In this paper, we propose three new metaheuristic implementations to address the problem of minimizing the makespan in a hybrid flexible flowshop with sequence-dependent setup times. The first metaheuristic is a genetic algorithm (GA) embedding two new crossover operators, and the second is an ant colony optimization (ACO) algorithm which incorporates a transition rule featuring lookahead infor...

in this paper, the distribution network for multi-level supply chain has been studied. products produced in factories are sent to customers through warehouses and distribution centers based on specific demands. warehouses as holding inventory facilities are located close to factories and the distribution centers are placed in the most accessible locations for services near customers. each item ...

The genetic algorithm (GA) have good global search characteristics and local optimizing algorithm (LOA) have good local search characteristics. In the present work, best characteristics of GA and LOA are combined to develop a hybrid genetic algorithm (HGA). A bank of GA s are used to get a good starting solution for a conjugate gradient algorithm. The number of GA banks is selected using an aut...

A wireless local area network (WLAN) is designed for an IC factory in Hong Kong using the hierarchical genetic algorithm (HGA). The HGA is capable of handling multiobjective functions and discrete constraints. Because of this uniqueness, together with the adopting of a Pareto ranking scheme, a solution can be reached even when skewed multiobjective functions and constraints confinements are bei...

A hybrid algorithm which combines particle swarm optimization (PSO) and iterated local search (ILS) is proposed for solving the hybrid flowshop scheduling (HFS) problem with preventive maintenance (PM) activities. In the proposed algorithm, different crossover operators and mutation operators are investigated. In addition, an efficient multiple insert mutation operator is developed for enhancin...

although several papers have studied no-idle scheduling problems, they all focus on flow shops, assuming one processor at each working stage. but, companies commonly extend to hybrid flow shops by duplicating machines in parallel in stages. this paper considers the problem of scheduling no-idle hybrid flow shops. a mixed integer linear programming model is first developed to mathematically form...

in this study, a hybrid intelligent model has been designed to predict groundwater inflow to a mine pit during its advance. novel hybrid method coupling artificial neural network (ann) with genetic algorithm (ga) called ann-ga, was utilised. ratios of pit depth to aquifer thickness, pit bottom radius to its top radius, inverse of pit advance time and the hydraulic head (hh) in the observation w...

in this paper, we have proposed a new algorithm which combines pso and ga in such a way that the new algorithm is more effective and efficient.the particle swarm optimization (pso) algorithm has shown rapid convergence during the initial stages of a global search but around global optimum, the search process will become very slow. on the other hand, genetic algorithm is very sensitive to the in...

in this study, a hybrid algorithm is presented to tackle multi-variables robust design problem. the proposed algorithm comprises neural networks (nns) and co-evolution genetic algorithm (cga) in which neural networks are as a function approximation tool used to estimate a map between process variables. furthermore, in order to make a robust optimization of response variables, co-evolution algor...

We describe a hybrid meta-heuristic algorithm for combinatorial optimization problems with a specific reference to the travelling salesman problem (TSP). The method is a combination of a genetic algorithm (GA) and greedy randomized adaptive search procedure (GRASP). A new adaptive fuzzy a greedy search operator is developed for this hybrid method. Computational experiments using a wide range of...