Multiple Container Packing Research Articles

Three Dimensional Offline Packing Optimization Problem Based on Genetic Simulated Annealing Algorithm

In this paper, we try to solve 3D offline packing optimization problem by combining two methods-genetic algorithm’ global performance and simulated annealing algorithm’ local performance. Given Heuristic rules in loading conditions, we use the optimal preservation strategy and the roulette wheel method to choose selection operator, integrating simulated annealing algorithm into genetic algorithm , and achieving code programming and algorithms by Matlab.This paper carries out an actual loading in a vehicle company in Changchun City, then makes a contrast between the final optimization results and each suppliers’ current packing data.The experimental results show that the algorithm has a certain validity and practicability in multiple container packing problem.

Hybrid genetic algorithms for the three-dimensional multiple container packing problem

The three-dimensional multiple container packing problem (3DMCPP) is used to determine non-overlapping packing of a set of finite three-dimensional rectangular items into the minimum number of identical containers. The decision framework consists of two main activities: item assignment and packing. This paper presents new hybrid genetic algorithms (HGAs) that address current limitations related to the 3DMCPP and enable use of relatively few containers. Rotation constraints are also addressed. An HGA is developed for small problems, and another HGA is created for large problems. Both of the HGAs combine the largest left space first item assignment strategy, a basic genetic algorithm, and the deepest bottom left with fill strategy. Experiments were conducted to demonstrate the performances of the HGAs with two different types of data sets. The results show that the proposed HGAs yield solutions, in a reasonable amount of time, in which relatively few containers are needed.

Development of a stochastic optimisation tool for solving the multiple container packing problems

Abstract Marine logistics has become increasingly important as the amount of global trade has increased. Products are usually packed in various sizes of boxes, which are then arranged into containers before shipping. Shipping companies aim to optimise the use of space when packing heterogeneous boxes into containers. The container packing problem (CPP) aims to optimise the packing of a number of rectangular boxes into a set of containers. The problems may be classified as being homogeneous (identical boxes), weakly heterogeneous (a few different sizes) or strongly heterogeneous (many different boxes). The CPP is categorised as an NP hard problem, which means that the amount of computation required to find solutions increases exponentially with problem size. This work describes the development and application of an Artificial Immune System (AIS), Particle Swarm Optimisation (PSO) and a Genetic Algorithm (GA) for solving multiple container packing problems (MCPP). The stochastic optimisation tool was written in Microsoft Visual Basic. A sequential series of experiments was designed to identify the best parameter settings and configuration of the algorithms for solving MCPP. The work optimised the packing of a standard marine container for a strongly heterogeneous problem. The experimental results were analysed using the general linear model form of analysis of variance to identify appropriate algorithm configuration and parameter settings. It was found that each algorithm's parameters were statistically significant with a 95% confidence interval. The best configurations were then used in a sequential experiment that compared the performance of the AIS, PSO and GA algorithms for solving 21 heterogeneous MCPP. It was found that the average best-so-far solutions obtained from AIS were marginally better than those produced by GA and PSO for all problem sizes but AIS required longer computational time than GA.

A memetic algorithm for the quadratic multiple container packing problem

In this paper, we propose a new memetic algorithm for the quadratic multiple container packing problem. The proposed memetic algorithm is based on the adaptive link adjustment evolutionary algorith...

The improved adaptive link adjustment evolutionary algorithm for the multiple container packing problem

In this paper, we propose a new version of Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA) for the network optimization problems, and apply it to the multiple container packing problem (MCPP). Because the proposed algorithm uses a different encoding method from that of the original ALA-EA, we also need different decoding methods for the new algorithm. In addition, to improve the performance of the proposed algorithm, we incorporate heuristic local improvement approaches into it. To verify the effectiveness of the proposed algorithm we compare it with the existing evolutionary approaches for several instances, which are known to be extremely difficult to them. Computational tests show that the algorithm is superior to the existing evolutionary approaches and the original ALA-EA in both of the solution quality and the computational time. Moreover, the performance seems to be not affected by an instance property.

Models and Algorithms of Production Scheduling in Tandem Cold Rolling

The complexity of production scheduling problem in cold rolling line is analyzed, which is formulated as two parts, namely, the coil-merging optimization and the rolling batch planning. The optimization of steel coil merging is constructed as a multiple container packing problem (MCPP) that is computed by a new proposed algorithm, discrete differential evolution (DDE), in this paper. A specific double traveling salesman problem (DTSP) is modeled for the rolling batch planning, and a hybrid heuristic method on the basis of evolutionary mechanism and local search is presented to solve this model. The experimental results with real production data from Shanghai Baosteel Co. Ltd. show that the production scheduling method suggested in this paper is effective.

An effective evolutionary algorithm for the multiple container packing problem

This paper focuses on a new optimization problem, which is called “The Multiple Container Packing Problem (MCPP)” and proposes a new evolutionary approach for it. The proposed evolutionary approach uses “Adaptive Link Adjustment Evolutionary Algorithm (ALA-EA)” as a basic framework and it incorporates a heuristic local improvement approach into ALA-EA. The first step of the local search algorithm is to raise empty space through the exchange among the packed items and then to improve the fitness value through packing unpacked items into the raised empty space. The second step is to exchange the packed items and the unpacked items one another toward improving the fitness value. The proposed algorithm is compared to the previous evolutionary approaches at the benchmark instances (with the same container capacity) and the modified benchmark instances (with different container capacity) and that the algorithm is proved to be superior to the previous evolutionary approaches in the solution quality.

The multiple container packing problem

This paper presents a genetic algorithm (GA) approach to the problem of choosing C disjoint subsets of n items to be packed into distinct containers, such that the total value of the selected items is maximized, without exceeding the capacity of each of the containers. This so-called multiple container packing problem (MCPP) has applications in naval as well as financial management. It is a hard combinatorial optimization problem comprising similarities to the knapsack problem and the bin packing problem.A novel technique for encoding MCPP solutions is used within the GA: The genotype is a vector of numerical weights associated with the items of the problem. The corresponding phenotype is obtained by temporarily modifying the original problem according to these weights and applying a greedy decoding heuristic for the MCPP to the new problem. This solution is then evaluated using the original problem data again. Two different techniques for biasing the original problem and four decoding heuristics are discussed. They were tested in a weight-coded steady-state GA on a variety of MCPP instances. One biasing technique and one decoding heuristic turned out to be clearly more effective than the others, and the GA using them found solutions of significantly higher quality than direct-encoded and order-based GAs from a previous work.