Hajinejad et al., 2011 - Google Patents
A fast hybrid particle swarm optimization algorithm for flow shop sequence dependent group scheduling problemHajinejad et al., 2011
View HTML- Document ID
- 14673677822884580714
- Author
- Hajinejad D
- Salmasi N
- Mokhtari R
- Publication year
- Publication venue
- Scientia Iranica
External Links
Snippet
Abstract A Particle Swarm Optimization (PSO) algorithm for a Flow Shop Sequence Dependent Group Scheduling (FSDGS) problem, with minimization of total flow time as the criterion (F m| fmls, S plk, prmu|∑ C j), is proposed in this research. An encoding scheme …
- 239000002245 particle 0 title abstract description 45
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
- 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
- G06F9/00—Arrangements for programme control, e.g. control unit
- G06F9/06—Arrangements for programme control, e.g. control unit using stored programme, i.e. using internal store of processing equipment to receive and retain programme
- G06F9/46—Multiprogramming arrangements
-
- 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
- G06F17/50—Computer-aided design
-
- 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
- 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
- G06Q10/04—Forecasting or optimisation, e.g. linear programming, "travelling salesman problem" or "cutting stock problem"
-
- 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
- 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
- G06N5/00—Computer systems utilising knowledge based models
- G06N5/02—Knowledge representation
- G06N5/022—Knowledge engineering, knowledge acquisition
-
- 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
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Hajinejad et al. | A fast hybrid particle swarm optimization algorithm for flow shop sequence dependent group scheduling problem | |
Jamrus et al. | Hybrid particle swarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing time for semiconductor manufacturing | |
Reddy et al. | An effective hybrid multi objective evolutionary algorithm for solving real time event in flexible job shop scheduling problem | |
Naderi et al. | Hybrid flexible flowshop problems: Models and solution methods | |
Banharnsakun et al. | Job shop scheduling with the best-so-far ABC | |
Karaboga et al. | A quick artificial bee colony (qABC) algorithm and its performance on optimization problems | |
Liao et al. | A discrete version of particle swarm optimization for flowshop scheduling problems | |
Zabihzadeh et al. | Two meta-heuristic algorithms for flexible flow shop scheduling problem with robotic transportation and release time | |
Deb et al. | A genetic algorithm based augmented Lagrangian method for constrained optimization | |
Mes et al. | Approximate dynamic programming by practical examples | |
Gupta et al. | Hybrid grey wolf optimizer with mutation operator | |
Singh et al. | A hybrid algorithm using particle swarm optimization for solving transportation problem | |
Li et al. | Multi-robot task allocation based on cloud ant colony algorithm | |
Pan et al. | A novel hybrid GWO-FPA algorithm for optimization applications | |
Pang et al. | Mass personalization-oriented integrated optimization of production task splitting and scheduling in a multi-stage flexible assembly shop | |
Zhang et al. | Improved slime mould algorithm based on hybrid strategy optimization of Cauchy mutation and simulated annealing | |
Afshar-Nadjafi et al. | Project scheduling with limited resources using an efficient differential evolution algorithm | |
Liu et al. | Advanced scatter search approach and its application in a sequencing problem of mixed-model assembly lines in a case company | |
Hong et al. | A dynamic demand-driven smart manufacturing for mass individualization production | |
Waris et al. | Framework for product innovation using SOEKS and decisional DNA | |
Babaei et al. | Analysis and behavior control of a modified singular prey–predator model | |
Singh et al. | Differential evolution: An overview | |
Mohanty et al. | Genetic algorithm for multi-choice integer linear programming problems | |
Chen et al. | Hybrid grey wolf optimizer for solving permutation flow shop scheduling problem | |
Cheung et al. | A supervised particle swarm algorithm for real-parameter optimization |