A constructive bin-oriented heuristic for the two-dimensional bin packing problem with guillotine cuts

A new heuristic algorithm for solving the two-dimensional bin-packing problem with guillotine cuts (2DBP|?|G)$(2\mathrm{DBP}|?|\mathrm{G})$ is presented. The heuristic constructs a solution by packing a bin at a time. Central to the adopted solution scheme is the principle of average-area sufficiency proposed by the authors for guiding selection of items to fill a bin. The algorithm is tested on a set of standard benchmark problem instances and compared with existing heuristics producing the best-known results. The results presented attest to the efficacy of the proposed scheme.     

Three insertion heuristics and a justification improvement heuristic for two-dimensional bin packing with guillotine cuts

The problem of packing two-dimensional items into two-dimensional bins is considered in which patterns of items allocated to bins must be guillotine-cuttable and item rotation might be allowed (2BP|?|G)$(2\mathrm{BP}|?|\mathrm{G})$. Three new constructive heuristics, namely, first-fit insertion heuristic, best-fit insertion heuristic, and critical-fit insertion heuristic, and a new justification improvement heuristic are proposed. All new heuristics use tree structures to represent guillotine-cuttable patterns of items and proceed by inserting one item at a time in a partial solution. Central to all heuristics are a new procedure for enumerating possible insertions and a new fitness criterion for choosing the best insertion. All new heuristics have quadratic worst-case computational complexity except for the critical-fit insertion heuristic which has a cubic worst-case computational complexity. The efficiency and effectiveness of the proposed heuristics is demonstrated by comparing their empirical performance on a standard benchmark data set against other published approaches.     

二维离线非旋转装箱问题的一个混合算法

针对二维离线非旋转装箱问题,在凹角和适应值的思想的基础上,提出了一个改进型的Best-Fit启发式算法,并结合基于自然数编码的遗传算法构建了混合算法。同时在遗传迭代过程中,引入二维装箱问题的下界思想作为迭代的终止条件之一,减少了遗传算法无效迭代次数,另外根据问题自身特点,有效地降低了染色体长度,提高了整体的计算速度。在36个标准测试案例的测试基础上与一些经典的算法进行了比较,实验结果表明该算法在工业生产可接受的时间内与其他经典的算法相比能够获得更为满意的结果。     

A single front genetic algorithm for parallel multi-objective optimization in dynamic environments

This paper proposes a new parallel evolutionary procedure to solve multi-objective dynamic optimization problems along with some measures to evaluate multi-objective optimization in dynamic environments. These dynamic optimization problems appear in quite different real-world applications with actual socio-economic relevance. In these applications, the objective functions, the constraints, and hence, also the solutions, can change over time and usually demand to be solved online whilst the size of the changes is unknown. Although parallel processing could be very useful in these problems to meet the solution quality requirements and constraints, to date, not many parallel approaches have been reported in the literature. Taking this into account, we introduce a multi-objective optimization procedure for dynamic problems that are based on PSFGA, a parallel evolutionary algorithm previously proposed by us for multi-objective optimization. It uses an island model where a process divides the population among the remaining processes and allows the communication and coordination among the subpopulations in the different islands. The proposed algorithm makes an exclusive use of non-dominating individuals for the selection and variation operator and applies a crowding mechanism to maintain the diversity and the distribution of the solutions in the Pareto front. We also propose a model to understand the benefits of parallel processing in multi-objective problems and the speedup figures obtained in our experiments.     

