Academic literature on the topic 'PROFIT MANAGEMENT ALGORITHM'
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Journal articles on the topic "PROFIT MANAGEMENT ALGORITHM"
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Wang, Xiaoli, Fang Wang, Shi Yan, and ZhanBo Liu. "Application of Sequential Pattern Mining Algorithm in Commodity Management." Journal of Electronic Commerce in Organizations 16, no. 3 (2018): 94–106. http://dx.doi.org/10.4018/jeco.2018070108.
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This article describes how by analyzing historical sales data of supermarkets, once data is cleaned, sampled and put through a series of operations, it can be transformed into a sequence database. Finally, the data is used in the SPM of Map-Reduce algorithm to data mining. This experiment has two stages. In the first stage, the sequences of product categories are mined to place the product categories. In the second stage, the products are mined for each category, add profit targets for calculating sequential pattern values. This is so that it can reorder the results to find the most profit products. Thus, readers can adjust products placement and improve the profits of the supermarket.
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Amudha, A., and C. Christober Asir Rajan. "On Line Application of Profit Based Unit Commitment Using Hybrid Algorithms of Memory Management Algorithm." Advanced Materials Research 403-408 (November 2011): 3965–72. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3965.
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As the electrical industry restructures, many of the traditional algorithms for controlling generating units need modification or replacement. In the past, utilities had to produce power to satisfy their customers with the objective to minimize costs and actual demand/reserve were met. But it is not necessary in a restructured system. The main objective of restructured system is to maximize their own profit without the responsibility of satisfying the forecasted demand. The PBUC is a highly dimensional mixed-integer optimization problem, which might be very difficult to solve. Hence a new software tool is developed in java using Memory Management Algorithm (MMA) by Best Fit (BF) & Worst Fit (WF) allocation for web based application. The proposed method MMA using Best Fit & Worst Fit allocation for generator scheduling in order to receive the maximum profit by considering the softer demand. Also this method gives an idea regarding how much power and reserve should be sold in markets. The Madurai Power Grid Corporation in Tamil Nadu, India demonstrates the effectiveness of the proposed approach; extensive studies have also been performed for different power systems consisting of 3, 10and 7 generating units. Simulations of the proposed are carried out for maximizing profit and computation time and results are compared with existing methods.
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Mahesh Prabhu, R., G. Hema, Srilatha Chepure, and M. Nageswara Guptha. "Logistics Optimization in Supply Chain Management using Clustering Algorithms." Scalable Computing: Practice and Experience 21, no. 1 (2020): 107–14. http://dx.doi.org/10.12694/scpe.v21i1.1628.
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Today’s business environment, survival and making profit in market are the prime requirement for any enterprise due to competitive environment. Innovation and staying updated are commonly identified two key parameters for achieving success and profit in business. Considerably supply chain management is also accountable for profit. As a measure to maximize the profit, supply chain process is to be streamlined and optimized. Appropriate grouping of various suppliers for the benefit of shipment cost reduction is proposed. Data relating to appropriate attributes of supplier logistics are collected. A methodology is proposed to optimize the supplier logistics using clustering algorithm. In the proposed methodology data preprocessing, clustering and validation process have been carried out. The Z-score normalization is used to normalize the data, which converts the data to uniform scales for improving the clustering performance. By employing Hierarchical and K-means clustering algorithms the supplier logistics are grouped and performance of each method is evaluated and presented. The supplier logistics data from different country is experimented. Outcome of this work can help the buyers to select the cost effective supplier for their business requirements.
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Huo, Jia-Zhen, Yan-Ting Hou, Feng Chu, and Jun-Kai He. "A Combined Average-Case and Worst-Case Analysis for an Integrated Hub Location and Revenue Management Problem." Discrete Dynamics in Nature and Society 2019 (March 12, 2019): 1–13. http://dx.doi.org/10.1155/2019/8651728.
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This paper investigates joint decisions on airline network design and capacity allocation by integrating an uncapacitated single allocation p-hub median location problem into a revenue management problem. For the situation in which uncertain demand can be captured by a finite set of scenarios, we extend this integrated problem with average profit maximization to a combined average-case and worst-case analysis of this integration. We formulate this problem as a two-stage stochastic programming framework to maximize the profit, including the cost of installing the hubs and a weighted sum of average and worst case transportation cost and the revenue from tickets over all scenarios. This model can give flexible decisions by putting the emphasis on the importance of average and worst case profits. To solve this problem, a genetic algorithm is applied. Computational results demonstrate the outperformance of the proposed formulation.
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Kim, Yohan, Sunyong Kim, and Hyuk Lim. "Reinforcement Learning Based Resource Management for Network Slicing." Applied Sciences 9, no. 11 (2019): 2361. http://dx.doi.org/10.3390/app9112361.
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Network slicing to create multiple virtual networks, called network slice, is a promising technology to enable networking resource sharing among multiple tenants for the 5th generation (5G) networks. By offering a network slice to slice tenants, network slicing supports parallel services to meet the service level agreement (SLA). In legacy networks, every tenant pays a fixed and roughly estimated monthly or annual fee for shared resources according to a contract signed with a provider. However, such a fixed resource allocation mechanism may result in low resource utilization or violation of user quality of service (QoS) due to fluctuations in the network demand. To address this issue, we introduce a resource management system for network slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning approach from each tenant’s point of view. First, the resource management for network slicing is modeled as a Markov Decision Process (MDP) with the state space, action space, and reward function. Then, we propose a Q-learning-based dynamic resource adjustment algorithm that aims at maximizing the profit of tenants while ensuring the QoS requirements of end-users. The numerical simulation results demonstrate that the proposed algorithm can significantly increase the profit of tenants compared to existing fixed resource allocation methods while satisfying the QoS requirements of end-users.
