Optimization on the container loading sequence based on hybrid dynamic programming

Retrieving export containers from a container yard is an important part of the ship loading process during which arranging the retrieving sequence to enhance port efficiency has become a vital issue. This paper presents a twophase hybrid dynamic algorithm aiming at obtaining an optimized container loading sequence for a crane to retrieve all the containers from the yard to the ship. The optimization goal is to minimize the number of relocation operations which has a direct impact upon container loading operation efficiency. The two phases of the proposed dynamic algorithms are designed as follows: at the first phase, a heuristic algorithm is developed to retrieve the containers subset which needs no relocation and may be loaded directly onto the ship; at the second phase, a dynamic programming with heuristic rules is applied to solve the loading sequence problem for the rest of the containers. Numerical experiments showed the effectiveness and practicability of the model and the algorithm by comparing with the loading proposals from an existing research and actual rules respectively. First published online: 14 Jan 201

The block retrieval problem

Retrieving containers from a bay in a port terminal yard is a time consuming activity. The Block Retrieval Problem (BRTP) aims at minimizing the number of relocations, the unproductive moves of hindering con- tainers, while retrieving target containers belonging to a customer. The choice of relocations leads to alternative bay configurations, some of which would minimize the relocations of forthcoming retrievals. The Bi-objective Block Retrieval Problem (2BRTP) includes a secondary objective, the minimization of the expected number of relocations for retrieving the containers of the next customer. This paper pro- vides N P -Hardness proofs for both the BRTP and 2BRTP. A branch-and-bound algorithm and a linear time heuristic are developed for the BRTP; a branch-and-bound algorithm and a beam search algorithm are presented for the 2BRTP. Extensive computational tests on randomly generated instances as well as instances adapted from the literature are performed, and the results are presented

Research into container reshuffling and stacking problems in container terminal yards

Container stacking and reshuffling are important issues in the management of operations in a container terminal. Minimizing the number of reshuffles can increase productivity of the yard cranes and the efficiency of the terminal. In this research, the authors improve the existing static reshuffling model, develop five effective heuristics, and analyze the performance of these algorithms. A discrete-event simulation model is developed to animate the stacking, retrieving, and reshuffling operations and to test the performance of the proposed heuristics and their extended versions in a dynamic environment with arrivals and retrievals of containers. The experimental results for the static problem show that the improved model can solve the reshuffling problem more quickly than the existing model and the proposed extended heuristics are superior to the existing ones. The experimental results for the dynamic problem show that the results of the extended versions of the five proposed heuristics are superior or similar to the best results of the existing heuristics and consume very little time

Hybrid Metaheuristic Approach to Solve the Problem of Containers Reshuffling in an Inland Terminal

The paper deals with the problem of minimizing the reshuffling of containers in an inland intermodal terminal. The problem is tackled according to a hybrid approach that combines a preliminary selection of heuristics and a genetic algorithm. The heuristics are used to determine the initial population for the genetic algorithm, which aims to optimize the locations of the containers to store in the yard in order to minimize the operational costs. A simulation model computes the costs related to storage and pick-up operations in the yard bay. The proposed optimization method has been calibrated by selecting the optimal parameters of the genetic algorithm in a toy case and has been tested on a theoretical example of realistic size. Results highlighted that the use of a suitable heuristic to generate the initial population outperforms the genetic algorithm, initialized with a random solution, by 20%