Facility layout problem is associated with the arrangement of facilities in a plant. It is a critical issue in the early stages of designing a manufacturing system because it affects the total manufacturing cost significantly. Dynamic and robust layouts are flexible enough to cope with fluctuations and uncertainties in product demands in volatile environment of flexible manufacturing systems. Since the facility layout is a hard combinatorial optimization problem, intelligent approaches are the most appropriate methods for solving the large size of this problem in reasonable computational time. In this paper, first of all, dynamic and robust layouts are surveyed. After a quick look of different mathematical models, including quadratic assignment, quadratic set covering, mixed integer programming, and graph theoretic models, the various solution methods especially intelligent approaches along with their advantages and disadvantages are surveyed. Finally, after review of hybrid algorithms, the conclusion of this paper is reported.

No abstract

Design of a robust production facility layout with minimum handling cost (MHC) presents an appropriate approach to tackle facility layout problems in a dynamic volatile environment, in which product demands randomly change in each planning period. The objective of the design is to find the robust facility layout with minimum total material handling cost over the entire multiperiod planning horizon. This paper proposes a new mathematical model for designing robust machine layout in the stochastic dynamic environment of manufacturing systems using quadratic assignment problem (QAP) formulation. In this investigation, product demands are assumed to be normally distributed random variables with known expected value, variance, and covariance that randomly change from period to period. The proposed model was verified and validated using randomly generated numerical data and benchmark examples. The effect of dependent product demands and varying interest rate on the total cost function of the proposed model has also been investigated. Sensitivity analysis on the proposed model has been performed. Dynamic programming and simulated annealing optimization algorithms were used in solving the modeled example problems.

No abstract

This paper aims at proposing a quadratic assignment-based mathematical model to deal with the stochastic dynamic facility layout problem. In this problem, product demands are assumed to be dependent normally distributed random variables with known probability density function and covariance that change from period to period at random. To solve the proposed model, a novel hybrid intelligent algorithm is proposed by combining the simulated annealing and clonal selection algorithms. The proposed model and the hybrid algorithm are verified and validated using design of experiment and benchmark methods. The results show that the hybrid algorithm has an outstanding performance from both solution quality and computational time points of view. Besides, the proposed model can be used in both of the stochastic and deterministic situations.