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Some research has been conducted on inbound shipments scheduling. However, most research on DCs focuses on the put-away and order-picking processes because they have a more direct impact on traditional measures of DC performance and are the most labor-intensive processes (Boeve, 2016). Additionally, Jackson (2005) states, “There is also an organizational belief that outbound is always more important than inbound since it is the customer-facing part of the warehouse” (p. 10). As a result, insufficient research has been conducted specifically on the inbound vendor receiving process, a fact that became apparent to the restaurant chain that is the subject of this case study and that required more efficient and automated scheduling. This is especially true in the area of MPRS.
The inbound vendor receiving process starts with demand planning, in which planners forecast how many goods will be sold. Based on the resulting demand schedule, material requirements planning (MRP) logic will generate purchase quantities using multiple approaches, as outlined in Monczka et al. (2020). Methodologies include economic order quantity, reorder point, just-in-time, kanban, etc. Nagpal et al. (2022) proposed an innovative inventory optimization model for short-life cycle products that is suitable for the high-tech industry. Thurman (2021) assessed different inventory replenishment strategies for Target Corporation. Hwang (2009) studied a dynamic lot sizing vendor-managed inventory warehouse model to illustrate the importance of the minimum replenishment quantity. Pandey (2022) outlined a two-step approach that first generates a so-called “pristine order” to meet just-in-time needs. Then in the second step, different constraints, including product availability and truck load needs, are used to rationalize the “pristine order.”
Many papers address the DC operations aspects of the problem. Cochran and Ramanujam (2006) analyzed optimal combinations of third-party logistics providers, container sizes, packaging (such as pallets and slip sheets), and additional services to minimize costs. Traditional dock scheduling approaches have been studied, and more recently there has been research on more advanced algorithms such as genetic algorithms (Fong et al., 2013). Similarly, truck optimization models using mixed-integer linear programming have been developed for DCs with a mixed service-mode dock area (Correa Issi et al., 2020). Some models also take into account the different types of vehicles with different capacities in DC cross docking and minimize operation time, lateness, and earliness of product delivery (Mohtashami, 2020). However, dock scheduling focuses on the short term, since each truck’s exact time slot must be determined and it is difficult to do so weeks and months ahead.
From a transportation perspective, books like that of Novack et al. (2018) provide a good summary of transportation management. The simplex method has been used in solving some transportation optimization problems for years (Dantzig, 1951). Boeve (2016) has introduced improvements to the inbound system based on available space, staff capacity, and variability in vendor deliveries, and Jackson (2005) has analyzed the impact of flexible staffing and work schedules. There are other publications focused on truck load optimization such as Morabito et al. (2000) and Alonso et al. (2019). Calabrò et al. (2020) focuses on minimizing truck travel distance by using an ant-colony simulation-based optimization for finding routes, and Wang and Chen (2019) have developed a time-indexed integer linear programming model to minimize truck transportation time in supplier parks. Yang et al. (2023) proposed an integrated deep reinforcement learning-based logistics management model (DELLMM) to improve the logistic distribution.