The Self-Learning Particle Swarm Optimization approach for routing pickup and delivery of multiple products with material handling in multiple cross-docks

2016 ◽  
Vol 91 ◽  
pp. 208-226 ◽  
Author(s):  
Mu-Chen Chen ◽  
Yu-Hsiang Hsiao ◽  
Reddivari Himadeep Reddy ◽  
Manoj Kumar Tiwari
Keyword(s):  
Particle Swarm Optimization ◽  
Material Handling ◽  
Particle Swarm ◽  
The Self ◽  
Pickup And Delivery ◽  
Optimization Approach ◽  
Swarm Optimization ◽  
Multiple Products ◽  
Multiple Cross ◽  
Self Learning

A Self-Learning Particle Swarm Optimization for Robust Multi-Echelon Capacitated Location–Allocation–Inventory Problem

2019 ◽  
Vol 18 (04) ◽  
pp. 677-694 ◽  
Author(s):  
Erfan Babaee Tirkolaee ◽  
Javad Mahmoodkhani ◽  
Mehdi Ranjbar Bourani ◽  
Reza Tavakkoli-Moghaddam
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Upper And Lower Bounds ◽  
Mixed Integer ◽  
Optimization Approach ◽  
Inventory Problem ◽  
Swarm Optimization ◽  
Location Allocation ◽  
Self Learning ◽  
Capacitated Location

This paper addresses a multi-echelon capacitated location–allocation–inventory problem under uncertainty by providing a robust mixed integer linear programming (MILP) model considering production plants at level one, central warehouses at level two, and the retailers at level three in order to design an optimal supply chain network. In this model, the retailer’s demand parameter is uncertain and just its upper and lower bounds within an interval are known. In order to deal with this uncertainty, a robust optimization approach is used. Then, a self-learning particle swarm optimization (SLPSO) algorithm is developed to solve the problem. The results show that the proposed algorithm outperforms the exact method by providing high quality solutions in the reasonable amount of computational runtime.

scholarly journals A Novel Particle Swarm Optimization with Improved Learning Strategies and Its Application to Vehicle Path Planning

2019 ◽  
Vol 2019 ◽  
pp. 1-16 ◽  
Author(s):  
En Lu ◽  
Lizhang Xu ◽  
Yaoming Li ◽  
Zheng Ma ◽  
Zhong Tang ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Path Planning ◽  
Learning Strategies ◽  
Learning Strategy ◽  
Particle Swarm ◽  
The Self ◽  
Swarm Optimization ◽  
Vehicle Path ◽  
Improved Learning ◽  
Self Learning

In order to balance the exploration and exploitation capabilities of the PSO algorithm to enhance its robustness, this paper presents a novel particle swarm optimization with improved learning strategies (ILSPSO). Firstly, the proposed ILSPSO algorithm uses a self-learning strategy, whereby each particle stochastically learns from any better particles in the current personal history best position (pbest), and the self-learning strategy is adjusted by an empirical formula which expresses the relation between the learning probability and evolution iteration number. The cognitive learning part is improved by the self-learning strategy, and the optimal individual is reserved to ensure the convergence speed. Meanwhile, based on the multilearning strategy, the global best position (gbest) of particles is replaced with randomly chosen from the top k of gbest and further improve the population diversity to prevent premature convergence. This strategy improves the social learning part and enhances the global exploration capability of the proposed ILSPSO algorithm. Then, the performance of the ILSPSO algorithm is compared with five representative PSO variants in the experiments. The test results on benchmark functions demonstrate that the proposed ILSPSO algorithm achieves significantly better overall performance and outperforms other tested PSO variants. Finally, the ILSPSO algorithm shows satisfactory performance in vehicle path planning and has a good result on the planned path.

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