Solving permutation flowshop scheduling problems with a discrete differential evolution algorithm

In this paper a new discrete Differential Evolution algorithm for the Permutation Flowshop Scheduling Problem with the total flowtime and makespan criteria is proposed. The core of the algorithm is the distance-based differential mutation operator defined by means of a new randomized bubble sort algorithm. This mutation scheme allows the Differential Evolution to directly navigate the permutations search space. Experiments were held on a well known benchmarks suite and they show that the proposal reaches very good performances compared to other state-of-the-art algorithms. The results are particularly satisfactory on the total flowtime criterion where also new upper bounds that improve on the state-of-the-art have been found.

[1]  Thomas Stützle,et al.  A simple and effective iterated greedy algorithm for the permutation flowshop scheduling problem , 2007, Eur. J. Oper. Res..

[2]  Alfredo Milani,et al.  An Algebraic Differential Evolution for the Linear Ordering Problem , 2015, GECCO.

[3]  Alfredo Milani,et al.  Linear Ordering Optimization with a Combinatorial Differential Evolution , 2015, 2015 IEEE International Conference on Systems, Man, and Cybernetics.

[4]  Mehmet Fatih Tasgetiren,et al.  A discrete differential evolution algorithm for the permutation flowshop scheduling problem , 2008, Comput. Ind. Eng..

[5]  Godfrey C. Onwubolu,et al.  Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization , 2009 .

[6]  Xiangtao Li,et al.  An opposition-based differential evolution algorithm for permutation flow shop scheduling based on diversity measure , 2013, Adv. Eng. Softw..

[7]  Quan-Ke Pan,et al.  A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime , 2013, Comput. Oper. Res..

[8]  F. Glover,et al.  Fundamentals of Scatter Search and Path Relinking , 2000 .

[9]  Xiao Xu,et al.  An asynchronous genetic local search algorithm for the permutation flowshop scheduling problem with total flowtime minimization , 2011, Expert Syst. Appl..

[10]  Mauro Birattari,et al.  Tuning Metaheuristics - A Machine Learning Perspective , 2009, Studies in Computational Intelligence.

[11]  Jiyin Liu,et al.  Constructive and composite heuristic solutions to the P// Sigma Ci scheduling problem , 2001, Eur. J. Oper. Res..

[12]  Inyong Ham,et al.  A heuristic algorithm for the m-machine, n-job flow-shop sequencing problem , 1983 .

[13]  Alfredo Milani,et al.  A Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem with Total Flow Time Criterion , 2014, PPSN.

[14]  Alexander Mendiburu,et al.  A Distance-Based Ranking Model Estimation of Distribution Algorithm for the Flowshop Scheduling Problem , 2014, IEEE Transactions on Evolutionary Computation.

[15]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[16]  I. Herstein,et al.  Topics in algebra , 1964 .

[17]  Jatinder N. D. Gupta,et al.  Flowshop scheduling research after five decades , 2006, Eur. J. Oper. Res..

[18]  E. Mokotoff Multi-objective Simulated Annealing for Permutation Flow Shop Problems , 2009 .

[19]  Shih-Wei Lin,et al.  Minimizing makespan and total flowtime in permutation flowshops by a bi-objective multi-start simulated-annealing algorithm , 2013, Comput. Oper. Res..

[20]  Janez Brest,et al.  Self-Adapting Control Parameters in Differential Evolution: A Comparative Study on Numerical Benchmark Problems , 2006, IEEE Transactions on Evolutionary Computation.

[21]  Thomas Stützle,et al.  A review of metrics on permutations for search landscape analysis , 2007, Comput. Oper. Res..

[22]  Franz Rothlauf,et al.  Representations for genetic and evolutionary algorithms , 2002, Studies in Fuzziness and Soft Computing.

[23]  Hideo Tanaka,et al.  Genetic algorithms for flowshop scheduling problems , 1996 .

[24]  Alfredo Milani,et al.  A Discrete Differential Evolution Algorithm for Multi-Objective Permutation Flowshop Scheduling , 2015, IPS@AI*IA.

[25]  Q. Wang,et al.  Efficient composite heuristics for total flowtime minimization in permutation flow shops , 2009 .

[26]  Alfredo Milani,et al.  Experimental evaluation of pheromone models in ACOPlan , 2011, Annals of Mathematics and Artificial Intelligence.

[27]  Marco César Goldbarg,et al.  New VNS heuristic for total flowtime flowshop scheduling problem , 2012, Expert Syst. Appl..

[28]  Rubén Ruiz,et al.  A comprehensive review and evaluation of permutation flowshop heuristics to minimize flowtime , 2013, Comput. Oper. Res..

[29]  R. Storn,et al.  Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series) , 2005 .

[30]  Alfredo Milani,et al.  Community of scientist optimization: An autonomy oriented approach to distributed optimization , 2012, AI Commun..

[31]  Rubén Ruiz,et al.  A Review and Evaluation of Multiobjective Algorithms for the Flowshop Scheduling Problem , 2008, INFORMS J. Comput..

[32]  Alfredo Milani,et al.  Algebraic Differential Evolution Algorithm for the Permutation Flowshop Scheduling Problem With Total Flowtime Criterion , 2016, IEEE Transactions on Evolutionary Computation.

[33]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[34]  Teofilo F. Gonzalez,et al.  Flowshop and Jobshop Schedules: Complexity and Approximation , 1978, Oper. Res..

[35]  Chandrasekharan Rajendran,et al.  A Multi-Objective Ant-Colony Algorithm for Permutation Flowshop Scheduling to Minimize the Makespan and Total Flowtime of Jobs , 2009 .

[36]  Éric D. Taillard,et al.  Benchmarks for basic scheduling problems , 1993 .