This paper proposes a novel hybrid algorithm in which simulated annealing algorithm starts with a population of good initial solutions constructed by combining ant colony, clonal selection, and robust layout design approaches. The proposed algorithm can be used to solve a dynamic (multi-period) facility layout problem in both deterministic and stochastic cases. In the stochastic environment, product demands are assumed to be normally distributed random variables with known probability density function that changes from period to period at random. In addition, a quadratic assignment-based mathematical model, which is used in the proposed hybrid algorithm, is developed to design a robust layout for the stochastic dynamic layout problem. Finally, the performance of the proposed algorithm is evaluated by solving a large number of randomly generated test problems and some test problems from the literature in stochastic and deterministic cases respectively. The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time points of view.

Many real-world problems in manufacturing system, for instance, the scheduling problems, are formulated by defining several objectives for problem solving and decision making. Recently, research on dispatching rules allocation has attracted substantial attention. Although many dispatching rules methods have been developed, multi-objective scheduling problems remain inherently difficult to solve by any single rule. In this paper, a hybrid genetic-based gravitational search algorithm (GSA) in weighted dispatching rule is proposed to tackle a scheduling problem by achieving both time and job-related objectives. Genetic algorithm (GA) is used to select two appropriate dispatching rules to combine as a weighted multi-attribute function, while the GSA is used to optimize the contribution weightage of each rule in each stage of the flow shop. The results show that the proposed algorithm is significantly better than the traditional dispatching rules and the rules allocation algorithm. The proposed algorithm not only improved the quality of the schedule in multi-objective problems but also maintained the advantages of traditional dispatching rules in terms of ease of implementation.