Abstract
The Resource Constrained Scheduling Problem (RCSP) represents an important research area. Not only exact solution but also many heuristic methods have been proposed to solve RCPSP (Resource Constrained Project Scheduling Problem). It is an NP hard problem. Heuristic methods are designed to solve large and highly Resource Constrained software projects. We have solved the problem of resource constrained scheduling problem and named as GASolver. It is implemented in C# using .net platform. We have used Dependency Injection to make the problem loosely coupled, so that other arena of scheduling like Time Cost Tradeoff (CT), Payment Scheduling (PS) etc can be merged with same solution in the future. We have implemented GASolver using Genetic Algorithm (GA).
Highlights
The Resource Constrained Scheduling Problem (RCSP) represents an important research area
Genetic algorithm (GA) [1][2][3] is a pioneering method of metaheuristic optimization which originated from the studies of cellular automata of Holland in the 1970s
A project is best represented as a Task Precedence Graph (TPG).A TPG is an acyclic directed graph consisting of a set of tasks and a set of precedence relationships
Summary
Genetic algorithms (GAs) are search algorithms that are conceptually based on the methods that living organisms adapt. A new set of string structures is created from (bits and pieces of) the fittest strings from the previous generation and occasionally a randomly altered new part This process of exploiting historical data allows the GA to speculate on new search points that will improve performance producing better solutions. The Genetic Algorithm attempts to improve performance leading to an optimal solution In this process, there are two distinct steps, (1) the process of improvement and (2) reaching the optimum itself. Genetic algorithm (GA) [1][2][3] is a pioneering method of metaheuristic optimization which originated from the studies of cellular automata of Holland in the 1970s It is known as an evolutionary algorithm and a search technique that copies from biological evolution.
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