Simulated annealing and tabu search approaches for the Corridor Allocation Problem

•The Corridor Allocation Problem (CAP) is considered.•Applications of the CAP include the arrangement of rooms in office buildings, hospitals, shopping centers or schools.•Tabu search and simulated annealing algorithms are presented to minimize the total flow cost among facilities.•Large CAP instanc...

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Bibliographic Details
Published inEuropean journal of operational research Vol. 232; no. 1; pp. 221 - 233
Main Authors Ahonen, H., de Alvarenga, A.G., Amaral, A.R.S.
Format Journal Article
LanguageEnglish
Published Amsterdam Elsevier B.V 01.01.2014
Elsevier Sequoia S.A
Subjects
Algorithms
Allocations
Combinatorial optimization
Corridors
Facilities management
Facilities planning and design
Heuristic
Hospitals
Mathematical problems
Optimization
Optimization algorithms
Shopping centers
Simulated annealing
Studies
Tabu search
Facilities planning and design
Tabu search
Combinatorial optimization
Simulated annealing
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Summary:•The Corridor Allocation Problem (CAP) is considered.•Applications of the CAP include the arrangement of rooms in office buildings, hospitals, shopping centers or schools.•Tabu search and simulated annealing algorithms are presented to minimize the total flow cost among facilities.•Large CAP instances with up to 70 facilities are tested. In the Corridor Allocation Problem, we are given n facilities to be arranged along a corridor. The arrangements on either side of the corridor should start from a common point on the left end of the corridor. In addition, no space is allowed between two adjacent facilities. The problem is motivated by applications such as the arrangement of rooms in office buildings, hospitals, shopping centers or schools. Tabu search and simulated annealing algorithms are presented to minimize the sum of weighted distances between every pair of facilities. The algorithms are evaluated on several instances of different sizes either randomly generated or available in the literature. Both algorithms reached the optimal (when available) or best-known solutions of the instances with n⩽30. For larger instances with size 42⩽n⩽70, the simulated annealing implementation obtained smaller objective values, while requiring a smaller number of function evaluations.
Bibliography:ObjectType-Article-2
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content type line 23
ISSN:0377-2217
1872-6860
DOI:10.1016/j.ejor.2013.07.010