A grouping genetic algorithm with controlled gene transmission for the bin packing problem

In this study, the one-dimensional Bin Packing Problem (BPP) is approached. The BPP is a classical optimization problem that is known for its applicability and complexity. We propose a method that is referred to as the Grouping Genetic Algorithm with Controlled Gene Transmission (GGA-CGT) for Bin Packing. The proposed algorithm promotes the transmission of the best genes in the chromosomes without losing the balance between the selective pressure and population diversity. The transmission of the best genes is accomplished by means of a new set of grouping genetic operators, while the evolution is balanced with a new reproduction technique that controls the exploration of the search space and prevents premature convergence of the algorithm. The results obtained from an extensive computational study confirm that (1) promoting the transmission of the best genes improves the performance of each grouping genetic operator; (2) adding intelligence to the packing and rearrangement heuristics enhances the performance of a GGA; (3) controlling selective pressure and population diversity tends to lead to higher effectiveness; and (4) GGA-CGT is comparable to the best state-of-the-art algorithms, outperforming the published results for the class of instances Hard28, which appears to have the greatest degree of difficulty for BPP algorithms.     

A greedy search for the three-dimensional bin packing problem: the packing static stability case

We suggest a greedy search heuristic for solving the three-dimensional bin packing problem (3D-BPP) where in addition to the usual requirement of minimum amount of bins being used, the resulting packing of items into the bins must be physically stable. The problem is NP-hard in the strong sense and imposes severe computational strain for solving it in practice. Computational experiments are also presented and the results are compared with those obtained by the Martello, Pisinger and Vigo (2000) heuristic.     

多箱型三维装箱问题及其优化研究

对多箱型三维装箱问题进行了描述,提出了求解该问题的禁忌算法。算法基于自然数编码,设计了货物的摆放规则和序列生成方式,采用三种邻域,利用邻域操作控制集装箱数量限制,采用惩罚函数处理集装箱重量约束和重心约束。介绍了算法的原理,给出了具有代表性算例实验结果并且进行了分析。实验结果表明了提出的禁忌算法对优化多箱型三维装箱问题的有效性。     

An ant colony optimization algorithm for load balancing in parallel machines with sequence-dependent setup times

This study introduces the problem of minimizing average relative percentage of imbalance (ARPI) with sequence-dependent setup times in a parallel-machine environment. A mathematical model that minimizes ARPI is proposed. Some heuristics, and two metaheuristics, an ant colony optimization algorithm and a genetic algorithm are developed and tested on various random data. The proposed ant colony optimization method outperforms heuristics and genetic algorithm. On the other hand, heuristics using the cumulative processing time obtain better results than heuristics using setup avoidance and a hybrid rule in assignment.     

A new analytical method for parallel,diffusion-type load balancing

We propose a new proof technique which can be used to analyse many parallel load balancing algorithms. The technique is designed to handle concurrent load balancing actions, which are often the main obstacle in the analysis. We demonstrate the usefulness of the approach by analysing various natural diffusion-type protocols. Our results are similar to, or better than, previously existing ones, while our proofs are much easier.     

A novel dynamic load balancing scheme for parallel systems

Adaptive mesh refinement (AMR) is a type of multiscale algorithm that achieves high resolution in localized regions of dynamic, multidimensional numerical simulations. One of the key issues related to AMR is dynamic load balancing (DLB), which allows large-scale adaptive applications to run efficiently on parallel systems. In this paper, we present an efficient DLB scheme for structured AMR (SAMR) applications. This scheme interleaves a grid-splitting technique with direct grid movements (e.g., direct movement from an overloaded processor to an underloaded processor), for which the objective is to efficiently redistribute workload among all the processors so as to reduce the parallel execution time. The potential benefits of our DLB scheme are examined by incorporating our techniques into a SAMR cosmology application, the ENZO code. Experiments show that by using our scheme, the parallel execution time can be reduced by up to 57% and the quality of load balancing can be improved by a factor of six, as compared to the original DLB scheme used in ENZO.     

Load balancing strategies for a parallel ray-tracing system based on constant subdivision

Static and dynamic load balancing strategies for a multiprocessor system for a ray tracing algorithm based on constant subdivision are presented. An object space is divided into regular cubes (subspaces), whose boundary planes are perpendicular to the coordinate axes, and these are allocated to the processors in the system. Here, load balancing among the processors is the most important problem. Firstly, in a category of static load balancing, strategies for mapping the subspaces into the processors are evaluated by simulation. Moreover, we propose a hierarchical multiprocessor system in order to realize dynamic load balancing with the static one. Its architecture can overcome the limitation of the static load balancing in a large scale multiprocessor system.     

