Analysis of Profit Maximization Problem in Multiperiod Energy Markets with Uncertain Photovoltaics Power

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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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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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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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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Optimizing Discount and Reputation Trade-Offs in E-Commerce Systems: Characterization and Online Learning

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33017992 ◽

2019 ◽

Vol 33 ◽

pp. 7992-7999 ◽

Cited By ~ 1

Author(s):

Hong Xie ◽

Yongkun Li ◽

John C. S. Lui

Keyword(s):

Learning Algorithm ◽

Profit Maximization ◽

Projection Algorithm ◽

Reputation Systems ◽

Maximization Problem ◽

Transaction Data ◽

Q Learning ◽

Price Discounts ◽

Trade Offs ◽

Forward Projection

Feedback-based reputation systems are widely deployed in E-commerce systems. Evidences showed that earning a reputable label (for sellers of such systems) may take a substantial amount of time and this implies a reduction of profit. We propose to enhance sellers’ reputation via price discounts. However, the challenges are: (1) The demands from buyers depend on both the discount and reputation; (2) The demands are unknown to the seller. To address these challenges, we first formulate a profit maximization problem via a semiMarkov decision process (SMDP) to explore the optimal trade-offs in selecting price discounts. We prove the monotonicity of the optimal profit and optimal discount. Based on the monotonicity, we design a QLFP (Q-learning with forward projection) algorithm, which infers the optimal discount from historical transaction data. We conduct experiments on a dataset from to show that our QLFP algorithm improves the profit by as high as 50% over both the classical Q-learning and speedy Q-learning algorithm. Our QLFP algorithm also improves the profit by as high as four times over the case of not providing any price discount.

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STOCK-HARVEST DYNAMICS IN MULTIAGENT FISHERIES

International Journal of Bifurcation and Chaos ◽

10.1142/s0218127411028477 ◽

2011 ◽

Vol 21 (01) ◽

pp. 363-372

Author(s):

EN-GUO GU

Keyword(s):

Initial Conditions ◽

Profit Maximization ◽

Fish Stock ◽

Maximization Problem ◽

Chaotic Attractors ◽

Two Dimensional ◽

Fishery Resource ◽

Global Dynamic ◽

Existence And Stability ◽

Fishery Model

In this work, we build a two-dimensional dynamical fishery model in which the total harvest is obtained by a multiagent game with best reply strategy and naive expectations, i.e. each agent decides the harvest quantity by solving a profit maximization problem. Special attention is paid to the global dynamic analysis in the light of feasible domains (initial conditions giving non-negative trajectories converging to an equilibrium), which is related to the crisis of extinction. We also study the existence and stability of non-negative equilibria for models through mathematical analysis and numerical simulations. We discover the increase in the margin price of fish stock may lead to instability of the fixed point and make the system sink into chaotic attractors. Thus the fishery resource may fluctuate in a stochastic form.

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The Study of Resource Allocation among Software Development Phases: An Economics-Based Approach

Advances in Software Engineering ◽

10.1155/2011/579292 ◽

2011 ◽

Vol 2011 ◽

pp. 1-21 ◽

Cited By ~ 1

Author(s):

Peleg Yiftachel ◽

Irit Hadar ◽

Dan Peled ◽

Eitan Farchi ◽

Dan Goldwasser

Keyword(s):

Resource Allocation ◽

Software Development ◽

Empirical Studies ◽

Profit Maximization ◽

Maximization Problem ◽

Software Production ◽

Software Product ◽

Development Processes ◽

Development Phases ◽

Novel Concept

This paper presents an economics-based approach for studying the problem of resource allocation among software development phases. Our approach is structured along two parallel axes: theoretical and empirical. We developed a general economic model for analyzing the allocation problem as a constrained profit maximization problem. The model, based on a novel concept of software production function, considers the effects of different allocations of development resources on output measures of the resulting software product. An empirical environment for evaluating and refining the model is presented, and a first exploratory study for characterizing the model's components and developers' resource allocation decisions is described. The findings illustrate how the model can be applied and validate its underlying assumptions and usability. Future quantitative empirical studies can refine and substantiate various aspects of the proposed model and ultimately improve the productivity of software development processes.

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A Dynamic Decision Model of Technology Adoption under Uncertainty: Case of Herbicide-Resistant Rice

Journal of Agricultural and Applied Economics ◽

10.1017/s1074070800007173 ◽

2005 ◽

Vol 37 (1) ◽

pp. 161-172 ◽

Cited By ~ 4

Author(s):

Mamane M. Annou ◽

Eric J. Wailes ◽

Michael R. Thomsen

Keyword(s):

Technology Adoption ◽

Decision Model ◽

Profit Maximization ◽

Maximization Problem ◽

Red Rice ◽

Herbicide Resistant ◽

Programming Framework ◽

Optimal Rotation ◽

Dynamic Decision Model ◽

Potential Tool

Herbicide-resistant (HR) rice technology is a potential tool for control of red rice in commercial rice production. Using anex antemathematical programming framework, this research presents an empirical analysis of HR rice technology adoption under uncertainty. The analysis accounts for stochastic germination of red rice and sheath blight to model a profit maximization problem of crop rotation among HR rice, regular rice, and soybeans. The results demonstrate that risk attitudes and technology efficiency determine adoption rates and optimal rotation patterns.

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PROFIT MAXIMIZATION PROBLEM

Economic Inquiry ◽

10.1111/j.1465-7295.1997.tb01970.x ◽

1997 ◽

Vol 35 (4) ◽

pp. 862-863

Author(s):

DAVID HEMENWAY ◽

ELON KOHLBERG

Keyword(s):

Profit Maximization ◽

Maximization Problem

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Inventory Replenishment for Profit Maximization over a Finite Horizon under One-time Cost Changes

Global Business Review ◽

10.1177/0972150918758099 ◽

2018 ◽

Vol 19 (3_suppl) ◽

pp. S235-S248

Author(s):

F. J. Arcelus ◽

T. P. M. Pakkala ◽

G. Srinivasan

Keyword(s):

Cost Minimization ◽

Profit Maximization ◽

Planning Horizon ◽

Finite Horizon ◽

Maximization Problem ◽

Decision Variable ◽

Time Cost ◽

For Profit ◽

Optimal Policies ◽

The Cost

This article considers the optimal inventory ordering, purchasing and holding policies of the profit-maximization problem, as against the well-known cost-minimization case, over a finite horizon of length H, under two special conditions. First, there is change in at least one of the inventory costs, that is, in the cost of ordering and/or purchasing/holding, at some point, Tc < H, during the planning horizon. Second, it is not necessary to satisfy the demand, at a rate of R units per year, for the entire horizon. Rather, the objective is to meet the demand for a period of length H1 ≤ H. In fact, if the retailer does not have the obligation to meet the entire demand, this article shows the conditions wherein it may be more profitable to meet only a portion or may be even none of the demand. Further, such a determination can be made up front, with H1 as a decision variable and the optimal policies of the cost-minimization models, by fulfilling the entire demand, will result in lower profits. Numerical examples are included to identify the demand fulfilment and the profit differences between the cost-minimization and profit-maximization optimal policies, under the different one-time cost changes.

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