A multi-objective model for solar industry closed-loop supply chain by using particle swarm optimization

2014 ◽  
Author(s):  
YiWen Chen ◽  
Li-Chih Wang ◽  
Tzu-Li Chen ◽  
Allen Wang ◽  
Chen-Yeng Cheng
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Closed Loop ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Closed Loop Supply Chain ◽  
Multi Objective ◽  
Objective Model

Designing a Bi-Objective Closed-Loop Supply Chain Problem with Shortage and All Unit Discount: “Nondominated Sorting Genetic Algorithm II” and “Multi-Objective Particle Swarm Optimization”

2020 ◽  
Vol 19 (04) ◽  
pp. 701-736
Author(s):  
Masoomeh Vazifeh Pirnagh ◽  
Hamed Davari-Ardakani ◽  
Seyed Hamid Reza Pasandideh
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Closed Loop ◽  
Nsga Ii ◽  
Swarm Optimization ◽  
Closed Loop Supply Chain ◽  
The Real ◽  
Multi Objective

Nowadays, due to environmental issues, government rules and economic interests have increased attention to the collection and recovery of products, which has led to the formation of new concepts such as reverse and closed-loop supply chains. The implementation of the closed-loop supply chain as a solution to sustainable development is expanding from one hand and increasing the profitability of companies on the other. For this purpose, a mathematical model was developed to design an integrated closed-loop supply chain network, which is a combination of two-problem localization problems and flow optimization. The proposed model was designed to minimize network costs and to maximize the level of responsiveness to customers. The cost parameters of establishing centers in this model are uncertain; to overcome the model’s uncertainties, stochastic programming is used. In the mathematical model, supplier, manufacturer, distributor and customer in the direct supply chain and collection/rehabilitation, destruction, recycling centers and, second-type distribution center for sale of second-hand products as well as second-hand products customers in the reverse flow are considered, to be closer to the real today world. This model is multi-periodic mix integer nonlinear programming where the shortage has allowed. To motivate and encourage customers to buy more, in addition to getting closer to the real world and it happens more in practice, is considered all units of discount for transportation cost in the forward flow. To solve this model Non-Dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) is using. The parameter tuning was done using the Taguchi method. Then, the important criteria for measurement and comparison of performance algorithms have used, including the Mean Ideal Distance, Diversification Metric, Number of Pareto-optimal Solutions, and the Quality Metric. Results of the Comparative metrics show that NSGA-II outperforms MOPSO in almost all cases in achieving the best trade-off solutions.

scholarly journals A robust multi-objective model for managing the distribution of perishable products within a green closed-loop supply chain

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Maedeh Agahgolnezhad Gerdrodbari ◽  
Fatemeh Harsej ◽  
Mahboubeh Sadeghpour ◽  
Mohammad Molani Aghdam
Keyword(s):  
Supply Chain ◽  
Closed Loop ◽  
Perishable Products ◽  
Closed Loop Supply Chain ◽  
Multi Objective ◽  
Objective Model

scholarly journals Solving a multi-objective model of job rotation minimizing the chemical exposure and cost by particle swarm optimization

2019 ◽  
Vol 11 ◽  
pp. 184797901986783 ◽  
Author(s):  
Mahdi Maleki
Keyword(s):  
Particle Swarm Optimization ◽  
Harmful Effect ◽  
Particle Swarm ◽  
Chemical Exposure ◽  
Pso Algorithm ◽  
Job Rotation ◽  
Swarm Optimization ◽  
Multi Objective ◽  
Objective Model ◽  
Plastic Injection

Today in developing countries, perilous chemicals are widely used that have harmful effect on human resources. National and global organizations developed different approaches to control chemicals exposure and their consequences in that one of them is job rotation. Since job rotation is categorized as nondeterministic polynomial-time hardness problem, meta-heuristic methods are used to solve it such as particle swarm optimization (PSO) algorithm. In this article, because job rotation can have different and contradictory objectives, the multi-objective PSO (MOPSO) method with parallel vector evaluated PSO approach is implemented. At first, a new MOPSO algorithm is presented that solves job rotation problems to minimize chemical exposures and is finally compared with non-dominated sorting genetic algorithm. The achievement of this algorithm reduces chemicals exposure in manufacturing processes such as casting, welding, foam injection, plastic injection, which ensure workers’ health.

scholarly journals A multi-objective particle swarm optimization for production-distribution planning in supply chain network

2012 ◽  
Vol 2 (2) ◽  
pp. 603-614 ◽  
Author(s):  
alireza Pourrousta ◽  
Saleh dehbari ◽  
Reza Tavakkoli-Moghaddam ◽  
Mohsen sadegh amalnik
Keyword(s):  
Particle Swarm Optimization ◽  
Supply Chain ◽  
Particle Swarm ◽  
Supply Chain Network ◽  
Swarm Optimization ◽  
Distribution Planning ◽  
Multi Objective ◽  
Production Distribution
Sign in / Sign up

Export Citation Format

Share Document