Comparing performance of genetic and discrete invasive weed optimization algorithms for solving the inventory routing problem with an incremental delivery

Hadi Jahangir (), Mohammad Mohammadi (), Seyed Hamid Reza Pasandideh () and Neda Zendehdel Nobari ()
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Hadi Jahangir: Kharazmi University
Mohammad Mohammadi: Kharazmi University
Seyed Hamid Reza Pasandideh: Kharazmi University
Neda Zendehdel Nobari: Kharazmi University

Journal of Intelligent Manufacturing, 2019, vol. 30, issue 6, No 2, 2327-2353

Abstract: Abstract This article formulates an inventory routing problem in which backorders are allowed, each vehicle is used at most once, a central depot distributes a single product to a set of customers with an incremental delivery, each customer is served at most once over a finite planning period and the objective is minimizing transportation and inventory costs. Since the proposed model is an Np-Hard problem, for solving large scale problems two meta-heuristics, discrete invasive weed optimization and Genetic algorithm are presented. Tuning the parameters of the algorithms are performed by regression approach. In this approach, equation of fitness is found in terms of the parameters and the best value of the parameters is found in a way that the equation is minimized. Performance of the algorithms for solving the IRP is compared with statistical and multi-attribute decision making approach in terms of computational time and quality of solutions.

Keywords: Genetic algorithm; Discrete invasive weed optimization; Inventory routing; Meta-heuristic (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (4)

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DOI: 10.1007/s10845-018-1393-z

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