Green Job Shop Scheduling Problem With Discrete Whale Optimization Algorithm

In the last few decades, production scheduling problems have been studied for optimizing production efficiency involving the time-related indicators, such as completion time, earliness/tardiness time, or flow time. Currently, with the consideration of sustainable development, the green scheduling problem has been paid more and more attention. Here, a green job shop scheduling problem is considered to minimize the sum of energy-consumption cost and completion-time cost in the workshop. In this paper, a mathematical model is first established with the consideration of multi-speed machines. A discrete whale optimization algorithm (DWOA) is then proposed for solving the model. In the proposed algorithm, a two-string encoding is adopted to represent the two sub-problems: job permutation and speed selection. Then, a heuristic method is used to initialize the population to enhance the quality of initial solutions. By considering the discrete characteristics of the problem, the individual updating operators are redesigned to ensure the algorithm work directly in a discrete scheduling domain. In addition, a variable neighborhood search strategy is embedded to further improve the search ability. The extensive experiments have been performed to test the DWOA. The computational data reveal the promising advantages of the DWOA on the considered problem.

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

[2]  Diego Oliva,et al.  Parameter estimation of photovoltaic cells using an improved chaotic whale optimization algorithm , 2017 .

[3]  Arun Kumar Sangaiah,et al.  A modified nature inspired meta-heuristic whale optimization algorithm for solving 0–1 knapsack problem , 2019, Int. J. Mach. Learn. Cybern..

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

[5]  Jianzhou Wang,et al.  A novel hybrid system based on a new proposed algorithm-Multi-Objective Whale Optimization Algorithm for wind speed forecasting , 2017 .

[6]  Mohamed Abdel-Basset,et al.  A hybrid whale optimization algorithm based on local search strategy for the permutation flow shop scheduling problem , 2018, Future Gener. Comput. Syst..

[7]  Raymond Chiong,et al.  Solving the energy-efficient job shop scheduling problem: a multi-objective genetic algorithm with enhanced local search for minimizing the total weighted tardiness and total energy consumption , 2016 .

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

[9]  Raymond Chiong,et al.  Parallel Machine Scheduling Under Time-of-Use Electricity Prices: New Models and Optimization Approaches , 2016, IEEE Transactions on Automation Science and Engineering.

[10]  Marco Taisch,et al.  Multi-objective genetic algorithm for energy-efficient job shop scheduling , 2015 .

[11]  Alper Hamzadayi,et al.  Testing the performance of teaching-learning based optimization (TLBO) algorithm on combinatorial problems: Flow shop and job shop scheduling cases , 2014, Inf. Sci..

[12]  Ada Che,et al.  Energy-conscious unrelated parallel machine scheduling under time-of-use electricity tariffs , 2017 .

[13]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

[14]  Shuning Zhang,et al.  Multi-Objective Parallel Variable Neighborhood Search for Energy Consumption Scheduling in Blocking Flow Shops , 2018, IEEE Access.

[15]  Chao Zhang,et al.  Energy-Efficient Scheduling for a Job Shop Using Grey Wolf Optimization Algorithm with Double-Searching Mode , 2018, Mathematical Problems in Engineering.

[16]  Xiao-Long Zheng,et al.  A Collaborative Multiobjective Fruit Fly Optimization Algorithm for the Resource Constrained Unrelated Parallel Machine Green Scheduling Problem , 2018, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[17]  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.

[18]  T. C. Edwin Cheng,et al.  Two-agent flowshop scheduling to maximize the weighted number of just-in-time jobs , 2017, J. Sched..

[19]  Adriana Giret,et al.  A genetic algorithm for energy-efficiency in job-shop scheduling , 2016 .

[20]  George Q. Huang,et al.  A Branch-and-Bound Algorithm for Minimizing the Energy Consumption in the PFS Problem , 2013 .

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

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

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

[24]  S. Afshin Mansouri,et al.  Green scheduling of a two-machine flowshop: Trade-off between makespan and energy consumption , 2016, Eur. J. Oper. Res..

[25]  Cheng Wu,et al.  Carbon-efficient scheduling of flow shops by multi-objective optimization , 2016, Eur. J. Oper. Res..

[26]  Majdi M. Mafarja,et al.  Hybrid Whale Optimization Algorithm with simulated annealing for feature selection , 2017, Neurocomputing.

[27]  Tianhua Jiang,et al.  Optimizing the Low-Carbon Flexible Job Shop Scheduling Problem Considering Energy Consumption , 2018, IEEE Access.