Order Picking Problem in a Warehouse with Bi-Objective Genetic Algorithm Approach: Case Study

Abstract

In this paper, an order picking problem with the capacitated forklift in a warehouse is studied by considering the total distance and the penalized earliness/tardiness. These objectives are important to reduce transportation costs and to satisfy customer expectations. Since this problem has been known as NP-hard, a genetic algorithm GA is proposed to solve the bi-objective order picking problem. The proposed approach is applied to auto components industry that produces wire harnesses responsible for all electrical functions in the vehicle. Experimental design is used for tuning the influential parameters of the proposed GA. The GA approach was solved by weighted sum scalarization.

Keywords

• Experimental design, Genetic algorithm, Order picking, weighted sum scalarization

İki Amaçlı Genetik Algoritma Yaklaşımı ile Bir Depoda Sipariş Toplama Problemi: Vaka Çalışması

Abstract

Bu çalışmada, toplam uzaklık ve cezalı erkenlik/gecikme durumlarını dikkate alan bir depoda kapasiteli forklift ile bir sipariş toplama problemi çalışılmıştır. Bu amaçlar, ulaşım maliyetlerini azaltmak ve müşteri beklentilerini karşılamak için önemlidir. Bu problem NP-zor olarak bilindiğinden iki amaçlı sipariş toplama problem çözümü için bir genetik algoritma önerilmiştir. Önerilen yaklaşım, araçtaki tüm elektriksel fonksiyonların çalışmasını sağlayan kablo demetleri üreten bir oto bileşenleri endüstrisine uygulanmıştır. Önerilen GA’nın etkili parametreleri için deney tasarımı kullanılmıştır. GA yaklaşımı ağırlıklı toplam skalerleştirme yöntemi ile çözülmüştür

Keywords

• Deney tasarımı, Genetik Algoritma, Sipariş toplama, Ağırlıklı toplam skalerleştirme yöntemi

References

1. Bean, J.C. (1994). Genetic algorithms and random keys for sequencing and optimization. ORSA Journal on Computing, 6, 2, 154-160.
2. Bogdanović, M. (1989). An ILP formulation and genetic algorithm for the Maximum Degree-Bounded Connected Subgraph problem. Computers & Mathematics with Applications, 59(9), 3029-3038.
3. Chakrabortia, D., Biswasb, P. and Palc, B.B. (2013). FGP Approach for solving fractional Multiobjective Decision Making Problems using GA with Tournament Selection and Arithmetic Crossover. Procedia Technology, 10, 505–514.
4. Ghannadpour, S. F., Noori, S., T.-Moghaddam R. and Ghoseiri, K. (2014). A multi- objective dynamic vehicle routing problem with fuzzy time windows: Model, solution and application. Applied Soft Computing, 14, Part C, 504-527.
5. Gils, T., Ramaekers, K., Braekers, K., Depaire, B. and Caris, A. (2017). Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions. International Journal of Production Economics. https://doi.org/10.1016/j.ijpe.2017.11.021
6. Gils,T., Ramaekers, K., Caris,A. and Koster, R.B.M. (2017). Designing efficient order picking systems by combining planning problems: State-of-the-art classification and https://doi.org/10.1016/j.ejor.2017.09.002 Journal of Operational Research, 1–15.
7. Goldberg, D.E. (1989). Genetic algorithms in search, optimization & machine learning. MA: Addison-Wesley, Reading.
8. Koster, R., Le-Duc, T. and Roodbergen, K.J. (2007). Design and control of warehouse order picking: A literature review. European Journal of Operational Research, 182, 481–501.

9. Lawler, E.L., Lenstra, J.K., Rinnooy Kan, A.H.G. and Shmoys, D.B. (1995). The Traveling Salesman Problem. Chicheste: Wiley.
10. Lenstra, J.K. and Rinnooy Kan, A.H.G. (1981). Complexity of Vehicle and Scheduling Problems. Networks, 11, 221-227.
11. Nagy,G. and Salhi, S. (2005). Heuristic algorithms for single and multiple depot vehicle routing problems with pickups and deliveries, European Journal of Operational Research, 162, 126–141.
12. Rao, Y.Q., Meng-Chang Wang, M.C., Wang, K.P. and Wu, T.M. (2013). Scheduling a single vehicle in the just-in-time part supply for a mixed-model assembly line, Computers & Operations Research, 40, 2599–2610.
13. Rubrico J.I.U., Higashi, T., Tamura, H. and Ota, J. (2011). Online rescheduling of multiple picking agents for warehouse management. Robotics and Computer- Integrated Manufacturing, 27, 62–71.
14. Serna, M.D.A., Uran, C.A.S., Cortes, J.A.Z. and Benitez, A.F.A. (2014). Vehicle routing to multiple warehouses using a memetic algorithm, Procedia - Social and Behavioral Sciences, 160, 587 – 596.
15. Solomon, M.M. and Desrosiers, J. (1988). Time Window Constrained Routing and Scheduling Problem. Transportation Science, 22, 1-13.
16. Teekeng, W. and Thammano, A. (2012). Modified Genetic Algorithm for Flexible Job-Shop Scheduling Problems. Procedia Computer Science, 12, 122-128.
17. Tonci Caric, T., and Gold, H. (Eds.) (2008). Vehicle Routing Problem. Austria: In- The.
18. Wei, L., Zhang,Z., Zhang, D. and Lim, A. (2015). A variable neighborhood search for the capacitated vehicle routing problem with two-dimensional loading constraints. European Journal of Operational Research, 243, 798–814.