Applying Genetic Algorithms for Production Scheduling and Resource Allocation. Special Case: A Small Size Manufacturing Company

Contreras, A Ricardo; Valero, C. Virginia; Pinninghoff, J. M. Angélica

doi:10.1007/11504894_74

Cited by 7 publications

(5 citation statements)

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“…Genetic algorithms adapted the principle of natural genetic systems in searching and optimization problems. Genetic algorithms are widely used in production scheduling, dynamic process control and complex design [9], [10]. For fuzzy logic, this technique can makes the decisions based on incomplete information by simulating the ability of the human mind when dealing with ambiguity.…”

Section: Data Mining Task and Methodsmentioning

confidence: 99%

Data mining in production planning and scheduling: A review

Ismail

Othman

Bakar

2009

2009 2nd Conference on Data Mining and Optimization

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The paper reviews about the data mining tasks and methods, and its application in production planning and scheduling. Data mining will be reviewed in four classifications of data mining systems according to the kinds of databases mined, knowledge to discover, techniques utilized and the applications adapted. This paper also reviews in production planning and scheduling that focused in time frame range either short-to mid-range or long-range planning. In production planning, there are a lot of planning such as process planning, strategic capacity planning, aggregate planning, master scheduling, material requirements planning and order scheduling. From these activities different problems are arise because of the different time, product and environment of production.

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Section: Data Mining Task and Methodsmentioning

confidence: 99%

Data mining in production planning and scheduling: A review

Ismail

Othman

Bakar

2009

2009 2nd Conference on Data Mining and Optimization

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“…) context bFlow, oal, (businessG Agent Artificial (3) where bFlow is the business process instance currently associated to the artificial agent, and context represents the pool of shared resources.…”

Section: Process Optimizationmentioning

confidence: 99%

“…In [3], authors propose a method for optimizing production scheduling in terms of cost and usability. The proposed method combines a genetic algorithm with tabu search to identify the optimal production schedule.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Business process optimization using bio-inspired methods — Ants or bees intelligence?

Pop

Chifu

Salomie

et al. 2012

2012 IEEE 8th International Conference on Intelligent Computer Communication and Processing

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This paper presents two bio-inspired methods addressing the problem of business process optimization. We provide a comparative approach for optimal business process flow selection and resource allocation using two of the most important bio-inspired meta-heuristics: Ant Colony Optimization and Bee Colony Optimization. Our approach does not use predefined rules for making the decisions when selecting the flow in the business process model. Given a business process, our approach starts from the business goal and reconstructs the optimal process flow by going backwards in the process in an iterative manner and solving everything that needs to be done for that goal to be completed. This reconstruction is realized by combining the bio-inspired algorithms with resource allocation strategies. The bio-inspired optimizations have been comparatively evaluated on a set of manufacturing scenarios.

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“…Constraints in ship routing arise from the existence of obstacles (islands) and general international or national navigation rules. The penalty encoding method perhaps is the most popular approach used in Genetic Algorithms for constrained optimization problems because of its simplicity and ease of implementation ( [21], [22], [23], [24], [25] and [26]). On the other hand, one can take extra actions in order to limit the members of the population in the feasible region of the search space.…”

Section: Constraints In Ship Routingmentioning

confidence: 99%

Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm

Kosmas,

Vlachos

2008

Preprint

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Optimization of ship routing depends on several parameters, like ship and cargo characteristics, environmental factors, topography, international navigation rules, crew comfort etc. The complex nature of the problem leads to oversimplifications in analytical techniques, while stochastic methods like simulated annealing can be both time consuming and sensitive to local minima. In this work, a hybrid parallel genetic algorithm -estimation of distribution algorithm is developed in the island model, to operationally calculate the optimal ship routing. The technique, which is applicable not only to clusters but to grids as well, is very fast and has been applied to very difficult environments, like the Greek seas with thousands of islands and extreme micro-climate conditions.

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Applying Genetic Algorithms for Production Scheduling and Resource Allocation. Special Case: A Small Size Manufacturing Company

Cited by 7 publications

References 1 publication

Data mining in production planning and scheduling: A review

Data mining in production planning and scheduling: A review

Business process optimization using bio-inspired methods — Ants or bees intelligence?

Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm

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Applying Genetic Algorithms for Production Scheduling and Resource Allocation. Special Case: A Small Size Manufacturing Company

Cited by 7 publications

References 1 publication

Data mining in production planning and scheduling: A review

Data mining in production planning and scheduling: A review

Business process optimization using bio-inspired methods &#x2014; Ants or bees intelligence?

Operational Optimal Ship Routing Using a Hybrid Parallel Genetic Algorithm

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Product

Resources

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Business process optimization using bio-inspired methods — Ants or bees intelligence?