Integrated inventory control and scheduling decision framework for packaging and products on a reusable transport item sharing platform

References

• Accorsi, Riccardo, Susan Cholette, Riccardo Manzini, and Lorenzo Mucci. 2022. “Managing Uncertain Inventories, Washing, and Transportation of Reusable Containers in Food Retailer Supply Chains.” Sustainable Production and Consumption 31: 331–345. doi:10.1016/j.spc.2022.02.014.
   Web of Science ®Google Scholar
• Albuquerque, Vitória, Miguel Sales Dias, and Fernando Bacao. 2021. “Machine Learning Approaches to Bike-Sharing Systems: A Systematic Literature Review.” ISPRS International Journal of Geo-Information 10 (2): 62. doi:10.3390/ijgi10020062.
   Web of Science ®Google Scholar
• Ambilkar, Priya, Vishwas Dohale, Angappa Gunasekaran, and Vijay Bilolikar. 2021. “Product Returns Management: A Comprehensive Review and Future Research Agenda.” International Journal of Production Research 60 (12): 3920–3944. doi:10.1080/00207543.2021.1933645.
   Web of Science ®Google Scholar
• Azevedo, S. G., K. Govindan, H. Carvalho, and V. Cruz-Machado. 2012. “An Integrated Model to Assess the Leanness and Agility of the Automotive Industry.” Resources Conservation and Recycling 66: 85–94. doi:10.1016/j.resconrec.2011.12.013.
   Web of Science ®Google Scholar
• Banks, Jerry. 2005. Discrete Event System Simulation. Forth Edition. Delhi, India: Pearson Education India.
   Google Scholar
• Bottani, Eleonora, Roberto Montanari, Marta Rinaldi, and Giuseppe Vignali. 2015. “Modeling and Multi-Objective Optimization of Closed Loop Supply Chains: A Case Study.” Computers & Industrial Engineering 87: 328–342. doi:10.1016/j.cie.2015.05.009
   Web of Science ®Google Scholar
• Carrasco-Gallego, Ruth, and Eva Ponce-Cueto. 2009. “Forecasting the Returns in Reusable Containers’ Closed-Loop Supply Chains. A Case in the LPG Industry.” Paper presented at the XIII Congreso de Ingeniería de Organización.
   Google Scholar
• Carrasco-Gallego, Ruth, Eva Ponce-Cueto, and Rommert Dekker. 2012. “Closed-loop Supply Chains of Reusable Articles: A Typology Grounded on Case Studies.” International Journal of Production Research 50 (19): 5582–5596. doi:10.1080/00207543.2011.649861.
   Web of Science ®Google Scholar
• China Automobile Logistics Association of CFLP. 2021. China Automotive Logistics Development Report. Beijing, China: China Fortune Press.
   Google Scholar
• Cobb, Barry R. 2016a. “Estimating Cycle Time and Return Rate Distributions for Returnable Transport Items.” International Journal of Production Research 54 (14): 4356–4367. doi:10.1080/00207543.2016.1162920.
   Web of Science ®Google Scholar
• Cobb, Barry R. 2016b. “Inventory Control for Returnable Transport Items in a Closed-Loop Supply Chain.” Transportation Research Part E-Logistics and Transportation Review 86: 53–68. doi:10.1016/j.tre.2015.12.010.
   Web of Science ®Google Scholar
• Coelho, Patricia Megale, Blanca Corona, Roland ten Klooster, and Ernst Worrell. 2020. “Sustainability of Reusable Packaging–Current Situation and Trends.” Resources, Conservation & Recycling: X 6: 100037. doi:10.1016/j.rcrx.2020.100037.
   Google Scholar
• Dessain, Jean. 2022. “Machine Learning Models Predicting Returns: Why Most Popular Performance Metrics are Misleading and Proposal for an Efficient Metric.” Expert Systems with Applications 199: 116970. doi:10.1016/j.eswa.2022.116970.
   Web of Science ®Google Scholar
• DHL Supply Chain. n.d. Closing the Loop: Returnable Packaging Boosts Supply Chain Efficiency.
   Google Scholar
• Erkayman, Burak. 2019. “Transition to a JIT Production System Through ERP Implementation: A Case from the Automotive Industry.” International Journal of Production Research 57 (17): 5467–5477. doi:10.1080/00207543.2018.1527048.
