In this research, unequal area facility layout and relayout problems are 
studied under uncertainty. Two important notions, robustness to 
uncertainty and flexibility for future changes are introduced and
incorporated into the usual objective of cost minimization under the 
assumption of dependent product demands. A tabu search heuristic procedure 
has been developed and successfully applied to the following versions of 
the stochastic facility layout problem. First, uncertainty is assumed only 
for product demand, i.e., volume uncertainty, and a method using a 
robustness measure which weights the quality of alternative layouts over 
a continuous metric of scenarios is proposed. Second, a simulation 
approach is proposed to deal with volume uncertainty and routing 
flexibility in the same layout design. Third, expansion flexibility is 
studied by assuming that the facility is able to expand towards one side 
(i.e., the last bay). Departments which are most likely to expand in the 
future are encouraged to be located in the last bay by penalizing layouts 
which do not locate them in the defined vertical border. A multi-objective 
tabu search approach has been proposed to solve the stochastic facility 
relayout problem, in which material handling cost and relayout cost are 
optimized. This procedure uniformly and randomly rotates the objective 
function between the two objectives of the problem in each step and, by 
doing so, eliminates the problems of weighting each objective and scaling 
the two objectives. The developed approach is flexible in handling various 
aspects of the stochastic facility relayout problem such as fixed portions 
(i.e., monuments), addition of new departments, and changes in the 
department and facility areas. Next, the developed multi-objective tabu 
search approach is adapted to solve some versions of the stochastic 
facility layout problem. The first problem is one with volume uncertainty 
where the robustness measure and the variance of the material handling cost 
are both optimized. The second problem considers expansion flexibility, where
the robustness measure of the material handling cost and the expansion penalty 
are both optimized. This research is a novel in using tabu search for 
multi-objective optimization by alternating the objectives. Computational 
experiments show that both the TS and MOTS approaches are effective and 
tractable. CPU time per replication ranges from a few seconds to a few minutes.