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.

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.

Supply Chain Design Including Quality Considerations

2014 ◽  
pp. 102-140 ◽  
Author(s):  
Krystel K. Castillo-Villar ◽  
Neale R. Smith
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Simulated Annealing ◽  
Network Design ◽  
Parameter Tuning ◽  
Supply Chain Design ◽  
Background Information ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Solution Methods

This chapter introduces the reader to Supply Chain Network Design (SCND) models that include the Cost Of Quality (COQ) among the relevant costs. In contrast to earlier models, the COQ is computed internally as a function of decisions taken as part of the design of the supply chain. Earlier models assume exogenously given COQ functions. Background information is provided on previous COQ modeling and on supply chain network design models. The authors’ COQ modeling is described in detail as is the SCND model that incorporates COQ. The COQ modeling includes prevention, appraisal, and both internal and external failure costs. Solution methods based on metaheuristics such as simulated annealing and the genetic algorithm are provided, including details on parameter tuning and computational testing. A genetic algorithm was found to yield the best results, followed by the simulated annealing approach. Topics for further research are provided as well as an extensive list of references for further reading.

scholarly journals An Efficient Hybrid Genetic Approach for Solving the Two-Stage Supply Chain Network Design Problem with Fixed Costs

Mathematics ◽  
2020 ◽  
Vol 8 (5) ◽  
pp. 712
Author(s):  
Ovidiu Cosma ◽  
Petrică C. Pop ◽  
Cosmin Sabo
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Optimization Problem ◽  
Hybrid Genetic Algorithm ◽  
Network Design Problem ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Fixed Costs ◽  
Distribution Centers ◽  
Two Stage

This paper deals with a complex optimization problem, more specifically the two-stage transportation problem with fixed costs. In our investigated transportation problem, we are modeling a distribution network in a two-stage supply chain. The considered two-stage supply chain includes manufacturers, distribution centers, and customers, and its principal feature is that in addition to the variable transportation costs, we have fixed costs for the opening of the distribution centers, as well as associated with the routes. In this paper, we describe a different approach for solving the problem, which is an effective hybrid genetic algorithm. Our proposed hybrid genetic algorithm is constructed to fit the challenges of the investigated supply chain network design problem, and it is achieved by incorporating a linear programming optimization problem within the framework of a genetic algorithm. Our achieved computational results are compared with the existing solution approaches on a set of 150 benchmark instances from the literature and on a set of 50 new randomly generated instances of larger sizes. The outputs proved that we have developed a very competitive approach as compared to the methods that one can find in the literature.

scholarly journals A Multimodal Smart Quantum Particle Swarm Optimization for Electromagnetic Design Optimization Problems

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4613
Author(s):  
Shah Fahad ◽  
Shiyou Yang ◽  
Rehan Ali Khan ◽  
Shafiullah Khan ◽  
Shoaib Ahmed Khan
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Quantum Particle ◽  
Particle Swarm ◽  
Continuous Variables ◽  
Design Problems ◽  
Swarm Optimization ◽  
Electromagnetic Design ◽  
Quantum Particle Swarm Optimization ◽  
Engineering Design Problems

Electromagnetic design problems are generally formulated as nonlinear programming problems with multimodal objective functions and continuous variables. These can be solved by either a deterministic or a stochastic optimization algorithm. Recently, many intelligent optimization algorithms, such as particle swarm optimization (PSO), genetic algorithm (GA) and artificial bee colony (ABC), have been proposed and applied to electromagnetic design problems with promising results. However, there is no universal algorithm which can be used to solve engineering design problems. In this paper, a stochastic smart quantum particle swarm optimization (SQPSO) algorithm is introduced. In the proposed SQPSO, to tackle the premature convergence problem in order to improve the global search ability, a smart particle and a memory archive are adopted instead of mutation operations. Moreover, to enhance the exploration searching ability, a new set of random numbers and control parameters are introduced. Experimental results validate that the adopted control policy in this work can achieve a good balance between exploration and exploitation. Finally, the SQPSO has been tested on well-known optimization benchmark functions and implemented on the electromagnetic TEAM workshop problem 22. The simulation result shows an outstanding capability of the proposed algorithm in speeding convergence compared to other algorithms.

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