Material Scheduling with Storage Windows Based on Modified Particle Swarm Algorithm for Material Manufacturing System

2012 ◽  
Vol 252 ◽  
pp. 343-348
Author(s):  
Xin Min Zhang ◽  
Su Zhang
Keyword(s):  
Manufacturing System ◽  
Time Windows ◽  
Particle Swarm ◽  
Superior Performance ◽  
Particle Swarm Algorithm ◽  
Delivery Time ◽  
Routing Problem ◽  
Modified Particle Swarm Optimization ◽  
Automobile Assembly ◽  
Low Efficiency

In order to solve the logistics problems in replenishment delay, low efficiency, high delivery time cost occurred on the side of automobile assembly line, a combination of “Workstation - Supermarket” is proposed to optimize vehicle scheduling. Storage windows are firstly proposed to replace pervious time windows. In this paper, based on analyzing the JIT material flow of line side, an optimization scheduling model with storage windows is established by taking the minimum of total delivery time as the objective function. A modified particle swarm optimization algorithm (MPSO) is introduced to solve the model. The result of simulation showed that the MPSO has superior performance on the proposed vehicle routing problem with storage windows (VRPSW). Furthermore, it is shown that MPSO is more efficiently on VRP in material manufacturing system.

scholarly journals Application of Strongly Constrained Space Particle Swarm Optimization to Optimal Operation of a Reservoir System

2018 ◽  
Vol 10 (12) ◽  
pp. 4445 ◽  
Author(s):  
Lejun Ma ◽  
Huan Wang ◽  
Baohong Lu ◽  
Changjun Qi
Keyword(s):  
Particle Swarm Optimization ◽  
Computational Efficiency ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Optimal Operation ◽  
Computational Time ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Swarm Algorithm ◽  
Low Efficiency

In view of the low efficiency of the particle swarm algorithm under multiple constraints of reservoir optimal operation, this paper introduces a particle swarm algorithm based on strongly constrained space. In the process of particle optimization, the algorithm eliminates the infeasible region that violates the water balance in order to reduce the influence of the unfeasible region on the particle evolution. In order to verify the effectiveness of the algorithm, it is applied to the calculation of reservoir optimal operation. Finally, this method is compared with the calculation results of the dynamic programming (DP) and particle swarm optimization (PSO) algorithm. The results show that: (1) the average computational time of strongly constrained particle swarm optimization (SCPSO) can be thought of as the same as the PSO algorithm and lesser than the DP algorithm under similar optimal value; and (2) the SCPSO algorithm has good performance in terms of finding near-optimal solutions, computational efficiency, and stability of optimization results. SCPSO not only improves the efficiency of particle evolution, but also avoids excessive improvement and affects the computational efficiency of the algorithm, which provides a convenient way for particle swarm optimization in reservoir optimal operation.

Solving a large multi-product production-routing problem with delivery time windows

Omega ◽  
2019 ◽  
Vol 86 ◽  
pp. 154-172 ◽  
Author(s):  
Fábio Neves-Moreira ◽  
Bernardo Almada-Lobo ◽  
Jean-François Cordeau ◽  
Luís Guimarães ◽  
Raf Jans
Keyword(s):  
Time Windows ◽  
Delivery Time ◽  
Routing Problem ◽  
Production Routing

A Novel Particle Swarm Algorithm to Optimize QoS Unicast Routing

2011 ◽  
Vol 230-232 ◽  
pp. 377-383 ◽  
Author(s):  
An Xin Ye ◽  
Jian Bin Wu
Keyword(s):  
Particle Swarm Optimization Algorithm ◽  
Routing Algorithm ◽  
Particle Swarm ◽  
Particle Swarm Algorithm ◽  
The Novel ◽  
Routing Problem ◽  
Unicast Routing ◽  
Np Complete ◽  
Chaotic Particle Swarm Optimization ◽  
Searching Ability

Unicast routing service is becoming a important requirement of computer networks supporting multimedia applications. And unicast routing problem has been demonstrated technically as a NP-complete. This paper proposes a novel QoS-based unicast routing algorithm using the Chaotic Particle Swarm Optimization algorithm (CPSO).The algorithm enhance the global searching ability when some particles have trapped in local minimums by chaotic series .The novel algorithm makes use of the ergodicity of chaotic search to improve the capability of precise search and keep the balance between the global search and the local search. The result of QoS shows that the CPSO algorithm has the advantage over the conventional algorithms in efficiency.

