Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA) is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multiobjective Particle Swarm Optimization (OMOPSO), and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

[1]  Mitsuo Gen,et al.  Multi-objective multi-population biased random-key genetic algorithm for the 3-D container loading problem , 2015, Comput. Ind. Eng..

[2]  Gunther Reinhart,et al.  Energy-orientated Machine Scheduling for Hybrid Flow Shops☆ , 2015 .

[3]  L. J. Eshelman,et al.  chapter Real-Coded Genetic Algorithms and Interval-Schemata , 1993 .

[4]  Massimo Paolucci,et al.  Energy-aware scheduling for improving manufacturing process sustainability: A mathematical model for flexible flow shops , 2012 .

[5]  Andreas C. Nearchou,et al.  Scheduling with controllable processing times and compression costs using population-based heuristics , 2010 .

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

[7]  Alistair Duffy,et al.  Lean Cables ? A Step towards Competitive, Sustainable and Profitable Processes , 2014 .

[8]  Christian Gahm,et al.  Energy-oriented scheduling based on Evolutionary Algorithms , 2015, Comput. Oper. Res..

[9]  Jorge M. S. Valente.  Beam search heuristics for quadratic earliness and tardiness scheduling , 2010, J. Oper. Res. Soc..

[10]  Mehmet Bayram Yildirim,et al.  Single-Machine Sustainable Production Planning to Minimize Total Energy Consumption and Total Completion Time Using a Multiple Objective Genetic Algorithm , 2012, IEEE Transactions on Engineering Management.

[11]  W. Spears,et al.  On the Virtues of Parameterized Uniform Crossover , 1995 .

[12]  Fengxing Zou,et al.  Mathematical model and genetic optimization for hybrid flow shop scheduling problem based on energy consumption , 2008, 2008 Chinese Control and Decision Conference.

[13]  Jorge M. S. Valente,et al.  Beam search algorithms for the early/tardy scheduling problem with release dates , 2005 .

[14]  Lin Lin,et al.  Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey , 2014, J. Intell. Manuf..

[15]  Ming Zhou,et al.  Optimizing green production strategies: An integrated approach , 2013, Comput. Ind. Eng..

[16]  Sanja Petrovic,et al.  An investigation into minimising total energy consumption and total weighted tardiness in job shops , 2014 .

[17]  Mehmet Fatih Tasgetiren,et al.  A hybrid harmony search algorithm for the blocking permutation flow shop scheduling problem , 2011, Comput. Ind. Eng..

[18]  Axel Tuma,et al.  Energy-efficient scheduling in manufacturing companies: A review and research framework , 2016, Eur. J. Oper. Res..

[19]  John W. Sutherland,et al.  A new approach to scheduling in manufacturing for power consumption and carbon footprint reduction , 2011 .

[20]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[21]  Aimin Zhou,et al.  A Multiobjective Evolutionary Algorithm Based on Decomposition and Preselection , 2015, BIC-TA.

[22]  Fei Liu,et al.  A bi-objective model for job-shop scheduling problem to minimize both energy consumption and makespan , 2005 .

[23]  Mostafa Zandieh,et al.  A genetic algorithm for JIT single machine scheduling with preemption and machine idle time , 2011, Expert Syst. Appl..

[24]  Kenneth R. Baker,et al.  Minimizing earliness and tardiness costs in stochastic scheduling , 2014, Eur. J. Oper. Res..

[25]  Xin Yao,et al.  Mathematical modeling and multi-objective evolutionary algorithms applied to dynamic flexible job shop scheduling problems , 2015, Inf. Sci..

[26]  John W. Sutherland,et al.  Flow shop scheduling with peak power consumption constraints , 2013, Ann. Oper. Res..

[27]  Liang Gao,et al.  Energy-efficient multi-pass turning operation using multi-objective backtracking search algorithm , 2016 .

[28]  Mehmet Bayram Yildirim,et al.  A framework to minimise total energy consumption and total tardiness on a single machine , 2008 .

