Anti-Predatory NIA for Unconstrained Mathematical Optimization Problems

Kumar²,

2020

IJIEI

Self Cite

E-commerce is expanding their roots in every business. Fast, efficient, reliable, timely delivery of goods and optimal transportation cost are the major challenges in e-commerce. To overcome these challenges, e-commerce companies are using a well-planned arrangement of warehouses and distribution centres (logistics network). This logistics network also reduces the operational cost and capital investment of an e-commerce company. This study proposes a novel solution to deal with e-commerce logistics problem (ECLP) using anti-predatory NIA (APNIA). The proposed approach is useful for identifying cities where warehouses and distribution centres can be established; and allocating the distribution centres to warehouse in order to reduce total cost of goods transportation. The proposed approach is also useful for predicting the number of warehouses to be established for optimal logistics network. The experimental evaluation reveals that the proposed method achieves 2.30% lower gap value and 20% more consistent optimal results as compared to the genetic algorithm.

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Section: Objective Function and Its Constraintsmentioning

confidence: 99%

“…Based on the 'fearness' value, a frog tries to protect itself by 'colouration', 'toxin secretion', 'flee towards safe a position', 'puffing up' and 'playing dead' (Sachan and Kushwaha, 2020). These anti-predatory mechanisms are modelled by mathematical equations (Sachan and Kushwaha, 2019).…”

Section: Objective Function and Its Constraintsmentioning

confidence: 99%

Solving the e-commerce logistics problem using anti-predatory NIA

Kumar²,

2020

IJIEI

Self Cite

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“…APNIA is a Single-Objective Optimization Algorithm (SOOA). Its performance has been evaluatedonvariousunconstrainedbenchmarkfunctions (SachanandKushwaha,2019;2020a)and engineeringoptimizationproblems,likesoftwareeffortestimation(SachanandKushwaha,2020d), p-hublocationproblem(SachanandKushwaha,2021b),e-commercelogisticsproblem(Sachanet al,2020c.Theseevaluationsestablish APNIAasapowerfuloptimizationalgorithmandproveitseffectivenessandrobustnessforsolving theSOOPs.Thesimultaneousexplorationandexploitationfeature(SachanandKushwaha,2021a) ofAPNIAcanprovidetheabilitytohandlemultipleobjectives.Itcanbeextendedtodevelopa generalized,robust,efficient,posteriorandMulti-Objective(MO)variantofthealgorithm.…”

Section: Introductionmentioning

confidence: 99%

A Multi-Objective Anti-Predatory NIA for E-Commerce Logistics Optimization Problem

International Journal of Applied Metaheuristic Computing

2021

Nature-inspired algorithms (NIAs) have established their promising performance to solve both single-objective optimization problems (SOOPs) and multi-objective optimization problems (MOOPs). Anti-predatory NIA (APNIA) is one of the recently introduced single-objective algorithm based on the self-defense behavior of frogs. This paper extends APNIA as multi-objective algorithm and presents the first proposal of APNIA to solve MOOPs. The proposed algorithm is a posteriori version of APNIA, which is named as multi-objective anti-predatory NIA (MO-APNIA). It uses the concept of Pareto dominance to determine the non-dominated solutions. The performance of the MO-APNIA is established through the experimental evaluation and statistically verified using the Friedman rank test and Holm-Sidak test. MO-APNIA is also employed to solve a multi-objective variant of hub location problem (HLP) from the perspective of the e-commerce logistics. Results indicate that the MO-APNIA is also capable to finds the non-dominated solutions of HLP. This finds immense use in logistics industry.

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Warehouse Setup Problem in Logistics: A Truck Transportation Cost Model

Advances in Intelligent Systems and Computing

2020