Price, 1976 - Google Patents
An interactive objective function generator for goal programmesPrice, 1976
- Document ID
- 11483575886726555014
- Author
- Price W
- Publication year
- Publication venue
- Multiple Criteria Decision Making: Proceedings of a Conference Jouy-en-Josas, France May 21–23, 1975
External Links
Snippet
The problem of multi-objective programming has been treated in the literature, both in the development of solution algorithms ([2],[3],[4],[5],[6],[7]) and in the reporting of applications ([8],[9],[10]). Some authors ([11],[12]) have examined the problem with a desire to outline the …
- 230000002452 interceptive 0 title description 7
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce, e.g. shopping or e-commerce
- G06Q30/02—Marketing, e.g. market research and analysis, surveying, promotions, advertising, buyer profiling, customer management or rewards; Price estimation or determination
- G06Q30/0202—Market predictions or demand forecasting
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06Q—DATA PROCESSING SYSTEMS OR METHODS, SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL, SUPERVISORY OR FORECASTING PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management, e.g. organising, planning, scheduling or allocating time, human or machine resources; Enterprise planning; Organisational models
- G06Q10/063—Operations research or analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/30—Information retrieval; Database structures therefor; File system structures therefor
- G06F17/30286—Information retrieval; Database structures therefor; File system structures therefor in structured data stores
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/12—Computer systems based on biological models using genetic models
- G06N3/126—Genetic algorithms, i.e. information processing using digital simulations of the genetic system
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/04—Inference methods or devices
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Gonzalez-Vidal et al. | A methodology for energy multivariate time series forecasting in smart buildings based on feature selection | |
US11423336B2 (en) | Method and system for model integration in ensemble learning | |
Sangaiah et al. | Towards an efficient risk assessment in software projects–Fuzzy reinforcement paradigm | |
Ha et al. | Application of data mining tools to hotel data mart on the Intranet for database marketing | |
Reeves et al. | A simplified interactive multiple objective linear programming procedure | |
Misaghian et al. | An approach for requirements prioritization based on tensor decomposition | |
von Lücken et al. | An overview on evolutionary algorithms for many‐objective optimization problems | |
CN111797333B (en) | Public opinion spreading task display method and device | |
Hefny et al. | Fuzzy multi-criteria decision making model for different scenarios of electrical power generation in Egypt | |
Kim et al. | A deep generative model for feasible and diverse population synthesis | |
Yameogo et al. | Generating a two-layered synthetic population for French municipalities: Results and evaluation of four synthetic reconstruction methods | |
Hesam Hafezi et al. | Framework for development of the Scheduler for Activities, Locations, and Travel (SALT) model | |
Behera et al. | XGBoost regression model-based electricity tariff plan recommendation in smart grid environment | |
Louviere et al. | A review of recent advances in decompositional preference and choice models | |
Donadello et al. | Machine learning for utility prediction in argument-based computational persuasion | |
Wang et al. | Personalized federated learning for buildings energy consumption forecasting | |
Price | An interactive objective function generator for goal programmes | |
Ben-Akiva | Passenger travel demand forecasting: applications of disaggregate models and directions for research | |
Keles et al. | IBMMS decision support tool for management of bank telemarketing campaigns | |
Amadini et al. | An extensive evaluation of portfolio approaches for constraint satisfaction problems | |
Pandey et al. | Experimental Analysis on Banking Customer Segmentation using Machine Learning Techniques | |
Ananthi et al. | An insight of deep neural networks based on demand forecasting in using: ANN algorithm | |
Telelis et al. | Combinatorial optimization through statistical instance-based learning | |
Lee et al. | Computational results for a quantum computing application in real-life finance | |
Kutiel et al. | What's behind the mask: Estimating uncertainty in image-to-image problems |