Applying meta-heuristic algorithms for an integrated production-distribution problem in a two level supply chain

Bank, M.; Mazdeh, M.; Heydari, M.

Growing Science » Uncertain Supply Chain Management » Applying meta-heuristic algorithms for an integrated production-distribution problem in a two level supply chain

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Uncertain Supply Chain Management

ISSN 2291-6830 (Online) - ISSN 2291-6822 (Print) Quarterly Publication Volume 8 Issue 1 pp. 77-92 , 2020

Applying meta-heuristic algorithms for an integrated production-distribution problem in a two level supply chain Pages 77-92 Download PDF

Authors: Maedeh Bank, Mohammad Mahdavi Mazdeh, Mahdi Heydari

DOI: 10.5267/j.uscm.2019.8.004

Keywords: Scheduling, Supply chain, Lifespan, Simulated Annealing, Genetic Algorithm

Abstract: Supply Chain Management (SCM) is the set of approaches used for the appropriate integration and utilization of suppliers, manufacturers, warehouses and retailers to ensure the production and delivery of products to end users in the right quantities and at the right time. Integration of the stages in the supply chain can make it more effective and profitable as a whole. In the present study, an integrated production and distribution problem in a two-stage supply chain is considered. The supply chain consists of m manufacturers with different locations and rates of production, and a distributer that delivers the ordered products to customers in different locations. Here, products are seasonal and perishable and must be delivered before a specified time. To characterize the problem, a Mixed Integer Programming (MIP) model is proposed and to solve the proposed model, a Hybrid Simulated Annealing (HSA) and a Genetic Algorithm (GA) with mixed repair and penalize strategies are introduced. Computational results of HSA are compared with those of the GA algorithm as the current best algorithm for solving similar problems in the literature.

How to cite this paper

 Bank, M., Mazdeh, M & Heydari, M. (2020). Applying meta-heuristic algorithms for an integrated production-distribution problem in a two level supply chain.Uncertain Supply Chain Management, 8(1), 77-92.

Refrences

Armstrong, R., Gao, S., & Lei, L. (2008). A zero-inventory production and distribution problem with a fixed customer sequence. Annals of Operations Research, 159(1), 395-414.
Chan, F. T. S., Chung, S. H., & Chan, P. L. Y. (2005). An adaptive genetic algorithm with dominated genes for distributed scheduling problems. Expert Systems with Applications, 29(2), 364–371.
Delavar, M. R., Hajiaghaei-Keshteli, M., & Molla-Alizadeh-Zavardehi, S. (2010). Genetic algorithms for coordinated scheduling of production and air transportation. Expert Systems with Applications, 37(12), 8255-8266.
Chang, Y. C., & Lee, C. Y. (2004). Machine scheduling with job delivery coordination. European Journal of Operational Research, 158(2), 470-487.
Chang, Y. C., Chang, K. H., & Kang, T. C. (2015). Applied variable neighborhood search-based approach to solve two-stage supply chain scheduling problems. Journal of Testing and Evaluation, 44(3), 1337-1349.
Fahimnia, B., Luong, L., & Marian, R. (2012). Genetic algorithm optimisation of an integrated aggregate production–distribution plan in supply chains. International Journal of Production Research, 50(1), 81-96.
Guo, Z., Zhang, D., Leung, S. Y. S., & Shi, L. (2016). A bi-level evolutionary optimization approach for integrated production and transportation scheduling. Applied Soft Computing, 42, 215-228.
Hall, N. G., & Potts, C. N. (2003). Supply chain scheduling: Batching and delivery. Operations Research, 51(4), 566-584.
Hamidinia, A., Khakabimamaghani, S., Mazdeh, M. M., & Jafari, M. (2012). A genetic algorithm for minimizing total tardiness/earliness of weighted jobs in a batched delivery system. Computers & Industrial Engineering, 62(1), 29-38.
Hao, J., Cao, L., & Jiang, D. (2015). Integrated production-distribution scheduling problem with multiple independent manufacturers. Mathematical Problems in Engineering, Article ID 579893, 5 pages
Jia, Z. H., Zhuo, X. X., Leung, J. Y., & Li, K. (2019). Integrated production and transportation on parallel batch machines to minimize total weighted delivery time. Computers & Operations Research, 102, 39-51.
Karaoğlan, İ., & Kesen, S. E. (2017). The coordinated production and transportation scheduling problem with a time-sensitive product: a branch-and-cut algorithm. International Journal of Production Research, 55(2), 536-557.
Hu, Y. (2004). Performance analysis and optimization for directional residual based fault isolation (Doctoral dissertation, George Mason University).
Lejeune, M. A. (2006). A variable neighborhood decomposition search method for supply chain management planning problems. European Journal of Operational Research, 175(2), 959-976.
Lee, C. Y., & Chen, Z. L. (2001). Machine scheduling with transportation considerations. Journal of Scheduling, 4(1), 3-24.
Lee, Y. H., Kim, S. H., & Moon, C. (2002). Production-distribution planning in supply chain using a hybrid approach. Production Planning & Control, 13(1), 35-46.
Low, C., Chang, C. M., Li, R. K., & Huang, C. L. (2014). Coordination of production scheduling and delivery problems with heterogeneous fleet. International Journal of Production Economics, 153, 139-148.
Mazdeh M. M., Sarhadi M., & Hindi K.S. (2007). A branch-and-bound algorithm for single-machine scheduling with batch delivery minimizing flow times and delivery costs. European Journal of Operational Research, 183(1), 74–86.
Mazdeh, M. M., Sarhadi, M., & Hindi, K. S. (2008). A branch-and-bound algorithm for single-machine scheduling with batch delivery and job release times. Computers & Operations Research, 35(4), 1099-1111.
Mazdeh, M. M., Shashaani, S., Ashouri, A., & Hindi, K. S. (2011). Single-machine batch scheduling minimizing weighted flow times and delivery costs. Applied Mathematical Modelling, 35(1), 563-570.
Moon, C., Kim, J., & Hur, S. (2002). Integrated process planning and scheduling with minimizing total tardiness in multi-plants supply chain. Computers & Industrial Engineering, 43(1-2), 331-349.
Su, C. S., Pan, J. C. H., & Hsu, T. S. (2009). A new heuristic algorithm for the machine scheduling problem with job delivery coordination. Theoretical Computer Science, 410(27-29), 2581-2591.
Taheri, S. M. R., & Beheshtinia, M. A. (2019). A Genetic Algorithm Developed for a Supply Chain Scheduling Problem. Iranian Journal of Management Studies, 12(2), 107-132.
Yin, Y., Cheng, T. C. E., Hsu, C. J., & Wu, C. C. (2013). Single-machine batch delivery scheduling with an assignable common due window. Omega, 41(2), 216-225.
Zandieh, M., & Molla-Alizadeh-Zavardehi, S. (2009). Synchronizing production and air transportation scheduling using mathematical programming models. Journal of computational and Applied mathematics, 230(2), 546-558.
Zegordi, S. H., Abadi, I. K., & Nia, M. B. (2010). A novel genetic algorithm for solving production and transportation scheduling in a two-stage supply chain. Computers & Industrial Engineering, 58(3), 373-381.
Zhong, W., Dósa, G., & Tan, Z. (2007). On the machine scheduling problem with job delivery coordination. European Journal of Operational Research, 182(3), 1057-1072.