بهینه سازی مسئله زمانبندی کامیون ها در انبار متقاطع چنددربی با در نظر گرفتن اثر یادگیری و زوال پذیری کارها

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، گروه مهندسی صنایع، دانشگاه پیام نور، تهران، ایران

2 استاد، دانشکده مهندسی صنایع، پردیس دانشکده‌های فنی، دانشگاه تهران، تهران، ایران

10.22034/tri.2022.119317

چکیده

به طور کلی هر زنجیره‌ی تامین شامل سه مرحله‌ی اصلی تهیه، تولید و توزیع است. استفاده از سیستم انبار متقاطع یک استراتژی جدید در مرحله توزیع برای بهبود زمان پاسخگویی به مشتریان با انتقال محصولات به طور مستقیم از کامیون های دریافت به کامیون های ارسالی است. به طور کلی برای پردازش یک فعالیت، هر دو منبع ماشین و منابع انسانی مورد نیاز است. بسیاری از محققان تا‌کنون روشهای برنامه ریزی متعددی برای سیستم های انبار متقاطع توسعه داده اند، اما اکثراً محدودیت های مهم منابع انسانی را نادیده گرفته اند. در این مقاله برای اولین بار به بررسی مسئله زمانبندی کامیون‌ها در انبار متقاطع چند دربی با در نظر گرفتن اثرات عوامل انسانی و زوال پذیری کارها برای پر کردن شکاف بین مدل های برنامه‌ریزی نظری و آنچه در دنیای واقعی انجام می گیرد پرداخته‌ایم و برای این منظور یک مدل برنامه‌ریزی عدد صحیح مختلط برای مسئله یاد شده ارائه شده است. با توجه به ادبیات تحقیق زمان حل مدل ارائه شده توسط روش های دقیق با افزایش اندازه مساله به سرعت افزایش می یابد تا حدی که روش‌های دقیق به سختی می‌تواند به جواب بهینه دست پیدا کنند. برای حل مسائل در مقیاس بزرگ از چهار الگوریتم فراابتکاری شامل الگوریتم‌های ژنتیک (GA)، رقابت استعماری (ICA)، کشتل (KA) و بهینه سازی مهندسی اجتماعی (SEO) استفاده شده است. در نهایت نتایج عددی بدست آمده از تمامی الگوریتم‌های فرا‌ابتکاری مورد بررسی و تحلیل حساسیت قرار گرفته‌اند. الگوریتم‌های فراابتکاری را بر اساس معیار های بهترین، میانگین‌ جواب‌ها، Rpd و زمان مورد مقایسه قرار داده‌ایم. در نتیجه الگوریتم‌های SEO و الگوریتم کشتل از نظر کیفیت جواب بهتر از سایر الگوریتم‌ها عمل نمودند.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Optimization for a truck scheduling problem in multi-door cross dokcing with learning effect and deteriorating jobs

نویسندگان [English]

  • Iman Seyedi 1
  • Maryam Hamedi 1
  • Reza Tavakkoli-Moghaddam 2
1 Assistant Professor, Department of Industrial Engineering, Payame Noor University, Tehran, Iran.
2 Professor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran.
چکیده [English]

In general, each supply chain consists of three main stages of procurement, production and distribution. The use of the cross-docking system is a new strategy at the distribution stage to improve customer response time by moving products directly from pickup trucks to delivery trucks. Generally, for an activity to be done both machine and human resources are needed. Many researchers have already developed numerous planning methods for cross-docking systems, but human resource constraints have largely ignored. In this paper, for the first time, we examine the problem of truck scheduling in multi-door cross-dock considering the learning effects and the deterioration of tasks to fill the gap between theoretical planning models and what is happening in the real world. We have proposed a mixed integer programming model for this problem. According to the research literature, with increasing the size of the problem, the complexity of integer programming model is expanding rapidly so that the exact methods can hardly achieve the optimal solution. To solve large-scale problems, five meta-heuristic algorithms are used including Genetic Algorithms (GA), Imperial Competitive Algorithm (ICA), Keshtel Algorithm (KA), and Social Engineering Optimization (SEO). Finally, the numerical results obtained from all meta-heuristic algorithms are analyzed. We compare the meta- heuristic algorithms based on the best, average, Rpd and time criteria. As a result, the SEO and KA algorithm performed better than the other algorithms in terms of solution quality.

کلیدواژه‌ها [English]

