A COMBINED APPROACH TO ADAPTIVE DIFFERENTIAL EVOLUTION

A Bi-Objective Approach to an Assembly Line Re-Balancing Problem: Model and Differential Evolution Algorithms

Assembly lines are special kinds of production systems which are of great importance in the industrial production of high quantity commodities. In many practical manufacturing systems, configuration of assembly lines is fixed and designing a new line may be incurred huge amount of costs and thereby it is not desirable for practitioners. When some changes related to market demand occur, it is wo...

Developing Adaptive Differential Evolution as a New Evolutionary Algorithm, Application in Optimization of Chemical Processes

An enhanced reliability-oriented workforce planning model for process industry using combined fuzzy goal programming and differential evolution approach

This paper draws on the “human reliability” concept as a structure for gaining insight into the maintenance workforce assessment in a process industry. Human reliability hinges on developing the reliability of humans to a threshold that guides the maintenance workforce to execute accurate decisions within the limits of resources and time allocations. This concept offers a worthwhile point of de...

a bi-objective approach to an assembly line re-balancing problem: model and differential evolution algorithms

assembly lines are special kinds of production systems which are of great importance in the industrial production of high quantity commodities. in many practical manufacturing systems, configuration of assembly lines is fixed and designing a new line may be incurred huge amount of costs and thereby it is not desirable for practitioners. when some changes related to market demand occur, it is wo...

Combined Mutation Differential Evolution to Solve Systems of Nonlinear Equations

This paper presents a differential evolution heuristic to compute a solution of a system of nonlinear equations through the global optimization of an appropriate merit function. Three different mutation strategies are combined to generate mutant points. Preliminary numerical results show the effectiveness of the presented heuristic.