A modified adaptive genetic algorithm for multi-product multi-period inventory routing problem

Mahjoob, M. J.; Fazeli, Seyed Sajjad; Milanlouei, Soodabeh; Tavassoli, Leyla Sadat; Mirmozaffari, Mirpouya

doi:10.1016/j.susoc.2021.08.002

Cited by 37 publications

(13 citation statements)

References 22 publications

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“…In order to create new chromosomes, recombining strings (component materials) is conducted using simple analogies of genetic crossover and mutation operator. Crossover is the primary instrument of variation and innovation in GA to obtain better characteristics and form the most suitable solution among generations (Mahjoob et al, 2021). Mutation includes flipping the bit at a randomly chosen locus (i.e.…”

Section: The Proposed Ga Modelsmentioning

confidence: 99%

An Efficient Inventory Model-Based Ga for Food Deterioration Products in the Tourism Industry

‎Al-Salami

Saleh²,

Al-Bazi

2022

Pesqui. Oper.

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Background: The inventory control practice of deteriorating food products that are subject to an expiration date is a challenging process. Inappropriate inventory control practice leads to substantial waste of products and significant holding and purchasing costs. Purpose: This paper aims to develop an inventory control model-based Genetic Algorithm (GA) to minimize the Total Annual Inventory Cost (TAIC) function developed explicitly for the proposed model. Methodology: GA is used and tailored to provide the best reorder level of deteriorating food products. A case study of one of the five-star hotels in Iraq is conducted, followed by a sensitivity analysis study to validate the proposed model for varying reorder levels. Results and Conclusion:A minimum inventory cost is obtained with an optimum reorder level achieved by running GA. It is concluded that the optimal reorder level provided by the proposed GA minimized the monthly inventory cost of products.

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Section: The Proposed Ga Modelsmentioning

confidence: 99%

An Efficient Inventory Model-Based Ga for Food Deterioration Products in the Tourism Industry

‎Al-Salami

Saleh²,

Al-Bazi

2022

Pesqui. Oper.

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“…Although making an appropriate decision is urgent, the process is often too challenging [7] . So far, a wide range of decision-making tools and methods have been used to reach the most desirable decision 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 . However, the research in this area is scarce and has not been conducted in responding to pandemics.…”

Section: Introductionmentioning

confidence: 99%

An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic

Mirmozaffari

Yazdani

Shadkam

et al. 2022

Sustainable Operations and Computers

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The spread of coronavirus disease around the world has had an immense impact on most economic sectors. Yet amid the turmoil and chaos from the worldwide pandemic, one industry is thriving noticeably. The coronavirus disease is a once in a lifetime business opportunity for pharmaceutical companies. This study presents an artificial intelligence method composed of optimization and machine learning. Data envelopment analysis (DEA) is used to measure productivities and efficiencies of pharmaceutical companies during the COVID-19 pandemic using the additive model in window analysis, the BCC (Banker-Charnes-Cooper) model, and the CCR (Charnes-Cooper-Rhodes) model. The three models are assessed using DataStream financial information with research and development (R&D) investment. The results indicated the additive model's superiority in window analysis, followed by the BCC and CCR models. In the end, some of well-known data mining algorithms, based on the suggested data, have been evaluated in various tools to find the most efficient tool and algorithm.

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“…It has the characteristics of fast search speed, simple process and the flexibility is strong [2]. However, the optimization results easily fall into local optimal values due to its fixed crossover probability and mutation probability [3][4].…”

Section: Introductionmentioning

confidence: 99%

Optimization Performance Analysis for Adaptive Genetic Algorithm with Nonlinear Probabilities

Sun¹,

Su²,

Yuan³

et al. 2022

IJACSA

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Genetic Algorithm (GA) has been proven to be easy in falling into local optimal value due to its fixed crossover probability and mutation probability, while Adaptive Genetic Algorithm (AGA) has strong global search capability because the two probabilities adjust adaptively. There are two categories of AGA according to the different adjustment methods for crossover and mutation probabilities: probabilistic linear adjustment AGA and probabilistic non-linear adjustment AGA. AGA with linear adjustment of probability values cannot solve the problems of local optimal value and premature convergence. The nonlinear adaptive probability adjustment strategy can avoid premature convergence, poor stability and slow convergence speed. The typical AGA with nonlinear adjustment of probabilities are compared and analyzed through benchmark functions. The optimization performance of typical AGA algorithms is compared and analyzed by 10 benchmark functions. Compared with traditional GA and other AGA algorithms, AGA with crossover and mutation probabilities adjusted nonlinearly at both ends of the average fitness value has higher computational stability and is easy to find the global optimal solution, which provides ideas for the application of adaptive genetic algorithm.

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A modified adaptive genetic algorithm for multi-product multi-period inventory routing problem

Cited by 37 publications

References 22 publications

An Efficient Inventory Model-Based Ga for Food Deterioration Products in the Tourism Industry

An Efficient Inventory Model-Based Ga for Food Deterioration Products in the Tourism Industry

An integrated artificial intelligence model for efficiency assessment in pharmaceutical companies during the COVID-19 pandemic

Optimization Performance Analysis for Adaptive Genetic Algorithm with Nonlinear Probabilities

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