A variable neighborhood search heuristic algorithm for production routing problems

Qiu, Yuzhuo; Liang, Wang; Xu, Xiaoling; Fang, Xuanjing; Pardalos, Pãnos M.

doi:10.1016/j.asoc.2018.02.032

Cited by 70 publications

(19 citation statements)

References 25 publications

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“…The variable neighborhood search (VNS), introduced by Mladenovic and Hansen [ 29 ], has effective results in solving combination problems [ 30 – 33 ]. In this section, we propose a VNS heuristic to solve the proposed problem, which uses the ideas from the bi-level programing formulations proposed in Sect.…”

Section: The Variable Neighborhood Searchmentioning

confidence: 99%

A variable neighborhood search for the last-mile delivery problem during major infectious disease outbreak

et al. 2021

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Section: The Variable Neighborhood Searchmentioning

confidence: 99%

A variable neighborhood search for the last-mile delivery problem during major infectious disease outbreak

et al. 2021

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“…Then, several heuristics have been developed for its www.ijacsa.thesai.org resolution. Without being exhaustive, there are the Greedy Randomized Adaptive Search Procedure (GRASP) [6], [7], the Adaptive Large Neighborhood Search (ALNS) procedure [8], [9], the Tabu research (TS) [10], the Variable Neighborhood Search (VNS) [11], the Memetic Algorithm [12] ,the heuristic based on Mixed Integer Programming (MIP) and iterative MIP [13], [14] .Decomposition heuristics are resolution approaches that consist in dividing the problem into several subproblems. the subproblems are then solved sequentially.…”

Section: Literature Reviewsmentioning

confidence: 99%

Managing an External Depot in a Production Routing Problem

Kayé¹,

Diaby²,

N'Takpé³

et al. 2020

IJACSA

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This paper addresses a production and distribution problem in a supply chain. The supply chain consists of a plant with no storage capacity that produces only one type of product. The manufactured products are then transported to a depot for storage. Customers demand is met by a homogeneous fleet of vehicles that begins and ends their trips at the depot. The objective of the study is to minimize the overall cost of production, inventory and transport throughout the supply chain. A Branch-and-Cut and a hybrid Two Phases Decomposition Heuristic using a Mixed Integer Programming and a Genetic Algorithm have been developed to solve the problem.

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“…Prosedur ini efektif untuk meningkatkan intensifikasi yang memakan waktu [10]. VNS efisien dalam meningkatkan hasil pencarian [11].…”

Section: Pendahuluanunclassified

Hyper-heuristik untuk Penyelesaian Masalah Optimasi Lintas Domain dengan Seleksi Heuristik berdasarkan Variable Neighborhood Search

Djunaidy

Angresti

Mukhlason

2019

khif

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Metode state-of-the-art untuk menyelesaikan permasalahan optimasi kombinatorik yang diketahui sebagai permasalahan non-deterministic polynomial hard (NP-hard) adalah meta-heuristics. Kelemahan dari metode meta-heuristics adalah dibutuhkanya parameter tuning yang spesifik untuk setiap problem domain berbeda. Hal ini menyebabkan pendekatan meta-heuristics kurang efektif untuk menyelesaikan permasalahan lintas problem domain. Untuk mengatasi permasalahan tersebut, muncul pendekatan baru, yaitu hyper-heuristics. Dengan meningkatkan level search space dari solution space ke low-level heuristics space, hyper-heuristics diharapkan dapat menjadi pendekatan yang lebih general dan efektif untuk menyelesaikan permasalahan lintas problem domain. Penelitian ini bertujuan untuk menginvestigasi performa algoritma variable neighborhood search (VNS) sebagai strategi untuk memilih low-level heuristics dalam kerangka kerja hyper-heuristics. Hal ini berbeda dengan penelitian-penelitian sebelumnya di mana VNS digunakan dalam kerangka kerja meta-heuristics. Metode yang diusulkan dalam penelitian ini diuji coba pada 6 problem domain yang berbeda, yaitu satisfiability (SAT), one dimensional bin packing, permutation flow shop, personel scheduling, travelling salesman problem (TSP), dan vehicle routing problem (VRP). Hasil komputasi menunjukkan bahwa metode VNS ini memiliki performa lebih baik dibandingkan dengan metode seleksi low-level heuristics pembanding, yaitu Simple Random. Secara lebih spesifik, VNS lebih unggul pada 5 dari 6 problem domain.

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A variable neighborhood search heuristic algorithm for production routing problems

Cited by 70 publications

References 25 publications

A variable neighborhood search for the last-mile delivery problem during major infectious disease outbreak

A variable neighborhood search for the last-mile delivery problem during major infectious disease outbreak

Managing an External Depot in a Production Routing Problem

Hyper-heuristik untuk Penyelesaian Masalah Optimasi Lintas Domain dengan Seleksi Heuristik berdasarkan Variable Neighborhood Search

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