Metaheuristic algorithms for one-dimensional bin-packing problems: A survey of recent advances and applications

Munien, Chanaleä; Ezugwu, Absalom E.

doi:10.1515/jisys-2020-0117

Cited by 29 publications

(12 citation statements)

References 66 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Ref. [40] presents a survey of the recent advances for one-dimensional BP, with specific interest on population-based metaheuristic algorithms. This paper wishes to give a reference guide for researchers to explore and develop more robust state-of-the-art metaheuristic algorithms for solving the emerging variants of the bin-parking problems.…”

Section: Very Recent Relevant Publicationsmentioning

confidence: 99%

Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

et al. 2021

View full text Add to dashboard Cite

Bin Packing is one of the research areas of Operations Research with many industrial applications, as well as rich theoretical impact. In this article, the authors deal with Bin Packing on the practical side: they consider two Bin Packing Benchmark classes. These benchmark problems are often used to check the “usefulness”, efficiency of algorithms. The problem is well-known to be NP-hard. Instead of introducing some exact, heuristic, or approximation method (as usual), the problem is attacked here with some kind of greedy algorithm. These algorithms are very fast; on the other hand, they are not always able to provide optimal solutions. Nevertheless, they can be considered as pre-processing algorithms for solving the problem. It is shown that almost all problems in the considered two benchmark classes are, in fact, easy to solve. In case of the Schwerin class, where there are 200 instances, it is obtained that all instances are solved by the greedy algorithm, optimally, in a very short time. The Falkenauer U class is a little bit harder, but, here, still more than 91% of the instances are solved optimally very fast, with the help of another greedy algorithm. Based on the above facts, the main contribution of the paper is to show that pre-processing is very useful for solving such kinds of problems.

show abstract

Section: Very Recent Relevant Publicationsmentioning

confidence: 99%

Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

et al. 2021

View full text Add to dashboard Cite

show abstract

“…i i m V are (13,1), (7,17) and (4,1), respectively. The sum of these three vectors is (24,19), which is lexicographically less than the capacity…”

Section: Letmentioning

confidence: 99%

“…The variable neighborhood search algorithm is developed for solving the bin packing problem (BPP) with compatible categories by Santos et al [31]. A recent survey by Munien and Ezugwu [24] on different solution approaches for the 1DHom-VBPP represents The fitness-dependent optimizer (FDO), Cuckoo search via Lévy flights, Whale optimization algorithm (WOA), Squirrel search algorithm and Genetic algorithms as successful metaheuristic approaches to this type of the Bin packing problem. Another review by Ramos et al [30] on grouping problems, among which is bin 2021/4/50 packing as well, classify the applied metaheuristics as: Neighborhood searches, Evolutionary algorithms, Swarm intelligence algorithms.…”

Section: Introductionmentioning

confidence: 99%

A Reduced Variable Neighborhood Search Approach to the Heterogeneous Vector Bin Packing Problem

Stakić

Živković

Anokić³

2021

ITC

View full text Add to dashboard Cite

The two-dimensional heterogeneous vector bin packing problem (2DHet-VBPP) consists of packing the set of items into the set of various type bins, respecting their two resource limits. The problem is to minimize the total cost of all bins. The problem, known to be NP-hard, can be formulated as a pure integer linear program, but optimal solutions can be obtained by the CPLEX Optimizer engine only for small instances. This paper proposes a metaheuristic approach to the 2DHet-VBPP, based on Reduced variable neighborhood search (RVNS). All RVNS elements are adapted to the considered problem and many procedures are designed to improve efficiency of the method. As the Two-dimensional Homogeneous-VBPP (2DHom-VBPP) is more often treated, we considered also a special version of the RVNS algorithm to solve the 2DHom-VBPP. The results obtained and compared to both CPLEX results and results on benchmark instances from literature, justify the use of the RVNS algorithm to solve large instances of these optimization problems.

show abstract

“…Performance analysis of metaheuristics are not simple, since it is highly random in nature. MCs are one of the widely used methods to analyze the performance of metaheuristics [17][18][19][20][21][22][23][24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

Search Graph Magnification in Rapid Mixing of Markov Chains Associated with the Local Search-Based Metaheuristics

Shenoy

Pai

2021

Mathematics

View full text Add to dashboard Cite

The structural property of the search graph plays an important role in the success of local search-based metaheuristic algorithms. Magnification is one of the structural properties of the search graph. This study builds the relationship between the magnification of a search graph and the mixing time of Markov Chain (MC) induced by the local search-based metaheuristics on that search space. The result shows that the ergodic reversible Markov chain induced by the local search-based metaheuristics is inversely proportional to magnification. This result indicates that it is desirable to use a search space with large magnification for the optimization problem in hand rather than using any search spaces. The performance of local search-based metaheuristics may be good on such search spaces since the mixing time of the underlying Markov chain is inversely proportional to the magnification of search space. Using these relations, this work shows that MC induced by the Metropolis Algorithm (MA) mixes rapidly if the search graph has a large magnification. This indicates that for any combinatorial optimization problem, the Markov chains associated with the MA mix rapidly i.e., in polynomial time if the underlying search graph has large magnification. The usefulness of the obtained results is illustrated using the 0/1-Knapsack Problem, which is a well-studied combinatorial optimization problem in the literature and is NP-Complete. Using the theoretical results obtained, this work shows that Markov Chains (MCs) associated with the local search-based metaheuristics like random walk and MA for 0/1-Knapsack Problem mixes rapidly.

show abstract

Metaheuristic algorithms for one-dimensional bin-packing problems: A survey of recent advances and applications

Cited by 29 publications

References 66 publications

Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

Efficient Pre-Solve Algorithms for the Schwerin and Falkenauer_U Bin Packing Benchmark Problems for Getting Optimal Solutions with High Probability

A Reduced Variable Neighborhood Search Approach to the Heterogeneous Vector Bin Packing Problem

Search Graph Magnification in Rapid Mixing of Markov Chains Associated with the Local Search-Based Metaheuristics

Contact Info

Product

Resources

About