Mathematical formulation and hybrid meta-heuristic solution approaches for dynamic single row facility layout problem

Şahin R., Niroomand S., Durmaz E. D., Molla-Alizadeh-Zavardehi S.

ANNALS OF OPERATIONS RESEARCH, cilt.295, sa.1, ss.313-336, 2020 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 295 Sayı: 1
  • Basım Tarihi: 2020
  • Doi Numarası: 10.1007/s10479-020-03704-7
  • Dergi Adı: ANNALS OF OPERATIONS RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, ABI/INFORM, Applied Science & Technology Source, Business Source Elite, Business Source Premier, Computer & Applied Sciences, INSPEC, Public Affairs Index, zbMATH, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.313-336
  • Anahtar Kelimeler: Dynamic single row facility layout problem, NP-hard problem, Meta-heuristic algorithm, Restart strategy, Parameter tuning, DIMENSIONAL SPACE ALLOCATION, CLOSED-LOOP LAYOUT, GENETIC ALGORITHM, OPTIMIZATION, NETWORKS, DESIGN
  • Gazi Üniversitesi Adresli: Evet

Özet

In this study, for the first time, the classical single row facility layout problem is extended to its dynamic type by considering several planning periods. This new problem consists of two types of costs e.g. material handling cost and rearrangement cost of the departments at the beginning of each period. The problem is formulated by a mixed integer linear programming model. Because of the high complexity of the problem, two well-known meta-heuristic algorithms e.g. the GA and the SA are proposed to solve the problem. In addition, both of the algorithms are hybridized considering the restart and acceptance probability strategies. In order to study the performance of the proposed algorithms, 20 benchmark problems are generated randomly. Considering one of the generated benchmarks, the parameters of the algorithms are tuned by a typical method and final experiments are performed accordingly. The obtained results strongly prove the superiority of the SA hybridized by the restart strategy as it shows much better performance comparing to other proposed algorithms in more than 60% of the benchmarks.