An Improved Genetic Algorithm for Single-Machine Inverse Scheduling Problem

The goal of the scheduling is to arrange operations on suitable machines with optimal sequence for corresponding objectives. In order to meet market requirements, scheduling systems must own enough flexibility against uncertain events. These events can change production status or processing parameters, even causing the original schedule to no longer be optimal or even to be infeasible. Traditional scheduling strategies, however, cannot cope with these cases. Therefore, a new idea of scheduling called inverse scheduling has been proposed. In this paper, the inverse scheduling with weighted completion time (SMISP) is considered in a single-machine shop environment. In this paper, an improved genetic algorithm (IGA) with a local searching strategy is proposed. To improve the performance of IGA, efficient encoding scheme, fitness evaluation mechanism, feasible initialization methods, and a local search procedure have been employed in the paper. Because of the local improving method, the proposed IGA can balance its exploration ability and exploitation ability. We adopt 27 instances to verify the effectiveness of the proposed algorithm. The experimental results illustrated that the proposed algorithm can generate satisfactory solutions. This approach also has been applied to solve the scheduling problem in the real Chinese shipyard and can bring some benefits.

[1]  Colin R. Reeves,et al.  Genetic Algorithms for the Operations Researcher , 1997, INFORMS J. Comput..

[2]  Tang Guo-chun.  Inverse Problems of a Single Machine Scheduling to Minimize the Total Completion Time , 2005 .

[3]  Daniel Vanderpooten,et al.  General approximation schemes for min-max (regret) versions of some (pseudo-)polynomial problems , 2010, Discret. Optim..

[4]  Iraj Mahdavi,et al.  A genetic algorithm for the single machine preemptive scheduling problem with linear earliness and quadratic tardiness penalties , 2013 .

[5]  Savas Balin,et al.  Non-identical parallel machine scheduling using genetic algorithm , 2011, Expert Syst. Appl..

[6]  Chou-Jung Hsu,et al.  Single-Machine Scheduling with Aging Effects and Optional Maintenance Activity Considerations , 2013 .

[7]  Li-Chen Fu,et al.  NNMA: An effective memetic algorithm for solving multiobjective permutation flow shop scheduling problems , 2011, Expert Syst. Appl..

[8]  Wayne E. Smith.  Various optimizers for single‐stage production , 1956 .

[9]  Peter Brucker,et al.  Inverse scheduling: two-machine flow-shop problem , 2011, J. Sched..

[10]  Cheol Min Joo,et al.  Single-Machine Total Completion Time Scheduling with Position-Based Deterioration and Multiple Rate-Modifying Activities , 2011 .

[11]  P. Chang,et al.  The development of gradual-priority weighting approach for the multi-objective flowshop scheduling problem , 2002 .

[12]  Pyung-Hoi Koo,et al.  Scheduling a single batch processing machine with arbitrary job sizes and incompatible job families , 2005 .

[13]  Byung Soo Kim,et al.  Genetic algorithms for single machine scheduling with time-dependent deterioration and rate-modifying activities , 2013, Expert Syst. Appl..

[14]  Christos Koulamas.  Inverse scheduling with controllable job parameters , 2005 .

[15]  M. Azizoglu,et al.  Single machine scheduling with preventive maintenances , 2009 .

[16]  Shih-Hsin Chen,et al.  Artificial chromosomes with genetic algorithm 2 (ACGA2) for single machine scheduling problems with sequence-dependent setup times , 2014, Appl. Soft Comput..

[17]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[18]  Louis Anthony Cox,et al.  Wiley encyclopedia of operations research and management science , 2011 .

[19]  Felix T. S. Chan,et al.  A genetic algorithm-based scheduler for multiproduct parallel machine sheet metal job shop , 2011, Expert Syst. Appl..

[20]  Xianyu Yu,et al.  Minimizing the Makespan for Scheduling Problems with General Deterioration Effects , 2013 .

[21]  Tang Guo-chun.  Inverse Problems of Supply Chain Scheduling and Flowshop Scheduling , 2009 .

[22]  Peng Liu,et al.  Two-Agent Single-Machine Scheduling with Resource-Dependent Starting Times , 2013 .

[23]  Domagoj Jakobovic,et al.  Dynamic Scheduling with Genetic Programming , 2006, EuroGP.

[24]  Mitsuo Gen,et al.  A tutorial survey of job-shop scheduling problems using genetic algorithms—I: representation , 1996 .

[25]  I. Elamvazuthi,et al.  Swarm intelligence and gravitational search algorithm for multi-objective optimization of synthesis gas production , 2013 .

[26]  Albert Tarantola,et al.  Inverse problem theory - and methods for model parameter estimation , 2004 .

[27]  Adam Kurpisz,et al.  Approximating a two-machine flow shop scheduling under discrete scenario uncertainty , 2012, Eur. J. Oper. Res..

[28]  T. C. Edwin Cheng,et al.  Bicriterion scheduling with equal processing times on a batch processing machine , 2009, Comput. Oper. Res..

[29]  Dan Boneh,et al.  On genetic algorithms , 1995, COLT '95.

[30]  Pandian Vasant,et al.  Hybrid LS-SA-PS methods for solving fuzzy non-linear programming problems , 2013, Math. Comput. Model..

[31]  Longcheng Liu,et al.  Inverse maximum flow problems under the weighted Hamming distance , 2006, J. Comb. Optim..

[32]  P. Vasant,et al.  Hybrid Mesh Adaptive Direct Search Genetic Algorithms and Line Search Approaches for Fuzzy Optimization Problems in Production Planning , 2013, Handbook of Optimization.

[33]  Ismail Karaoglan,et al.  A hybrid genetic algorithm for the location-routing problem with simultaneous pickup and delivery , 2010, The 40th International Conference on Computers & Indutrial Engineering.

[34]  Yasuhiro Tsujimura,et al.  A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: hybrid genetic search strategies , 1999 .

[35]  Ravindra K. Ahuja,et al.  Inverse Optimization , 2001, Oper. Res..

[36]  Der-Chiang Li,et al.  A Genetic Algorithm-Based Approach for Single-Machine Scheduling with Learning Effect and Release Time , 2014 .

[37]  Lizhi Wang,et al.  Cutting plane algorithms for the inverse mixed integer linear programming problem , 2009, Oper. Res. Lett..

[38]  Clemens Heuberger,et al.  Inverse Combinatorial Optimization: A Survey on Problems, Methods, and Results , 2004, J. Comb. Optim..

[39]  Pei-Chann Chang,et al.  Genetic algorithms applied in BOPP film scheduling problems: minimizing total absolute deviation and setup times , 2003, Appl. Soft Comput..

[40]  Omar Ghrayeb,et al.  A hybrid random-key genetic algorithm to minimize weighted number of late deliveries for a single machine , 2013 .

[41]  Alireza Fallahi,et al.  Dynamic scheduling in flexible job shop systems by considering simultaneously efficiency and stability , 2010 .

[42]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[43]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[44]  Pandian Vasant,et al.  Hybrid Optimization Techniques for Optimization in a Fuzzy Environment , 2013, Handbook of Optimization.

[45]  Peter Brucker,et al.  Inverse scheduling with maximum lateness objective , 2009, J. Sched..