An Optimal Management Strategy of Carbon Forestry with a Stochastic Price

An analysis for the value of carbon forestry needs to be provided for the successful establishment of the carbon offset market in Korea. We present an optimal management strategy for a forest owner who participates in the offset market. Given a stochastic process of the timber price following a geometric Brownian motion, the profit maximization problem of the forest owner is solved. The model finds an optimal harvest time in the presence of the carbon and timber revenues with opposing time effects. Sensitivity analysis is performed with respect to the volatility rate of the timber price and the discount rate. The presented model is applied to the study of the Korean larch case to identify the threshold timber price above which it is optimal to harvest trees.

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Genetic Algorithm for the Retailers’ Shelf Space Allocation Profit Maximization Problem

Applied Sciences ◽

10.3390/app11146401 ◽

2021 ◽

Vol 11 (14) ◽

pp. 6401

Author(s):

Kateryna Czerniachowska ◽

Karina Sachpazidu-Wójcicka ◽

Piotr Sulikowski ◽

Marcin Hernes ◽

Artur Rot

Keyword(s):

Genetic Algorithm ◽

Profit Maximization ◽

Allocation Rule ◽

Maximization Problem ◽

Shelf Space ◽

Allocation Model ◽

Space Allocation ◽

Shelf Space Allocation ◽

To Receive

This paper discusses the problem of retailers’ profit maximization regarding displaying products on the planogram shelves, which may have different dimensions in each store but allocate the same product sets. We develop a mathematical model and a genetic algorithm for solving the shelf space allocation problem with the criteria of retailers’ profit maximization. The implemented program executes in a reasonable time. The quality of the genetic algorithm has been evaluated using the CPLEX solver. We determine four groups of constraints for the products that should be allocated on a shelf: shelf constraints, shelf type constraints, product constraints, and virtual segment constraints. The validity of the developed genetic algorithm has been checked on 25 retailing test cases. Computational results prove that the proposed approach allows for obtaining efficient results in short running time, and the developed complex shelf space allocation model, which considers multiple attributes of a shelf, segment, and product, as well as product capping and nesting allocation rule, is of high practical relevance. The proposed approach allows retailers to receive higher store profits with regard to the actual merchandising rules.

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Defining the Optimal Management Strategy for Small Renal Masses

Kidney Cancer ◽

10.3233/kca-200008 ◽

2021 ◽

pp. 1-2

Author(s):

Marc Dall’Era ◽

Primo N. Lara

Keyword(s):

Management Strategy ◽

Optimal Management ◽

Small Renal Masses ◽

Renal Masses

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Predicted harvest time effects on switchgrass moisture content, nutrient concentration, yield, and profitability

Biomass and Bioenergy ◽

10.1016/j.biombioe.2017.09.017 ◽

2018 ◽

Vol 108 ◽

pp. 74-89 ◽

Cited By ~ 4

Author(s):

Karen R. Lindsay ◽

Michael P. Popp ◽

Charles P. West ◽

Amanda J. Ashworth ◽

Alexandre Caldeira Rocateli ◽

...

Keyword(s):

Moisture Content ◽

Nutrient Concentration ◽

Harvest Time ◽

Time Effects

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A Novel Integrated Profit Maximization Model for Retailers under Varied Penetration Levels of Photovoltaic Systems

Energies ◽

10.3390/en14010092 ◽

2020 ◽

Vol 14 (1) ◽

pp. 92

Author(s):

Ioannis P. Panapakidis ◽

Nikolaos Koltsaklis ◽

Georgios C. Christoforidis

Keyword(s):

Profit Maximization ◽

Energy Markets ◽

Maximization Problem ◽

Selling Price ◽

Retail Market ◽

Stochastic Variable ◽

Wholesale Market ◽

Market Operation ◽

Pv Penetration ◽

Marginal Price

In contemporary energy markets, the Retailer acts as the intermediate between the generation and demand sectors. The scope of the Retailer is to maximize its profits by selecting the appropriate procurement mechanism and selling price to the consumers. The wholesale market operation influences the profits since the mix of generation plants determines the system marginal price (SMP). In the related literature, the SMP is treated as a stochastic variable, and the wholesale market conditions are not taken into account. The present paper presents a novel methodology that aims at connecting the wholesale and retail market operations from a Retailer’s perspective. A wholesale market clearing problem is formulated and solved. The scope is to examine how different photovoltaics (PV) penetration levels in the generation side influences the profits of the Retailer and the selling prices to the consumers. The resulting SMPs are used as inputs in a retailer profit maximization problem. This approach allows the Retailer to minimize economic risks and maximize profits. The results indicate that different PV implementation levels on the generation side highly influences the profits and the selling prices.

