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 Retailer's optimal pricing and replenishment policy for new product and optimal take-back quantity of used product

2018 ◽  
Vol 28 (3) ◽  
pp. 345-353 ◽  
Author(s):  
Nita Shah ◽  
Chetansinh Vaghela
Keyword(s):  
Profit Maximization ◽  
New Product ◽  
Critical Issue ◽  
Point Of View ◽  
Maximization Problem ◽  
Selling Price ◽  
Take Back ◽  
Replenishment Policy ◽  
Total Profit ◽  
Optimal Ordering

In the world of limited resources, recovery of used products for reselling or recycling is a critical issue from the economic and environmental point of view. In this paper, we have assumed that a retailer sells the new product to customers as well as collects and sells the used products. We adopt a price dependent quadratic demand function, and the return of used product as a price and time-dependent linear function. The proposed problem is formulated as a profit maximization problem for the retailer. The objective is to find the optimal selling price, the optimal ordering quantity for the new product, and the optimal quantity of used product simultaneously such that the retailers total profit is maximized. The model is validated by a numerical example and sensitivity analysis is performed for the key parameters.

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 Prosumer Community Portfolio Optimization via Aggregator: The Case of the Iberian Electricity Market and Portuguese Retail Market

Energies ◽  
2021 ◽  
Vol 14 (13) ◽  
pp. 3747
Author(s):  
Ricardo Faia ◽  
Tiago Pinto ◽  
Zita Vale ◽  
Juan Manuel Corchado
Keyword(s):  
Portfolio Optimization ◽  
Electricity Markets ◽  
Electricity Market ◽  
Retail Market ◽  
Wholesale Market ◽  
Generation Capacity ◽  
Wholesale Electricity Market ◽  
Portfolio Optimization Model ◽  
Photovoltaic Generators

The participation of household prosumers in wholesale electricity markets is very limited, considering the minimum participation limit imposed by most market participation rules. The generation capacity of households has been increasing since the installation of distributed generation from renewable sources in their facilities brings advantages for themselves and the system. Due to the growth of self-consumption, network operators have been putting aside the purchase of electricity from households, and there has been a reduction in the price of these transactions. This paper proposes an innovative model that uses the aggregation of households to reach the minimum limits of electricity volume needed to participate in the wholesale market. In this way, the Aggregator represents the community of households in market sales and purchases. An electricity transactions portfolio optimization model is proposed to enable the Aggregator reaching the decisions on which markets to participate to maximize the market negotiation outcomes, considering the day-ahead market, intra-day market, and retail market. A case study is presented, considering the Iberian wholesale electricity market and the Portuguese retail market. A community of 50 prosumers equipped with photovoltaic generators and individual storage systems is used to carry out the experiments. A cost reduction of 6–11% is achieved when the community of households buys and sells electricity in the wholesale market through the Aggregator.

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.

Nonsubmodular Constrained Profit Maximization in Attribute Networks

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.

scholarly journals Multi-item fuzzy inventory problem with space constraint via geometric programming method

2006 ◽  
Vol 16 (1) ◽  
pp. 55-66 ◽  
Author(s):  
Kumar Mandal ◽  
Kumar Roy ◽  
Manoranjan Maiti
Keyword(s):  
Space Constraint ◽  
Geometric Programming ◽  
Profit Maximization ◽  
Inventory Model ◽  
Programming Method ◽  
Unit Price ◽  
Selling Price ◽  
Inventory Problem ◽  
Set Up ◽  
Fuzzy Inventory

In this paper, a multi-item inventory model with space constraint is developed in both crisp and fuzzy environment. A profit maximization inventory model is proposed here to determine the optimal values of demands and order levels of a product. Selling price and unit price are assumed to be demand-dependent and holding and set-up costs sock dependent. Total profit and warehouse space are considered to be vague and imprecise. The impreciseness in the above objective and constraint goals has been expressed by fuzzy linear membership functions. The problem is then solved using modified geometric programming method. Sensitivity analysis is also presented here.

A survey of edible fresh fish resources in Iewduh (Bara Bazaar) in Shillong, Meghalaya

2019 ◽  
Vol 6 (1) ◽  
pp. 11-20
Author(s):  
Jasmine T. Sawian ◽  
Elizabeth Nemhoihkim
Keyword(s):  
Foreign Exchange ◽  
Food Production ◽  
Retail Market ◽  
Wholesale Market ◽  
Fresh Fish ◽  
Livelihood Security ◽  
Fish Resources ◽  
Fish Market ◽  
North Eastern ◽  
Traditional Market

Indian fisheries are an important sector of food production, providing nutritional and livelihood security to a vast majority of the population and contributes significantly to the foreign exchange earnings. There is a big demand of fish in north-eastern states of India. Fish market infrastructures include wholesale market, retail market and fish shops. Iewduh, also called Bara Bazaar, is one of the oldest and largest traditional market and trade centre in the northeast. A variety of fish are being sold in Iewduh in Shillong, Meghalaya. Majority of the fishes are sourced from other parts of the country. A total of 30 distinguishable taxa were observed in the market, representing 18 families. There was a predominance of different carp species and a number of catfishes were also available.

Optimal Integrated Inventory Policy for Deteriorating Units Under Selling-Price-Dependent Demand When Holding Cost Is Capacity-Utilization Dependent

2018 ◽  
pp. 74-89
Author(s):  
Ishaben Talati ◽  
Poonam Prakash Mishra
Keyword(s):  
Supply Chain ◽  
Trade Credit ◽  
Profit Maximization ◽  
Capacity Utilization ◽  
Optimal Number ◽  
Sensitivity Analyses ◽  
Selling Price ◽  
Sustainable Supply Chain ◽  
Modern Approach ◽  
Holding Cost

Conventional EOQ models always discussed profit maximization for one player at a time. But modern approach of supply chain suggests that growing and sustainable supply chain is possible only when benefits of all members of chain are protected. This chapter proposes an integrated model of supply chain where units in inventory are subjected to time dependent deterioration. Since demand is inversely proportional to selling price of the item, it is assumed selling price dependent. To make it more practical and feasible permissible delay on payments is offered only on purchase of a certain amount of quantity. This chapter helps to offer an algorithm to attain optimal number of orders, quantity, selling price and trade credit to maximize the joint profit of supply chain. Isolated profit of supply chain is compared with overall system profit. Results are validated by numerical examples and further sensitivity analyses of important parameters are discussed. Conclusion obtained from the chapter is useful to supply chains involved with FMCGs, Drugs, Fashion goods and home decor textile.

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

2019 ◽  
Vol 33 ◽  
pp. 7992-7999 ◽  
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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