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Brester, Christina, Ivan Ryzhikov, and Eugene Semenkin. "Multi-objective Optimization Algorithms with the Island Metaheuristic for Effective Project Management Problem Solving." Organizacija 50, no. 4 (2017): 364–73. http://dx.doi.org/10.1515/orga-2017-0027.
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Abstract Background and Purpose: In every organization, project management raises many different decision-making problems, a large proportion of which can be efficiently solved using specific decision-making support systems. Yet such kinds of problems are always a challenge since there is no time-efficient or computationally efficient algorithm to solve them as a result of their complexity. In this study, we consider the problem of optimal financial investment. In our solution, we take into account the following organizational resource and project characteristics: profits, costs and risks. Design/Methodology/Approach: The decision-making problem is reduced to a multi-criteria 0-1 knapsack problem. This implies that we need to find a non-dominated set of alternative solutions, which are a trade-off between maximizing incomes and minimizing risks. At the same time, alternatives must satisfy constraints. This leads to a constrained two-criterion optimization problem in the Boolean space. To cope with the peculiarities and high complexity of the problem, evolution-based algorithms with an island meta-heuristic are applied as an alternative to conventional techniques. Results: The problem in hand was reduced to a two-criterion unconstrained extreme problem and solved with different evolution-based multi-objective optimization heuristics. Next, we applied a proposed meta-heuristic combining the particular algorithms and causing their interaction in a cooperative and collaborative way. The obtained results showed that the island heuristic outperformed the original ones based on the values of a specific metric, thus showing the representativeness of Pareto front approximations. Having more representative approximations, decision-makers have more alternative project portfolios corresponding to different risk and profit estimations. Since these criteria are conflicting, when choosing an alternative with an estimated high profit, decision-makers follow a strategy with an estimated high risk and vice versa. Conclusion: In the present paper, the project portfolio decision-making problem was reduced to a 0-1 knapsack constrained multi-objective optimization problem. The algorithm investigation confirms that the use of the island meta-heuristic significantly improves the performance of genetic algorithms, thereby providing an efficient tool for Financial Responsibility Centres Management.
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Truschenko, Irina. "Algorithm for making management decisions on the use of outsourcing by entrepreneurial structures." Russian Journal of Management 9, no. 1 (2021): 216–20. http://dx.doi.org/10.29039/2409-6024-2021-9-1-216-220.
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Due to the economic downturn and high competition in the market, business structures are constantly looking for ways to optimize financial resources, as well as ways to increase their profits. Thus, a number of business structures use outsourcing as one of the effective tools for managing their resources. The transfer of individual business processes can allow the entrepreneurial structure to focus on strategically important areas of activity, due to which the profit of the entrepreneurial structure can be increased.
 According to the author's observations, the functioning of any business structures in the conditions of the economic crisis and the situation that has developed in connection with the spread of the new coronavirus infection COVID-19, outsourcing is a very relevant tool to increase competitiveness in the market.
 The article discusses the developed algorithm for making management decisions on the use of outsourcing by entrepreneurial structures. The process of making management decisions regarding the use of outsourcing is associated with a number of risks. The algorithm proposed by the author can help to minimize the risks arising when making a managerial decision on the use of outsourcing by entrepreneurial structures.
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Kumari, Monika, and G. Sahoo. "Design and Analysis of Sustainable and Seasonal Profit Scaling Model in Cloud Environment." Scientific Programming 2019 (October 24, 2019): 1–14. http://dx.doi.org/10.1155/2019/7457938.
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Cloud is a widely used platform for intensive computing, bulk storage, and networking. In the world of cloud computing, scaling is a preferred tool for resource management and performance determination. Scaling is generally of two types: horizontal and vertical. The horizontal scale connects users’ agreement with the hardware and software entities and is implemented physically as per the requirement and demand of the datacenter for its further expansion. Vertical scaling can essentially resize server without any change in code and can increase the capacity of existing hardware or software by adding resources. The present study aims at describing two approaches for scaling, one is a predator-prey method and second is genetic algorithm (GA) along with differential evolution (DE). The predator-prey method is a mathematical model used to implement vertical scaling of task for optimal resource provisioning and genetic algorithm (GA) along with differential evolution(DE) based metaheuristic approach that is used for resource scaling. In this respect, the predator-prey model introduces two algorithms, namely, sustainable and seasonal scaling algorithm (SSSA) and maximum profit scaling algorithm (MPSA). The SSSA tries to find the approximation of resource scaling and the mechanism for maximizing sustainable as well as seasonal scaling. On the other hand, the MPSA calculates the optimal cost per reservation and maximum sustainable profit. The experimental results reflect that the proposed logistic scaling-based predator-prey method (SSSA-MPSA) provides a comparable result with GA-DE algorithm in terms of execution time, average completion time, and cost of expenses incurred by the datacenter.
