Genetic algorithm for integrated model of berth allocation problem and quay crane scheduling with noncrossing safety and distance constraint

Berth Allocation and Quay Crane Scheduling are the most important part of container terminal operations since berth and quay cranes are an interface of ocean-side and landside in any port container terminal operation. Their operations significantly influence the efficiency of port container terminals and need to be solved simultaneously. Based on the situation, this study focuses on an integrated model of Continuous Berth Allocation Problem and Quay Crane Scheduling Problem. A comprehensive analysis of safety distance for vessel and non-crossing constraint for quay crane is provided. There are two integrated model involved. For the first integrated model, non-crossing constraints are added wherein quay cranes cannot cross over each other since they are on the same track. The second integrated model is focused on the safety distance between vessels while berthing at the terminal and at the same time, quay crane remains not to cross each other. These two constraints were selected to ensure a realistic model based on the real situation at the port. The objective of this model is to minimise the processing time of vessels. A vessel's processing time is measured between arrival and departure including the waiting time to be berthed and servicing time. A new algorithm is developed to obtain the good solution. Genetic Algorithm is chosen as a method based on flexibility and can apply to any problems. There are three layers of algorithm that provide a wider search to the solution space for vessel list, berth list, and hold list developed in this study. The new Genetic Algorithm produced a better solution than the previous research, where the objective function decreases 5 to 12 percent. Numerical experiments were conducted and the results show that both integrated models are able to minimize the processing time of vessels and can solve problem quickly even involving a large number of vessels. Studies have found that the safety distance set as 5 percent of vessel length gives the best solution. By adding safety distance to the integrated model with non-crossing constraint, the result indicates no improvement in the model objective function due to increasing distance between vessels. The objective function increases in the range of 0.4 to 8.6 percent. However, the safety distance constraint is important for safety and realistic model based on the port’s real situation

A reclaimer scheduling problem arising in coal stockyard management

We study a number of variants of an abstract scheduling problem inspired by the scheduling of reclaimers in the stockyard of a coal export terminal. We analyze the complexity of each of the variants, providing complexity proofs for some and polynomial algorithms for others. For one, especially interesting variant, we also develop a constant factor approximation algorithm.Comment: 26 page

Dynamic Quay Crane Allocation

We introduce simple rules for quay cranes to handle containers along a berth where vessels arrive continuously in time. We first analyze a model where workload is continuous. Our analysis shows that if the system is configured properly, it will always converge to a state with the maximum possible throughput regardless of external disruptions or changes in workload. Numerical simulations based on a discrete workload model suggest that, by following the same rules, the system can still converge to state with throughput that is very close to its upper bound

Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems

In this article, a novel approach to solve combinatorial optimization problems is proposed. This approach makes use of a heuristic algorithm to explore the search space tree of a problem instance. The algorithm is based on Monte Carlo tree search, a popular algorithm in game playing that is used to explore game trees. By leveraging the combinatorial structure of a problem, several enhancements to the algorithm are proposed. These enhancements aim to efficiently explore the search space tree by pruning subtrees, using a heuristic simulation policy, reducing the domains of variables by eliminating dominated value assignments and using a beam width. They are demonstrated for two specific combinatorial optimization problems: the quay crane scheduling problem with non-crossing constraints and the 0-1 knapsack problem. Computational results show that the algorithm achieves promising results for both problems and eight new best solutions for a benchmark set of instances are found for the former problem. These results indicate that the algorithm is competitive with the state-of-the-art. Apart from this, the results also show evidence that the algorithm is able to learn to correct the incorrect choices made by constructive heuristics

A Comparison of Priority Rules for Non-passing Automated Stacking Cranes

A recent trend in container ports is to operate dual non-passing Automated Storage Cranes (ASCs) that collaborate to serve storage and retrieval requests from opposite ends of a storage block. Since the ASCs are unable to pass each other, there is an exchange zone that serves as a temporary storage location so that one crane can start a request and leave it to the other crane to complete it. In this study, twelve priority rules are introduced and evaluated to determine which rule minimizes the total makespan for serving all requests, given the sequence in which each ASC will serve the requests. Preliminary results from 12 randomly generated experiments indicate that the priority rules favoring the crane furthest away from the origin of the next request (LonOri) and the longest individual completion times (LonTot) outperformed all other rules in terms of the average percent difference with the best found solution and in terms of the percent of times the priority rule yield the best found solution. Also, combining priority rules AdvFun and LonRem yields the best makespan in 11 of the 12 (91.67%) problem instances tested. Results of this study transcend container ports as it is applicable to any material handling system composed of non-passing MHE and that has pickup/deposit points at the ends of the system

Adaptive autotuning mathematical approaches for integrated optimization of automated container terminal

With the development of automated container terminals (ACTs), reducing the loading and unloading time of operation and improving the working efficiency and service level have become the key point. Taking into account the actual operation mode of loading and unloading in ACTs, a mixed integer programming model is adopted in this study to minimize the loading and unloading time of ships, which can optimize the integrated scheduling of the gantry cranes (QCs), automated guided vehicles (AGVs), and automated rail-mounted gantries (ARMGs) in automated terminals. Various basic metaheuristic and improved hybrid algorithms were developed to optimize the model, proving the effectiveness of the model to obtain an optimized scheduling scheme by numerical experiments and comparing the different performances of algorithms. The results show that the hybrid GA-PSO algorithm with adaptive autotuning approaches by fuzzy control is superior to other algorithms in terms of solution time and quality, which can effectively solve the problem of integrated scheduling of automated container terminals to improve efficiency.info:eu-repo/semantics/publishedVersio