   Web of Science ®Google Scholar
• Essien, A., and C. Giannetti. 2020. “A Deep Learning Model for Smart Manufacturing Using Convolutional LSTM Neural Network Autoencoders.” IEEE Transactions on Industrial Informatics 16 (9): 6069–6078. doi:10.1109/Tii.2020.2967556.
   Web of Science ®Google Scholar
• Glock, Christoph H. 2017. “Decision Support Models for Managing Returnable Transport Items in Supply Chains: A Systematic Literature Review.” International Journal of Production Economics 183: 561–569. doi:10.1016/j.ijpe.2016.02.015.
   Web of Science ®Google Scholar
• Glock, Christoph H., and Taebok Kim. 2014. “Container Management in a Single-Vendor-Multiple-Buyer Supply Chain.” Logistics Research 7 (1): 112. doi: 10.1007/s12159-014-0112-1.
   Google Scholar
• Goodall, P., R. Sharpe, and A. West. 2019. “A Data-Driven Simulation to Support Remanufacturing Operations.” Computers in Industry 105: 48–60. doi:10.1016/j.compind.2018.11.001.
   Web of Science ®Google Scholar
• Guda, Harish, and Upender Subramanian. 2019. “Your Uber Is Arriving: Managing on-Demand Workers Through Surge Pricing, Forecast Communication, and Worker Incentives.” Management Science 65 (5): 1995–2014. doi:10.1287/mnsc.2018.3050.
   Web of Science ®Google Scholar
• Hariga, Moncer, Christoph H. Glock, and Taebok Kim. 2016. “Integrated Product and Container Inventory Model for a Single-Vendor Single-Buyer Supply Chain with Owned and Rented Returnable Transport Items.” International Journal of Production Research 54 (7): 1964–1979. doi:10.1080/00207543.2015.1071896.
   Web of Science ®Google Scholar
• Hellström, Daniel. 2009. “The Cost and Process of Implementing RFID Technology to Manage and Control Returnable Transport Items.” International Journal of Logistics-Research and Applications 12 (1): 1–21. doi:10.1080/13675560802168526.
   Web of Science ®Google Scholar
• Hellström, Daniel, and Ola Johansson. 2010. “The Impact of Control Strategies on the Management of Returnable Transport Items.” Transportation Research Part E: Logistics and Transportation Review 46 (6): 1128–1139. doi:10.1016/j.tre.2010.05.006.
   Web of Science ®Google Scholar
• Huang, S. D., G. Z. Cao, Z. Y. He, J. F. Pan, J. A. Duan, and Q. Q. Qian. 2015. “Nonlinear Modeling of the Inverse Force Function for the Planar Switched Reluctance Motor Using Sparse Least Squares Support Vector Machines.” IEEE Transactions on Industrial Informatics 11 (3): 591–600. doi:10.1109/Tii.2015.2411438.
   Web of Science ®Google Scholar
• Iassinovskaia, Galina, Sabine Limbourg, and Fouad Riane. 2017. “The Inventory-Routing Problem of Returnable Transport Items with Time Windows and Simultaneous Pickup and Delivery in Closed-Loop Supply Chains.” International Journal of Production Economics 183: 570–582. doi:10.1016/j.ijpe.2016.06.024.
   Web of Science ®Google Scholar
• Kim, Taebok, and Christoph H. Glock. 2014. “On the use of RFID in the Management of Reusable Containers in Closed-Loop Supply Chains Under Stochastic Container Return Quantities.” Transportation Research Part E: Logistics and Transportation Review 64: 12–27. doi:10.1016/j.tre.2014.01.011.
   Web of Science ®Google Scholar
• Kim, Taebok, Christoph H. Glock, and Yongjang Kwon. 2014. “A Closed-Loop Supply Chain for Deteriorating Products Under Stochastic Container Return Times.” Omega-International Journal of Management Science 43: 30–40. doi:10.1016/j.omega.2013.06.002.
   Web of Science ®Google Scholar
• Kim, Byeong Soo, Bong Gu Kang, Seon Han Choi, and Tag Gon Kim. 2017. “Data Modeling Versus Simulation Modeling in the big Data era: Case Study of a Greenhouse Control System.” SIMULATION 93 (7): 579–594. doi:10.1177/0037549717692866.