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Jamali, Gholamreza, Shib Sankar Sana, and Reza Moghdani. "Hybrid improved cuckoo search algorithm and genetic algorithm for solving Markov-modulated demand." RAIRO - Operations Research 52, no. 2 (2018): 473–97. http://dx.doi.org/10.1051/ro/2017076.
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One of the fundamental problems in supply chain management is to design the effective inventory control policies for models with stochastic demands because efficient inventory management can both maintain a high customers’ service level and reduce unnecessary over and under-stock expenses which are significant key factors of profit or loss of an organization. In this study, a new formulation of an inventory system is analyzed under discrete Markov-modulated demand. We employ simulation-based optimization that combines simulated annealing pattern search and ranking selection (SAPS&RS) methods to approximate near-optimal solutions of this problem. After determining the values of demand, we employ novel approach to achieve minimum cost of total SCM (Supply Chain Management) network. In our proposed approach, hybrid improved cuckoo search algorithm (ICS) and genetic algorithm (GA) are presented as main platform to solve this problem. The computational results demonstrate the effectiveness and applicability of the proposed approach.
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Qu, Shou Ning, Qin Wang, Kui Liu, and De Jun Xu. "Research and Application in Supply Chain Management Based on Correlation Analyze of Association Rules Algorithm." Materials Science Forum 532-533 (December 2006): 1024–27. http://dx.doi.org/10.4028/www.scientific.net/msf.532-533.1024.
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In this paper, the association rule algorithm and its defects were analyzed. An improved algorithm was put forward for applying it to analysis the association of products fittings in SCM. The application of improved algorithm can mine which kinds of fittings or sets of items being matched to get a salable product or gain the higher profit. So it can not only instruct customer’s consumption but also can help the entrepreneur make a detailed and efficient internal plan.
Dissertations / Theses on the topic "PROFIT MANAGEMENT ALGORITHM"
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Плюхина, Т. Н., та T. N. Plyukhina. "Методические подходы к управлению прибылью предприятий малого и среднего бизнеса : магистерская диссертация". Master's thesis, б. и, 2020. http://hdl.handle.net/10995/86606.
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In a complex and dynamic market economy, for stability and maintaining a high level of competitiveness, its participants need constant work on all the main aspects of the formation, distribution and use of profits at the enterprise. Indeed, it is profit that is the main source of financing the activities of a business entity, satisfying the financial interests of the owners of the enterprise, its employees and the state. The aim of the master's thesis is to develop guidelines for managing the profit of small and medium-sized enterprises. The work examines the genesis of the concept of “profit” and the theoretical and methodological aspects of managing the profit of small and medium-sized enterprises. The sources used educational and methodological and research literature, the results of empirical research of the author and corporate reporting data. In the master's thesis, an algorithm for managing the profit of small and medium-sized enterprises was developed, which is based on the choice of a vector for managing profit taking into account the proposed classification, which allows an objective assessment of the financial results of the enterprise to determine scenarios of its development.<br>В условиях сложной и динамичной рыночной экономики для устойчивости и поддержания высокого уровня конкурентоспособности её участникам необходима постоянная работа по всем основным аспектам формирования, распределения и использования прибыли на предприятии. Ведь именно прибыль является основным источником финансирования деятельности субъекта хозяйствования, удовлетворения финансовых интересов собственников предприятия, его сотрудников и государства. Целью магистерской диссертации является разработка методических рекомендаций по управлению прибылью предприятий малого и среднего бизнеса. В работе рассматривается генезис понятия «прибыль» и теоретические и методические аспекты управления прибылью предприятий малого и среднего бизнеса. В качестве источников использовалась учебно-методическая и научно-исследовательская литература, результаты эмпирических исследований автора и данные корпоративной отчетности. В магистерской диссертации был разработан алгоритм управления прибылью предприятий малого и среднего бизнеса, в основу которого положен выбор вектора управления прибылью с учетом предложенной классификации, что позволяет получить объективную оценку финансовых результатов предприятия для определения сценариев его развития.
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Andreu, Altava Ramon. "Calcul du profil optimal d'un aéronef dans les phases de descente et d'approche." Thesis, Toulouse 3, 2020. http://www.theses.fr/2020TOU30026.
