scholarly journals An Algorithm for Generating a Dispersed Population of Feasible Schedules for Flexible Job Shop Problems

2017 ◽  
Vol 15 (3) ◽  
pp. 16-19
Author(s):  
L. Kirilov ◽  
V. Guliashki
Keyword(s):  
Job Shop ◽  
Flexible Job Shop ◽  
Np Hard ◽  
Important Class ◽  
Scheduling Problems ◽  
Hard Problems ◽  
Np Hard Problems

Abstract The flexible job shop problems (FJSP) are an important class of scheduling problems and they have a significant practical value. Unfortunately it is not easy to solve job shop problems and in particular FJSPs because they are NP-hard problems. In this paper we propose a method for generating a set of feasible schedules for a given FJSP.

scholarly journals Application of genetic algorithm to industrial scheduling and problems of parameters evaluation

2021 ◽  
Vol 47 ◽  
Author(s):  
Edgaras Šakurovas ◽  
Narimantas Listopadskis
Keyword(s):  
Genetic Algorithm ◽  
Job Shop ◽  
Flow Shop ◽  
Np Hard ◽  
Scheduling Problems ◽  
Optimal Parameters ◽  
Algorithm Performance ◽  
Hard Problems ◽  
Np Hard Problems ◽  
Real World Problems

Genetic algorithms are widely used in various mathematical and real world problems. They are approximate metaheuristic algorithms, commonly used for solving NP-hard problems in combinatorial optimisation. Industrial scheduling is one of the classical NP-hard problems. We analyze three classical industrial scheduling problems: job-shop, flow-shop and open-shop. Canonical genetic algorithm is applied for those problems varying its parameters. We analyze some aspects of parameters such as selecting optimal parameters of algorithm, influence on algorithm performance. Finally, three strategies of algorithm – combination of parameters and new conceptualmodel of genetic algorithm are proposed.

scholarly journals Improved Differential Evolution Algorithm for Flexible Job Shop Scheduling Problems

2019 ◽  
Vol 24 (3) ◽  
pp. 80 ◽  
Author(s):  
Prasert Sriboonchandr ◽  
Nuchsara Kriengkorakot ◽  
Preecha Kriengkorakot
Keyword(s):  
Differential Evolution ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Differential Evolution Algorithm ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling ◽  
Relative Errors ◽  
Improved Differential Evolution Algorithm

This research project aims to study and develop the differential evolution (DE) for use in solving the flexible job shop scheduling problem (FJSP). The development of algorithms were evaluated to find the solution and the best answer, and this was subsequently compared to the meta-heuristics from the literature review. For FJSP, by comparing the problem group with the makespan and the mean relative errors (MREs), it was found that for small-sized Kacem problems, value adjusting with “DE/rand/1” and exponential crossover at position 2. Moreover, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 3.25. For medium-sized Brandimarte problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave a mean relative error of 7.11. For large-sized Dauzere-Peres and Paulli problems, value adjusting with “DE/best/2” and exponential crossover at position 2 gave an MRE of 4.20. From the comparison of the DE results with other methods, it was found that the MRE was lower than that found by Girish and Jawahar with the particle swarm optimization (PSO) method (7.75), which the improved DE was 7.11. For large-sized problems, it was found that the MRE was lower than that found by Warisa (1ST-DE) method (5.08), for which the improved DE was 4.20. The results further showed that basic DE and improved DE with jump search are effective methods compared to the other meta-heuristic methods. Hence, they can be used to solve the FJSP.

Multiobjective Optimization for Complex Flexible Job-Shop Scheduling Problems

2021 ◽  
Author(s):  
Karim Tamssaouet ◽  
Stéphane Dauzère-Pérès ◽  
Sebastian Knopp ◽  
Abdel Bitar ◽  
Claude Yugma
Keyword(s):  
Multiobjective Optimization ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Flexible Job Shop ◽  
Scheduling Problems ◽  
Flexible Job Shop Scheduling ◽  
Shop Scheduling

scholarly journals A Dispatching-Fuzzy AHP-TOPSIS Model for Scheduling Flexible Job-Shop Systems in Industry 4.0 Context

2021 ◽  
Vol 11 (11) ◽  
pp. 5107
Author(s):  
Miguel Ortíz-Barrios ◽  
Antonella Petrillo ◽  
Fabio De Felice ◽  
Natalia Jaramillo-Rueda ◽  
Genett Jiménez-Delgado ◽  
...  
Keyword(s):  
Job Shop ◽  
Selection Process ◽  
Fuzzy Ahp ◽  
Flexible Job Shop ◽  
Criteria Weights ◽  
Dispatching Algorithm ◽  
Scheduling Method ◽  
Hard Problems ◽  
Fourth Step

Scheduling flexible job-shop systems (FJSS) has become a major challenge for different smart factories due to the high complexity involved in NP-hard problems and the constant need to satisfy customers in real time. A key aspect to be addressed in this particular aim is the adoption of a multi-criteria approach incorporating the current dynamics of smart FJSS. Thus, this paper proposes an integrated and enhanced method of a dispatching algorithm based on fuzzy AHP (FAHP) and TOPSIS. Initially, the two first steps of the dispatching algorithm (identification of eligible operations and machine selection) were implemented. The FAHP and TOPSIS methods were then integrated to underpin the multi-criteria operation selection process. In particular, FAHP was used to calculate the criteria weights under uncertainty, and TOPSIS was later applied to rank the eligible operations. As the fourth step of dispatching the algorithm, the operation with the highest priority was scheduled together with its initial and final time. A case study from the smart apparel industry was employed to validate the effectiveness of the proposed approach. The results evidenced that our approach outperformed the current company’s scheduling method by a median lateness of 3.86 days while prioritizing high-throughput products for earlier delivery.

scholarly journals In some curved spaces, one can solve NP-hard problems in polynomial time

2009 ◽  
Vol 158 (5) ◽  
pp. 727-740 ◽  
Author(s):  
V. Kreinovich ◽  
M. Margenstern
Keyword(s):  
Polynomial Time ◽  
Np Hard ◽  
Curved Spaces ◽  
Hard Problems ◽  
Np Hard Problems

NP vs QMA_\log(2)

2010 ◽  
Vol 10 (1&2) ◽  
pp. 141-151
Author(s):  
S. Beigi
Keyword(s):  
Quantum Algorithms ◽  
Np Hard ◽  
Complete Problems ◽  
Hard Problems ◽  
Np Complete ◽  
Np Hard Problems

Although it is believed unlikely that $\NP$-hard problems admit efficient quantum algorithms, it has been shown that a quantum verifier can solve NP-complete problems given a "short" quantum proof; more precisely, NP\subseteq QMA_{\log}(2) where QMA_{\log}(2) denotes the class of quantum Merlin-Arthur games in which there are two unentangled provers who send two logarithmic size quantum witnesses to the verifier. The inclusion NP\subseteq QMA_{\log}(2) has been proved by Blier and Tapp by stating a quantum Merlin-Arthur protocol for 3-coloring with perfect completeness and gap 1/24n^6. Moreover, Aaronson et al. have shown the above inclusion with a constant gap by considering $\widetilde{O}(\sqrt{n})$ witnesses of logarithmic size. However, we still do not know if QMA_{\log}(2) with a constant gap contains NP. In this paper, we show that 3-SAT admits a QMA_{\log}(2) protocol with the gap 1/n^{3+\epsilon}} for every constant \epsilon>0.

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