Optimizing the Static and Dynamic Scheduling problem of Automated Guided Vehicles in Container Terminals

Todays, using automated guide vehicles (AGV) for container handling in ports and flexible material handling in manufacturing are getting more attentions. These vehicles are without drivers and are controlled by computers. One of the challenges for these vehicles is to schedule several vehicles with some constraints in appointment and delivery times of container jobs. This type of problem is often modelled as Minimum Cost Flow (MCF) problem, which is one of the most well-known problems in the area of network optimisation. To tackle the MCF problem, Network Simplex Algorithm (NSA) is the fastest solution method. NSA has three extensions, namely Network Simplex plus Algorithm (NSA+), Dynamic Network Simplex Algorithm (DNSA) and Dynamic Network Simplex plus Algorithm (DNSA+). NSA and NSA+ start from scratch without reconsidering the pre-established schedules. DNSA and DNSA+ repair the solution rather than starting from scratch. The objectives of the research reported in this paper are to simulate and investigate the advantages and disadvantages of NSA compared with those of the three extensions in practical situations. To perform the evaluations, an application of these algorithms to the scheduling problem of automated guided vehicles in container terminal is used. In the experiments, the number of iterations, CPU-time required to solve the problems, overheads and complexity are considered. The experimental results show that the main advantage of the dynamic algorithms over NSA and NSA+ is their performance.