有卸货顺序约束的集装箱装载问题及算法研究

对有卸货顺序约束的三维集装箱问题进行了描述.基于禁忌规则,采用了求解该问题的模拟退火算法,设计了货物的摆放规则和序列生成方式.采用3种邻域,根据邻域的不同,构造了2种禁忌表.根据问题的特点,在模拟退火算法抽样过程中加入了禁忌规则.介绍了算法的原理,给出了具有代表性算例试验结果并且进行了分析.试验结果表明,提出的混合算法对有卸货顺序约束的集装箱三维装载问题的有效性.     

Tree-decomposition based heuristics for the two-dimensional bin packing problem with conflicts

This paper deals with the two-dimensional bin packing problem with conflicts (BPC-2D). Given a finite set of rectangular items, an unlimited number of rectangular bins and a conflict graph, the goal is to find a conflict-free packing of the items minimizing the number of bins used. In this paper, we propose a new framework based on a tree-decomposition for solving this problem. It proceeds by decomposing a BPC-2D instance into subproblems to be solved independently. Applying this decomposition method is not straightforward, since merging partial solutions is hard. Several heuristic strategies are proposed to make an effective use of the decomposition. Computational experiments show the practical effectiveness of our approach.     

A decomposition based memetic algorithm for multi-objective vehicle routing problem with time windows

Multi-objective evolutionary algorithm based on decomposition (MOEA/D) provides an excellent algorithmic framework for solving multi-objective optimization problems. It decomposes a target problem into a set of scalar sub-problems and optimizes them simultaneously. Due to its simplicity and outstanding performance, MOEA/D has been widely studied and applied. However, for solving the multi-objective vehicle routing problem with time windows (MO-VRPTW), MOEA/D faces a difficulty that many sub-problems have duplicated best solutions. It is well-known that MO-VRPTW is a challenging problem and has very few Pareto optimal solutions. To address this problem, a novel selection operator is designed in this work to enhance the original MOEA/D for dealing with MO-VRPTW. Moreover, three local search methods are introduced into the enhanced algorithm. Experimental results indicate that the proposed algorithm can obtain highly competitive results on Solomon׳s benchmark problems. Especially for instances with long time windows, the proposed algorithm can obtain more diverse set of non-dominated solutions than the other algorithms. The effectiveness of the proposed selection operator is also demonstrated by further analysis.     

Resource scheduling algorithm with load balancing for cloud service provisioning

Cloud computing uses scheduling and load balancing for virtualized file sharing in cloud infrastructure. These two have to be performed in an optimized manner in cloud computing environment to achieve optimal file sharing. Recently, Scalable traffic management has been developed in cloud data centers for traffic load balancing and quality of service provisioning. However, latency reducing during multidimensional resource allocation still remains a challenge. Hence, there necessitates efficient resource scheduling for ensuring load optimization in cloud. The objective of this work is to introduce an integrated resource scheduling and load balancing algorithm for efficient cloud service provisioning. The method constructs a Fuzzy-based Multidimensional Resource Scheduling model to obtain resource scheduling efficiency in cloud infrastructure. Increasing utilization of Virtual Machines through effective and fair load balancing is then achieved by dynamically selecting a request from a class using Multidimensional Queuing Load Optimization algorithm. A load balancing algorithm is then implemented to avoid underutilization and overutilization of resources, improving latency time for each class of request. Simulations were conducted to evaluate the effectiveness using Cloudsim simulator in cloud data centers and results shows that the proposed method achieves better performance in terms of average success rate, resource scheduling efficiency and response time. Simulation analysis shows that the method improves the resource scheduling efficiency by 7% and also reduces the response time by 35.5 % when compared to the state-of-the-art works.     

