in pinpointing key areas where conservation practices should be implemented, ensuring the sustainability of the Hassan II dam situated downstream in the Upper Moulouya catchment. [ABSTRACT FROM AUTHOR]

In tourist facilities, managing ticket sales can reduce congestion imbalances. This study reports on the results of a pilot experiment conducted at Kaiyukan in Osaka, one of the largest aquariums in Japan. The experiment utilized pre-sale ticket data for controlling admission time intervals, smoothing the number of admissions through dynamic pricing, and predicting visitor numbers. The study reviewed the effectiveness of each of these approaches to alleviate congestion inside Kaiyukan. We then report on a method to predict the number of visitors from the pre-sale conditions of Kaiyukan admission tickets. It was found that setting entry times to 15-minute intervals was most operationally advantageous for the Kaiyukan. Moreover, the behavioral effects induced by variable pricing were more effective when prices were changed based on time slots rather than on a daily basis. Compared to the Holt–Winters' method, we were able to maintain stable prediction accuracy even during consecutive holidays and long school vacation seasons. [ABSTRACT FROM AUTHOR]

Purpose: This study aims to propose a risk-induced game theoretic forecasting model to predict average daily rate (ADR) under COVID-19, using an advanced recurrent neural network. Design/methodology/approach: Using three data sets from upper-midscale hotels in three locations (i.e. urban, interstate and suburb), from January 1, 2018, to August 31, 2020, three long-term, short-term memory (LSTM) models were evaluated against five traditional forecasting models. Findings: The models proposed in this study outperform traditional methods, such that the simplest LSTM model is more accurate than most of the benchmark models in two of the three tested hotels. In particular, the results show that traditional methods are inefficient in hotels with rapid fluctuations of demand and ADR, as observed during the pandemic. In contrast, LSTM models perform more accurately for these hotels. Research limitations/implications: This study is limited by its use of American data and data from midscale hotels as well as only predicting ADR. Practical implications: This study produced a reliable, accurate forecasting model considering risk and competitor behavior. Theoretical implications: This paper extends the application of game theory principles to ADR forecasting and combines it with the concept of risk for forecasting during uncertain times. Originality/value: This study is the first study, to the best of the authors' knowledge, to use actual hotel data from the COVID-19 pandemic to determine an appropriate neural network forecasting method for times of uncertainty. The application of Shapley value and operational risk obtained a game-theoretic property-level model, which fits best. [ABSTRACT FROM AUTHOR]

State‐of‐the‐art revenue management systems combine forecasting and optimization algorithms with human decision‐making. However, only a few existing contributions consider the behavioral aspects of revenue management. To extend the related research, we examine the impact of nonstationary demand and two dynamic decision tasks. We examine human decision‐making strategies and biases by implementing a related experimental design in a laboratory study and comparing participant decisions to systematic heuristics. Our results highlight that participants struggle to accommodate a nonstationary willingness to pay. In that, they exhibit a combination of optimism and loss aversion biases. We further find that participants anchor their decisions on customers' willingness to pay. We draw implications and further research opportunities to behaviorally inform the design of symbiotic analytics systems from these results. [ABSTRACT FROM AUTHOR]