   Google Scholar
• Kroon, Leo, and Gaby Vrijens. 1995. “Returnable Containers: An Example of Reverse Logistics.” International Journal of Physical Distribution & Logistics Management 25 (2): 56–68. doi:10.1108/09600039510083934.
   Google Scholar
• Li, J. Y., F. C. Guo, A. Sivakumar, Y. J. Dong, and R. Krishnan. 2021. “Transferability Improvement in Short-Term Traffic Prediction Using Stacked LSTM Network.” Transportation Research Part C-Emerging Technologies 124: 102977. doi:10.1016/j.trc.2021.102977.
   Web of Science ®Google Scholar
• Liu, Guoquan, Lei Li, Jianghang Chen, and Fei Ma. 2020. “Inventory Sharing Strategy and Optimization for Reusable Transport Items.” International Journal of Production Economics 228: 107742. doi:10.1016/j.ijpe.2020.107742.
   Web of Science ®Google Scholar
• Ma, C. X., G. W. Dai, and J. B. A. Zhou. 2022. “Short-Term Traffic Flow Prediction for Urban Road Sections Based on Time Series Analysis and LSTM_BILSTM Method.” IEEE Transactions on Intelligent Transportation Systems 23 (6): 5615–5624. doi:10.1109/Tits.2021.3055258.
   Web of Science ®Google Scholar
• Mahmoudi, Monirehalsadat, and Irandokht Parviziomran. 2020. “Reusable Packaging in Supply Chains: A Review of Environmental and Economic Impacts, Logistics System Designs, and Operations Management.” International Journal of Production Economics 228: 107730. doi:10.1016/j.ijpe.2020.107730.
   Web of Science ®Google Scholar
• Martin, Dan. 2021. Reusable Packaging in a Circular Economy: Panel Discussion.
   Google Scholar
• McKerrow, Don. 1996. “What Makes Reusable Packaging Systems Work.” Logistics Information Management 9 (4): 39–42. doi:10.1108/09576059610123169.
   Google Scholar
• Meherishi, Lavanya, Sushmita A. Narayana, and K. S. Ranjani. 2021. “Integrated Product and Packaging Decisions with Secondary Packaging Returns and Protective Packaging Management.” European Journal of Operational Research 292 (3):930-952. doi:10.1016/j.ejor.2020.11.022.
   Web of Science ®Google Scholar
• Muir, William A., Stanley E. Griffis, and Judith M. Whipple. 2019. “A Simulation Model Of Multi-Echelon Retail Inventory With Cross-Channel Product Returns.” Journal of Business Logistics 40 (4): 322–338. doi:10.1111/jbl.12209.
   Web of Science ®Google Scholar
• Net, China Automotive Information. 2019. “Monthly sales data of Automotive.” In edited by China Automotive Information Net.
   Google Scholar
• Poormoaied, S. 2022. “Inventory Decision in a Periodic Review Inventory Model with two Complementary Products.” Annals of Operations Research 315 (2): 1937–1970. doi:10.1007/s10479-021-03949-w.
   Web of Science ®Google Scholar
• Poormoaied, S., and Z. Atan. 2020. “A Continuous Review Policy for two Complementary Products with Interrelated Demand.” Computers & Industrial Engineering 150: 106980. doi:10.1016/j.cie.2020.106980.
   Web of Science ®Google Scholar
• Punia, Sushil, Konstantinos Nikolopoulos, Surya Prakash Singh, Jitendra K. Madaan, and Konstantia Litsiou. 2020. “Deep Learning with Long Short-Term Memory Networks and Random Forests for Demand Forecasting in Multi-Channel Retail.” International Journal of Production Research 58 (16): 4964–4979. doi:10.1080/00207543.2020.1735666.
   Web of Science ®Google Scholar
• Robinson, S. 2007. “A Statistical Process Control Approach to Selecting a Warm-up Period for a Discrete-Event Simulation.” European Journal of Operational Research 176 (1): 332–346. doi:10.1016/j.ejor.2005.07.014
   Web of Science ®Google Scholar
• Sari, K. 2010. “Exploring the Impacts of Radio Frequency Identification (RFID) Technology on Supply Chain Performance.” European Journal of Operational Research 207 (1): 174–183. doi:10.1016/j.ejor.2010.04.003.
