Coordination in retail transportation logistics
Carriers in retail logistics often face congestion at warehouses at certain times of the day which force them to wait before their trucks can be unloaded. Warehouses suffer from unbalanced utilization due to a lack of coordination between carriers. This has a significant negative impact on logistics efficiency. According to a survey among more than 500 transport companies, 18\% of them have an average waiting time of more than two hours and 51\% have an average waiting time between one to two hours at each warehouse. Another problem is insufficient parking capacity for waiting trucks.
The main reasons for waiting times include shortages of resources (staff and infrastructure) and uncoordinated arrivals of trucks, especially at peak times. These problems are interconnected, since uncoordinated arrivals of trucks make appropriate staffing difficult. In addition, congestion can interfere with other processes due to a high number of trucks at the facility. Proposed remedies for these problems are time slot management, and increased infrastructure capacities. Capacity increases require high investments compared to improved coordination.
We plan to develop new or adapt existing mechanisms for the coordination of retailers in our domain. There are several forms of coordination among the market participants to mitigate the problem: Carriers can make a reservation for a time slot with a retailer via a first-come, first-served system (with or without payments). Alternatively, coordination can be facilitated via an economic mechanism where carriers express their preferences for various routes and respective time slots and the mechanism assigns time slots to carriers such that waiting times are avoided as far as possible while meeting the carriers' preferences, i.e., the routes and times of the day causing the least costs for him.
This project area includes all three major research challenges of a networked economy. Due to the number of retail warehouses and carriers and vehicles involved, already sub-problems to be solved by individual decision makers are large-scale optimization problems under uncertainty. For efficient outcomes, coordination and allocation of resources in a decentralized, multiple decision maker environment is necessary.