Solving a multi-objective job shop scheduling problem with sequence-dependent setup times by a Pareto archive PSO combined with genetic operators and VNS

Tavakkoli–Moghaddam, Reza; Azarkish, M.; Barkousaraie, Azar Sadeghnejad

doi:10.1007/s00170-010-2847-4

Cited by 40 publications

(9 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…It is similar to genetic algorithms. PSO is performed by a population (called swarm) of individuals (called particles) that are updated from iteration to iteration [24,25]. The particle of improved PSO-SVM is composed of two parts, c and  .…”

Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network

et al. 2014

View full text Add to dashboard Cite

Abstract:In electric power systems, power cable operation under normal conditions is very important. Various cable faults will happen in practical applications. Recognizing the cable faults correctly and in a timely manner is crucial. In this paper we propose a method that an annealed chaotic competitive learning network recognizes power cable types. The result shows a good performance using the support vector machine (SVM) and improved Particle Swarm Optimization (IPSO)-SVM method. The experimental result shows that the fault recognition accuracy reached was 96.2%, using 54 data samples. The network training time is about 0.032 second. The method can achieve cable fault classification effectively.

show abstract

Section: Particle Swarm Optimization Algorithm (Pso)mentioning

confidence: 99%

Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network

et al. 2014

View full text Add to dashboard Cite

show abstract

“…Therefore, characteristics are added to the beginning of the Tabu list and exit from the end of it (finishing the Tabu tenure) [28]. In order to investigate the quality of the solutions generated by the proposed SS, the MSPSP is also solved by using the TS algorithm [29], as in The details of Figure 7 can be described as follows:…”

Section: Tabu Search (Ts)mentioning

confidence: 99%

Solving a novel multi-skilled project scheduling model by scatter search

Kazemipoor¹,

Tavakkoli–Moghaddam²,

Shahnazari-Shahrezaei³

2013

sajie

View full text Add to dashboard Cite

A multi-skilled project scheduling problem (MSPSP) has generally been presented to schedule information technology projects in deterministic conditions. The contribution of this model is to consider the resources, called 'staff members'. These members are regarded as valuable, renewable, and discrete resources with different multiple skills. The different skills of staff members, as well as the project network's activity requirement of different skills, cause this problem to become a special type of multi-mode resourceconstrained project scheduling problem (MM-RCPSP), with a huge number of modes. Taking into account the importance of this issue and the few studies performed on this problem, a novel mathematical model for the MSPSP is presented. Since the complexity of this problem is NP-hard, an efficient scatter search (SS) algorithm is developed to solve such a difficult problem. This proposed SS is capable of generating optimised solutions in small sizes, and the excellent solutions in large sizes are compared with the solutions reported by a proposed Tabu search (TS) algorithm. OPSOMMING'n Veeldoel projekskeduleringsvraagstuk (MSPSP) word voorgehou vir skedulering onder deterministiese toestande van inligtingstegnologieprojekte. Die model word aangewend vir hulpbronne genaamd "personeellede". Die bronne is waardevol, hernubaar, diskreet en beskik oor uiteenlopende vaardighede. Die eienskappe van die vraagstuk berus by uiteenlopendheid. Vir die voorafgaande omstandighede word 'n nuwe model geskep vir die hantering van eienaardighede. Aangesien die problem NP-moeilik is, word 'n doeltreffende spreisoek-algoritme gebruik met groot sukses.

show abstract

“…For this reason several works have recently tackled multi-objective job shop scheduling problems. A genetic algorithm for a multi-objective job shop scheduling problem that minimizes the mean weighted completion time and the sum of the weighted tardiness/earliness costs simultaneously was developed by Tavakkoli-Moghaddam et al [54].…”

Section: Job Shop Scheduling Problemmentioning

confidence: 99%

Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems

Ławrynowicz

2011

Foundations of Management

View full text Add to dashboard Cite

Genetic Algorithms for Solving Scheduling Problems in Manufacturing SystemsScheduling manufacturing operations is a complicated decision making process. From the computational point of view, the scheduling problem is one of the most notoriously intractable NP-hard optimization problems. When the manufacturing system is not too large, the traditional methods for solving scheduling problem proposed in the literature are able to obtain the optimal solution within reasonable time. But its implementation would not be easy with conventional information systems. Therefore, many researchers have proposed methods with genetic algorithms to support scheduling in the manufacturing system. The genetic algorithm belongs to the category of artificial intelligence. It is a very effective algorithm to search for optimal or near-optimal solutions for an optimization problem. This paper contains a survey of recent developments in building genetic algorithms for the advanced scheduling. In addition, the author proposes a new approach to the distributed scheduling in industrial clusters which uses a modified genetic algorithm.

show abstract

Solving a multi-objective job shop scheduling problem with sequence-dependent setup times by a Pareto archive PSO combined with genetic operators and VNS

Cited by 40 publications

References 38 publications

Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network

Power Cable Fault Recognition Based on an Annealed Chaotic Competitive Learning Network

Solving a novel multi-skilled project scheduling model by scatter search

Genetic Algorithms for Solving Scheduling Problems in Manufacturing Systems

Contact Info

Product

Resources

About