Genetic algorithms to schedule container transfers at multimodal terminals

Abstract

Optimising the container transfer schedule at the multimodal terminals is known to be NP‐hard, which implies that the best solution becomes computationally infeasible as problem sizes increase. Genetic Algorithm (GA) techniques are used to reduce container handling/transfer times and ships' time at the port by speeding up handling operations. The GA is chosen due to the relatively good results that have been reported even with the simplest GA implementations to obtain near‐optimal solutions in reasonable time. Also discussed, is the application of the model to assess the consequences of increased scheduled throughput time as well as different strategies such as the alternative plant layouts, storage policies and number of yard machines. A real data set used for the solution and subsequent sensitivity analysis is applied to the alternative plant layouts, storage policies and number of yard machines.

The Operational Planning Model of Transhipment Processes in the Port

“…The containers must be stored in such a way, so that the amount of handling needed to place a container in the storage area and to remove it when required, is minimised. Therefore, it is important to minimise the total throughput time that is the handling time of loading all the containers onto the ships at berth [5].…”

“…The problem of managing a container exchange facility with multiple RMG is solved proposing an integer programming-based heuristic method [4]. Genetic algorithm (GA) techniques, used to reduce container handling/transfer time and ship's time at the port by speeding up handling operations, are applied for optimising the container transfer schedule at multimodal terminals [5]. An optimization methodology for container handling using genetic algorithm is also implemented in [6].…”

Analysis of the operations of an intermodal barge terminal

“…When looking in detail at optimisation techniques to solve the various planning problems per subprocess, it appears that the majority of scientific papers suggest the combination of two methods. In many cases, the planning problem is formally modelled and defined as a specific mathematical programming problem which is then solved with an appropriate (meta)heuristic (e.g., Kozan and Preston, 1999;Sammarra et al, 2007).…”

A Genetic Algorithm for the Container Pick-Up Problem

“…Their objective was to minimize the container handling time, which consists of the total traveling distance of the yard crane and the setup time of the yard crane, by determining the sequence of yard-bays to be visited and the number of containers to be picked-up at each yard-bay. Kozan and Preston (1999) studied the scheduling of transfer operation of containers without considering actual sequencing issues. The research of a single yard crane operation was also studied by Kim (1999a), (1999c) and Narasimhan and Palekar (2002).…”