نشریه پژوهش های مهندسی صنایع در سیستم های تولید

نشریه پژوهش های مهندسی صنایع در سیستم های تولید

تصمیمات مشترک درخصوص قیمت‌گذاری و تجمیع سفارش‌های مشتری در گام آخر تحویل کالا در شرایط تقاضای غیر‌قطعی

نوع مقاله : مقاله پژوهشی

نویسندگان
1 گروه مهندسی سیستم‌ها، دانشکدۀ مهندسی صنایع و سیستم‏های مدیریت، دانشگاه صنعتی امیرکبیر، تهران‌، ایران
2 'گروه مهندسی صنایع، دانشکدۀ مهندسی صنایع، دانشگاه تهران، تهران‌، ایران
چکیده
امروزه با توسعه روزافزون فروشگاه‏ها‏ی اینترنتی و کسب‌وکارهای آنلاین، بخش زیادی از نیازهای مشتریان از این راه پاسخ داده می‏شود. تمرکز این پژوهش، بر قیمت‌گذاری محصولات و حل مسأله تحویل جداگانه سفارش‌ها در گام آخر تحویل کالا است. تحویل جداگانه سفارش‌ها، هنگامی‌که چندین سفارش یک مشتری در یک دوره زمانی مشخص، در چند سفر جداگانه ارسال می‌شود موضوعیت دارد. این پژوهش، یک زنجیره‌تأمین دو سطحی شامل یک مرکز تحویل و تعدادی از مشتریان نهایی را مورد هدف قرار می‎دهد. هر یک از مشتریان، در طول یک دوره زمانی مشخص، می‌توانند چندین سفارش ثبت ‌کنند. همچنین در این پژوهش، تنوع محصول وجود دارد و زمان ثبت سفارش هر محصول توسط هر مشتری غیرقطعی و تحت هر سناریو متفاوت است. برای مواجهه با این عدم قطعیت، یک مدل برنامه‌ریزی غیرخطی عدد صحیح با رویکرد بهینه‌سازی استوار براساس مدل سناریو محور مالوی ارائه شده است. برای حل مدل ریاضی در ابعاد بزرگ از یک الگوریتم ابتکاری استفاده می‌شود. در نهایت، مسأله به‌صورت عددی در ابعاد مختلف، حل و عملکرد خوب الگوریتم ابتکاری با دقت 95% تا 99% و حداکثر زمان حل 9 ثانیه برای بزرگ‌ترین نمونه نسبت به روش حل دقیق توسط نرم‌افزار گمز که بیش از 7200 ثانیه برای همگرایی نیاز به زمان دارد، نشان داده می‌شود. همچنین، برتری سیاست تجمیع سفارش‌ها نسبت به روش خروج به ترتیب ورود روی پارامترهای مختلف حاصل می‌شود. نتایج حاصل از تحلیل حساسیت بر روی پارامترهای تأثیرگذار مسأله، دیدگاه‌های مدیریتی ارزشمندی برای به‌کار بردن در عمل ایجاد می‌کند.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Joint Decisions of Pricing and Shipment Consolidation per Customer for The Last-Mile Delivery with Uncertain Demand

نویسندگان English

Melina Safari Nasab 1
Mohsen Sheikh Sajadieh 1
Matineh Ziari 2
1 Department of Systems and Industrial Engineering, Amirkabir University of Technology, Tehran, Iran. M.Sc. Graduate, Faculty of Industrial Engineering and Management Systems, Amirkabir University of Technology, Tehran, Iran
2 Department of Industrial Engineering, Faculty of Industrial Engineering, University of Tehran, Tehran, Iran
چکیده English

With the increasing growth of online stores and e-commerce businesses, a significant portion of customer needs are now met through these channels. This research focuses on product pricing and solving the problem of separate order deliveries in the final stage of product delivery. Separate order deliveries occur when multiple orders from a single customer are shipped in separate trips within a specific time period. This study targets a two-level supply chain consisting of a delivery center and a number of end customers. Each customer can place multiple orders over a given time period. Additionally, the research considers product variety, and the time at which each customer places an order for a product is uncertain and varies under different scenarios. To address this uncertainty, a robust nonlinear integer programming model based on the scenario-based Mulvey robust optimization approach is proposed. To solve the mathematical model in large-scale instances, a heuristic algorithm is employed. Finally, the problem is numerically solved in various dimensions, demonstrating the strong performance of the heuristic algorithm with an accuracy of 95% to 99% and a maximum solution time of 9 seconds for the largest instance—compared to the exact solution method using GAMS software, which requires over 7,200 seconds to converge. Furthermore, the order consolidation policy proves superior to the first-come-first-served approach across various parameters. The results of sensitivity analysis on key problem parameters provide valuable managerial insights for practical implementation.

