Loading and Unloading Scheduling Problem in a Multiple-Multiple Logistics Network: Modeling and Solving
Most of the supply chain networks have many nodes starting from the suppliers’ side up to the customers’ side that each node sends/receives the raw materials/products from/to the other nodes. One of the major concerns in this kind of supply chain network is finding the best schedule for loading/unloading the shipments through the whole network by which all the constraints in the source and destination nodes are met and all the shipments are delivered on time. One of the main constraints in this problem is the loading/unloading capacity in each source/destination node at each time slot (e.g., per week/day/hour). Because of the different characteristics of different products/groups of products, the capacity of each node might differ based on each group of products. In most supply chain networks (especially in the Fast-moving consumer goods (FMCG) industry), there are different planners/planning teams working separately in different nodes to determine the loading/unloading timeslots in source/destination nodes to send/receive the shipments. In this paper, a mathematical problem has been proposed to find the best timeslots for loading/unloading the shipments minimizing the overall delays subject to respecting the capacity of loading/unloading of each node, the required delivery date of each shipment (considering the lead-times), and working-days of each node. This model was implemented on Python and solved using Python-MIP on a sample data set. Finally, the idea of a heuristic algorithm has been proposed as a way of improving the solution method that helps to implement the model on larger data sets in real business cases, including more nodes and shipments.Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 393
 Xuting Sun, Sai-Ho Chung, Tsan-Ming Choi, Jiuh-Biing Sheu, Hoi Lam Ma, "Combating lead-time uncertainty in global supply chain’s shipment-assignment: Is it wise to be risk-averse?," Transportation Research, vol. 138, p. 406–434, 2020.
 H. Matsuo, "the weighted total tardiness problem with fixed shipping times and overtime utilization," Operations Research, vol. 36, no. 2, pp. 293 - 307, 1988.
 Nicholas G. Hall, 'Maseka Lesaoana, Chris N. Potts, "Scheduling with Fixed Delivery Dates," Operations Research, vol. 49, no. 1, pp. 134 - 146, 2001.
 T.-F. Liang, "interactive multi-objective transportation planning decisions using fuzzy linear programming," Asia - Pacific Journal of Operational Research, vol. 28, no. 1, pp. 11 - 31, 2008.
 Eray Cakicia, Scott J. Masonb and Mary E. Kurzb, "Multi-objective analysis of an integrated supply chain scheduling problem," International Journal of Production Research, vol. 50, no. 10, pp. 2624 - 2638, 2011.
 Srikant Gupta, Irfan Ali, Aquil Ahmed, "Multi-objective capacitated transportation problem with mixed constraint: a case study of certain and uncertain environment," OPSEARCH, vol. 55, pp. 447 - 477, 2018.
 Morteza Rasti-Barzoki, Seyed Reza Hejazi, "Minimizing the weighted number of tardy jobs with due date assignment and capacity-constrained deliveries for multiple customers in supply chains," European Journal of Operational Research, vol. 228, no. 2, pp. 345 - 357, 2013.
 Morteza Rasti-Barzokia, Seyed Reza Hejazia, Mohammad Mahdavi Mazdeh, "A Branch and Bound Algorithm to Minimize the Total Weighed Number of Tardy Jobs and Delivery Costs," Applied Mathematical Modeling, vol. 37, no. 7, pp. 4924 - 4937, 2013.