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<p>Tournamenting approach plays most vital and important role for selecting best individual in knockout tournament system. This idea is used for developing hybridization of algorithm based on teaching learning based optimization technique and named as tournament teaching learning based optimization algorithm (TTLBO). The main goal of this work is to apply hybrid TTLBO for solving maximization problems corresponding to the proposed non-instantaneous inventory model for single deteriorating item with trade-credit financing, partially backlogging and Weibull distributed deterioration. Demand depends on credit period and selling price of item. Now, our aim is to determine optimal order quantity, cycle length, selling price and maximum quantity of shortage by maximizing the retailer’s average profit. The validity of the developed model is tested with the help of an example. Also, the same example is solved by existing nine algorithms, viz. ABC, GQPSO, AQPSO, DE, RAO-1, RAO-2, RAO-3, HBO and TLBO algorithms to compare the performance and efficiency of the proposed TTLBO algorithm. Moreover, the analyses of sensitivity are studied to investigate the impact of different parameters involving in the model on the best found policy. Also, two nonparametric statistical tests, viz. Wilcoxon rank sum test and Friedman test are used to compare the statistical significant of proposed algorithm and existing nine algorithms. Finally, from numerical illustration and sensitivity analysis, a fruitful conclusion of this study is drawn.</p>

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Optimization of a deteriorated inventory model with bi-level credit periods and variable demand via tournament teaching learning based optimization algorithm

  • 期刊论文

  • 发表日期：

    2025年4月6日

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  • Goutam Mandal,
  • Amalesh Kumar Manna,
  • Subhash Chandra Das,
  • Asoke Kumar Bhunia

摘要

Tournamenting approach plays most vital and important role for selecting best individual in knockout tournament system. This idea is used for developing hybridization of algorithm based on teaching learning based optimization technique and named as tournament teaching learning based optimization algorithm (TTLBO). The main goal of this work is to apply hybrid TTLBO for solving maximization problems corresponding to the proposed non-instantaneous inventory model for single deteriorating item with trade-credit financing, partially backlogging and Weibull distributed deterioration. Demand depends on credit period and selling price of item. Now, our aim is to determine optimal order quantity, cycle length, selling price and maximum quantity of shortage by maximizing the retailer’s average profit. The validity of the developed model is tested with the help of an example. Also, the same example is solved by existing nine algorithms, viz. ABC, GQPSO, AQPSO, DE, RAO-1, RAO-2, RAO-3, HBO and TLBO algorithms to compare the performance and efficiency of the proposed TTLBO algorithm. Moreover, the analyses of sensitivity are studied to investigate the impact of different parameters involving in the model on the best found policy. Also, two nonparametric statistical tests, viz. Wilcoxon rank sum test and Friedman test are used to compare the statistical significant of proposed algorithm and existing nine algorithms. Finally, from numerical illustration and sensitivity analysis, a fruitful conclusion of this study is drawn.