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Le contexte actuel de croissance du trafic aérien, qui double tous les quinze ans, pose des problèmes environnementaux et remet en cause le développement durable de l'aviation. De plus, d'autres facteurs comme l'entrée en vigueur de nouveaux décrets relatifs aux questions environnementales, la volatilité des cours du pétrole et aussi la concurrence exacerbée du marché des compagnies aériennes conduisent au fait que les sujets de recherche liés à l'optimisation fine du profil de vol de l'avion et à l'amélioration de l'efficacité des opérations aériennes sont devenus des enjeux majeurs pour l'aviation. Le système de gestion du vol, ou FMS selon l'acronyme anglais, est un système de navigation embarqué, courant dans tous les avions de transport commercial, qui permet à l'équipage de gérer le plan de vol latéral et vertical. Du fait que les systèmes avioniques aient des performances limitées, les algorithmes embarqués font des calculs sur la base d'hypothèses très conservatrices. Ceci conduit à des écarts notoires entre les calculs du FMS et le profil réellement volé par l'avion dans un environnement dynamique du vol. L'objectif de cette thèse est donc de développer une fonction bord intégrée au concept de poste de pilotage des futurs cockpit Airbus, permettant de générer des trajectoires optimisées mais aussi tenant compte de l'environnement dynamique de l'avion. Pour cela, cette nouvelle fonction bord qui a été développée adapte la stratégie et le profil de vol de façon régulière pour minimiser le coût global de l'opération. Les principes de gestion énergétique d'un aéronef sont utilisés pour optimiser le profil vertical de vol dans les phases de descente et d'approche dans le but de réduire la consommation carburant, les émissions de gaz à effet de serre et potentiellement le bruit généré par les moteurs et les surfaces aérodynamiques. La fonction proposée est basée sur les principes de la programmation dynamique et plus particulièrement sur l'algorithme A*. Elle cherche à minimiser une fonction de coût en traversant un espace de recherche généré au fur et à mesure que l'algorithme avance dans ses calculs. Non seulement la trajectoire résultante est optimale mais aussi relie la position courante de l'avion avec le seuil de piste de l'aéroport d'arrivée indépendamment du mode de guidage et des conditions énergétiques, ce qui est une nouveauté par rapport au FMS.[...]<br>The continued increase of air traffic, which doubles every 15 years, produces large economic benefits but poses environmental issues that put at risk the sustainable development of air transport. Other factors such as jet fuel prices volatility, the introduction of new environmental regulations and intense competition in the airline industry, have stimulated in the last years research on trajectory optimization and flight efficiency topics. The Flight Management System (FMS) is an onboard avionic system, standard in all transport aircraft, which is used by flight crews to manage the lateral and vertical flight-plan. Since current avionic systems are limited in terms of computational capacity, the computations performed by their algorithms are usually done on the basis of conservative hypotheses. Thus, notorious deviations may occur between FMS computations and the actual flight profile flown by the aircraft. The goal of this thesis is to develop an onboard function, which could be integrated in future Airbus cockpits, that computes optimal trajectories, readjusts the flight strategy according to the dynamic aircraft condition and minimizes operating costs. Flight energy management principles has been used for optimizing aircraft trajectories in descent and approach phases with respect to fuel consumption, greenhouse gas and noise emissions. The proposed function has been developed on the basis of dynamic programming techniques, in particular the A* algorithm. The algorithm minimizes a certain objective function by generating incrementally the search space. The exploration of the search space gives the optimal profile that links the aircraft current position to the runway threshold, independently of the current flight mode and aircraft energy condition. Results show 13% fuel savings and a decrease of 12% in gas emissions compared with a best-in-class FMS. Furthermore, the algorithm proposes the flight strategy to dissipate the excess of energy in situations where aircraft fly too high and/or too fast close to the destination runway. A preliminary operational evaluation of the computed trajectories has been conducted in the flight simulators. These tests demonstrate that the computed trajectories can be tracked with current guidance modes, although new modes should be required to decrease the workload of flight crews. In conclusion, this paper constitutes a solid background for the generation of real-time optimal trajectories in light of the automation of descent and approach flight phases
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Pitiot, Paul. "Amélioration des techniques d'optimisation combinatoire par retour d'expérience dans le cadre de la sélection de scénarios de Produit/Projet." Thesis, Toulouse, INPT, 2009. http://www.theses.fr/2009INPT021H/document.
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La définition et l’utilisation d'un modèle couplant la conception de produit et la conduite du projet dès les phases amont de l’étude d’un système correspondent à une forte demande industrielle. Ce modèle permet la prise en compte simultanée de décisions issues des deux environnements produit/projet mais il représente une augmentation conséquente de la dimension de l'espace de recherche à explorer pour le système d'aide à la décision, notamment lorsque il s'agit d'une optimisation multiobjectif. Les méthodes de type métaheuristique tel que les algorithmes évolutionnaires, sont une alternative intéressante pour la résolution de ce problème fortement combinatoire. Ce problème présente néanmoins une particularité intéressante et inexploitée : Il est en effet courant de réutiliser, en les adaptant, des composants ou des procédures précédemment mis en œuvre dans les produits/projets antérieurs. L'idée mise en avant dans ce travail consiste à utiliser ces connaissances « a priori » disponibles afin de guider la recherche de nouvelles solutions par l'algorithme évolutionnaire. Le formalisme des réseaux bayésiens a été retenu pour la modélisation interactive des connaissances expertes. De nouveaux opérateurs évolutionnaires ont été définis afin d'utiliser les connaissances contenues dans le réseau. De plus, le système a été complété par un processus d'apprentissage paramétrique en cours d'optimisation permettant d'adapter le modèle si le guidage ne donne pas de bons résultats. La méthode proposée assure à la fois une optimisation plus rapide et efficace, mais elle permet également de fournir au décideur un modèle de connaissances graphique et interactif associé au projet étudié. Une plateforme expérimentale a été réalisée pour valider notre approche<br>The definition and use of a model coupling product design and project management in the earliest phase of the study of a system correspond to a keen industrial demand. This model allows simultaneous to take into account decisions resulting from the two environments (product and project) but it represents a consequent increase of the search space dimension for the decision-making system, in particular when it concerns a multiobjective optimization. Metaheuristics methods such as evolutionary algorithm are an interesting way to solve this strongly combinative problem. Nevertheless, this problem presents an interesting and unexploited characteristic: It is indeed current to re-use, by adapting them, the components or the procedures previously implemented in pasted product or project. The idea proposed in this work consists in using this “a priori” knowledge available in order to guide the search for new solutions by the evolutionary algorithm. Bayesian network was retained for the interactive modeling of expert knowledge. New evolutionary operators were defined in order to use knowledge contained in the network. Moreover, the system is completed by a process of parametric learning during optimization witch make it possible to adapt the model if guidance does not give good results. The method suggested ensures both a faster and effective optimization, but it also makes it possible to provide to the decision maker a graphic and interactive model of knowledge linked to studied project. An experimental platform was carried out to validate our approach
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Stecík, Július. "Algoritmy ve správě barev." Master's thesis, Vysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií, 2013. http://www.nusl.cz/ntk/nusl-220212.
