scholarly journals Evaluating effort regulation in mixed fisheries: a Monte Carlo approach

2019 ◽  
Vol 76 (7) ◽  
pp. 2114-2124
Author(s):  
Xiaozi Liu ◽  
Mikko Heino
Keyword(s):  
Monte Carlo ◽  
Profit Maximization ◽  
Monte Carlo Sampling ◽  
Abundance Ratio ◽  
Relative Strength ◽  
Maximization Problem ◽  
Key Factors ◽  
Species Abundances ◽  
Effort Regulation ◽  
Stock Abundance

Abstract This paper evaluates whether effort regulation could achieve the goal of protecting low-abundance species in mixed fisheries. We construct a two-species bio-economic model and compare the stock abundance ratio in the end of the fishing season with the ratio prior to the fishing. Fishers’ profit maximization problem is governed by three key factors: (i) the overall efficiency of catching different species (catchability), (ii) the price of different species, and (iii) their ability to catch the favoured species separately from the less-favoured species (separability). Using a Monte Carlo sampling of feasible parameters space, we show that effort regulation has good chances (87% of the cases) of maintaining the end stock ratio near equal levels (1/2< stock ratio <2) when the initial stock ratio is equal. If the initial stock ratio is not equal, however, there is a high risk (about 50% of the cases) that effort control increases differences in the relative species abundances, rather than diminishing them. The effects depend on whether the key factors determining fishing profitability are counteracting or reinforcing each other, and their relative strength. Our results warn against placing too much faith on the ability of effort regulation to protect species at low abundances from excessive exploitation.

scholarly journals Economic Analysis of a Multi-Sided Platform for Sensor-Based Services in the Internet of Things

Sensors ◽  
2019 ◽  
Vol 19 (2) ◽  
pp. 373
Author(s):  
Luis Guijarro ◽  
Jose Vidal ◽  
Vicent Pla ◽  
Maurizio Naldi
Keyword(s):  
Sensor Networks ◽  
Business Model ◽  
Mobile Apps ◽  
Profit Maximization ◽  
User Participation ◽  
Relative Strength ◽  
Wireless Sensor ◽  
Maximization Problem ◽  
Price Structure ◽  
The Internet Of Things

A business model for sensor-based services is proposed where a platform creates a multi-sided market. The business model comprises a platform that serves as an intermediary between human users, app developers, and sensor networks, so that the users use the apps and the apps process the data supplied by the sensor networks. The platform, acting as a monopolist, posts a fee for each of the three sides so as to maximize its profit. This business model intends to mimic the market-creating innovation that main mobile apps platforms have generated in the smartphone sector. We conduct an analysis of the profit maximization problem faced by the platform, show that optimum prices exist for any parameter value, and show that these prices always induce an equilibrium in the number of agents from each side that join the platform. We show that the relative strength of the value that advertisers attach to the users determines the platform price structure. Depending on the value of this relative strength, two alternative subsidizing strategies are feasible: to subsidize either the users’ subscription or the developers’ registration. Finally, all agents benefit from an increase in the population at any of the three sides. This result provides a rationale for incentivizing not only the user participation, but also the entry of developer undertakings and the deployment of wireless sensor network infrastructure.

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

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.

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

Energies ◽  
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.

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

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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