Volume 28, Number 1 (IJIEPR 2017)                   IJIEPR 2017, 28(1): 33-45 | Back to browse issues page



DOI: 10.22068/ijiepr.28.1.33

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Mehdizadeh E, Fatehi-Kivi A. A Vibration Damping Optimization Algorithm for Solving the Single-item Capacitated Lot-sizing Problem with Fuzzy Parameters . IJIEPR. 2017; 28 (1) :33-45
URL: http://ijiepr.iust.ac.ir/article-1-702-en.html

Associate professor Islamic Azad University, Qazvin Branch , emehdi@qiau.ac.ir
Abstract:   (246 Views)

In this paper, we propose a vibration damping optimization algorithm to solve a fuzzy mathematical model for the single-item capacitated lot-sizing problem. At first, a fuzzy mathematical model for the single-item capacitated lot-sizing problem is presented. The possibility approach is chosen to convert the fuzzy mathematical model to crisp mathematical model. The obtained crisp model is in the form of mixed integer linear programming (MILP) which can be solved by existing solver in crisp environment to find optimal solution. Due to the complexity and NP-hardness of the problem, a vibration damping optimization (VDO) is used to solve the model for large-scale problems.  To verify the performance of the proposed algorithm, we computationally compared the results obtained by the VDO algorithm with the results of the branch-and-bound method and two other well-known meta-heuristic algorithms namely simulated annealing (SA) and genetic algorithm (GA). Additionally, Taguchi method is used to calibrate the parameters of the meta-heuristic algorithms. Computational results on a set of randomly generated instances show that the VDO algorithm compared with the other algorithms can obtain appropriate solutions.

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Highlights

  • Presenting a fuzzy mathematical model for the single-item capacitated lot-sizing problem.

  • Choosing the possibility approach to convert the fuzzy mathematical model to crisp mathematical model.

  • Proposing a vibration damping optimization algorithm to solve the crisp mathematical model.

  • Comparing the VDO algorithm with the results of genetic algorithm (GA) and simulated annealing (SA) algorithm.

Applying Taguchi method for parameter setting.


Type of Study: Research | Subject: Production Planning & Control
Received: 2016/12/9 | Accepted: 2017/06/11 | Published: 2017/06/18

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