Two-sided assembly line is usually used for the assembly of large products such as cars,buses,and trucks.With the development of technical progress,the assembly line needs to be reconfigured and the cycle time of the ...Two-sided assembly line is usually used for the assembly of large products such as cars,buses,and trucks.With the development of technical progress,the assembly line needs to be reconfigured and the cycle time of the line should be optimized to satisfy the new assembly process.Two-sided assembly line balancing with the objective of minimizing the cycle time is called TALBP-2.This paper proposes an improved artificial bee colony(IABC)algorithm with the MaxTF heuristic rule.In the heuristic initialization process,the MaxTF rule defines a new task's priority weight.On the basis of priority weight,the assignment of tasks is reasonable and the quality of an initial solution is high.In the IABC algorithm,two neighborhood strategies are embedded to balance the exploitation and exploration abilities of the algorithm.The employed bees and onlooker bees produce neighboring solutions in different promising regions to accelerate the convergence rate.Furthermore,a well-designed random strategy of scout bees is developed to escape local optima.The experimental results demonstrate that the proposed MaxTF rule performs better than other heuristic rules,as it can find the best solution for all the 10 test cases.A comparison of the IABC algorithm and other algorithms proves the effectiveness of the proposed IABC algorithm.The results also denote that the IABC algorithm is efficient and stable in minimizing the cycle time for the TALBP-2,and it can find 20 new best solutions among 25 large-sized problem cases.展开更多
In this paper, recent developments of some heuristic algorithms were discussed. The focus was laid on the improvements of ant-cycle (AC) algorithm based on the analysis of the performances of simulated annealing (SA) ...In this paper, recent developments of some heuristic algorithms were discussed. The focus was laid on the improvements of ant-cycle (AC) algorithm based on the analysis of the performances of simulated annealing (SA) and AC for the traveling salesman problem (TSP). The Metropolis rules in SA were applied to AC and turned out an improved AC. The computational results obtained from the case study indicated that the improved AC algorithm has advantages over the sheer SA or unmixed AC.展开更多
The traveling salesman problem has long been regarded as a challenging application for existing optimization methods as well as a benchmark application for the development of new optimization methods. As with many exi...The traveling salesman problem has long been regarded as a challenging application for existing optimization methods as well as a benchmark application for the development of new optimization methods. As with many existing algorithms, a traditional genetic algorithm will have limited success with this problem class, particularly as the problem size increases. A rule based genetic algorithm is proposed and demonstrated on sets of traveling salesman problems of increasing size. The solution character as well as the solution efficiency is compared against a simulated annealing technique as well as a standard genetic algorithm. The rule based genetic algorithm is shown to provide superior performance for all problem sizes considered. Furthermore, a post optimal analysis provides insight into which rules were successfully applied during the solution process which allows for rule modification to further enhance performance.展开更多
文摘Two-sided assembly line is usually used for the assembly of large products such as cars,buses,and trucks.With the development of technical progress,the assembly line needs to be reconfigured and the cycle time of the line should be optimized to satisfy the new assembly process.Two-sided assembly line balancing with the objective of minimizing the cycle time is called TALBP-2.This paper proposes an improved artificial bee colony(IABC)algorithm with the MaxTF heuristic rule.In the heuristic initialization process,the MaxTF rule defines a new task's priority weight.On the basis of priority weight,the assignment of tasks is reasonable and the quality of an initial solution is high.In the IABC algorithm,two neighborhood strategies are embedded to balance the exploitation and exploration abilities of the algorithm.The employed bees and onlooker bees produce neighboring solutions in different promising regions to accelerate the convergence rate.Furthermore,a well-designed random strategy of scout bees is developed to escape local optima.The experimental results demonstrate that the proposed MaxTF rule performs better than other heuristic rules,as it can find the best solution for all the 10 test cases.A comparison of the IABC algorithm and other algorithms proves the effectiveness of the proposed IABC algorithm.The results also denote that the IABC algorithm is efficient and stable in minimizing the cycle time for the TALBP-2,and it can find 20 new best solutions among 25 large-sized problem cases.
文摘In this paper, recent developments of some heuristic algorithms were discussed. The focus was laid on the improvements of ant-cycle (AC) algorithm based on the analysis of the performances of simulated annealing (SA) and AC for the traveling salesman problem (TSP). The Metropolis rules in SA were applied to AC and turned out an improved AC. The computational results obtained from the case study indicated that the improved AC algorithm has advantages over the sheer SA or unmixed AC.
文摘The traveling salesman problem has long been regarded as a challenging application for existing optimization methods as well as a benchmark application for the development of new optimization methods. As with many existing algorithms, a traditional genetic algorithm will have limited success with this problem class, particularly as the problem size increases. A rule based genetic algorithm is proposed and demonstrated on sets of traveling salesman problems of increasing size. The solution character as well as the solution efficiency is compared against a simulated annealing technique as well as a standard genetic algorithm. The rule based genetic algorithm is shown to provide superior performance for all problem sizes considered. Furthermore, a post optimal analysis provides insight into which rules were successfully applied during the solution process which allows for rule modification to further enhance performance.