Diagnosis for Vibration Fault of Steam Turbine Based on Modified Particle Swarm Optimization Support Vector Machine

2011 ◽  
Vol 128-129 ◽  
pp. 113-116 ◽  
Author(s):  
Zhi Biao Shi ◽  
Quan Gang Song ◽  
Ming Zhao Ma
Keyword(s):  
Support Vector Machine ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Steam Turbine ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Support Vector ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Modified Particle Swarm Optimization

Due to the influence of artificial factor and slow convergence of particle swarm algorithm (PSO) during parameters selection of support vector machine (SVM), this paper proposes a modified particle swarm optimization support vector machine (MPSO-SVM). A Steam turbine vibration fault diagnosis model was established and the failure data was used in fault diagnosis. The results of application show the model can get automatic optimization about the related parameters of support vector machine and achieve the ideal optimal solution globally. MPSO-SVM strategy is feasible and effective compared with traditional particle swarm optimization support vector machine (PSO-SVM) and genetic algorithm support vector machine (GA-SVM).

The Application of Quantum-Behaved Particle Swarm Algorithm in Intelligent Test Paper Generation

2013 ◽  
Vol 694-697 ◽  
pp. 2378-2382 ◽  
Author(s):  
Xin Ran Li
Keyword(s):  
Particle Swarm ◽  
Optimal Solution ◽  
Particle Aggregation ◽  
Particle Swarm Algorithm ◽  
Test Paper ◽  
Inertia Weight ◽  
Local Optimal Solution ◽  
Swarm Algorithm ◽  
Low Efficiency

Aiming at solving the low efficiency and low quality of the existing test paper generation algorithm, this paper proposes an improved particle swarm algorithm, a new algorithm for intelligent test paper generation. Firstly, the paper conducts mathematically modeling based on item response theory. Secondly, in the new algorithm, the inertia weight is expressed as functions of particle evolution velocity and particle aggregation by defining particle evolution velocity and particle aggregation so that the inertia weight has adaptability. At the same time, slowly varying function is introduced to the traditional location updating formula so that the local optimal solution can be effectively overcome. Finally, simulation results show that compared with the quantum-behaved particle swarm algorithm, the proposed algorithm has better performance in success rate and composing efficiency.

scholarly journals Penyelesaian Urban Transit Routing Problem Menggunakan Algoritma Hyper-heuristics berbasis Modified Particle Swarm Optimization based on Gravitational field Interactions

2021 ◽  
Vol 8 (4) ◽  
pp. 1984-1997
Author(s):  
Shof Rijal Ahlan Robbani
Keyword(s):  
Particle Swarm Optimization ◽  
Gravitational Field ◽  
Public Transport ◽  
Particle Swarm ◽  
Hill Climbing ◽  
Urban Transit ◽  
Swarm Optimization ◽  
Routing Problem ◽  
Modified Particle Swarm Optimization

Kemacetan lalu lintas dapat diatasi dengan adanya public transport. Penerapan public transport yang optimal perlu dilakukan penentuan rute yang baik. Untuk mendapatkan rute public transport yang optimal, maka perlu dilakukan beberapa percobaan kombinasi antara jarak titik awal dan tujuan. Sehingga masalah dapat dikatakan sebagai masalah kombinatorik. VRP merupakan permasalahan kombinatorik. Oleh karena itu permasalahan dapat diselesaikan menggunakan metode metaheuristik. Penelitian ini akan menggunakan algoritma Modified Particle Swarm Optimization (MPSO-GI) dengan pendekatan Hyper-heuristics untuk menyelesaikan masalah penentuan rute public transport. Data yang digunakan merupakan dataset Mumford dan Mandl yang digunakan pada beberapa penelitian sebelumnya. Penelitian dilakukan dengan membandingkan hasil solusi yang dihasilkan oleh metode yang ditawarkan dengan hasil pada penelitian sebelumnya. Sehingga dapat diketahui kelebihan dan kekurangan dari metode yang ditawarkan. Berdasarkan hasil uji coba dapat ketahui bahwa algoritma MPSO-GI dengan pendekatan Hyper-Heuristics dapat diimpelmentasikan dan menyelesaikan masalah UTRP. MPSO-GI dengan pendekatan Hyper-Heuristics berhasil memperbaiki solusi hill-climbing di hamper semua dataset dengan nilai yang stabil. Hasil metode MPSO-GI dengan pendekatan Hyper-Heuristics unggul dalam menghasilkan solusi biaya penumpang pada dataset Mandl4, Mandl6, Mandl7, Mandl8 dan biaya operator pada dataset Mandl4 dan Mandl6 jika dibandingkan dengan metode pada penelitian sebelumnya.

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