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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]

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]

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]

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]

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]

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]

Grounded on the shareholder theory, this study aims to relate the effects of management systems standards (MSSs) ISO 9001, ISO 14001 and/or ISO 45001 on firm financial performance (FFP) in terms of return on equity (ROE) and dividend per share yield (DY). With a balanced data sample between 2010 and 2019 composed of more than 10,000 observations, this paper performs a two-step system generalised-method-of-moments model to find a causal relationship between single, double and triple certifications, and ROE and DY. The results find no impact of single certifications on FFP. However, double certifications that include ISO 9001 promote ROE. Furthermore, all multiple certifications are found to promote DY. It is then argued that operating with multiple MSSs promotes shareholder wealth. Thus, this research adds to the continuing academic discourse on the connection between MSS certifications and FFP by presenting findings from a worldwide longitudinal dataset. Additionally, it adopts a shareholder-focused perspective and introduces DY as a relevant FFP metric in this line of research. [ABSTRACT FROM AUTHOR]

Revenue management (RM) systems forecast demand and optimize prices to maximize a hotel's revenue. The RM function operates in coordination between a system and an analyst. Systems provide recommendations while analysts review the forecasts and prices to approve or make subjective adjustments. In many cases the recommendations are a "black box" with little insight regarding how recommendations are derived. This article proposes the k-Nearest Neighbor (k-NN) algorithm as a forecasting approach that can transition the "black box" to a "glass box." The benefits of the k-NN are discussed in detail and compared with neural networks. The analysis is conducted on 35 hotels in partnership with a leading RM service provider. The results indicate similar performance for both techniques, leading to an important discussion on model evaluation outside of accuracy. In particular, the article discusses some of the unique advantages k-NN provides for the RM discipline. [ABSTRACT FROM AUTHOR]

Waitlists are commonly used to allocate scarce resources, such as public housing or organs. Waitlist policies attempt to prioritize agents who wait longer by assigning them priority points (à la first come, first served). We show that such point systems can lead to severe inequality across the agents' assignment probabilities unless they use randomization. In particular, deterministic point systems lead to a more unequal allocation than any other rule that prioritizes earlier arrivals, an axiom that ensures that agents who wait longer are treated (weakly) better. Among the policies abiding by this axiom, we show that service in random order (SIRO) leads to the most equal allocation. From a utilitarian perspective, we show that the planner faces no trade-off between equity and efficiency when the flow utility from waiting is nonnegative or negative and increasing over time. In these cases, SIRO is also the most efficient policy. However, when the flow cost of waiting increases over time, then the planner may face an efficiency–equity trade-off: SIRO remains the most equitable policy but may not be the most efficient one. This paper was accepted by Omar Besbes, revenue management and market analytics. [ABSTRACT FROM AUTHOR]

Many firms delegate pricing decisions to sales agents that directly interact with customers. A premise behind this practice is that sales agents can gather informative signals about the customer's valuation for the good of interest. The information acquired through this interaction with the customer can then be used to make better pricing decisions. We study the underlying principal-agent problem that arises in such situations. In this setting, the agent can exert costly effort to learn a customer's valuation and then decide on the price to quote to the customer, whereas the firm needs to offer a contract to the agent to induce its desired joint learning and pricing behavior. We analyze two versions of this problem: a base model where there is a single customer and a single good, and a generalization where there are multiple customers and limited inventory of the good. For both problems, we find a family of contracts whose payoffs can approach first-best payoffs arbitrarily closely even if the agent has limited liability, that is, garners nonnegative payments in all states of the world, and shed light on the structure and implementation of such contracts. Under reasonable assumptions, these contracts can be implemented with commissions that are convex increasing in revenues up to some cap. These contracts continue to perform well under practical adjustments such as commissions with a revenue-sharing structure. This paper was accepted by Itai Ashlagi, revenue management and market analytics. Supplemental Material: The e-companion and data are available at https://doi.org/10.1287/mnsc.2023.4939. [ABSTRACT FROM AUTHOR]

We study the problem of maximizing payoff generated over a period of time in a general class of closed queueing networks with a finite, fixed number of supply units that circulate in the system. Demand arrives stochastically, and serving a demand unit (customer) causes a supply unit to relocate from the "origin" to the "destination" of the customer. The key challenge is to manage the distribution of supply in the network. We consider general controls including customer entry control, pricing, and assignment. Motivating applications include shared transportation platforms and scrip systems. Inspired by the mirror descent algorithm for optimization and the backpressure policy for network control, we introduce a rich family of mirror backpressure (MBP) control policies. The MBP policies are simple and practical and crucially do not need any statistical knowledge of the demand (customer) arrival rates (these rates are permitted to vary in time). Under mild conditions, we propose MBP policies that are provably near optimal. Specifically, our policies lose at most O(KT+1K+ηK) payoff per customer relative to the optimal policy that knows the demand arrival rates, where K is the number of supply units, T is the total number of customers over the time horizon, and η is the demand process' average rate of change per customer arrival. An adaptation of MBP is found to perform well in numerical experiments based on data from NYC Cab. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Funding: Y. Kanoria was supported by the National Science Foundation's Division of Civil, Mechanical, and Manufacturing Innovation [Grant CMMI-1653477]. Supplemental Material: The data files and online appendices are available at https://doi.org/10.1287/mnsc.2023.4934. [ABSTRACT FROM AUTHOR]

