Abstract
ABSTRACT Chemical Reaction Optimization (CRO) is a metaheuristic for solving optimization problems, which mimics the interactions between molecules in a chemical reaction with the purpose of achieving a stable, low-energy state. In the present work, we utilize the CRO metaheuristic to solve, in an efficient manner, the capacitated p-median problem, in order to locate service stations. Results from solving small to medium-sized problems available in the literature, with up to 724 notes and 200 medians, are compared to their optimal or best-known values. Results show that CRO results are comparable, in terms of accuracy and execution time, to many existing successfulmetaheuristics, as well as exact and hybrid methods, having exceeding those in some cases.
Highlights
Facility layout and planning is an important topic that has a wide variety of applications in real life
We provide a speed comparison for reference purposes only, as it would not be fair to compare Chemical Reaction Optimization (CRO) results with the ones published by the aforementioned authors, considering that these processors were released more than a decade ago
Since most of these problems cannot be solved to optimality by MIP solvers in a timely fashion, we report in Table 7, their objective function values for the Best Know feasible Solution (BKS), regardless of being achieved by CRO or Iterated Reduction Matheuristic Algorithm” (IRMA)
Summary
Facility layout and planning is an important topic that has a wide variety of applications in real life. Fleszar & Hindi (2008) developed a hybrid heuristic that utilizes Variable Neighborhood Search to find suitable medians, reducing the CPMP to a generalized assignment problem, which was solved using IBM CPLEX. A simple heuristic, based on the first size-reduction heuristic proposed by Stefanello et al (2015) as part of their “Iterated Reduction Matheuristic Algorithm” (IRMA), along with a modified version of the λ-interchange mechanism, presented by Osman & Christofides (1994) is used during intensification (local search) phases This adapted version of CRO is used to solve a wide variety of instances available in the literature, with sizes ranging from 50 to 724 customers and 5 to 200 medians. Further information about the CRO metaheuristics can be obtained in Lam & Li (2010, 2012)
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