Green parallel machines scheduling problem: A bi-objective model and a heuristic algorithm to obtain Pareto frontier

Abstract Sustainability consciousness in manufacturing has become an interesting topic for many researchers in recent years. There is also more concern in many companies about reducing energy consumption in manufacturing. Improving environmental health and safety, production cost saving, access to governmental incentives such as grants and tax credits and also improving the brand image are the most important reasons which is leading many companies to an environmental-friendly production planning. For example, one of the most applicable scheduling problems deals with planning jobs on numbers of parallel machines. In such an application, different machines have different technologies and different speed and power of energy consumption in manufacturing similar jobs. This article introduced a mathematical formulation which models the green parallel machines scheduling problem with total energy consumption and total completion time as objectives. Due to high computational complexity of the proposed model, a heuristic algorithm is developed to obtain the exact Pareto frontier of these two objectives with a polynomial complexity. Numerical experiments are presented to show the efficiency and speed of the proposed algorithm compared to solving the model using the optimisation software directly.

[1]  Tom V. Mathew Genetic Algorithm , 2022 .

[2]  Ellis Horowitz,et al.  Exact and Approximate Algorithms for Scheduling Nonidentical Processors , 1976, JACM.

[3]  W. A. Horn Technical Note - Minimizing Average Flow Time with Parallel Machines , 1973, Oper. Res..

[4]  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 .

[5]  George Mavrotas,et al.  Effective implementation of the epsilon-constraint method in Multi-Objective Mathematical Programming problems , 2009, Appl. Math. Comput..

[6]  Oliver Alfred Gross,et al.  On the minimization of . , 1954 .

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

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

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

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

[11]  Damien Trentesaux,et al.  Sustainability in manufacturing operations scheduling: A state of the art review , 2015 .

[12]  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.

[13]  Jeng-Fung Chen,et al.  Total tardiness minimization on unrelated parallel machine scheduling with auxiliary equipment constraints , 2006 .

[14]  Xin Yao,et al.  Parallel Problem Solving from Nature PPSN VI , 2000, Lecture Notes in Computer Science.

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

[16]  Kay Chen Tan,et al.  A Competitive-Cooperative Coevolutionary Paradigm for Dynamic Multiobjective Optimization , 2009, IEEE Transactions on Evolutionary Computation.

[17]  Dehua Xu,et al.  Parallel machine scheduling with almost periodic maintenance and non-preemptive jobs to minimize makespan , 2008, Comput. Oper. Res..

[18]  Mostafa Zandieh,et al.  Parallel-machine scheduling problems with sequence-dependent setup times using an ACO, SA and VNS hybrid algorithm , 2009, Expert Syst. Appl..

[19]  C. Hwang Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[20]  Peter Brucker,et al.  Necessary and sufficient optimality conditions for scheduling unit time jobs on identical parallel machines , 2016, J. Sched..

[21]  Cheng-Hsiang Liu,et al.  Multi-objective parallel machine scheduling problems by considering controllable processing times , 2016, J. Oper. Res. Soc..

[22]  T.C.E. Cheng,et al.  A state-of-the-art review of parallel-machine scheduling research , 1990 .

[23]  Lingfa Lu,et al.  Parallel-machine scheduling with release dates and rejection , 2016, 4OR.

[24]  Hao Zhang,et al.  Energy-conscious flow shop scheduling under time-of-use electricity tariffs , 2014 .

[25]  Joseph Y.-T. Leung,et al.  Parallel machine scheduling problems in green manufacturing industry , 2016 .

[26]  Madjid Tavana,et al.  Multi-objective control chart design optimization using NSGA-III and MOPSO enhanced with DEA and TOPSIS , 2016, Expert Syst. Appl..

[27]  Kalyanmoy Deb,et al.  A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II , 2000, PPSN.

[28]  Tsai C. Kuo,et al.  Environmentally conscious design and manufacturing: A state-of-the-art survey , 1997 .

[29]  Katsundo Hitomi,et al.  Optimal Two-Stage Production Scheduling with Setup Times Separated , 1979 .

[30]  Haidong Yang,et al.  An Ant Optimization Model for Unrelated Parallel Machine Scheduling with Energy Consumption and Total Tardiness , 2015 .

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

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

[33]  Pradyumn Kumar Shukla,et al.  On the Normal Boundary Intersection Method for Generation of Efficient Front , 2007, International Conference on Computational Science.

[34]  Zhantao Li,et al.  Unrelated parallel machine scheduling problem with energy and tardiness cost , 2015, The International Journal of Advanced Manufacturing Technology.

[35]  Wu Cheng,et al.  A genetic algorithm for minimizing the makespan in the case of scheduling identical parallel machines , 1999, Artif. Intell. Eng..

[36]  Shanlin Yang,et al.  Non-identical parallel-machine scheduling research with minimizing total weighted completion times: Models, relaxations and algorithms ☆ , 2009 .

[37]  Meral Azizoglu,et al.  On the minimization of total weighted flow time with identical and uniform parallel machines , 1999, Eur. J. Oper. Res..