共查询到10条相似文献,搜索用时 125 毫秒
1.
This paper presents two novel genetic algorithms (GAs) for hard industrially relevant packing problems. The design of both
algorithms is inspired by aspects of molecular genetics, in particular, the modular exon-intron structure of eukaryotic genes.
Two representative packing problems are used to test the utility of the proposed approach: the bin packing problem (BPP) and
the multiple knapsack problem (MKP). The algorithm for the BPP, the exon shuffling GA (ESGA), is a steady-state GA with a
sophisticated crossover operator that makes maximum use of the principle of natural selection to evolve feasible solutions
with no explicit verification of constraint violations. The second algorithm, the Exonic GA (ExGA), implements an RNA inspired
adaptive repair function necessary for the highly constrained MKP. Three different variants of this algorithm are presented
and compared, which evolve a partial ordering of items using a segmented encoding that is utilised in the repair of infeasible
solutions. All algorithms are tested on a range of benchmark problems, and the results indicate a very high degree of accuracy
and reliability compared to other approaches in the literature. 相似文献
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3.
The more-dimensional bin packing problem (BPP) considered here requires packing a set of rectangular-shaped items into a minimum
number of identical rectangular-shaped bins. All items may be rotated and the guillotine cut constraint has to be respected.
A straightforward heuristic is presented that is based on a method for the container loading problem following a wall-building
approach and on a method for the one-dimensional BPP. 1,800 new benchmark instances are introduced for the two-dimensional
and three-dimensional BPP. The instances include more than 1,500 items on average. Applied to these very large instances,
the heuristic generates solutions of acceptable quality in short computation times. Moreover, the influence of different instance
parameters on the solution quality is investigated by an extended computational study. 相似文献
4.
This paper proposes a four corners’ heuristic for application in evolutionary algorithms (EAs) applied to two-dimensional packing problems. The four corners’ (FC) heuristic is specifically designed to increase the search efficiency of EAs. Experiments with the FC heuristic are conducted on 31 problems from the literature both with rotations permitted and without rotations permitted, using two different EA algorithms: a self-adaptive parallel recombinative simulated annealing (PRSA) algorithm, and a self-adaptive genetic algorithm (GA). Results on bin packing problems yield the smallest trim losses we have seen in the published literature; with the FC heuristic, zero trim loss was achieved on problems of up to 97 rectangles. A comparison of the self-adaptive GA to fixed-parameter GAs is presented and the benefits of self-adaption are highlighted. 相似文献
5.
Andrea Bettinelli Alberto Ceselli Giovanni Righini 《Annals of Operations Research》2010,179(1):221-241
In this paper we consider a variation of the bin packing problem in which bins of different types have different costs and
capacities. Furthermore, each bin has to be filled at least to a certain level, which depends on its type. We present a set
partitioning formulation and an exact optimization algorithm which exploits column generation and specialized heuristics.
We compare our algorithm with the general purpose solver ILOG CPLEX, running on two compact ILP formulations and we report
on experimental results on instances we have generated from data-sets for the variable size bin packing problem. 相似文献
6.
Shane Dye 《Discrete Applied Mathematics》2009,157(13):2958-2963
Minimum bounded edge-partition divides the edge set of a tree into the minimum number of disjoint connected components given a maximum weight for any component. It is an adaptation of the uniform edge-partition of a tree. An optimization algorithm is developed for this NP-hard problem, based on repeated bin packing of inter-related instances. The algorithm has linear running time for the class of ‘balanced trees’ common for the stochastic programming application which motivated investigation of this problem.Fast 2-approximation algorithms are formed for general instances by replacing the optimal bin packing with almost any bin packing heuristic. The asymptotic worst-case ratio of these approximation algorithms is never better than the absolute worst-case ratio of the bin packing heuristic used. 相似文献
7.
The bin packing problem is widely found in applications such as loading of tractor trailer trucks, cargo airplanes and ships, where a balanced load provides better fuel efficiency and safer ride. In these applications, there are often conflicting criteria to be satisfied, i.e., to minimize the bins used and to balance the load of each bin, subject to a number of practical constraints. Unlike existing studies that only consider the issue of minimum bins, a multiobjective two-dimensional mathematical model for bin packing problems with multiple constraints (MOBPP-2D) is formulated in this paper. To solve MOBPP-2D problems, a multiobjective evolutionary particle swarm optimization algorithm (MOEPSO) is proposed. Without the need of combining both objectives into a composite scalar weighting function, MOEPSO incorporates the concept of Pareto’s optimality to evolve a family of solutions along the trade-off surface. Extensive numerical investigations are performed on various test instances, and their performances are compared both quantitatively and statistically with other optimization methods to illustrate the effectiveness and efficiency of MOEPSO in solving multiobjective bin packing problems. 相似文献
8.
Weidong Chen Pengfei Zhai Heng Zhu Yongbo Zhang 《The Journal of the Operational Research Society》2014,65(7):1068-1077
In this paper, a rectangular layer-packing algorithm (RLPA) combined with modified genetic algorithm (GA) or particle swarm optimization (PSO) algorithm is developed to solve the problem with emerging restraints, which is raised from the two-dimensional rectangular packing problem with some small rectangles that need to be packed into a fixed rectangular object. RLPA is designed from the BL algorithm and lowest horizontal line algorithm. GA and PSO are also modified to satisfy the constraint conditions. Best GA or PSO parameters are obtained by conducting experiments on some typical instances. The results are also compared, which validate the quality of the solutions and show the effectiveness of the modified algorithm. 相似文献
9.
Shaohui Hong Defu Zhang Hoong Chuin Lau XiangXiang Zeng Yain-Whar Si 《European Journal of Operational Research》2014
In this paper, we consider the two-dimensional variable-sized bin packing problem (2DVSBPP) with guillotine constraint. 2DVSBPP is a well-known NP-hard optimization problem which has several real applications. A mixed bin packing algorithm (MixPacking) which combines a heuristic packing algorithm with the Best Fit algorithm is proposed to solve the single bin problem, and then a backtracking algorithm which embeds MixPacking is developed to solve the 2DVSBPP. A hybrid heuristic algorithm based on iterative simulated annealing and binary search (named HHA) is then developed to further improve the results of our Backtracking algorithm. Computational experiments on the benchmark instances for 2DVSBPP show that HHA has achieved good results and outperforms existing algorithms. 相似文献
10.
In this paper we consider a class of bin selection and packing problems (BPP) in which potential bins are of various types, have two resource constraints, and the resource requirement for each object differs for each bin type. The problem is to select bins and assign the objects to bins so as to minimize the sum of bin costs while meeting the two resource constraints. This problem represents an extension of the classical two-dimensional BPP in which bins are homogeneous. Typical applications of this research include computer storage device selection with file assignment, robot selection with work station assignment, and computer processor selection with task assignment. Three solution algorithms have been developed and tested: a simple greedy heuristic, a method based onsimulated annealing (SA) and an exact algorithm based onColumn Generation with Branch and Bound (CG). An LP-based method for generating tight lower bounds was also developed (LB). Several hundred test problems based on computer storage device selection and file assignment were generated and solved. The heuristic solved problems up to 100 objects in less than a second; average solution value was within about 3% of the optimum. SA improved solutions to an average gap of less than 1% but a significant increase in computing time. LB produced average lower bounds within 3% of optimum within a few seconds. CG is practical for small to moderately-sized problems — possibly as many as 50 objects. 相似文献