Neutrality in Flow Shop Scheduling Problems: Landscape Structure and Local Search

2013 ◽  
pp. 97-125
Keyword(s):  
Local Search ◽  
Landscape Structure ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Shop Scheduling

scholarly journals Optimal Results and Numerical Simulations for Flow Shop Scheduling Problems

2012 ◽  
Vol 2012 ◽  
pp. 1-9
Author(s):  
Tao Ren ◽  
Yuandong Diao ◽  
Xiaochuan Luo
Keyword(s):  
Local Search ◽  
Completion Time ◽  
Flow Shop ◽  
Asymptotic Optimality ◽  
Flow Shop Scheduling ◽  
Release Dates ◽  
Scheduling Problems ◽  
Improvement Strategy ◽  
Shop Scheduling ◽  
Flow Shop Problem

This paper considers them-machine flow shop problem with two objectives: makespan with release dates and total quadratic completion time, respectively. For Fm|rj|Cmax, we prove the asymptotic optimality for any dense scheduling when the problem scale is large enough. For Fm‖ΣCj2, improvement strategy with local search is presented to promote the performance of the classical SPT heuristic. At the end of the paper, simulations show the effectiveness of the improvement strategy.

Minimizing makespan and total flow time in permutation flow shop scheduling problems using modified gravitational emulation local search algorithm

2016 ◽  
Vol 232 (3) ◽  
pp. 534-545 ◽  
Author(s):  
R Sanjeev Kumar ◽  
KP Padmanaban ◽  
M Rajkumar
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Local Search Algorithm ◽  
Scheduling Problems ◽  
Total Flow ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

Permutation flow shop scheduling is a part of production scheduling problems. It allows “n” jobs to be processed on “m” machines. All the jobs are processed in all the machines, and the sequence of jobs being processed is the same in all the machines. It plays a vital role in both automated manufacturing industries and nondeterministic polynomial hard problem. Gravitational emulation local search algorithm is a randomization-based concept algorithm. It is used iteratively as the local search procedure for exploring the local optimum solution. Modified gravitational emulation local search algorithm is used for both exploring and exploiting the optimum solution for permutation flow shop scheduling problems. In this work, modified gravitational emulation local search algorithm is proposed to solve the permutation flow shop scheduling problems with the objectives such as minimization of makespan and total flow time. The computational results show that the performance solution of the proposed algorithm gives better results than the previous author’s approaches. Statistical tools are also used for finding out a relationship that exists between the two variables (makespan and total flow time) and to evaluate the performance of the proposed approach against the previous approaches in the literature.

Solution for flow shop scheduling problems using chaotic hybrid firefly and particle swarm optimization algorithm with improved local search

2021 ◽  
Vol 25 (10) ◽  
pp. 7143-7154
Author(s):  
Serkan Kaya ◽  
Abdülkadir Gümüşçü ◽  
İbrahim Berkan Aydilek ◽  
İzzettin Hakan Karaçizmeli ◽  
Mehmet Emin Tenekeci
Keyword(s):  
Particle Swarm Optimization ◽  
Local Search ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Flow Shop ◽  
Particle Swarm ◽  
Flow Shop Scheduling ◽  
Scheduling Problems ◽  
Swarm Optimization ◽  
Shop Scheduling

scholarly journals A Hybrid Crow Search Algorithm for Solving Permutation Flow Shop Scheduling Problems

2019 ◽  
Vol 9 (7) ◽  
pp. 1353 ◽  
Author(s):  
Ko-Wei Huang ◽  
Abba Girsang ◽  
Ze-Xue Wu ◽  
Yu-Wei Chuang
Keyword(s):  
Local Search ◽  
Flow Shop ◽  
Optimization Problems ◽  
Search Algorithm ◽  
Flow Shop Scheduling ◽  
Neighborhood Search ◽  
Scheduling Problems ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Permutation Flow Shop

The permutation flow shop scheduling problem (PFSP) is a renowned problem in the scheduling research community. It is an NP-hard combinatorial optimization problem that has useful real-world applications. In this problem, finding a useful algorithm to handle the massive amounts of jobs required to retrieve an actionable permutation order in a reasonable amount of time is important. The recently developed crow search algorithm (CSA) is a novel swarm-based metaheuristic algorithm originally proposed to solve mathematical optimization problems. In this paper, a hybrid CSA (HCSA) is proposed to minimize the makespans of PFSPs. First, to make the CSA suitable for solving the PFSP, the smallest position value rule is applied to convert continuous numbers into job sequences. Then, the HCSA uses a Nawaz–Enscore–Ham (NEH) technique to create a population with the required levels of quality and diversity. We apply a local search to enhance the quality of the solutions and avoid premature convergence; simulated annealing enhances the local search of a method based on a variable neighborhood search. Computational tests are used to evaluate the algorithm using PFSP benchmarks with job sizes between 20 and 500. The tests indicate that the performance of the proposed HCSA is significantly superior to that of other algorithms.

scholarly journals A Local Search-Based Generalized Normal Distribution Algorithm for Permutation Flow Shop Scheduling

2021 ◽  
Vol 11 (11) ◽  
pp. 4837
Author(s):  
Mohamed Abdel-Basset ◽  
Reda Mohamed ◽  
Mohamed Abouhawwash ◽  
Victor Chang ◽  
S. S. Askar
Keyword(s):  
Local Search ◽  
Search Strategy ◽  
Flow Shop ◽  
Flow Shop Scheduling ◽  
Discrete Problem ◽  
Mutation Operator ◽  
Shop Scheduling ◽  
Permutation Flow Shop Scheduling ◽  
Local Search Strategy ◽  
Generalized Normal Distribution

This paper studies the generalized normal distribution algorithm (GNDO) performance for tackling the permutation flow shop scheduling problem (PFSSP). Because PFSSP is a discrete problem and GNDO generates continuous values, the largest ranked value rule is used to convert those continuous values into discrete ones to make GNDO applicable for solving this discrete problem. Additionally, the discrete GNDO is effectively integrated with a local search strategy to improve the quality of the best-so-far solution in an abbreviated version of HGNDO. More than that, a new improvement using the swap mutation operator applied on the best-so-far solution to avoid being stuck into local optima by accelerating the convergence speed is effectively applied to HGNDO to propose a new version, namely a hybrid-improved GNDO (HIGNDO). Last but not least, the local search strategy is improved using the scramble mutation operator to utilize each trial as ideally as possible for reaching better outcomes. This improved local search strategy is integrated with IGNDO to produce a new strong algorithm abbreviated as IHGNDO. Those proposed algorithms are extensively compared with a number of well-established optimization algorithms using various statistical analyses to estimate the optimal makespan for 41 well-known instances in a reasonable time. The findings show the benefits and speedup of both IHGNDO and HIGNDO over all the compared algorithms, in addition to HGNDO.

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