### Facility Layout and Relayout under Uncertainty

Abstract

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.