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Thesis briefly discusses the issues of color perception and effects associated with it. Further describes color model and its mathematical definition, which are used by color management. Briefly analyzes important elements of ICC profile. In second part two java applications were designed and programmed. First one evaluates visible spectrum and graphically demonstrate procedure for obtaining trichromacy information from this spectrum. Second application analyzes ICC profile and derives gamut of described device.
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Kněžínek, Michal. "Návrh a využití automatického obchodního systému pro zhodnocení kapitálu podniku." Master's thesis, Vysoké učení technické v Brně. Fakulta podnikatelská, 2014. http://www.nusl.cz/ntk/nusl-224710.
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This diploma thesis discusses about the possibilities of investing in the capital market with a focus on the foreign exchange market. Analysis of the company, whose output is SWOT analysis, is focused on the economic justification of investments. The essence is the proposal of automatic trading systems that will automatically trade on the basis of information from the market and add value to ivested capital. This automatic trading systems are designed in analytic platform named MetaTrader and their parameters are optimized by genetic algorithms.
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Lemos, Bernardo dos Santos Rosa Santos de. "Management Algorithms for Charging Electric Vehicles in Residential Buildings." Master's thesis, 2017. http://hdl.handle.net/10316/83220.
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Dissertação de Mestrado Integrado em Engenharia Electrotécnica e de Computadores apresentada à Faculdade de Ciências e Tecnologia<br>A eletrificação do setor dos transportes irá desempenhar um papel fundamental no futuro de cidades mais inteligentes e mais sustentáveis. Devido à crescente adoção de veículos elétricos (EVs), se o carregamento não for controlado, poderão surgir eventuais problemas na rede elétrica, tal como sobretensões levando a perdas de energia. Assim, torna-se necessário a criação de estratégias de carregamento mais inteligentes.Esta dissertação apresenta um algoritmo inteligente de controlo de carregamento (SCMA) que simula o carregamento de um EV, numa residência, de acordo com as preferências do utilizador, bem como as da rede. Este algoritmo permite ao utilizador poupanças de energia e beneficiam, simultaneamente, a rede elétrica e o ambiente. Para o seu funcionamento, considera-se que a residência em causa tem uma tarifa energética variável no tempo, de hora em hora. Assume-se também que os dados da variação do preço da eletricidade, da previsão de procura e da geração energia renovável são comunicados ao SCMA.Os SCMA foram simulados de acordo com três objetivos: minimizar o custo de carregamento, minimizar o impacto ambiental e suavizar o diagrama de carga. Par tal, os algoritmos priorizam ou os intervalos de tempo com menor custo de eletricidade, aqueles com maior percentagem de energia renovável, ou ainda os que apresentem maior disponibilidade de potência. Depois, os algoritmos foram simulados para vários casos de estudo.Por último, os cenários de carregamento otimizado foram comparados entre eles e com um caso de carregamento não otimizado. O SCMA foi bem sucedido na otimização do carregamento do EV sob todos os objetivos.<br>The transport electrification will play an important role in future smarter and more sustainable cities. Due to the increasing adoption of electric vehicles (EVs), if the charging cycles are not managed, a number of grid related issues, such as overload, could arise, which can lead to power outages. Thus, smarter charging strategies are required.This dissertation presents smart charging management algorithm (SCMA) that simulate the charging of the EV according to the user’s preferences, as well as the grid’s requirements. Such algorithm allows further energy savings to the consumer, whilst benefiting the grid and the environment. It is considered that the residential dwelling is operating under time-varying electricity prices, that change on an hourly basis. This work assumes that electricity pricing variations, the load forecast and the renewable energy generation data are provided to the SCMA.The SCMA was simulated under three objectives: to minimize the charging cost, minimize the environmental impact and to flatten the load profile. In order to do this, the algorithms either prioritize the time slots with the lowest electricity price, or the ones with higher RES share, or the ones with more available power, respectively. Then, the algorithms were tested for a series of case studies.Lastly, the optimized charging scenarios were compared among them and to an unmanaged charging scenario. The SCMA successfully optimized the EV’s charging cycles under any objective. - - - - - -
Books on the topic "PROFIT MANAGEMENT ALGORITHM"
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Cevelev, Aleksandr. Material management of railway transport. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1064961.
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In the monograph reviewed the development of the inventory management of railway transport in the new economic environment of market economy. 
