A Continuous-Time Model for Multiple Yard Crane Scheduling with Last-Minute Job Arrivals

Abstract

Container terminal (CT) operations are often bottlenecked by slow YC (yard crane) movements. Efficient YC scheduling to reduce the PM waiting time is therefore critical in increasing a CT's throughput. This paper develops an efficient continuous time MILP model for YC scheduling. The model treats realistic operational constraints such as multiple inter-crane interference, fixed YC separation distances, simultaneous container storage/retrievals, realistic YC acceleration/deceleration stages and gantry time, and require far fewer integer variables than previous work. The model significantly improves the solution quality compared to the existing discrete time models and other heuristics found in the literature. Using heuristics and

Scheduling optimization of yard cranes with minimal energy consumption at container terminals

“…Recently, Wen et al (2010) described exact and heuristic approaches for scheduling multiple cranes that service a ship yard that produces multiple ships concurrently. Li et al (2011) aimed to generate CT applicable and an optimal schedule, they developed an efficient continuous-time model and applied a rolling-horizon algorithm for container YC work schedules. Yu and Qi (2013) studied the problem of improving the operations efficiency for retrieving inbound containers in a modern automatic container terminal, with the aim of reducing the expected external truck waiting time.…”

Planning and Scheduling for Maritime Container Yards

“…4 (Li et al 2012) is used as the benchmark and solved with GAMS/CPLEX 11; the clustering-reassigning approach is coded in C++. Both approaches are tested on Windows XP with a 2.2 GHz Pentium processor and 2 GB RAM.…”

“…Table 5.2 displays the performance of the clustering-reassigning approach compared with the results provided by the MILP model. We adopt the total completion time (TCT) as used in Li et al (2012) for the comparison purpose. Only pure storage test cases are compared; pure retrieval, and mixed storage and retrieval test cases are not comparable due to the fact that the weights assigned to retrieval earliness, retrieval lateness, and storage lateness between the two approaches are different.…”

“…It can be observed that the performance difference between the clusteringreassigning approach and the MILP model developed in Li et al (2012) is very narrow. For light workload test cases in 40-slot blocks, the maximum performance gap is 0.40 % while the minimum performance gap is 0.0 % which indicates that the clustering-reassigning algorithm achieves the same results as the MILP model, the average performance gap is 0.13 %.…”

A genetic algorithm to solve the container's location allocation problem in rear storage yard

“…Li et al (2012) developed an efficient continuous time MILP model for yard crane scheduling to reduce the PM waiting time, which is one of main factors of inefficiency.…”