Extracting New Dispatching Rules for Multi-objective Dynamic Flexible Job Shop Scheduling with Limited Buffer Spaces

Dispatching rules are among the most widely applied and practical methods for solving dynamic flexible job shop scheduling problems in manufacturing systems. Hence, the design of applicable and effective rules is always an important subject in the scheduling literature. The aim of this study is to propose a practical approach for extracting efficient rules for a more general type of dynamic job shop scheduling problem in which jobs arrive at the shop at different times and machine breakdowns occur stochastically. Limited-buffer conditions are also considered, increasing the problem complexity. Benchmarks are selected from the literature, with some modifications. Gene expression programming combined with a simulation model is used for the design of scheduling policies. The extracted rules are compared with several classic dispatching rules from the literature based on a multi-objective function. The new rules are found to be superior to the classic ones. They are robust and can be used for similar complex scheduling problems. The results prove the efficiency of gene expression programming as a nature-inspired method for dispatching rule extraction.

[1]  Z. Kamisli Ozturk,et al.  A Hybrid NSGA-II Algorithm for Multiobjective Quadratic Assignment Problems , 2017 .

[2]  Erhan Kozan,et al.  Scheduling trains as a blocking parallel-machine job shop scheduling problem , 2009, Comput. Oper. Res..

[3]  Bernd Scholz-Reiter,et al.  Generating dispatching rules for semiconductor manufacturing to minimize weighted tardiness , 2010, Proceedings of the 2010 Winter Simulation Conference.

[4]  Felix T.S. Chan,et al.  Job shop scheduling with a combination of four buffering constraints , 2018, Int. J. Prod. Res..

[5]  Reha Uzsoy,et al.  Rapid Modeling and Discovery of Priority Dispatching Rules: An Autonomous Learning Approach , 2006, J. Sched..

[6]  Hongbo Liu,et al.  Cognitively Inspired Artificial Bee Colony Clustering for Cognitive Wireless Sensor Networks , 2017, Cognitive Computation.

[7]  Aydin Teymourifar,et al.  Extracting priority rules for dynamic multi-objective flexible job shop scheduling problems using gene expression programming , 2018, Int. J. Prod. Res..

[8]  Erhan Kozan,et al.  Scheduling a flow-shop with combined buffer conditions , 2009 .

[9]  Martin Josef Geiger,et al.  Test Instances for the Flexible Job Shop Scheduling Problem with Work Centers , 2012 .

[10]  Wei Li,et al.  An effective heuristic for no-wait flow shop production to minimize makespan , 2016 .

[11]  Md. Jalil Piran,et al.  Survey of computational intelligence as basis to big flood management: challenges, research directions and future work , 2018 .

[12]  Cândida Ferreira,et al.  Gene Expression Programming and the Evolution of Computer Programs , 2004 .

[13]  Shou-yu Chen,et al.  Improved annual rainfall-runoff forecasting using PSO-SVM model based on EEMD , 2013 .

[14]  Hojjat Adeli,et al.  Nature Inspired Computing: An Overview and Some Future Directions , 2015, Cognitive Computation.

[15]  Bernd Scholz-Reiter,et al.  Evolutionary generation of dispatching rule sets for complex dynamic scheduling problems , 2013 .

[16]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[17]  Gary B. Lamont,et al.  Evolutionary Algorithms for Solving Multi-Objective Problems , 2002, Genetic Algorithms and Evolutionary Computation.

[18]  Graham Kendall,et al.  Searching the Hyper-heuristic Design Space , 2014, Cognitive Computation.

[19]  Hojjat Adeli,et al.  Nature-Inspired Chemical Reaction Optimisation Algorithms , 2017, Cognitive Computation.

[20]  Proceedings of the 2010 Winter Simulation Conference, WSC 2010, Baltimore, Maryland, USA, 5-8 December 2010 , 2010, Winter Simulation Conference.

[21]  Alexander Gepperth,et al.  A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems , 2016, Cognitive Computation.

[22]  Hugo Terashima-Marín,et al.  A Neuro-evolutionary Hyper-heuristic Approach for Constraint Satisfaction Problems , 2015, Cognitive Computation.

[23]  Quan-Ke Pan,et al.  Solving the large-scale hybrid flow shop scheduling problem with limited buffers by a hybrid artificial bee colony algorithm , 2015, Inf. Sci..

[24]  Bernd Scholz-Reiter,et al.  Towards improved dispatching rules for complex shop floor scenarios: a genetic programming approach , 2010, GECCO '10.

[25]  Bijaya K. Panigrahi,et al.  A Biologically Inspired Modified Flower Pollination Algorithm for Solving Economic Dispatch Problems in Modern Power Systems , 2015, Cognitive Computation.

[26]  R. Deo,et al.  Computational intelligence approach for modeling hydrogen production: a review , 2018 .

[27]  Ameur Soukhal,et al.  Complexity of flow shop scheduling problems with transportation constraints , 2005, Eur. J. Oper. Res..

[28]  Hossam Faris,et al.  Simultaneous Feature Selection and Support Vector Machine Optimization Using the Grasshopper Optimization Algorithm , 2018, Cognitive Computation.

[29]  Fuqing Zhao,et al.  An improved particle swarm optimisation with a linearly decreasing disturbance term for flow shop scheduling with limited buffers , 2014, Int. J. Comput. Integr. Manuf..

[30]  Bellie Sivakumar,et al.  Neural network river forecasting through baseflow separation and binary-coded swarm optimization , 2015 .

[31]  Rosni Abdullah,et al.  A Machine Learning Approach to Detect Router Advertisement Flooding Attacks in Next-Generation IPv6 Networks , 2018, Cognitive Computation.

[32]  Shanwen Zhang,et al.  Dimension Reduction Using Semi-Supervised Locally Linear Embedding for Plant Leaf Classification , 2009, ICIC.

[33]  Nhu Binh Ho,et al.  Evolving dispatching rules using genetic programming for solving multi-objective flexible job-shop problems , 2008, Comput. Ind. Eng..

[34]  Erhan Kozan,et al.  Parallel-identical-machine job-shop scheduling with different stage-dependent buffering requirements , 2016, Comput. Oper. Res..

[35]  Mark Johnston,et al.  A Computational Study of Representations in Genetic Programming to Evolve Dispatching Rules for the Job Shop Scheduling Problem , 2013, IEEE Transactions on Evolutionary Computation.

[36]  Nilanjan Dey,et al.  Optimization of Non-rigid Demons Registration Using Cuckoo Search Algorithm , 2017, Cognitive Computation.

[37]  Zhi-Hua Hu,et al.  Path-relinking Tabu search for the multi-objective flexible job shop scheduling problem , 2014, Comput. Oper. Res..

[38]  K. Chau,et al.  Modeling of groundwater level fluctuations using dendrochronology in alluvial aquifers , 2015 .

[39]  Peter Brucker,et al.  Job-shop scheduling with limited capacity buffers , 2006, OR Spectr..

[40]  Chengkuan Zeng,et al.  Scheduling of no buffer job shop cells with blocking constraints and automated guided vehicles , 2014, Appl. Soft Comput..