Random coefficient multinomial logit models are widely used to estimate customer preferences from sales data. However, these estimation models can only allow for products with positive sales; this selection leads to highly biased estimates in long tail markets, that is, markets where many products have zero or low sales. Such markets are increasingly common in areas such as online retail and other online marketplaces. In this paper, we propose a two-stage estimator that uses machine learning to correct for this bias. Our method first uses deep learning to predict the market shares of all products, where the neural network's structure mirrors the random coefficient multinomial logit model's data generating process. In the second stage, we use the predictions of the first stage to reweight the observed shares in a way that corrects for the induced bias and maintains the causal interpretation of the structural model. We show that the estimated parameters are consistent in the number of markets. Our method performs well on simulated and real long tail data, producing accurate estimates of customer behavior. These improved estimates can subsequently be used to provide prescriptive policy recommendations on important managerial decisions such as pricing, assortment, and so on. This paper was accepted by Gabriel Weintraub, revenue management and market analytics. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.4893. [ABSTRACT FROM AUTHOR]

The potato is a key crop in addressing global hunger, and deep learning is at the core of smart agriculture. Applying deep learning (e.g., YOLO series, ResNet, CNN, LSTM, etc.) in potato production can enhance both yield and economic efficiency. Therefore, researching efficient deep learning models for potato production is of great importance. Common application areas for deep learning in the potato production chain, aimed at improving yield, include pest and disease detection and diagnosis, plant health status monitoring, yield prediction and product quality detection, irrigation strategies, fertilization management, and price forecasting. The main objective of this review is to compile the research progress of deep learning in various processes of potato production and to provide direction for future research. Specifically, this paper categorizes the applications of deep learning in potato production into four types, thereby discussing and introducing the advantages and disadvantages of deep learning in the aforementioned fields, and it discusses future research directions. This paper provides an overview of deep learning and describes its current applications in various stages of the potato production chain. [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]

The Indian Railways has adopted a dynamic pricing mechanism for its premium trains like Shatabdi, Rajdhani, and Duronto. This led to an increase in its revenue but also a fall in passenger traffic. In this paper, we have analyzed the existing dynamic pricing model. A major flaw in the existing system is that the present system is only a fare hike system rather than a dynamic pricing system as there is no provision for a decrease in prices when the demand is low. Considering this, we have developed a new model that incorporates both inter-temporal pricing and demand-based pricing to come up with the dynamic fares along with the provision of having a downside in case of low demand. We developed a route selection criteria based on the key parameters identified by us where dynamic pricing would yield good results. The model was then tested on these routes using real-time data to determine the feasibility of the dynamic pricing system. [ABSTRACT FROM AUTHOR]

Ghana discovered crude oil in 2007 and started oil production in 2010. Ghana, consequently, enacted the Petroleum Revenue Management Act (PRMA) to regulate the oil revenue and ensure the optimal utilisation of the oil revenue. To this end, several state institutions have been assigned duties under the PRMA. Quality institutions aid the management of resource revenue. Ghana's constitutional architecture permits a new President to appoint new heads of state institutions. To this end, state institutions meant to serve as a check on executive authority have been found wanting. This article investigates the quality of state institutions with responsibilities under the PRMA concerning their functions to ascertain whether they possess the characteristics of strong institutions. The article argues that executive influence dominates Ghana's PRMA, leading to the institutions making decisions based on political expediency. This shows weak institutional structures. This article argues for the development of state institutions to serve as a check on the executive power. The article thus recommends constitutional amendment regarding the power of appointment of heads of state institutions to be vested in the President and the Public Services Commission. [ABSTRACT FROM AUTHOR]

Monitoring genetic gains within breeding programs is a critical component for continuous improvement. While several national breeding programs in Africa have assessed genetic gain using era studies, this study is the first to use two decades of historical data to estimate genetic trends within a national breeding program. The objective of this study was to assess genetic trends within the final two stages of Zimbabwe's Department of Research & Specialist Services maize breeding pipeline between 2002 and 2021. Data from 107 intermediate and 162 advanced variety trials, comprising of 716 and 398 entries, respectively, was analyzed. Trials were conducted under optimal, managed drought stress, low nitrogen stress, low pH, random stress, and disease pressure (maize streak virus (MSV), grey leaf spot (GLS), and turcicum leaf blight under artificial inoculation. There were positive and significant genetic gains for grain yield across management conditions (28-35 kg ha-1 yr-1), under high-yield potential environments (17-61 kg ha-1 yr-1), and under low-yield potential environments (0-16 kg ha-1 yr-1). No significant changes were observed in plant and ear height over the study period. Stalk and root lodging, as well as susceptibility to MSV and GLS, significantly decreased over the study period. New breeding technologies need to be incorporated into the program to further increase the rate of genetic gain in the maize breeding programs and to effectively meet future needs. [ABSTRACT FROM AUTHOR]

