in this paper, we present a genetic algorithm (ga) for optimization of a multi-mode resource constrained time cost trade off (mrctct) problem. the proposed ga, each activity has several operational modes and each mode identifies a possible executive time and cost of the activity. beyond earlier studies on time-cost trade-off problem, in mrctct problem, resource requirements of each execution mo...

Due to the variety of products, simultaneous production of different models has an important role in production systems. Moreover, considering the realistic constraints in designing production lines attracted a lot of attentions in recent researches. Since the assembly line balancing problem is NP-hard, efficient methods are needed to solve this kind of problems. In this study, a new hybrid met...

among various heuristics techniques, genetic algorithm (ga) is one of the most widely used techniques which has successfully been applied on a variety of complex combinatorial problems. the performance of ga largely depends on the proper selection of its parameters values; including crossover mechanism, probability of crossover, population size and mutation rate and selection percent. in this p...

A novel hybrid genetic algorithm (GA) for jointly optimizing source and channel codes is presented in this paper. The algorithm first uses GA for the coarse search of source and channel codes. An iterative search is then followed for the refinement of the coarse search. The hybrid GA enhances the robustness of the design of source and channel codes. The distributed GA scheme can also be used in...

This paper proposes a novel hybrid algorithm namely APSO-BFO which combines merits of Bacterial Foraging Optimization (BFO) algorithm and Adaptive Particle Swarm Optimization (APSO) algorithm to determine the optimal PID parameters for control of nonlinear systems. To balance between exploration and exploitation, the proposed hybrid algorithm accomplishes global search over the whole search spa...

In this paper, we present an application of evolved neural networks using a real coded genetic algorithm for simulations of monthly groundwater levels in a coastal aquifer located in the Shabestar Plain, Iran. After initializing the model with groundwater elevations observed at a given time, the developed hybrid genetic algorithm-back propagation (GA-BP) should be able to reproduce groundwater ...

abstract one of the major factors on the amount of water resources is river flow which is so dependent to the hydrologic and meteorologic phenomena. simulation and forecasting of river flow makes the decision maker capable to effectively manage the water resources projects. so, simulation and forecasting models such as artificial neural networks (anns) are commonly used for simulation and predi...

in this paper, we consider a flow shop scheduling problem with availability constraints (fsspac) for the objective of minimizing the makespan. in such a problem, machines are not continuously available for processing jobs due to preventive maintenance activities. we proposed a mixed-integer linear programming (milp) model for this problem which can generate non-permutation schedules. furthermor...