 According to the results of theoretical research, innovative and production potential of the supply system of railway transport the main directions and methods of transformation of the restructuring process under the corporate changes of JSC "RZD", positioned value system of the logistics of railway transportation, and developed a classification model used logistical resources. 
 Evaluation of activity of structural divisions of Russian Railways supply is proposed to be viewed through an integrated and comprehensive approach to the development of systems of balanced indicators of supply and prompt handling of material resources, the implementation of which allows to distribute the strategic objectives of the company "Russian Railways" activities in the system of logistics of the Railways and also to involve in economic circulation of excessive and unused inventories of material and technical resources and efficiently reallocate them among enterprises at the site of the railway. 
 Recommendations for the implementation of the developed algorithms and models are long term in nature and are based on the concept of logistics management and improve the business processes of the logistics system. 
 Will be useful for managers and specialists of directorates of logistics of Russian Railways supply, undergraduates and graduate students interested in the economy of railway transport.
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Cevelev, Aleksandr. The economy and material management on a railway transport. INFRA-M Academic Publishing LLC., 2020. http://dx.doi.org/10.12737/1085329.
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In the textbook in an accessible form presented and discussed the development of the economy and the inventory management of railway transport in the new economic environment. For the first time in Russian literature, made a theoretical attempt at a comprehensive review of the effectiveness of, and satisfaction of needs in material resources structural divisions, subsidiaries and affiliates of JSC "RZD". According to the results of theoretical research, innovative and production potential of the supply system of railway transport the main directions and methods of transformation of the restructuring process under the corporate changes of JSC "RZD", positioned value system of logistics of rail transport, a comprehensive approach to the development of systems of balanced indicators of supply and prompt handling of material resources. Recommendations for the implementation of the developed algorithms and models are long term in nature and are based on the concept of logistics management improve business processes, system logistics.
 For students and teachers, workers of enterprises of railway transport, and others interested in questions of transport Economics.
Book chapters on the topic "PROFIT MANAGEMENT ALGORITHM"
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Sudtasan, Tatcha, and Komsan Suriya. "Making Profit in Stock Investment Before XD Dates by Using Genetic Algorithm." In Innovative Management in Information and Production. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-4857-0_38.
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Xu, Xiujuan, Lifeng Jia, Zhe Wang, and Chunguang Zhou. "DualRank: A Dual-Phase Algorithm for Optimal Profit Mining in Retailing Market." In Advances in Computer Science – ASIAN 2005. Data Management on the Web. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11596370_17.
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Fernau, Henning, and Ulrike Stege. "Profit Parameterizations of Dominating Set." In Algorithmic Aspects in Information and Management. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-27195-4_10.
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Liu, Bin, Xiao Li, Huijuan Wang, Qizhi Fang, Junyu Dong, and Weili Wu. "Profit Maximization Problem with Coupons in Social Networks." In Algorithmic Aspects in Information and Management. Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-030-04618-7_5.
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Cotet, Costel Emil, and Diana Popescu. "Material Flow Management in Industrial Engineering." In Encyclopedia of Information Science and Technology, Third Edition. IGI Global, 2015. http://dx.doi.org/10.4018/978-1-4666-5888-2.ch373.
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The first part of the article presents fundamentals of the Material Flow Theory (MFT) centered on five major topics: material flow and generic associate architecture definitions and classifications; major characteristics of discrete, continuous and hybrid material flow; mathematical and virtual modeling of material flow; material flow simulating algorithms (including a general algorithm for optimizing the associated architecture determining the flow trajectory), as well as major MFT applications in different fields. In the second part, the article focuses on the specific applications of MFT in industrial engineering material flow management (MFM). The accent is put on exploring different possibilities to increase productivity and profit in manufacturing architectures using the MFM approach based on virtual modeling and simulation. The proposed main topics of this section are: definition and characteristics of diffused and concentrated manufacturing architectures, virtual modeling of the manufacturing architecture structural elements, MFM simulation algorithms, diagnosis and optimization for manufacturing architectures in industrial engineering using MFM.
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Marks, R., D. Midgley, and L. Cooper. "Co-Evolving Better Strategies in Oligopolistic Price Wars." In Handbook of Research on Nature-Inspired Computing for Economics and Management. IGI Global, 2007. http://dx.doi.org/10.4018/978-1-59140-984-7.ch052.
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Using empirical market data from brand rivalry in a retail ground-coffee market, we model each idiosyncratic brand’s pricing behavior using the restriction that marketing strategies depend only on profit-relevant state variables, and use the genetic algorithm to search for co-evolved equilibria, where each profit-maximizing brand manager is a stimulus-response automaton, responding to past prices in the asymmetric oligopolistic market. This chapter is part of a growing study of repeated interactions and oligopolistic behavior using the GA.
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Dekhici, Latifa, Khaled Guerraiche, and Khaled Belkadi. "Bat Algorithm With Generalized Fly for Combinatorial Production Optimization Problems." In Technological Innovations in Knowledge Management and Decision Support. IGI Global, 2019. http://dx.doi.org/10.4018/978-1-5225-6164-4.ch003.