کلیدواژه‌ها English

Time-Based Shipment Consolidation Policy, Last-Mile Delivery, Pricing, Customer&‌‌‌rsquo
s Willingness to Pay, Demand Uncertainty, Robust Optimization
[1]    Gevaers, R., E. Van de Voorde, and T. Vanelslander, (2014). Cost modelling and simulation of last-mile characteristics in an innovative B2C supply chain environment with implications on urban areas and cities. Procedia-Social and Behavioral Sciences, 125: p. 398-411.
[2]    Zhang, Y., et al., (2019). Order consolidation for the last-mile split delivery in online retailing. Transportation Research Part E: Logistics and Transportation Review, 122: p. 309-327.
[3]    Ballou, R.H., (2006). Revenue estimation for logistics customer service offerings. The International Journal of Logistics Management, 17(1): p. 21-37.
[4]    Wei, L., S. Jasin, and R. Kapuscinski, (2017). Shipping consolidation with delivery deadline and expedited shipment options. Ross School of Business Paper.
[5]    Goodarzi, A.H., et al., (2024). Evaluating the sustainability and resilience of an intermodal transport network leveraging consolidation strategies. Transportation Research Part E: Logistics and Transportation Review, 188: p. 103616.
[6]    Yu, V.F., et al., (2024). A fast simulated annealing heuristic for the multi-depot two-echelon vehicle routing problem with delivery options. Transportation Letters, 16(8): p. 921-932.
[7]    Moradi, N., et al., (2024). Two-echelon Electric Vehicle Routing Problem in Parcel Delivery: A Literature Review. arXiv preprint arXiv:2412.19395.
[8]    Yamada, K., et al., (2024). Drone scheduling for parcel delivery with an access grade to stops on a fixed truck route. Journal of Advanced Mechanical Design, Systems, and Manufacturing, 18(2): p. JAMDSM0021-JAMDSM0021.
[9]    Bányai, T., (2018). Real-time decision making in first mile and last mile logistics: How smart scheduling affects energy efficiency of hyperconnected supply chain solutions. Energies, 11(7): p. 1833.
[10] Karaoglan, I., et al., (2012). The location-routing problem with simultaneous pickup and delivery: Formulations and a heuristic approach. Omega, 40(4): p. 465-477.
[11] Zhou, L., et al., (2019). Model and algorithm for bilevel multisized terminal location‐routing problem for the last mile delivery. International Transactions in Operational Research, 26(1): p. 131-156.
[12] Tong, B., et al., (2025) A deep learning-based algorithm for the detection of personal protective equipment. PLoS One, 20(5): p. e0322115.
[13] Senarclens de Grancy, G. and M. Reimann, (2016). Vehicle routing problems with time windows and multiple service workers: a systematic comparison between ACO and GRASP. Central European Journal of Operations Research, 24: p. 29-48.
[14] Cortes, J.D. and Y. Suzuki, (2020). Vehicle routing with shipment consolidation. International Journal of Production Economics, 227: p. 107622.
[15] Ma, Y., et al. (2017). An improved ACO for the multi-depot vehicle routing problem with time windows. in Proceedings of the Tenth International Conference on Management Science and Engineering Management. Springer.
[16] Dror, M. and B.C. Hartman, (2007). Shipment consolidation: Who pays for it and how much? Management Science, 53(1): p. 78-87.
[17] Ülkü, M.A., (2012). Dare to care: Shipment consolidation reduces not only costs, but also environmental damage. International Journal of Production Economics, 139(2): p. 438-446.
[18] Berling, P. and F. Eng-Larsson, (2016). Pricing and timing of consolidated deliveries in the presence of an express alternative: Financial and environmental analysis. European Journal of Operational Research, 250(2): p. 590-601.
[19] Snoeck, A., D. Merchán, and M. Winkenbach, (2020). Revenue management in last-mile delivery: state-of-the-art and future research directions. Transportation Research Procedia, 46: p. 109-116.
[20] Hong, K.-s. and C. Lee, (2013). Optimal time-based consolidation policy with price sensitive demand. International Journal of Production Economics, 143(2): p. 275-284.
[21] Taleizadeh, A.A. and A. Rasuli-baghban, (2015). Pricing and inventory decisions for deteriorating products under shipment consolidation. International Journal of Advanced Logistics, 4(2): p. 89-99.
[22] Bookbinder, J.H., Q. Cai, and Q.-M. He, (2011). Shipment consolidation by private carrier: the discrete time and discrete quantity case. Stochastic Models, 27(4): p. 664-686.
[23] Ülkü, M.A. and J.H. Bookbinder, (2012). Optimal quoting of delivery time by a third party logistics provider: The impact of shipment consolidation and temporal pricing schemes. European Journal of Operational Research, 221(1): p. 110-117.
[24] Hanson, W. and R.K. Martin, (1990). Optimal bundle pricing. Management Science, 36(2): p. 155-174.
[25] Chen, J., M. Dong, and F.F. Chen, (2017). Joint decisions of shipment consolidation and dynamic pricing of food supply chains. Robotics and Computer-Integrated Manufacturing, 43: p. 135-147.
[26] Chen, J., M. Dong, and L. Xu, (2018). A perishable product shipment consolidation model considering freshness-keeping effort. Transportation Research Part E: Logistics and Transportation Review, 115: p. 56-86.
[27] Nguyen, C., M. Dessouky, and A. Toriello, (2014). Consolidation strategies for the delivery of perishable products. Transportation Research Part E: Logistics and Transportation Review, 69: p. 108-121.
[28] Yang, X., et al., (2016). Choice-based demand management and vehicle routing in e-fulfillment. Transportation science, 50(2): p. 473-488.
[29] Çetinkaya, S., F. Mutlu, and C.-Y. Lee, (2006). A comparison of outbound dispatch policies for integrated inventory and transportation decisions. European Journal of Operational Research, 171(3): p. 1094-1112.
[30] Mulvey, J.M., R.J. Vanderbei, and S.A. Zenios, (1995). Robust optimization of large-scale systems. Operations research, 43(2): p. 264-281.
[31] Leung, S., Y. Wu, and K. Lai, (2002). A robust optimization model for a cross-border logistics problem with fleet composition in an uncertain environment. Mathematical and Computer Modelling, 36(11-13): p. 1221-1234.
[32] Mirzapour Al-E-Hashem, S., H. Malekly, and M.B. Aryanezhad, (2011). A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. International journal of production economics, 134(1): p. 28-42.
[33] Ben-Tal, A. and A. Nemirovski, (1999). Robust solutions of uncertain linear programs. Operations research letters, 25(1): p. 1-13.
[34] Rajgopal, J., (2004). Principles and applications of operations research. Maynard’s Industrial Engineering Handbook. p. 11.27-11.44.