scholarly journals Algoritmo Híbrido para o Problema Flow Shop de Permutação Multiobjetivo

2020 ◽  
Author(s):  
Volmir Fiorini Júnior ◽  
Carolina Almeida ◽  
Sandra Venske
Keyword(s):  
Flow Shop ◽  
Np Hard ◽  
Nsga Ii ◽  
Total Flowtime

Os algoritmos evolucionários são uma abordagem não determinística para resolver problemas de otimização que não podem ser resolvidos em tempo polinomial, como problemas clássicos NP-Hard. O Flow Shop de Permutação (FSP) é um problema de otimização combinatória do ambiente de produção, em que tarefas devem ser processadas por máquinas, mantendo o mesmo fluxo de processamento. Neste trabalho a abordagem multiobjetivo foi utilizada para o FSP, tendo como objetivos de minimização o makespan e o total flowtime. Dois algoritmos híbridos compostos por NSGA-II com Busca Tabu foram considerados na abordagem e aplicados em 11 instâncias do FSP com diferentes dimensões. Uma análise foi feita sobre o uso de regras de proibição na Busca Tabu e sua restritividade. Os resultados foram analisados utilizando as métricas de qualidade IGD e Função de Conquista Empírica, comparando-os com o NSGA-II canônico.

scholarly journals An evaluation of three DoE-guided meta-heuristic-based solution methods for a three-echelon sustainable distribution network

2020 ◽  
Vol 296 (1-2) ◽  
pp. 421-469
Author(s):  
Sahar Validi ◽  
Arijit Bhattacharya ◽  
P. J. Byrne
Keyword(s):  
Distribution Network ◽  
Np Hard ◽  
Nsga Ii ◽  
Solution Approach ◽  
Logistics Network ◽  
Location Routing ◽  
Solution Methods ◽  
Order Preference ◽  
Operational Constraints ◽  
Transportation Routes

AbstractThis article evaluates the efficiency of three meta-heuristic optimiser (viz. MOGA-II, MOPSO and NSGA-II)-based solution methods for designing a sustainable three-echelon distribution network. The distribution network employs a bi-objective location-routing model. Due to the mathematically NP-hard nature of the model a multi-disciplinary optimisation commercial platform, modeFRONTIER®, is adopted to utilise the solution methods. The proposed Design of Experiment (DoE)-guided solution methods are of two phased that solve the NP-hard model to attain minimal total costs and total CO2 emission from transportation. Convergence of the optimisers are tested and compared. Ranking of the realistic results are examined using Pareto frontiers and the Technique for Order Preference by Similarity to Ideal Solution approach, followed by determination of the optimal transportation routes. A case of an Irish dairy processing industry’s three-echelon logistics network is considered to validate the solution methods. The results obtained through the proposed methods provide information on open/closed distribution centres (DCs), vehicle routing patterns connecting plants to DCs, open DCs to retailers and retailers to retailers, and number of trucks required in each route to transport the products. It is found that the DoE-guided NSGA-II optimiser based solution is more efficient when compared with the DoE-guided MOGA-II and MOPSO optimiser based solution methods in solving the bi-objective NP-hard three-echelon sustainable model. This efficient solution method enable managers to structure the physical distribution network on the demand side of a logistics network, minimising total cost and total CO2 emission from transportation while satisfying all operational constraints.

scholarly journals Analisis Perbandingan Aggregat Of Function (AOF) dengan Non-Dominated Sorting Genetic Algorithm (NSGA-II) dalam Menentukan Optimasi Multi-Objective pada Penjadwalan Mesin Produksi Flow Shop

2018 ◽  
Vol 17 (2) ◽  
pp. 158
Author(s):  
Fifin Sonata ◽  
Dede Prabowo Wiguna
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Total Tardiness ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Trade Off ◽  
Multi Objective

Penjadwalan mesin produksi dalam dunia industri memiliki peranan penting sebagai bentuk pengambilan keputusan. Salah satu jenis sistem penjadwalan mesin produksi adalah sistem penjadwalan mesin produksi tipe flow shop. Dalam penjadwalan flow shop, terdapat sejumlah pekerjaan (job) yang tiap-tiap job memiliki urutan pekerjaan mesin yang sama. Optimasi penjadwalan mesin produksi flow shop berkaitan dengan penyusunan penjadwalan mesin yang mempertimbangkan 2 objek yaitu makespan dan total tardiness. Optimasi kedua permasalahan tersebut merupakan optimasi yang bertolak belakang sehingga diperlukan model yang mengintegrasikan permasalahan tersebut dengan optimasi multi-objective A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimazitaion : NSGA-II. Dalam penelitian ini akan dibandingkan 2 buah metode yaitu Aggregat Of Function (AOF) dengan NSGA-II agar dapat terlihat nilai solusinya. Penyelesaian penjadwalan mesin produksi flow shop dengan algoritma NSGA-II untuk membangun jadwal dengan meminimalkan makespan dan total tardiness.Tujuan yang ingin dicapai adalah mengetahui bahwa model yang dikembangkan akan memberikan solusi penjadwalan mesin produksi flow shop yang efisien berupa solusi pareto optimal yang dapat memberikan sekumpulan solusi alternatif bagi pengambil keputusan dalam membuat penjadwalan mesin produksi yang diharapkan. Solusi pareto optimal yang dihasilkan merupakan solusi optimasi multi-objective yang optimal dengan trade-off terhadap seluruh objek, sehingga seluruh solusi pareto optimal sama baiknya.

