A variable neighborhood search algorithm for the bin packing problem with compatible categories

Santos, Luiz Felipe de Oliveira Moura; Iwayama, Renan Sallai; Cavalcanti, Luísa Brandão; Turi, Leandro Maciel; Morais, Fábio Emanuel De Souza; Mormilho, Gabriel; Cunha, Cláudio Barbieri da

doi:10.1016/j.eswa.2019.01.052

Cited by 38 publications

(14 citation statements)

References 29 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This method is suitable for dynamic optimization problems as the objective changes over time. An example of such an application is in De Sousa Santos et al (2019) that used this metric as a convergence in the stopping criterion. The third measure is the logarithmic convergence rate (Salomon 1998;Mortazavi et al 2019) that is defined by…”

Section: Rate Of Convergencementioning

confidence: 99%

“…However, in TTP, the profit is increased towards the optimum solution. In Bin-packing problems, a graphical correlation between algorithms for initial and final solutions related to the number of items required (either in percentage or instance categories) with respect to the number of bins is one of the fruitful comparisons for relative efficiency, which is presented partly in Santos et al (2019). Such graphical comparison can describe the relative performance of each algorithm (Grange et al 2018) and is used for numerous practical Bin-packing problems such as the optimization of the last-mile distribution of nano-stores in large cities (Santos et al 2019) and e-commerce logistic cost (Zhang et al 2018).…”

Section: Rate Of Convergencementioning

confidence: 99%

“…Examples such as Adekanmbi and Green (2015) and Lee et al (2019a) of best to worst solution as an indicator of algorithm improvement in engineering optimization and water distribution problems. Another example is Santos et al (2019) in the combinatorial Bin-packing problem that evaluates the ratio between the best and worst solutions of total bins. The average and variation of this ratio may also indicate the effectiveness of the algorithm over the others for n trials of solutions.…”

Section: 216mentioning

confidence: 99%

See 2 more Smart Citations

Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

2020

View full text Add to dashboard Cite

The simulation-driven metaheuristic algorithms have been successful in solving numerous problems compared to their deterministic counterparts. Despite this advantage, the stochastic nature of such algorithms resulted in a spectrum of solutions by a certain number of trials that may lead to the uncertainty of quality solutions. Therefore, it is of utmost importance to use a correct tool for measuring the performance of the diverse set of metaheuristic algorithms to derive an appropriate judgment on the superiority of the algorithms and also to validate the claims raised by researchers for their specific objectives. The performance of a randomized metaheuristic algorithm can be divided into efficiency and effectiveness measures. The efficiency relates to the algorithm's speed of finding accurate solutions, convergence, and computation. On the other hand, effectiveness relates to the algorithm's capability of finding quality solutions. Both scopes are crucial for continuous and discrete problems either in single-or multi-objectives. Each problem type has different formulation and methods of measurement within the scope of efficiency and effectiveness performance. One of the most decisive verdicts for the effectiveness measure is the statistical analysis that depends on the data distribution and appropriate tool for correct judgments.

show abstract

Section: Rate Of Convergencementioning

confidence: 99%

Section: Rate Of Convergencementioning

confidence: 99%

Section: 216mentioning

confidence: 99%

See 1 more Smart Citation

Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

2020

View full text Add to dashboard Cite

show abstract

“…The FFD heuristic requires a decreasing order of items before processing, whereas the proposed method does not require item sorting. With more constraints on packing items, a VNS can be used to solve the BPP with conflicts [37]. The Firefly algorithm was presented to solve the 1D-BPP with fixedsized bins [38].…”

Section: Literature Reviewmentioning

confidence: 99%

An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem

et al. 2020

View full text Add to dashboard Cite

In recent years, the one-dimensional bin packing problem (1D-BPP) has become one of the most famous combinatorial optimization problems. The 1D-BPP is a robust NP-hard problem that can be solved through optimization algorithms. This paper proposes an adaptive procedure using a recently optimized swarm algorithm and fitness-dependent optimizer (FDO), named the AFDO, to solve the BPP. The proposed algorithm is based on the generation of a feasible initial population through a modified well-known first fit (FF) heuristic approach. To obtain a final optimized solution, the most critical parameters of the algorithm are adapted for the problem. To the best of our knowledge, this is the first study to apply the FDO algorithm in a discrete optimization problem, especially for solving the BPP. The adaptive algorithm was tested on 30 instances obtained from benchmark datasets. The performance and evaluation results of this algorithm were compared with those of other popular algorithms, such as the particle swarm optimization (PSO) algorithm, crow search algorithm (CSA), and Jaya algorithm. The AFDO algorithm obtained the smallest fitness values and outperformed the PSO, CS, and Jaya algorithms by 16%, 17%, and 11%, respectively. Moreover, the AFDO shows superiority in terms of execution time with improvements over the execution times of the PSO, CS, and Jaya algorithms by up to 46%, 54%, and 43%, respectively. The experimental results illustrate the effectiveness of the proposed adaptive algorithm for solving the 1D-BPP. INDEX TERMS Bin packing, first fit heuristic, fitness-dependent optimizer, swarm intelligent algorithms.

show abstract

“…As duas últimas regras de ordenação, RO 7 e RO 8, utilizam o conceito de grau de incompatibilidade das tarefas, adaptado do problema de Bin Packing com compatibilidade entre objetos (SANTOS et al, 2019). Para este contexto, um par de tarefas ( , ) é considerado incompatível entre si caso elas não possam ser executadas pelo mesmo rebocador sem que haja rompimento de suas janelas de tempo, considerando suas durações e e o menor tempo de deslocamento possível (min{ , } / max ).…”

Section: Apresentação Do Algoritmo De Inserção De Tarefa Em Rotas Paralelasunclassified

Roteirização de rebocadores para atendimento de plataformas offshore, com operações sincronizadas e janelas de tempo.

Cavalcanti¹

Self Cite

View full text Add to dashboard Cite

show abstract

A variable neighborhood search algorithm for the bin packing problem with compatible categories

Cited by 38 publications

References 29 publications

Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

Performance assessment of the metaheuristic optimization algorithms: an exhaustive review

An Adaptive Fitness-Dependent Optimizer for the One-Dimensional Bin Packing Problem

Roteirização de rebocadores para atendimento de plataformas offshore, com operações sincronizadas e janelas de tempo.

Contact Info

Product

Resources

About