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Non-submodular model for group profit maximization problem in social networks

Computational Social Networks ◽

10.1186/s40649-020-00085-6 ◽

2021 ◽

Vol 8 (1) ◽

Author(s):

Jianming Zhu ◽

Smita Ghosh ◽

Weili Wu ◽

Chuangen Gao

Keyword(s):

Social Networks ◽

Profit Maximization ◽

Randomized Algorithm ◽

Political Orientation ◽

Submodular Functions ◽

Maximization Problem ◽

Modular Algorithm ◽

The Difference ◽

Group Enterprise ◽

Theoretical Results

AbstractIn social networks, there exist many kinds of groups in which people may have the same interests, hobbies, or political orientation. Sometimes, group decisions are made by simply majority, which means that most of the users in this group reach an agreement, such as US Presidential Elections. A group is called activated if $$\beta$$ β percent of users are influenced in the group. Enterprise will gain income from all influenced groups. Simultaneously, to propagate influence, enterprise needs pay advertisement diffusion cost. Group profit maximization (GPM) problem aims to pick k seeds to maximize the expected profit that considers the benefit of influenced groups with the diffusion cost. GPM is proved to be NP-hard and the objective function is proved to be neither submodular nor supermodular. An upper bound and a lower bound which are difference of two submodular functions are designed. We propose a submodular–modular algorithm (SMA) to solve the difference of two submodular functions and SMA is shown to converge to a local optimal. We present an randomized algorithm based on weighted group coverage maximization for GPM and apply sandwich framework to get theoretical results. Our experiments verify the efficiency of our methods.

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Integrating Systemic Therapies into the Multimodality Treatment of Resectable Colorectal Liver Metastases

Gastroenterology Research and Practice ◽

10.1155/2018/4326082 ◽

2018 ◽

Vol 2018 ◽

pp. 1-4 ◽

Cited By ~ 2

Author(s):

Omar Abdel-Rahman ◽

Winson Y. Cheung

Keyword(s):

Liver Metastases ◽

Management Strategy ◽

Multimodality Treatment ◽

Treatment Modalities ◽

Considerable Proportion ◽

Optimal Management ◽

Related Factors ◽

Multiple Treatment ◽

Current Review ◽

Systemic Therapies

Colorectal carcinoma (CRC) is one of the most common cancers worldwide. A considerable proportion of CRC patients may present with metastatic disease either at upfront presentation (synchronous with the primary) or following diagnosis and treatment of the primary tumor (metachronous). Management of CRC liver metastases is a challenging endeavor which frequently necessitates proper assessment of patient- and disease-related factors. There is an opportunity within the management of CRC liver metastases to incorporate multiple treatment modalities (including surgery, other locoregional treatments, and systemic therapy). The current review aims to provide an updated overview on the optimal management strategy for CRC patients with liver metastases with a specific focus on the integration of systemic and/or locoregional treatments among patients with resectable or potentially resectable disease.

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Combined Metabolome and Transcriptome Profiling Reveal Optimal Harvest Strategy Model Based on Different Production Purposes in Olive

Foods ◽

10.3390/foods10020360 ◽

2021 ◽

Vol 10 (2) ◽

pp. 360

Author(s):

Guodong Rao ◽

Jianguo Zhang ◽

Xiaoxia Liu ◽

Xue Li ◽

Chenhe Wang

Keyword(s):

Gene Expression ◽

Olive Oil ◽

Expression Patterns ◽

Transcriptome Profiling ◽

Minor Components ◽

Harvest Time ◽

Rna Seq ◽

Optimal Harvest ◽

Harvest Strategy ◽

Different Color

Olive oil has been favored as high-quality edible oil because it contains balanced fatty acids (FAs) and high levels of minor components. The contents of FAs and minor components are variable in olive fruits of different color at harvest time, which render it difficult to determine the optimal harvest strategy for olive oil producing. Here, we combined metabolome, Pacbio Iso-seq, and Illumina RNA-seq transcriptome to investigate the association between metabolites and gene expression of olive fruits at harvest time. A total of 34 FAs, 12 minor components, and 181 other metabolites (including organic acids, polyols, amino acids, and sugars) were identified in this study. Moreover, we proposed optimal olive harvesting strategy models based on different production purposes. In addition, we used the combined Pacbio Iso-seq and Illumina RNA-seq gene expression data to identify genes related to the biosynthetic pathways of hydroxytyrosol and oleuropein. These data lay the foundation for future investigations of olive fruit metabolism and gene expression patterns, and provide a method to obtain olive harvesting strategies for different production purposes.

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Nonsubmodular Constrained Profit Maximization in Attribute Networks

Asia Pacific Journal of Operational Research ◽

10.1142/s0217595922400061 ◽

2021 ◽

Author(s):

Liman Du ◽

Wenguo Yang ◽

Suixiang Gao

Keyword(s):

Profit Maximization ◽

Logistic Function ◽

Influence Maximization ◽

Maximization Problem ◽

Cardinality Constraint ◽

Three Phase ◽

Marginal Increment ◽

The Difference ◽

Classical Influence ◽

Influence Maximization Problem

The number of social individuals who interact with their friends through social networks is increasing, leading to an undeniable fact that word-of-mouth marketing has become one of the useful ways to promote sale of products. The Constrained Profit Maximization in Attribute network (CPMA) problem, as an extension of the classical influence maximization problem, is the main focus of this paper. We propose the profit maximization in attribute network problem under a cardinality constraint which is closer to the actual situation. The profit spread metric of CPMA calculates the total benefit and cost generated by all the active nodes. Different from the classical Influence Maximization problem, the influence strength should be recalculated according to the emotional tendency and classification label of nodes in attribute networks. The profit spread metric is no longer monotone and submodular in general. Given that the profit spread metric can be expressed as the difference between two submodular functions and admits a DS decomposition, a three-phase algorithm named as Marginal increment and Community-based Prune and Search(MCPS) Algorithm frame is proposed which is based on Louvain algorithm and logistic function. Due to the method of marginal increment, MPCS algorithm can compute profit spread more directly and accurately. Experiments demonstrate the effectiveness of MCPS algorithm.

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