Problem solving of container loading using genetic algorithm based on modified random keys (original) (raw)
Related papers
IJERT-Solving Container Loading Problem Using Improved Genetic Algorithm
International Journal of Engineering Research and Technology (IJERT), 2012
https://www.ijert.org/solving-container-loading-problem-using-improved-genetic-algorithm https://www.ijert.org/research/solving-container-loading-problem-using-improved-genetic-algorithm-IJERTV1IS8595.pdf In this paper we present Elitism based Compact Genetic Algorithm to solve three dimensional bin packing or Container Loading Problem. The three dimensional bin packing problem is the problem of orthogonally packing of set of boxes into a minimum of three dimensional bin. This algorithm uses a probability vector to represent the bit probability of 0 and 1 and model the distribution of generation. Unlike previous works which concentrates on using either a heuristic rule or an optimization technique to find an optimal sequence of the packages which must be loaded into the containers, the proposed heuristic rule is used to partition the entire loading sequence into a number of shorter sequences. Each-partitioned sequence is then represented by a species member. The procedure involves the use of a heuristic rule and a genetic algorithm search. The heuristic rule used covers a scheme which involves a classification of the packages into three distinct groups: large-sized, medium-sized and small-sized package groups.
Heuristic Algorithm for Constrained 3D Container Loading Problem: A Genetic Approach
This paper presents an heuristic Genetic Algorithm for solving 3-Dimensional Single container packing optimization problem. The 3D container loading problem consists of 'n' number of boxes being to be packed in to a container of standard dimension in such a way to maximize the volume utilization and inturn profit. Furthermore, various practical constraints like box orientation, stack priority, container stability, etc also applied. Boxes to be packed are of various sizes and of heterogeneous shapes. In this research work, several heuristic improvements were proposed over Genetic Algorithm (GA) to solve the container loading problem that significantly improves the search efficiency and to load most of heterogeneous boxes into a container along with the optimal position of loaded boxes, box orientation and boxes to be loaded by satisfying practical constraints. In this module, both the guillotine and non-guillotine moves were allowed. In general, these heuristic GA solutions being substantially better and satisfactory than those obtained by applying heuristics to the bin packing directly.
Journal of Mathematical Modelling and Algorithms, 2012
This paper presents a new hybrid genetic algorithm for solving the container loading problem in the general case, precisely when the boxes have no orientation constraints. In order to improve the genetic algorithm efficiency, we developed a hybrid method, based on deterministic approaches combining the wall-building, level-slice approach and strip packing. A serie of experiments was achieved on 47 related benchmarks from the OR-Library. We could reach an average utilization of 94.47% and an average computation time of 840s on a 1.7 GHz core duo.
A Genetic algorithm to solve the container storage space allocation problem
This paper presented a genetic algorithm (GA) to solve the container storage problem in the port. This problem is studied with different container types such as regular, open side, open top, tank, empty and refrigerated containers. The objective of this problem is to determine an optimal containers arrangement, which respects customers' delivery deadlines, reduces the re-handle operations of containers and minimizes the stop time of the container ship. In this paper, an adaptation of the genetic algorithm to the container storage problem is detailed and some experimental results are presented and discussed. The proposed approach was compared to a Last In First Out (LIFO) algorithm applied to the same problem and has recorded good results.
Solving 3D Container Loading Problems Using Physics Simulation for Genetic Algorithm Evaluation
IEICE Transactions on Information and Systems
In this work, an optimization method for the 3D container loading problem with multiple constraints is proposed. The method consists of a genetic algorithm to generate an arrangement of cargo and a fitness evaluation using a physics simulation. The fitness function considers not only the maximization of the container density and fitness value but also several different constraints such as weight, stack-ability, fragility, and orientation of cargo pieces. We employed a container shaking simulation for the fitness evaluation to include constraint effects during loading and transportation. We verified that the proposed method successfully provides the optimal cargo arrangement for small-scale problems with about 10 pieces of cargo.
SMART EVOLUTIONARY ALGORITHM FOR CONSTRAINED CONTAINER LOADING PROBLEM
This paper addresses the issue of identifying the best Bin Packing pattern from the available bins by satisfying the packing constraints. This research adopted the concept of smart operator that allows setting the genetic parameters for any kind of situations and thereby increasing the performance of any packing problem. The result obtained from this heuristic algorithm matches with several lower bounds proposed in the literature. The developed smart operators have many applications.
Application of Genetic Algorithms to Container Loading Optimization
International Journal of Trade, Economics and Finance, 2013
Standardization of transport means, such as, containers has a direct impact on the transportation efficiency sought by European transport policies. In this paper, we present a genetic algorithm application to the container loading problem trying to maximize the cargo volume accommodated in the container whilst ensuring that loading restrictions are met, and thus achieving a reduction in the number of freight to hire and thereby a reduction in costs. The proposed method has been compared to similar models, and the results obtained are similar or even improved.
A Genetic Algorithm for Solving a Container Storage Problem Using a Residence Time Strategy
Studies in Informatics and Control, 2017
At each port of destination, some containers are unloaded from a vessel and stored in the terminal to be delivered to their customers. One of the strategies used to arrange the containers in a terminal is residence time strategy: based on their delivery deadlines, each incoming container being assigned to a priority class. The aim of this study is to determine a valid arrangement of incoming containers in a block (part) of the terminal, in the shortest amount of time, with higher priority containers located above lower priority ones. In this way, some of the main objectives of a container terminal may be achieved: avoiding further reshuffles (number of relocations) and reducing the vessel berthing time. We developed a genetic algorithm and its performance is evaluated against a random stacking strategy used as benchmark for the experiments, and through several sets of tests on control parameters. All the tests showed that, if a reliable estimation of the delivery time can be assigned to every incoming container, the proposed method may be a useful tool for container terminal operators.
A genetic algorithm to solve the storage space allocation problem in a container terminal
Computers & Industrial Engineering, 2009
In this paper, an efficient genetic algorithm (GA) is presented to solve an extended storage space allocation problem (SSAP) in a container terminal. The SSAP is defined as the temporary allocation of the inbound/outbound containers to the storage blocks at each time period with aim of balancing the workload between blocks in order to minimize the storage/retrieval times of containers. An extended version of a SSAP proposed in the literature is considered in this paper in which the type of container affects on making the decision on the allocation of containers to the blocks. In real-world cases, there are different types (as well as different sizes) of containers consisting of several different goods such as regular, empty and refrigerated containers. The extended SSAP is solved by an efficient GA for real-sized instances. Because of existing the several equality constraints in the extended model, the implementation of the GA in order to quick and facilitate achieve to the feasible solutions is one of the outstanding advantages of this paper. The performance of the extended model and proposed GA is verified by a number of numerical examples.
Storage Management of Hazardous Containers Using the Genetic Algorithm
Transport and Telecommunication Journal, 2016
This work discusses the problem of dangerous containers storage in a container terminal. Container terminal represents an essential intermodal interfaces for global transportation network. Several materials handling possible to move containers at the port to better meet the needs of ships awaiting loading or unloading. Have a good organization of the…