A Genetic Algorithm Developed for a Supply Chain Scheduling Problem

Document Type: Research Paper

Authors

Department of Industrial Engineering, University of Semnan, Semnan, Iran

Abstract

This paper concentrates on the minimization of total tardiness and earliness of orders in an integrated production and transportation scheduling problem in a two-stage supply chain. Moreover, several constraints are also considered, including time windows due dates, and suppliers and vehicles availability times. After presenting the mathematical model of the problem, a developed version of GA called Time Travel to History (TTH) algorithm, inspired from the idea of traveling through history, is proposed to solve the problem. In order to validate the performance of the proposed algorithm, the results of TTH algorithm are compared with two other genetic algorithms in the literature. The comparison results show the better performance of the proposed algorithm. Moreover, the results of implementing the sensitivity analysis to the main parameters of the algorithm show the behavior of the objective functions when the parameters are changed.

Keywords

Main Subjects


Article Title [Persian]

ارایه یک الگوریتم ژنتیک توسعه یافته به منظور حل مساله زمانبندی در زنجیره تامین

Authors [Persian]

  • سید محمدرضا طاهری
  • محمدعلی بهشتی نیا
گروه مهندسی صنایع، دانشگاه سمنان، سمنان، ایران
Abstract [Persian]

این مقاله به کمینه سازی مجموع زمانهای زودکرد و دیرکرد در مساله زمانبندی یکپارچه تولید و حمل و نقل در یک زنجیره تامین دو مرحله ای می پردازد. به علاوه محدودیتهایی نظیر پنجر های زمانی تحویل و زمانهای فراهم بودن تامین کنندگان و وسایل نقلیه نیز در مساله در نظر گرفته شده اند. پس از ارائه مدل ریاضی مساله یک نسخه توسعه یافته از الگوریتم ژنتیک به نام الگوریتم سفر به تاریخ (TTH) با الهام از ایده سفر به تاریخ به منظور حل مساله ارایه شده است. به منظور ارزیابی الگوریتم پیشنهادی، نتایج الگوریتم TTH با دو الگوریتم ژنتیک دیگر در ادبیات موضوع مقایسه شده است. نتایج مقایسات نشان از عملکرد بهتر الگوریتم پیشنهادی دارد. بعلاوه نتایج پیاده سازی تحلیل حساسیت روی پارامترهای اصلی مساله، رفتار توابع هدف در نظر گرفته شده را هنگامی که پارامترهای درنظر گرفته شده تغییر پیدا می کنند را نشان می دهد.

Keywords [Persian]

  • الگوریتم ژنتیک
  • فراابتکاری
  • زنجیره تامین
  • زمانبندی
  • لجستیک
 Beheshtinia, M. A., & Ghasemi, A. (2017). A multi-objective and integrated model for supply chain scheduling optimization in a multi-site manufacturing system. Engineering Optimization, 50(9), 1415-1433.

 Beheshtinia, M. A., Ghasemi, A., & Farokhnia, M. (2017). Supply chain scheduling and routing in multi-site manufacturing system (case study: a drug manufacturing company). Journal of Modelling in Management, 13(1), 27-49.

Borumand, A., & Beheshtinia, M. A. (2018). A developed genetic algorithm for solving the multi-objective supply chain scheduling problem. Kybernetes, 47(7), 1401-1419.

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.

Han, B., & Zhang, W. J. (2015). On-line Supply Chain Scheduling Problem with Capacity Limited Vehicles. IFAC-PapersOnLine, 48(3), 1539-1544.

Holland, J. H. (1992). Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence. MIT press.

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.

Kumar, M., Vrat, P., & Shankar, R. (2004). A fuzzy goal programming approach for vendor selection problem in a supply chain. Computers & Industrial Engineering, 46(1), 69-85.

Low, C., Chang, C. M., & Gao, B. Y. (2017). Integration of production scheduling and delivery in two echelon supply chain. International Journal of Systems Science: Operations & Logistics, 4(2), 122-134.

Moon, C., Lee, Y. H., Jeong, C. S., & Yun, Y. (2008). Integrated process planning and scheduling in a supply chain. Computers & Industrial Engineering, 54(4), 1048-1061.

Selvarajah, E., & Zhang, R. (2014). Supply chain scheduling at the manufacturer to minimize inventory holding and delivery costs. International Journal of Production Economics, 147, 117-124.

Ullrich, C. A. (2013). Integrated machine scheduling and vehicle routing with time windows. European Journal of Operational Research, 227(1), 152-165.

Xu, S., Liu, Y., & Chen, M. (2017). Optimisation of partial collaborative transportation scheduling in supply chain management with 3PL using ACO. Expert Systems with Applications, 71, 173-191.

Yeung, W. K., Choi, T. M., & Cheng, T. C. E. (2011). Supply chain scheduling and coordination with dual delivery modes and inventory storage cost. International Journal of Production Economics, 132(2), 223-229.

Yimer, A. D., & Demirli, K. (2010). A genetic approach to two-phase optimization of dynamic supply chain scheduling. Computers & Industrial Engineering, 58(3), 411-422.

Yin, P. Y., Lyu, S. R., & Chuang, Y. L. (2016). Cooperative coevolutionary approach for integrated vehicle routing and scheduling using cross-dock buffering. Engineering Applications of Artificial Intelligence, 52, 40-53.

Zegordi, S. H., & Beheshti Nia, M. A. (2009). Integrating production and transportation scheduling in a two-stage supply chain considering order assignment. The International Journal of Advanced Manufacturing Technology, 44(9), 928-939.

Zegordi, S. H., & BeheshtiNia, M. A. (2009). Integrating production andtransportation scheduling in a two-stage supply chain considering order assignment. The International Journal of Advanced Manufacturing Technology, 44(9-10), 928-939.