ABSTRACT

The hybrid flowshop scheduling problem with unrelated parallel machines exists in many industrial manufacturers, which is an NP-hard combinatorial optimisation problem. To solve this problem more effectively, an improved gravitational search (IGS) algorithm is proposed which combines three strategies: generate new individuals using the mutation strategy of the standard differential evolution (DE) algorithm and preserve the optimal solution via a greedy strategy; substitute the exponential gravitational constant of the standard gravitational search (GS) algorithm with a linear function; improve the velocity update formula of the standard GS algorithm by mixing an adaptive weight and the global search strategy of the standard particle swarm optimisation (PSO) algorithm. Benchmark examples are solved to demonstrate the proposed IGS algorithm is superior to the standard genetic algorithm, DE, GS, DE with local search, estimation of distribution algorithm and artificial bee colony algorithms. Two more examples from a real-world water-meter manufacturing enterprise are effectively solved.

摘要

许多工业制造商都存在与并行机无关的混合流水车间调度问题，这是一个 NP-hard 组合优化问题。为了更有效地解决这个问题，提出了一种改进的引力搜索（IGS）算法，它结合了三种策略：使用标准差分进化（DE）算法的变异策略生成新个体，并通过贪婪策略保留最优解；用线性函数代替标准引力搜索（GS）算法的指数引力常数；通过混合自适应权重和标准粒子群优化（PSO）算法的全局搜索策略，改进标准GS算法的速度更新公式。解决基准示例以证明所提出的IGS算法优于标准遗传算法DE、GS、DE与局部搜索、分布估计算法和人工蜂群算法。另外两个真实世界水表制造企业的例子得到了有效的解决。