[29]  Candace Arai Yano,et al.  Algorithms for a class of single-machine weighted tardiness and earliness problems , 1991 .

[30]  Adriana Giret,et al.  Energy-efficient scheduling for a flexible flow shop using an improved genetic-simulated annealing algorithm , 2013 .

[31]  Kalyanmoy Deb,et al.  Simulated Binary Crossover for Continuous Search Space , 1995, Complex Syst..

[32]  Marco Laumanns,et al.  SPEA2: Improving the Strength Pareto Evolutionary Algorithm For Multiobjective Optimization , 2002 .

[33]  Shigeru Fujimura,et al.  Dynamic routing strategies for JIT production in hybrid flow shops , 2012, Comput. Oper. Res..

[34]  Jagadish Jampani,et al.  A moving block heuristic for minimizing earliness and tardiness on a single machine with unrestrictive common due dates , 2005 .

[35]  Mitsuo Gen,et al.  Effective multiobjective EDA for bi-criteria stochastic job-shop scheduling problem , 2017, J. Intell. Manuf..

[36]  C. Coello,et al.  Improving PSO-based Multi-Objective Optimization using Crowding , Mutation and �-Dominance , 2005 .

[37]  Cheng-Hsiang Liu,et al.  Approximate trade-off between minimisation of total weighted tardiness and minimisation of carbon dioxide (CO2) emissions in bi-criteria batch scheduling problem , 2014, Int. J. Comput. Integr. Manuf..

[38]  Abel García-Nájera,et al.  An evolutionary approach for multi-objective vehicle routing problems with backhauls , 2015, Comput. Ind. Eng..

[39]  Lawrence E. Whitman,et al.  Data collection framework on energy consumption in manufacturing , 2006 .

[40]  Timothy G. Gutowski,et al.  An Environmental Analysis of Machining , 2004 .

[41]  Janet M. Twomey,et al.  Operational methods for minimization of energy consumption of manufacturing equipment , 2007 .

[42]  Iraj Mahdavi,et al.  A drastic hybrid heuristic algorithm to approach to JIT policy considering controllable processing times , 2013 .

[43]  Guohui Zhang,et al.  Dynamic rescheduling in FMS that is simultaneously considering energy consumption and schedule efficiency , 2016 .

[44]  Iraj Mahdavi,et al.  Single machine scheduling with controllable processing times to minimize total tardiness and earliness , 2013, Comput. Ind. Eng..

[45]  Abdelhakim Artiba,et al.  Minimizing the weighted sum of maximum earliness and maximum tardiness costs on a single machine with periodic preventive maintenance , 2014, Comput. Oper. Res..

[46]  Dvir Shabtay,et al.  The single-machine earliness-tardiness scheduling problem with due date assignment and resource-dependent processing times , 2008, Ann. Oper. Res..

[47]  Joaquín B. Ordieres Meré,et al.  Optimizing the production scheduling of a single machine to minimize total energy consumption costs , 2014 .

[48]  A. Verl,et al.  ENERGY CONSUMPTION FORECASTING AND OPTIMISATION FOR TOOL MACHINES , 2009 .

[49]  Jan Karel Lenstra,et al.  Complexity of machine scheduling problems , 1975 .

[50]  George Q. Huang,et al.  Hybrid flow shop scheduling considering machine electricity consumption cost , 2013 .

[51]  Safia Kedad-Sidhoum,et al.  A faster branch-and-bound algorithm for the earliness-tardiness scheduling problem , 2008, J. Sched..

[52]  Liang Gao,et al.  An effective hybrid particle swarm optimization algorithm for multi-objective flexible job-shop scheduling problem , 2009, Comput. Ind. Eng..

[53]  Quan-Ke Pan,et al.  Pareto-based grouping discrete harmony search algorithm for multi-objective flexible job shop scheduling , 2014, Inf. Sci..