Maximisation of total supply chain profit and minimisation of bullwhip effect in a multi-echelon supply chain network using particle swarm optimisation and genetic algorithm

2017 ◽  
Vol 11 (2/3) ◽  
pp. 236 ◽  
Author(s):  
Nafisa Mahbub ◽  
Ahsan Akhtar Hasin ◽  
Ridwan Al Aziz ◽  
Afia Sharin
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Particle Swarm ◽  
Bullwhip Effect ◽  
Particle Swarm Optimisation ◽  
Supply Chain Network

scholarly journals Hybrid Algorithms for Fuzzy Reverse Supply Chain Network Design

2014 ◽  
Vol 2014 ◽  
pp. 1-16 ◽  
Author(s):  
Z. H. Che ◽  
Tzu-An Chiang ◽  
Y. C. Kuo ◽  
Zhihua Cui
Keyword(s):  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Simulated Annealing ◽  
Decision Model ◽  
Particle Swarm ◽  
Hybrid Algorithms ◽  
Supply Chain Network ◽  
Reverse Supply Chain ◽  
Swarm Optimization

In consideration of capacity constraints, fuzzy defect ratio, and fuzzy transport loss ratio, this paper attempted to establish an optimized decision model for production planning and distribution of a multiphase, multiproduct reverse supply chain, which addresses defects returned to original manufacturers, and in addition, develops hybrid algorithms such as Particle Swarm Optimization-Genetic Algorithm (PSO-GA), Genetic Algorithm-Simulated Annealing (GA-SA), and Particle Swarm Optimization-Simulated Annealing (PSO-SA) for solving the optimized model. During a case study of a multi-phase, multi-product reverse supply chain network, this paper explained the suitability of the optimized decision model and the applicability of the algorithms. Finally, the hybrid algorithms showed excellent solving capability when compared with original GA and PSO methods.

Genetic Algorithm and Particle Swarm Optimization Techniques in Supply Chain Design Problems

2020 ◽  
pp. 425-438
Author(s):  
Md. Ashikur Rahman ◽  
Pandian M. Vasant ◽  
Junzo Watada ◽  
Rajalingam Al Sokkalingam
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Memetic Algorithms ◽  
Supply Chain Design ◽  
Network Design Problem ◽  
Future Research ◽  
Supply Chain Network ◽  
Design Problems

Metaheuristics has become a top research area. Numerous optimization problems have been solved by metaheuristics as they showed comprehensive improvements to solve these intractable optimization problems. Complex problems like supply chain design problems need strategic decisions, and metaheuristics can intensify the decisions while designing supply chain network. In this chapter, the authors have introduced how nature memetic algorithms (e.g., genetic algorithm and particle swarm algorithms) are implemented to solve supply chain network design problem. A discussion about the recent research in this field shows an important direction to the future research.

Solving a Two-stage Supply Chain Network Design Problem with Fixed Costs by a Hybrid Genetic Algorithm

2021 ◽  
Author(s):  
Ovidiu Cosma ◽  
Petrică C Pop ◽  
Cosmin Sabo
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Network Design ◽  
Design Problem ◽  
Optimization Problem ◽  
Hybrid Genetic Algorithm ◽  
Network Design Problem ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Fixed Costs

Abstract In this paper we investigate a particular two-stage supply chain network design problem with fixed costs. In order to solve this complex optimization problem, we propose an efficient hybrid algorithm, which was obtained by incorporating a linear programming optimization problem within the framework of a genetic algorithm. In addition, we integrated within our proposed algorithm a powerful local search procedure able to perform a fine tuning of the global search. We evaluate our proposed solution approach on a set of large size instances. The achieved computational results prove the efficiency of our hybrid genetic algorithm in providing high-quality solutions within reasonable running-times and its superiority against other existing methods from the literature.

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