Abstract
Despite the reported effectiveness of analytical algorithms in facility layout planning, a detailed literature survey suggests a lack of new analytical methods in recent years. This paper focuses on open space facilities layout planning that involves modules with constant aspect ratios. We propose a construction-cum-improvement algorithm involving a novel combination of a boundary search-based heuristic placement and steepest descent-based analytical improvement. In the construction phase, the algorithm places a new module at the optimal location on the boundary of a previously constructed cluster of modules. In the improvement phase, the algorithm alternates between boundary search and steepest descent moves until it converges to a local optimum. Experiments with well-known test problems indicate that the proposed algorithm produced solutions superior both to published results and to those produced by VIP-PLANOPT, a popular, oft-cited and commercially available layout planning and optimisation software.
Highlights
Facilities layout planning (FLP) involves the allocation of space to activities [1,2]
The proposed algorithm was implemented using Matlab, and was used to solve the benchmark problems listed in Table 1, using a 2.0 GHz quad-core computer running on Windows 7 with a memory of 8 GB
Comparison of layouts obtained from the proposed adaptive cluster boundary search (ACS) algorithm with the verifiably best available published results and those obtained from VIP-PLANOPT 10 [57], a popular, oft-cited commercially available layout planning and optimisation software, are presented here
Summary
Facilities layout planning (FLP) involves the allocation of space to activities [1,2] It is a difficult combinatorial optimisation problem that has received considerable attention from researchers. The objective is to minimise the cost of inter-module flow by placing all the modules on the packing space without overlaps, in such a manner that the edges of Mi are parallel to the x and y axes respectively. It is a well-known NP-complete problem; and so a verifiably optimal solution cannot be known even for modest size problems [15,16,17]. Published research in this area relies largely on comparing the performance of new algorithms with solutions reported for previously published algorithms, without any attempts to obtain verifiably global optima
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