Sinriech et al., 1998 - Google Patents
Process selection and tool assignment in automated cellular manufacturing using genetic algorithmsSinriech et al., 1998
- Document ID
- 2842276040456409437
- Author
- Sinriech D
- Meir A
- Publication year
- Publication venue
- Annals of Operations Research
External Links
Snippet
The purpose of this study is to develop an efficient heuristic for the process selection and part cell assignment problem. The study assumes a production environment where each part has several process plans, each manifested by a required set of tools. These tools can …
- 238000000034 method 0 title abstract description 118
Classifications
-
- 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
- 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
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B2219/00—Program-control systems
- G05B2219/30—Nc systems
- G05B2219/32—Operator till task planning
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/418—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM]
- G05B19/41865—Total factory control, i.e. centrally controlling a plurality of machines, e.g. direct or distributed numerical control [DNC], flexible manufacturing systems [FMS], integrated manufacturing systems [IMS], computer integrated manufacturing [CIM] characterised by job scheduling, process planning, material flow
-
- G—PHYSICS
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B19/00—Programme-control systems
- G05B19/02—Programme-control systems electric
- G05B19/18—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form
- G05B19/4097—Numerical control [NC], i.e. automatically operating machines, in particular machine tools, e.g. in a manufacturing environment, so as to execute positioning, movement or co-ordinated operations by means of programme data in numerical form characterised by using design data to control NC machines, e.g. CAD/CAM
-
- 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
- G05—CONTROLLING; REGULATING
- G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
- G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Gen et al. | Genetic algorithms and their applications | |
| Coello | An updated survey of GA-based multiobjective optimization techniques | |
| Mill | Simulated co-evolution as the mechanism for emergent planning and scheduling | |
| Morad et al. | Genetic algorithms in integrated process planning and scheduling | |
| Sarker et al. | Evolutionary optimization | |
| Truong et al. | Simulation based optimization for supply chain configuration design | |
| Akpınar et al. | A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints | |
| Gupta | Design of manufacturing cells for flexible environment considering alternative routeing | |
| Rai et al. | Machine-tool selection and operation allocation in FMS: solving a fuzzy goal-programming model using a genetic algorithm | |
| Husbands et al. | Genetic algorithms, production plan optimisation and scheduling | |
| CN112907150A (en) | Production scheduling method based on genetic algorithm | |
| Proudlove et al. | Intelligent management systems in operations: a review | |
| Kamaruddin et al. | The impact of variety of orders and different number of workers on production scheduling performance: A simulation approach | |
| Yi et al. | Soft computing for scheduling with batch setup times and earliness-tardiness penalties on parallel machines | |
| Phanden et al. | Application of genetic algorithm and variable neighborhood search to solve the facility layout planning problem in job shop production system | |
| Abir et al. | Multi-objective optimization for sustainable closed-loop supply chain network under demand uncertainty: A genetic algorithm | |
| Nidhiry et al. | Scheduling optimization of a flexible manufacturing system using a modified NSGA-II algorithm | |
| Sinriech et al. | Process selection and tool assignment in automated cellular manufacturing using genetic algorithms | |
| CN114237166A (en) | Method for solving multi-rotating-speed energy-saving scheduling problem based on improved SPEA2 algorithm | |
| Chaudhry | Job shop scheduling problem with alternative machines using genetic algorithms | |
| Lawrynowicz | Genetic algorithms for solving scheduling problems in manufacturing systems | |
| Hemmati Far et al. | A flexible cell scheduling problem with automated guided vehicles and robots under energy-conscious policy | |
| Vijayan et al. | Simulation-based decision framework for hybrid layout production systems under disruptions | |
| Ehtesham Rasi | Optimization of the multi-objective flexible job shop scheduling model by applying NSGAII and NRGA algorithms | |
| Mehdizadeh et al. | An integrated mathematical programming model for a dynamic cellular manufacturing system with limited resources |