   Web of Science ®Google Scholar
• Sarkar, Biswajit, Mehran Ullah, and Seok-Beom Choi. 2019. “Joint Inventory and Pricing Policy for an Online to Offline Closed-Loop Supply Chain Model with Random Defective Rate and Returnable Transport Items.” Mathematics 7 (6): 497. doi:10.3390/math7060497
   Web of Science ®Google Scholar
• Sarkar, Biswajit, Mehran Ullah, and Namhun Kim. 2017. “Environmental and Economic Assessment of Closed-Loop Supply Chain with Remanufacturing and Returnable Transport Items.” Computers & Industrial Engineering 111: 148–163. doi:10.1016/j.cie.2017.07.003.
   Web of Science ®Google Scholar
• Schmidhuber, Jürgen, and Sepp Hochreiter. 1997. “Long Short-Term Memory.” Neural Computation 9 (8): 1735–1780. doi:10.1162/neco.1997.9.8.1735
   PubMed Web of Science ®Google Scholar
• Thoroe, Lars, Adam Melski, and Matthias Schumann. 2009. “The Impact of RFID on Management of Returnable Containers.” Electronic Markets 19 (2-3): 115–124. doi:10.1007/s12525-009-0013-3.
   Web of Science ®Google Scholar
• Tong, H. 2020. “Automotive Logistics Revolution, the Breaking Way of Energy Logistics.” The Tencent News. Online.
   Google Scholar
• Tornese, Fabiana, Maria Grazia Gnoni, Brian K. Thorn, Andres L. Carrano, and Jennifer A. Pazour. 2021. “Management and Logistics of Returnable Transport Items: A Review Analysis on the Pallet Supply Chain.” Sustainability 13 (22): 12747. doi:10.3390/su132212747.
   Web of Science ®Google Scholar
• Twede, Diana, and Robb Clarke. 2004. “Supply Chain Issues in Reusable Packaging.” Journal of Marketing Channels 12 (1): 7–26. doi:10.1300/J049v12n01_02.
   Google Scholar
• Uriarte, A. G., A. H. C. Ng, and M. U. Moris. 2020. “Bringing Together Lean and Simulation: A Comprehensive Review.” International Journal of Production Research 58 (1): 87–117. doi:10.1080/00207543.2019.1643512.
   Web of Science ®Google Scholar
• Von Rueden, Laura, Sebastian Mayer, Rafet Sifa, Christian Bauckhage, and Jochen Garcke. 2020. “Combining Machine Learning and Simulation to a Hybrid Modelling Approach: Current and Future Directions.” In Lecture Notes in Computer Science, 548–560. Springer International Publishing.
   Google Scholar
• Wang, S. J., C. T. Huang, W. L. Wang, and Y. H. Chen. 2010. “Incorporating ARIMA Forecasting and Service-Level Based Replenishment in RFID-Enabled Supply Chain.” International Journal of Production Research 48 (9): 2655–2677. doi:10.1080/00207540903564983
   Web of Science ®Google Scholar
• Wang, J. J., Y. L. Ma, L. B. Zhang, R. X. Gao, and D. Z. Wu. 2018. “Deep Learning for Smart Manufacturing: Methods and Applications.” Journal of Manufacturing Systems 48: 144–156. doi:10.1016/j.jmsy.2018.01.003.
   Web of Science ®Google Scholar
• Xu, Chengcheng, Junyi Ji, and Pan Liu. 2018. “The Station-Free Sharing Bike Demand Forecasting with a Deep Learning Approach and Large-Scale Datasets.” Transportation Research Part C: Emerging Technologies 95: 47–60. doi:10.1016/j.trc.2018.07.013
   Web of Science ®Google Scholar
• Zhang, Yipei, Ada Che, and Feng Chu. 2022. “Improved Model and Efficient Method for bi-Objective Closed-Loop Food Supply Chain Problem with Returnable Transport Items.” International Journal of Production Research 60 (3): 1051–1068. doi:10.1080/00207543.2020.1851057.
   Web of Science ®Google Scholar