Brief Introduction of optaplanner
Posted By : Pradeep Singh Kushwah | 28-Oct-2018
Introduction:- Most of the organizations some time faces planning problems: providing services or products with a limited set of constrained resources (assets, employees, money and time). OptaPlanner is an openSource product of RedHat which optimizes such planning to do fewer resources with more business.
Optaplanner is constraints solving engine which optimize the planning problem as much as possible. In other words we can say that optaplanner is a planning problem solver on the basis of constraints. It is a Java-based product which can be configured on the based on your planning problem or we can say that it helps java developer to solve such type of problems.
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shift rostering: timetabling employee
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Agenda scheduling: meetings scheduling, appointments, jobs maintenance
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Educational timetabling: lessons scheduling, exams, presentations conference
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Vehicle routing
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machine queue planning
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planning games
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Financial optimization
What is the planning problem?
A planning problem has an optimal goal, based on under specific constraints and limited resources. Optimal goals can be any things like.
- Maximize Profit
- Maximize Customer satisfaction
- Optimize Resource allocation
- Optimize Employee Rostering
Basically, All these use cases are probably NP-hard/NP-complete, which means in layman’s terms:
It’s easy to verify a given solution to a problem in a reasonable time.
There is no silver bullet to find an optimal solution of these problems in a reasonable time (*).
Note:- So if we try to solve these type of problem it's harder to find the best solution in a reasonable time.
There is a number of algorithms to solve this type of problem Like
Exhaustive Search (ES)
- Brute Force
- Branch And Bound
Construction heuristics (CH)
- First Fit
- First Fit Decreasing
- Weakest Fit
- Weakest Fit Decreasing
- Strongest Fit
- Strongest Fit Decreasing
- Cheapest Insertion
- Regret Insertion
Metaheuristics (MH)
Local Search (LS)
- Hill Climbing
- Tabu Search
- Simulated Annealing
- Late Acceptance
- Step Counting Hill Climbing
- Variable Neighborhood Descent
Evolutionary Algorithms (EA)
- Evolutionary Strategies
- Genetic Algorithms
Optaplanner provides advanced type algorithm to solve these problems and helps to find the optimal solution in a reasonable amount.
More information Look at this link; https://docs.optaplanner.org/7.11.0.Final/optaplanner-docs/html_single/index.html
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About Author
Pradeep Singh Kushwah
Pradeep is an accomplished Backend Developer with in-depth knowledge and hands-on experience in various cutting-edge technologies. He specializes in Core Java, Spring-Boot, Optaplanner, Angular, and databases such as MongoDB, Neo4j, Redis, and PostgreSQL. Additionally, he has worked with cloud services like AWS and Google Cloud, and he has experience with monitoring tools such as Datadog and Raygun. Pradeep has honed his skills in API Implementations, Integration, optimization, Webservices, Development Testings, and deployments, code enhancements, and has contributed to company values through his deliverables in various client projects, including Kairos, Slick Payroll, Captionlabs, and FarmQ. He is a creative individual with strong analytical skills and a passion for exploring and learning new technologies.