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A set of metaheuristics has proved its efficiency in solving rapidly NP-hard problems. Several combinatorial and continuous optimization areas drew profit from these powerful alternative techniques. This chapter intends to describe a discrete version of bat algorithm (BA) combined to generalized walk evolutionary (GEWA), also called bat algorithm with generalized fly or walk (BAG) in order to solve discrete industrial optimization. The first case of study is the well-known hybrid flow shop scheduling. The second one concerns the operating theatre that represents a critical manufacturing system, as the products delivered are patients. The last problem is the redundancy optimization (ROP) for series-parallel multi-state power system (MSS). Its resolution involves the selection of components with an appropriate level of redundancy to maximize system reliability with constrained cost. A universal moment generating function (UMGF) is used to estimate reliabilities. The modified bat algorithm on specific benchmarks was compared with the original one, and other results taken from the literature of each case study.
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Badhe, Vivek, R. S. Thakur, and G. S. Thakur. "Profit Pattern Mining Using Soft Computing for Decision Making." In Pattern and Data Analysis in Healthcare Settings. IGI Global, 2017. http://dx.doi.org/10.4018/978-1-5225-0536-5.ch011.
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Problem of decision making is a crucial task in every business. Profit Pattern Mining hit the target by minimizes the gap between statistical based pattern generation and value base decision making. But this job is found very difficult when it depends on the large, imprecise and vague environment, which is frequent in recent years. The concept of soft computing with data mining is novel way to address this difficulty. The general approaches to association rule mining focus on inducting rule by using correlation among data and finding frequent occurring patterns. The major technique uses support and confidence measures for generating rules which is not adequate nowadays as a measure of interest, since the data have become more multifaceted these days, it's a necessary to find solution that deals with such problems and uses some new measures like profit, significance etc. In this chapter, authors apply concept of pattern mining with vague set theory, Genetic algorithm theory and related properties to the commercial management to deal with business decision making problem.
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Shojai, Ali Zolghadr, Jamal Shahrabi, and Masoud Jenabi. "An Integrated Bi-Objective Reverse Logistics Network Design for Remanufacturing." In Exploring Innovative and Successful Applications of Soft Computing. IGI Global, 2014. http://dx.doi.org/10.4018/978-1-4666-4785-5.ch015.
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Growing environmental and economical concern has led to increasing attention towards management of product return flows. An effective and efficient reverse logistics network enables companies to gain more profit and customer satisfaction. Consequently, the reverse logistics network design problem has become a critical issue. After a brief introduction to the basic concepts of reverse logistics, the authors formulate a new integrated multi-stage, multi-period, multi-product reverse logistics model for a remanufacturing system where the inventory is considered. Two objectives, minimization of the costs and maximization of coverage, are addressed. Since such network design problems belong to a class of NP-hard problems, a multi-objective genetic algorithm and a multi-objective evolutionary strategy algorithm are developed in order to find the set of non-dominated solutions. Finally, the model is tested on test problems with different sizes, and the proposed algorithms are compared based on the number, quality, and distribution of non-dominated solutions that belong to the Pareto front.
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Jiang, Yan. "Dynamic Spectrum Auction and Load Balancing Algorithm in Heterogeneous Network." In Global Applications of Pervasive and Ubiquitous Computing. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2645-4.ch017.
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Mobile communication plays an important role in the future of communication systems. To meet personalized and intelligent requirements, mobile communication has evolved from single wireless cellular network to heterogeneous mobile communication network, including wireless cellular network, wireless local network, and wireless personal network. In the heterogeneous communication system, to entirely fulfill the spectrum resource complementary advantages of such a heterogeneous wireless network, the spectrum resource trade algorithm has attracted tremendous research efforts. In this paper, the advantages and disadvantages of dynamic spectrum allocation and load balancing are discussed and spectrum auction takes the place of spectrum allocation to maximize the operation revenue. The authors design a joint spectrum auction and load balancing algorithm (SALB) based on heterogeneous network environment to develop a more flexible and efficient spectrum management. The simulation result shows that SALB increases the operation profit significantly.
Conference papers on the topic "PROFIT MANAGEMENT ALGORITHM"
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Intal, Ramon Christhoper D. J., Elmer P. Dadios, and Alexis M. Fillone. "Maximizing the bus carrier's profit in Epifanio Delos Santos Avenue using genetic algorithm." In 2014 International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM). IEEE, 2014. http://dx.doi.org/10.1109/hnicem.2014.7016254.
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Bouyahyiouy, Karim El, and Adil Bellabdaoui. "An ant colony optimization algorithm for solving the full truckload vehicle routing problem with profit." In 2017 International Colloquium on Logistics and Supply Chain Management (LOGISTIQUA). IEEE, 2017. http://dx.doi.org/10.1109/logistiqua.2017.7962888.
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Xia, Jinfu, Yan Zhao, Guanfeng Liu, Jiajie Xu, Min Zhang, and Kai Zheng. "Profit-driven Task Assignment in Spatial Crowdsourcing." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/265.
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In Spatial Crowdsourcing (SC) systems, mobile users are enabled to perform spatio-temporal tasks by physically traveling to specified locations with the SC platforms. SC platforms manage the systems and recruit mobile users to contribute to the SC systems, whose commercial success depends on the profit attained from the task requesters. In order to maximize its profit, an SC platform needs an online management mechanism to assign the tasks to suitable workers. How to assign the tasks to workers more cost-effectively with the spatio-temporal constraints is one of the most difficult problems in SC. To deal with this challenge, we propose a novel Profit-driven Task Assignment (PTA) problem, which aims to maximize the profit of the platform. Specifically, we first establish a task reward pricing model with tasks' temporal constraints (i.e., expected completion time and deadline). Then we adopt an optimal algorithm based on tree decomposition to achieve the optimal task assignment and propose greedy algorithms to improve the computational efficiency. Finally, we conduct extensive experiments using real and synthetic datasets, verifying the practicability of our proposed methods.