To meet the growing international demand for aromatic rice, this study, conducted at Uttar Banga Krishi Viswavidyalaya in Cooch Behar, West Bengal, aimed to enhance the yield and quality of the 'Tulaipanji' rice cultivar through advanced establishment methods and the use of organic nutrients over two years. The research tested three planting techniques: mechanical transplanting, wet direct seeding (using a drum seeder), and traditional methods, alongside four nutrient management strategies: vermicompost, farmyard manure, a mix of both, and conventional fertilizers. Findings revealed that mechanical transplanting significantly increased yield by over 31.98% and 71.05% compared to traditional methods and wet direct seeding, respectively. Using vermicompost alone as a nutrient source not only boosted yields by 21.31% over conventional fertilizers but also enhanced the rice's nutritional value and cooking quality. Moreover, soils treated with vermicompost showed higher dehydrogenase activity, indicating better soil health. Economically, mechanical transplanting with vermicompost was the most beneficial, yielding the highest net returns and benefit–cost ratios in both years studied. This approach presents a viable model for improving the sustainability of aromatic rice production globally, emphasizing the economic and environmental advantages of adopting mechanical planting techniques and organic fertilization methods. [ABSTRACT FROM AUTHOR]

The use of polymer-coated urea (PCU) can improve nitrogen use efficiency (NUE), compared to the application of rapid-release urea (RU). However, the effect of PCU-based nitrogen management on grain yield and the NUE of rice and its underlying mechanism remain unclear. A japonica rice cultivar Jinxiangyu 1 was grown in the field with four treatments including N omission (0N), split application of RU (Control), one-time application of 100% PCU (T1), and one-time application of 70% PCU + 30% RU (T2). Results showed that, compared to the control, the grain yield was significantly increased in the T2 treatment, while it was comparable in the T1 treatment. This was mainly due to increased total spikelets in the T2 treatment. Root oxidation activity (ROA) and root zeatin (Z) + zeatin riboside (ZR) content during booting were the distinct advantages of the T2 treatment, compared to either the control or T1 treatment, exhibiting significant or highly significant correlations with leaf photosynthesis. This process contributed significantly to total spikelets and total N uptake. Additionally, the T2 treatment absorbed more N than the control without reducing the internal N use efficiency (IEN), primarily due to its unchanged harvest index (HI) driven by comparable non-structural carbohydrate remobilization. In conclusion, combining PCU with RU can enhance the coordination of root and shoot traits during booting while maintaining a competitive HI at maturity, thereby significantly improving grain yield and achieving a balance in N uptake and utilization. [ABSTRACT FROM AUTHOR]

Maize plays a significant global role as a food source, feed, and as a raw material in industry. However, it is affected by toxin-producing fungi, mainly Fusarium graminearum, Fusarium verticillioides, and Aspergillus flavus, which compromise its quality. This study, conducted in 2022 and 2023 at the Látókép long-term research site of the University of Debrecen, Hungary, investigated the effects of different nitrogen fertilization rates (0, 90 and 150 Kgha−1 N) on mycotoxin contamination (DON vs. FB vs. AFB1) in the kernels of three (3) maize hybrids: DKC4590 (tolerant), GKT376 (sensitive), and P9610 (undefined). The results showed a significant (p = 0.05) influence of nitrogen fertilization and maize genotype on mycotoxin levels. Sole nitrogen impacts were complex and did not define a clear trend, contrary to the hybrids selected, which followed superiority to resistance. Increased nitrogen fertilization was associated with higher DON production, while hybrid selection demonstrated a clearer trend in resistance to mycotoxins. Therefore, to maximize yield and minimize mycotoxin contamination, future research should focus on optimizing nitrogen application rates and breeding for resistance to balance yield and mycotoxin management. These results suggest that while nitrogen fertilization is crucial for maximizing yield, selecting less susceptible maize hybrids remains vital for minimizing mycotoxin contamination. [ABSTRACT FROM AUTHOR]

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]

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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]

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 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]

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]

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]

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]

Corporate social responsibility is becoming increasingly important to the modern organization as customers, employees and other stakeholders demand greater flexibility, transparency and accountability from companies. However, companies have as their main goal an increase in the capital of their owners. Under these conditions, measures to ensure corporate social responsibility are appropriate only if they contribute to the solution of this main task. Purpose of the work: to conduct a study of the influence of corporate social responsibility on revenue management in an organization, and also to identify whether they are interdependent. Research method: analytical methods were used in the research process. Results. The results indicate that companies with high corporate social responsibility scores are more likely to have effective earnings management practices. This is due to the fact that corporate social responsibility helps companies create long-term relationships with customers, improve brand reputation, and attract and retain talented employees. All of these factors can positively impact revenue management. Scope of results: The article offers a number of recommendations for company managers who want to improve corporate social responsibility and revenue management in their organizations. This includes investing in sustainable initiatives, establishing clear ethical standards and disclosing corporate social responsibility practices.

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]