A dynamic and adaptive load balancing strategy for parallel file system with large-scale I/O servers

Many solutions have been proposed to tackle the load imbalance issue of parallel file systems. However, all these solutions either adopt centralized algorithms, or lack considerations for both the network transmission and the tradeoff between benefits and side-effects of each dynamic file migration. Therefore, existing solutions will be prohibitively inefficient in large-scale parallel file systems. To address this problem, this paper presents SALB, a dynamic and adaptive load balancing algorithm which is totally based on a distributed architecture. To be also aware of the network transmission, SALB on the one hand adopts an adaptively adjusted load collection threshold in order to reduce the message exchanges for load collection, and on the other hand it employs an on-line load prediction model with a view to reducing the decision delay caused by the network transmission latency. Moreover, SALB employs an optimization model for selecting the migration candidates so as to balance the benefits and the side-effects of each dynamic file migration. Extensive experiments are conducted to prove the effectiveness of SALB. The results show that SALB achieves an optimal performance not only on the mean response time but also on the resource utilization among the schemes for comparison. The simulation results also indicate that SALB is able to deliver high scalability.     

基于新模型的多目标Memetic算法及收敛分析

将多目标函数优化问题转化成单目标约束优化问题.对转化后的问题提出了基于约束主导原理的选择方法,克服了多数方法只使用Pareto优胜关系作为选择策略而没有采用偏好信息这一缺陷;Memetic算法是求解多目标优化问题最有效的方法之一,它融合了局部搜索和进化计算.新的多目标Memetic算法引进C-metric,将模拟退火算法与遗传算法结合起米,改善了全局搜索能力.用概率论的有关知识证明了算法的收敛性.仿真结果表明该方法对不同的试验函数均可求出一组沿着Pareto前沿分布均匀且散布广泛的非劣解.     

A hybrid genetic algorithm for mixed model assembly line balancing problem with parallel workstations and zoning constraints

In this paper, we propose a hybrid genetic algorithm to solve mixed model assembly line balancing problem of type I (MMALBP-I). There are three objectives to be achieved: to minimize the number of workstations, maximize the workload smoothness between workstations, and maximize the workload smoothness within workstations. The proposed approach is able to address some particular features of the problem such as parallel workstations and zoning constraints. The genetic algorithm may lack the capability of exploring the solution space effectively. We aim to improve its exploring capability by sequentially hybridizing the three well known heuristics, Kilbridge & Wester Heuristic, Phase-I of Moodie & Young Method, and Ranked Positional Weight Technique, with genetic algorithm. The proposed hybrid genetic algorithm is tested on 20 representatives MMALBP-I and the results are compared with those of other algorithms.     

A repartitioning hypergraph model for dynamic load balancing

In parallel adaptive applications, the computational structure of the applications changes over time, leading to load imbalances even though the initial load distributions were balanced. To restore balance and to keep communication volume low in further iterations of the applications, dynamic load balancing (repartitioning) of the changed computational structure is required. Repartitioning differs from static load balancing (partitioning) due to the additional requirement of minimizing migration cost to move data from an existing partition to a new partition. In this paper, we present a novel repartitioning hypergraph model for dynamic load balancing that accounts for both communication volume in the application and migration cost to move data, in order to minimize the overall cost. The use of a hypergraph-based model allows us to accurately model communication costs rather than approximate them with graph-based models. We show that the new model can be realized using hypergraph partitioning with fixed vertices and describe our parallel multilevel implementation within the Zoltan load balancing toolkit. To the best of our knowledge, this is the first implementation for dynamic load balancing based on hypergraph partitioning. To demonstrate the effectiveness of our approach, we conducted experiments on a Linux cluster with 1024 processors. The results show that, in terms of reducing total cost, our new model compares favorably to the graph-based dynamic load balancing approaches, and multilevel approaches improve the repartitioning quality significantly.