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Katooli, Javad, Mohammad Ameri, and Mohammad Hadi Katooli. "The Application of a Microgrid Considering Wind Generation." In ASME 2015 9th International Conference on Energy Sustainability collocated with the ASME 2015 Power Conference, the ASME 2015 13th International Conference on Fuel Cell Science, Engineering and Technology, and the ASME 2015 Nuclear Forum. American Society of Mechanical Engineers, 2015. http://dx.doi.org/10.1115/es2015-49787.
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Conventional power networks have experienced a gradual evolution from a centralized nature to distributed and localized structures. The upgrading of power system toward a smart grid is being developed to improve reliability and facilitate the integration of different types of renewable energies and improve load management. Due to different uncertainties linked to electricity supply in renewable MGs, probabilistic energy management techniques are going to be necessary to analyze the system. In this study, the short-term operation planning of a typical microgrid (MG) with diverse units for achieving the maximum profit, considering technical and economical constraints, for the next 24 hours, using gravitational search algorithm (GSA) with SPSS software is presented and the effect of wind generation in the planning is investigated. The MG consists of a diverse variety of power system components such as wind turbine, microturbine, photovoltaic, fuel cell, Hydrogen storage tank, reformer, a boiler, and electrical and thermal loads. Moreover, MG is connected to an electrical grid for exchange of power. The MG is managed and controlled through a central controller. The system costs include the operational cost, thermal recovery, power trade with the local grid, and hydrogen production costs. The system costs include the operational cost, thermal recovery, power trade with the local grid, and hydrogen production costs. Total obtained profit from the MG, considering with US electricity and natural gas prices is $5.312902×103.
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Qiu, Dawei, Jianhong Wang, Junkai Wang, and Goran Strbac. "Multi-Agent Reinforcement Learning for Automated Peer-to-Peer Energy Trading in Double-Side Auction Market." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/401.
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With increasing prosumers employed with distributed energy resources (DER), advanced energy management has become increasingly important. To this end, integrating demand-side DER into electricity market is a trend for future smart grids. The double-side auction (DA) market is viewed as a promising peer-to-peer (P2P) energy trading mechanism that enables interactions among prosumers in a distributed manner. To achieve the maximum profit in a dynamic electricity market, prosumers act as price makers to simultaneously optimize their operations and trading strategies. However, the traditional DA market is difficult to be explicitly modelled due to its complex clearing algorithm and the stochastic bidding behaviors of the participants. For this reason, in this paper we model this task as a multi-agent reinforcement learning (MARL) problem and propose an algorithm called DA-MADDPG that is modified based on MADDPG by abstracting the other agents’ observations and actions through the DA market public information for each agent’s critic. The experiments show that 1) prosumers obtain more economic benefits in P2P energy trading w.r.t. the conventional electricity market independently trading with the utility company; and 2) DA-MADDPG performs better than the traditional Zero Intelligence (ZI) strategy and the other MARL algorithms, e.g., IQL, IDDPG, IPPO and MADDPG.
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Zhang, Zhi, and Fei Li. "Dynamic power management algorithms in maximizing net profit." In 2010 44th Annual Conference on Information Sciences and Systems (CISS). IEEE, 2010. http://dx.doi.org/10.1109/ciss.2010.5464902.
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Jianhui, Lin. "An Urban Criminal Statistic Profile Vector Algorithm." In 2009 International Conference on Information Management, Innovation Management and Industrial Engineering. IEEE, 2009. http://dx.doi.org/10.1109/iciii.2009.548.
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Caisheng Wang, S. P. McElmurry, C. J. Miller, and Junhui Zhao. "An integrated economic/emission/load profile management dispatch algorithm." In 2012 IEEE Power & Energy Society General Meeting. New Energy Horizons - Opportunities and Challenges. IEEE, 2012. http://dx.doi.org/10.1109/pesgm.2012.6345405.
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Li, Wenhu, Yong Yin, Yang Wang, Leiming Zhu, Yu Xia, and Yuhang Qi. "Rail Profile Detection Based on PNP Algorithm." In 2018 2nd IEEE Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). IEEE, 2018. http://dx.doi.org/10.1109/imcec.2018.8469520.
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Wang Bangji, Liu Qingxiang, Zhou Lei, Zhang Yanrong, Li Xiangqiang, and Zhang Jianqiong. "Velocity profile algorithm realization on FPGA for stepper motor controller." In 2011 2nd International Conference on Artificial Intelligence, Management Science and Electronic Commerce (AIMSEC). IEEE, 2011. http://dx.doi.org/10.1109/aimsec.2011.6009864.
Full textReports on the topic "PROFIT MANAGEMENT ALGORITHM"
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Jenkins, M., and L. Zieglar. Commercial National Security Algorithm (CNSA) Suite Profile of Certificate Management over CMS. RFC Editor, 2020. http://dx.doi.org/10.17487/rfc8756.
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