Green manufacturing: Order acceptance and scheduling subject to the budgets of energy consumption and machine launch

Abstract This paper investigates an order acceptance and scheduling problem with an energy consumption budget, a machine launch budget, and the order release time in a green manufacturing system. The phenomenon of deteriorating jobs is considered in the production of the accepted orders. The objective of the study is to maximize the net revenue and a modified variable neighborhood search (MVNS) Algorithm that combines a novel encoding and decoding procedure as well as a dynamic programming algorithm is developed to solve it. To show the effectiveness and efficiency of the proposed algorithm, we first employ the MVNS algorithm and seven VNS-based algorithms to solve the problems with different configurations of orders and machines. The results show that the MVNS algorithm obtains better solutions than existing VNS-based algorithms. Then, the proposed algorithm is compared with three meta-heuristic algorithms. The experimental results show that the proposed algorithm has significant advantages in terms of solution optimality.

[1]  Shiji Song,et al.  hybrid differential evolution algorithm for job shop scheduling problems with xpected total tardiness criterion , 2013 .

[2]  Xinyu Shao,et al.  Mathematical modelling and optimisation of energy-conscious hybrid flow shop scheduling problem with unrelated parallel machines , 2018, Int. J. Prod. Res..

[3]  Panos M. Pardalos,et al.  Single-machine scheduling with learning effect and resource-dependent processing times in the serial-batching production , 2017, Applied Mathematical Modelling.

[4]  Russell C. Eberhart,et al.  Human tremor analysis using particle swarm optimization , 1999, Proceedings of the 1999 Congress on Evolutionary Computation-CEC99 (Cat. No. 99TH8406).

[5]  Luiz Antonio Nogueira Lorena,et al.  A biased random-key genetic algorithm for the two-stage capacitated facility location problem , 2019, Expert Syst. Appl..

[6]  Panos M. Pardalos,et al.  Single-machine and parallel-machine serial-batching scheduling problems with position-based learning effect and linear setup time , 2019, Ann. Oper. Res..

[7]  Nenad Mladenovic,et al.  Less is more: Basic variable neighborhood search for minimum differential dispersion problem , 2016, Inf. Sci..

[8]  Susan A. Slotnick,et al.  Order acceptance and scheduling: A taxonomy and review , 2011, Eur. J. Oper. Res..

[9]  Deming Lei,et al.  A shuffled frog-leaping algorithm for flexible job shop scheduling with the consideration of energy consumption , 2017, Int. J. Prod. Res..

[10]  Gerd Finke,et al.  Minimizing the number of machines for minimum length schedules , 2009, Eur. J. Oper. Res..

[11]  Xinbao Liu,et al.  Parallel-batching scheduling with nonlinear processing times on a single and unrelated parallel machines , 2020, J. Glob. Optim..

[12]  Stefan Minner,et al.  Influence of order acceptance policies on optimal capacity investment with stochastic customer required lead times , 2015, Eur. J. Oper. Res..

[13]  Leyuan Shi,et al.  Engineering management for high-end equipment intelligent manufacturing , 2018 .

[14]  Anand Subramanian,et al.  Exact and heuristic algorithms for order acceptance and scheduling with sequence-dependent setup times , 2018, Comput. Oper. Res..

[15]  Panos M. Pardalos,et al.  Serial-batching group scheduling with release times and the combined effects of deterioration and truncated job-dependent learning , 2018, J. Glob. Optim..

[16]  Fuh-Der Chou,et al.  A modified particle swarm optimization algorithm for a batch-processing machine scheduling problem with arbitrary release times and non-identical job sizes , 2018, Comput. Ind. Eng..

[17]  Panos M. Pardalos,et al.  A hybrid BA-VNS algorithm for coordinated serial-batching scheduling with deteriorating jobs, financial budget, and resource constraint in multiple manufacturers , 2017, Omega.

[18]  Alok Singh,et al.  Hybrid evolutionary approaches for the single machine order acceptance and scheduling problem , 2017, Appl. Soft Comput..

[19]  F. Sibel Salman,et al.  Order acceptance and scheduling decisions in make-to-order systems , 2010 .

[20]  Haoxun Chen,et al.  Fix-and-optimize and variable neighborhood search approaches for multi-level capacitated lot sizing problems , 2015 .

[21]  Alkin Yurtkuran,et al.  A novel artificial bee colony algorithm for the workforce scheduling and balancing problem in sub-assembly lines with limited buffers , 2018, Appl. Soft Comput..

[22]  Gur Mosheiov,et al.  A two-stage flow shop batch-scheduling problem with the option of using Not-All-Machines , 2013 .

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

[24]  Dervis Karaboga,et al.  A powerful and efficient algorithm for numerical function optimization: artificial bee colony (ABC) algorithm , 2007, J. Glob. Optim..

[25]  James C. Bean,et al.  Genetic Algorithms and Random Keys for Sequencing and Optimization , 1994, INFORMS J. Comput..

[26]  Pierre Hansen,et al.  Variable Neighbourhood Search , 2003 .

