Development of a modified biogeography-based optimisation tool for solving the unequal-sized machine and multi-row configuration facility layout design problem

Abstract

An effective layout can reduce material flow distances and manufacturing lead-times, whilst increasing productivity, throughput and cost effectiveness. The facilities layout problem (FLP) is a non-deterministic polynomialtime hard problem, which means that the computational time taken to produce solutions increases exponentially with problem size. Metaheuristics are particularly suitable for solving such problems in reasonable time. Biogeographybased optimisation (BBO) is a well-known nature-inspired computing metaheuristic. Its mechanisms mimic an analogy with biogeography which relates to the migration, mutation and geographical distribution of biological organisms. This paper presents a novel BBO optimisation tool that solves the unequal area facilities layout problem to generate multirow solutions that minimise the total material flow distance. Two novel modifications were made to the conventional BBO: the use of a Genetic Algorithm crossover operator in the migration process; and a changed method for selecting candidate solutions. The local search approaches used data on flow intensities and machine adjacencies. Experiments were conducted using five benchmark datasets obtained from the literature. The statistical analysis of the computational results indicated that the proposed mBBOs produced statistically better solutions than the conventional BBO and other metaheuristics for all datasets and converged more rapidly with comparable execution times.

Bi-Objective Optimization for Multi-Row Facility Layout Problem Integrating Automated Guided Vehicle Path

“…Fischer et al [29] presented new mixed-integer linear programming models for the MRLP. Sooncharoen et al [30] presented a novel biogeography-based optimization tool to solve the unequal area MRLP to minimize the total material flow distance. Ahmadi et al [31] carried out a literature review on MRLP, and presented the applications, essential features, approaches, and resolution methodologies on MRLP.…”

İki̇ Amaçli Çok Sirali Yerleşi̇m Problemi̇ İçi̇n Bi̇r Di̇feransi̇yel Evri̇m Algori̇tmasi

“…Bu nedenle az sayıda çalışmada kesin çözüm yöntemi kullanılmıştır (Fischer ve diğ., 2019;Anjos ve Vieira, 2021;Anjos, Fischer ve Hungerländer, 2018;Hungerländer ve Anjos, 2015). Çok sıralı yerleşim literatürü incelendiğinde genetik algoritma (Safarzadeh ve Koosha, 2017;Lee, 1999;Ficko ve Brezocnik, 2004;Vitayasak, Pongcharoen ve Hicks, 2017), tavlama benzetimi (Tubaileh ve Siam, 2017), harmoni arama algoritması (Lenin ve Siva Kumar, 2021), arı koloni algoritması (Tubaileh ve Siam, 2017;Soimart ve Pongcharoen, 2011), parçacık sürü optimizasyonu algoritması (Hu ve Yang, 2019), değişken komşuluk arama algoritması (Herrán, Colmenar ve Duarte, 2021), öğretme-öğrenme tabanlı optimizasyon algoritması (Vitayasak ve Pongcharoen, 2018), guguk kuşu arama algoritması (Mahalingam ve Nagarajan, 2021), GRASP (Wan, Zuo, Li ve Zhao, 2022), biyocoğrafya temelli optimizasyon algoritması (Sooncharoen, Vitayasak, Pongcharoen ve Hicks, 2022) metasezgisel çözüm yaklaşımlarının kullanıldığı görülmektedir.…”