  • cross-docking
  • scheduling
  • Learning Effect
  • Deterioration
  • Meta Heuristic
Alpan, G., Ladier, A.-L., Larbi, R., & Penz, B., (2011), “Heuristic solutions for transshipment problems in a multiple door cross docking warehouse”, Computers & Industrial Engineering, 61(2), pp.402-408.
-Amini, A., & Tavakkoli-Moghaddam,
R., (2016), “A bi-objective truck scheduling problem in a cross-docking center with probability of breakdown for trucks”, Computers & Industrial Engineering, 96, pp.180-191.
-Amini, A., Tavakkoli-Moghaddam, R., & Omidvar, A., (2014), “Cross-docking truck scheduling with the arrival times for inbound trucks and the learning effect for unloading/loading processes”, Production & Manufacturing Research, 2(1), pp.784-804.
-Arabani, A. B., Ghomi, S. F., & Zandieh, M., (2011), “Meta-heuristics implementation for scheduling of trucks in a cross-docking system with temporary storage”, Expert systems with Applications, 38, pp. 1964-1979.
-Bartholdi III, J. J., & Gue, K. R., (2000), “Reducing labor costs in an LTL cross docking terminal”, Operations Research, 48(6),
pp.823-832.
-Bellanger, A., Hanafi, S., & Wilbaut, C., (2013),­“Three-stage hybrid-flow shop model for cross-docking”, Computers & Operations Research, 40(4), pp.1109-1121.
-Boysen, N., & Fliedner, M., (2010), “Cross dock scheduling: Classification, literature review and research agenda”, Omega, 38(6),
pp.413-422.
-Dulebenets, M. A., (2019), “A Delayed Start Parallel Evolutionary Algorithm for just-in-time truck scheduling at a cross-docking facility”, International Journal of Production Economics, 212, pp.236-258.
-Fonseca, G. B., Nogueira, T. H., & Ravetti, M. G., (2019), “A hybrid Lagrangian metaheuristic for the cross-docking flow shop scheduling problem”, European Journal of Operational Research, 275(1), pp.139-154.
-Golshahi-Roudbaneh, A., Hajiaghaei-Keshteli, M., & Paydar, M. M., (2017), Developing a lower bound and strong heuristics for a truck scheduling problem in a cross-docking center, Knowledge-Based Systems, 129,
pp.17-38.
-Gupta J.N.D., Gupta S.K., (1988), “Single facility scheduling with nonlinear processing times, Computers and Industrial Engineering 14, pp.387–393.
-Holland, J. H., (1992), “Adaptation in natural and artificial systems: an introductory analysis with applications to biology, control, and artificial intelligence”, MIT press.
-Huang, X., Wang, M. Z., & Ji, P., (2014), “Parallel machines scheduling with deteriorating and learning effects”, Optimization Letters,
pp.1-8.
-Ladier, A. L., & Alpan, G., (2016), “Cross-docking operations: Current research versus industry practice, Omega, 62, pp.145-162.
-Maknoon, M. Y., & Baptiste, P., (2010), "Moving freight inside cross docking terminals", Paper presented at the 2010 8th International Conference on Supply Chain Management and Information.
-Mir, M. S. S., & Rezaeian, J., (2016), "A robust hybrid approach based on particle swarm optimization and genetic algorithm to minimize the total machine load on unrelated parallel machines", Applied Soft Computing, 41,
pp.488-504.
-Mousavi, S. M., Tavakkoli-Moghaddam, R., & Jolai, F., (2013), "A possibilistic programming approach for the location problem of multiple cross-docks and vehicle routing scheduling under uncertainty", Engineering Optimization, 45(10), pp.1223-1249.
-Molavi, D., Shahmardan, A., & Sajadieh, M. S., (2018), "Truck scheduling in a cross docking systems with fixed due dates and shipment sorting", Computers & Industrial Engineering, 117, pp.29-40.
-Rijal, A., Bijvank, M., & de Koster, R., (2019), "Integrated scheduling and assignment of trucks at unit-load cross-dock terminals with mixed service mode dock doors", European Journal of Operational Research, 278(3), pp.752-771.
-Rostami, M., Pilerood, A. E., & Mazdeh, M. M., (2015), "Multi-objective parallel machine scheduling problem with job deterioration and learning effect under fuzzy environment", Computers & Industrial Engineering, 85,
pp.206-215.
-Rohrer, M., (1995), "Simulation and cross docking", Paper presented at the Simulation Conference Proceedings, Winter.
-Song, K., & Chen, F., (2007), "Scheduling cross docking logistics optimization problem with multiple inbound vehicles and one outbound vehicle", Paper presented at the 2007 IEEE International Conference on Automation and Logistics.
-Seyedi, I., Hamedi, M., Tavakkoli-Moghaddam, R., (2019), "Truck Scheduling in a Cross-Docking Terminal by Using Novel Robust Heuristics, International Journal of Engineering, 32(2), pp.296-305.
-Seyedi, I., Hamedi, M., & Tavakkoli-Moghadaam, R. (2021). Developing a mathematical model for a multi-door cross-dock scheduling problem with human factors: A modified imperialist competitive algorithm. Journal of Industrial Engineering and Management Studies, 8(1), pp.180-201.
-Seyedi, I. & Maleki-Daronkolaei, A. (2013). Solving a two-stage assembly flowshop scheduling problem to minimize the mean tardiness and earliness penalties by three meta-heuristics. Caspian Journal of Applied Sciences Research, 2(4), pp. 67-78.
-Seyedi, I., Mirzazadeh, S., Maleki-Daronkolaei, A., Mukhtar, M., & Sahran, S. (2016). An inventory model with reworking and setup time to consider effect of inflation and time value of money. Journal of engineering science and Technology, 11(3), pp. 416-430.
Seyedi, I., Maleki-Daronkolaei, A., & Kalashi, F. (2012). Tabu search and simulated annealing for new three-stage assembly flow shop scheduling with blocking. Interdisciplinary Journal of Contemporary Research in usiness, 4(8), pp.394-402.
-Vahdani, B., & Zandieh, M., (2010), "Scheduling trucks in cross-docking systems: Robust meta-heuristics", Computers & Industrial Engineering, 58(1), pp.12-24.
-Wisittipanich, W., & Hengmeechai, P., (2017), "Truck scheduling in multi-door cross docking terminal by modified particle swarm optimization", Computers & Industrial Engineering.
-Xu, J., Xu, X., & Xie, S. Q., (2011), "Recent developments in Dual Resource Constrained (DRC) system research", European Journal of Operational Research, 215(2), pp.309-318.
-Yu, W., (2002), "Operational strategies for cross docking systems".
-Yu, W., & Egbelu, P. J., (2008), "Scheduling of inbound and outbound trucks in cross docking systems with temporary storage", European Journal of Operational Research, 184(1),
pp.377-396.
-Zhao, Q. H., & Cheng, T. E., (2009), "An analytical study of the modification ability of distribution centers", European Journal of Operational Research, 194(3), pp.901-910.