Solving Task Allocation to the Worker Using Genetic Algorithm

This paper deals with the task-scheduling and worker-allocation problem, in which each skillful worker is capable to perform multiple tasks and has various regular and worker capacity to perform tasks, and the task allocation is operated with daily overhead. We propose a worker assignment model and develop a heuristic algorithm that is genetic algorithm whose performance is to be evaluated against the optimal seeking methods in terms of small sized problems. The genetic algorithm is applied in a way that reduces the amount of involvement required to understand the existing solution. Genetic algorithm is basically used to minimize the total make-spam for scheduling jobs and assigning task to the worker. An attempt is made with an analytic review of the literature on the Genetic Algorithmic approach to GAP (generalized assignment problem), which is proved to be convenient and efficient in deriving the required solutions. Here a crossover and mutation operator respectively has been defined by focusing to solve the assignment problems. Here we have taken the simulation result of different tasks and different workers and it is solved through various algorithms. And the result in the graph, the same data set task-worker set is being provided to GA, ACO (Ant colony optimization), simulated annealing and also tabu search and the time is calculated for the comparison purpose as shown with different task and different worker. Each column represents the task performed by worker. KeywordsGenetic algorithm, assignment problem, makespam, Ant colony algorithm, simulated annealing, tabu search.