Iterated Local Search and Other Algorithms for Buffered Two-Machine Permutation Flow Shops with Constant Processing Times on One Machine

2020 ◽  
pp. 1-25
Author(s):  
Hoang Thanh Le ◽  
Philine Geser ◽  
Martin Middendorf
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Iterated Local Search ◽  
Flow Shop Scheduling ◽  
Np Hard ◽  
Ant Colony Optimization Algorithm ◽  
Solution Quality ◽  
Processing Times ◽  
One Machine ◽  
Special Case

The two-machine permutation flow shop scheduling problem with buffer is studied for the special case that all processing times on one of the two machines are equal to a constant c. This case is interesting because it occurs in various applications, e.g., when one machine is a packing machine or when materials have to be transported. Different types of buffers and buffer usage are considered. It is shown that all considered buffer flow shop problems remain NP-hard for the makespan criterion even with the restriction to equal processing times on one machine. However, the special case where the constant c is larger or smaller than all processing times on the other machine is shown to be polynomially solvable by presenting an algorithm (2BF-OPT) that calculates optimal schedules in [Formula: see text] steps. Two heuristics for solving the NP-hard flow shop problems are proposed: i) a modification of the commonly used NEH heuristic (mNEH) and ii) an Iterated Local Search heuristic (2BF-ILS) that uses the mNEH heuristic for computing its initial solution. It is shown experimentally that the proposed 2BF-ILS heuristic obtains better results than two state-of-the-art algorithms for buffered flow shop problems from the literature and an Ant Colony Optimization algorithm. In addition, it is shown experimentally that 2BF-ILS obtains the same solution quality as the standard NEH heuristic, however, with a smaller number of function evaluations.

Study on Multi-Objective Optimization of Hybrid Flow Shop Scheduling Problem

2014 ◽  
Vol 1082 ◽  
pp. 529-534
Author(s):  
Zheng Ying Lin ◽  
Wei Zhang
Keyword(s):  
Flow Shop ◽  
Economic Benefits ◽  
Flow Shop Scheduling ◽  
Hybrid Flow Shop ◽  
Scheduling Problem ◽  
Multi Objective Optimization ◽  
Nsga Ii ◽  
Shop Scheduling ◽  
Multi Objective ◽  
Hybrid Flow Shop Scheduling

Due to several mutual conflicting optimized objectives in the hybrid flow shop scheduling problem, its optimized model, including three objectives of make-span, flow-time and tardiness, was firstly set up, instead of the single optimized objective. Furthermore, in order to improve the optimized efficiency and parallelism, after comparing the normal multi-objective optimized methods, an improved NSGA-II algorithm with external archive strategy was proposed. Finally, taking a piston production line as example, its performance was tested. The result showed that the multi-objective optimization of hybrid flow shop scheduling based on improved NSGA-II provided managers with a set of feasible solutions for selection in accordance to their own preference. Therefore the decision could be made more scientific and efficient, and thus brings to the factory more economic benefits.

Preemptive scheduling in a two-stage multiprocessor flow shop is NP-hard

1996 ◽  
Vol 89 (1) ◽  
pp. 172-175 ◽  
Author(s):  
J.A. Hoogeveen ◽  
J.K. Lenstra ◽  
B. Veltman
Keyword(s):  
Flow Shop ◽  
Preemptive Scheduling ◽  
Np Hard ◽  
Two Stage

scholarly journals Application of genetic algorithm to industrial scheduling and problems of parameters evaluation

2021 ◽  
Vol 47 ◽  
Author(s):  
Edgaras Šakurovas ◽  
Narimantas Listopadskis
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Flow Shop ◽  
Np Hard ◽  
Scheduling Problems ◽  
Optimal Parameters ◽  
Algorithm Performance ◽  
Hard Problems ◽  
Np Hard Problems ◽  
Real World Problems

Genetic algorithms are widely used in various mathematical and real world problems. They are approximate metaheuristic algorithms, commonly used for solving NP-hard problems in combinatorial optimisation. Industrial scheduling is one of the classical NP-hard problems. We analyze three classical industrial scheduling problems: job-shop, flow-shop and open-shop. Canonical genetic algorithm is applied for those problems varying its parameters. We analyze some aspects of parameters such as selecting optimal parameters of algorithm, influence on algorithm performance. Finally, three strategies of algorithm – combination of parameters and new conceptualmodel of genetic algorithm are proposed.

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