[27]  Ceyda Oguz,et al.  A tabu search algorithm for order acceptance and scheduling , 2012, Comput. Oper. Res..

[28]  Zhiwu Li,et al.  Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm , 2018, Journal of Intelligent Manufacturing.

[29]  Lei Wang,et al.  Integrated green scheduling optimization of flexible job shop and crane transportation considering comprehensive energy consumption , 2019, Journal of Cleaner Production.

[30]  Frank Werner,et al.  Minimizing the number of machines for scheduling jobs with equal processing times , 2009, Eur. J. Oper. Res..

[31]  Deming Lei,et al.  Hybrid flow shop scheduling with not-all-machines options via local search with controlled deterioration , 2016, Comput. Oper. Res..

[32]  Rainer Kolisch,et al.  Dynamic order acceptance and capacity planning in a stochastic multi-project environment with a bottleneck resource , 2018, Int. J. Prod. Res..

[33]  Ghasem Moslehi,et al.  A Lagrangian relaxation algorithm for order acceptance and scheduling problem: a globalised robust optimisation approach , 2016, Int. J. Comput. Integr. Manuf..

[34]  M. Selim Akturk,et al.  An exact approach to minimizing total weighted tardiness with release dates , 2000 .

[35]  Haibo Wang,et al.  Unrelated Parallel Machine Selection and Job Scheduling With the Objective of Minimizing Total Workload and Machine Fixed Costs , 2018, IEEE Transactions on Automation Science and Engineering.

[36]  Dong Cao,et al.  Parallel machine selection and job scheduling to minimize machine cost and job tardiness , 2005, Comput. Oper. Res..

[37]  Enrique Alba,et al.  Variable neighborhood search for the stochastic and dynamic vehicle routing problem , 2016, Ann. Oper. Res..

[38]  Mingzhou Jin,et al.  A genetic algorithm with neighborhood search for the resource‐constrained project scheduling problem , 2011 .

[39]  Rubén Ruiz,et al.  Scheduling unrelated parallel machines with optional machines and jobs selection , 2012, Comput. Oper. Res..

[40]  Pierre Hansen,et al.  Variable neighborhood search: Principles and applications , 1998, Eur. J. Oper. Res..

[41]  Gur Mosheiov,et al.  The optimal number of used machines in a two-stage flexible flowshop scheduling problem , 2014, J. Sched..

[42]  Ghasem Moslehi,et al.  A Benders decomposition approach for order acceptance and scheduling problem: a robust optimization approach , 2017 .

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

[44]  Pierre Hansen,et al.  Variable Neighborhood Search , 2018, Handbook of Heuristics.

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

[46]  T. C. Edwin Cheng,et al.  Parallel-machine scheduling of simple linear deteriorating jobs , 2009, Theor. Comput. Sci..

[47]  Mauricio G. C. Resende,et al.  Biased random-key genetic algorithms for combinatorial optimization , 2011, J. Heuristics.

[48]  Purushothaman Damodaran,et al.  A branch and price solution approach for order acceptance and capacity planning in make-to-order operations , 2011, Eur. J. Oper. Res..

[49]  Francisco Herrera,et al.  A practical tutorial on the use of nonparametric statistical tests as a methodology for comparing evolutionary and swarm intelligence algorithms , 2011, Swarm Evol. Comput..

[50]  Mostafa Hajiaghaei-Keshteli,et al.  The allocation of customers to potential distribution centers in supply chain networks: GA and AIA approaches , 2011, Appl. Soft Comput..

[51]  Xinyu Shao,et al.  MILP models for energy-aware flexible job shop scheduling problem , 2019, Journal of Cleaner Production.

[52]  Belén Melián-Batista,et al.  High-dimensional feature selection via feature grouping: A Variable Neighborhood Search approach , 2016, Inf. Sci..

[53]  Mostafa Hajiaghaei-Keshteli,et al.  Integrated scheduling of production and rail transportation , 2014, Comput. Ind. Eng..

[54]  Xiuli Wang,et al.  An enhanced ABC algorithm for single machine order acceptance and scheduling with class setups , 2016, Appl. Soft Comput..

[55]  Panos M. Pardalos,et al.  Parallel-batching scheduling of deteriorating jobs with non-identical sizes and rejection on a single machine , 2020, Optim. Lett..

[56]  Mehmet Emin Aydin,et al.  A Variable Neighbourhood Search Algorithm for Job Shop Scheduling Problems , 2006, EvoCOP.

[57]  E.L. Lawler,et al.  Optimization and Approximation in Deterministic Sequencing and Scheduling: a Survey , 1977 .

[58]  Peng Wang,et al.  A Knowledge-Based Ant Colony Optimization for Flexible Job Shop Scheduling Problems , 2010, Appl. Soft Comput..

[59]  Li-Ning Xing,et al.  Multi-population interactive coevolutionary algorithm for flexible job shop scheduling problems , 2011, Comput. Optim. Appl..

[60]  Imed Eddine Bennour,et al.  A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem , 2019, Swarm Intelligence.