Skip to main content

Advertisement

Log in

Two calibrated meta-heuristics to solve an integrated scheduling problem of production and air transportation with the interval due date

  • Methodologies and Application
  • Published:
Soft Computing Aims and scope Submit manuscript

Abstract

Contrary to previous methods in production management, today’s approaches mainly focus on the whole supply chain parties’ considerations. Considering production planning and distribution, as the two main functions in supply chain (SC) management, in an integrated manner in order to enhance the SC advantages is one of today’s main dilemma. Here, we have firstly proposed and investigated the integrated production and air transportation scheduling problem with time windows for the due date to minimize the total SC costs. Since the problem was NP-hard, two new coordinated and integrated solution procedures have been presented based on meta-heuristics. Four algorithms (i.e., simulated annealing (SA), genetic algorithm, particle swarm optimization/district PSO (PSO/DPSO), and hybrid variable neighborhood search–simulated annealing (H-VNS–SA)) have been developed in both procedures. For the first time in literature, we probe different encoding schemes in the proposed algorithms. In addition, by using Taguchi experimental design, the parameters of the algorithms have been tuned. Besides, to study the behavior of the algorithms, different problem sizes have been generated and the results of two procedures have been compared together and discussed. Finally, a comparison of the proposed algorithms with some state-of-art optimized algorithms has been presented to prove statistically better performance of the proposed algorithms in most cases.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23

Similar content being viewed by others

Notes

  1. International Civil Aviation Organization; http://www.icao.int/.

References

  • Al-Aomar R (2006) Incorporating robustness into genetic algorithm search of stochastic simulation outputs. Simul Model Pract Theory 14:201–223

    Google Scholar 

  • Al-Aomar R, Al-Okaily A (2006) A GA-based parameter design for single machine turning process with high-volume production. Comput Ind Eng 50:317–337

    Google Scholar 

  • Behnamian J, Zandieh M, Ghomi S (2009) Parallel-machine scheduling problems with sequence-dependent setup times using an ACO, SA and VNS hybrid algorithm. Expert Syst Appl 36(6):9637–9644

    Google Scholar 

  • Černý V (1985) Thermo dynamical approach to the traveling salesman problem: an efficient simulation algorithm. J Optim Theory Appl 45:41–51

    MathSciNet  MATH  Google Scholar 

  • Chen Z (2010) Integrated production and outbound distribution scheduling: review and extensions. Oper Res 58(1):130–148

    MATH  Google Scholar 

  • Chen K, Zhou F, Yin L, Wang Sh, Wang Y, Wan F (2018) A hybrid particle swarm optimizer with sine cosine acceleration coefficients. Inf Sci 422:218–241

    MathSciNet  Google Scholar 

  • De Armas J, Melián-Batista B (2015) Variable neighborhood search for a dynamic rich vehicle routing problem with time windows. Comput Ind Eng 85:120–131

    Google Scholar 

  • Devapriya P, Ferrell W, Geismar N (2017) Integrated production and distribution scheduling with a perishable product. Eur J Oper Res 259:906–916

    MathSciNet  MATH  Google Scholar 

  • Dib O, Moalic L, Manier MA, Caminada A (2017) An advanced GA–VNS combination for multicriteria route planning in public transit networks. Expert Syst Appl 72(15):67–82

    MATH  Google Scholar 

  • Eberhart RC, Kennedy J (1995) A new optimizer using particle swarm theory. In: Proceedings of the sixth international symposium on micromachine and human science, Nagoya, Japan, pp 39–43

  • Fathollahi-Fard AM, Hajiaghaei-Keshteli M (2018) A stochastic multi-objective model for a closed-loop supply chain with environmental considerations. Appl Soft Comput 69:232–249

    Google Scholar 

  • Fu B, Huo Y, Zhao H (2012) Coordinated scheduling of production and delivery with production window and delivery capacity constraints. Theor Comput Sci 422:39–51

    MathSciNet  MATH  Google Scholar 

  • Gen M, Altiparmak F, Lin L (2006) A genetic algorithm for two-stage transportation problem using priority-based encoding. OR Spectrum 28:337–354

    MathSciNet  MATH  Google Scholar 

  • Hajiaghaei-Keshteli M, Aminnayeri M (2014) Solving the integrated scheduling of production and rail transportation problem by Keshtel algorithm. Appl Soft Comput 25:184–203

    Google Scholar 

  • Hajiaghaei-Keshteli M, Fathollahi-Fard AM (2018) A set of efficient heuristics and metaheuristics to solve a two-stage stochastic bi-level decision-making model for the distribution network problem. Comput Ind Eng 123:378–395

    Google Scholar 

  • Hajiaghaei-Keshteli M, Aminnayeri M, Fatemi Ghomi SMT (2014) Integrated scheduling of production and rail transportation. Comput Ind Eng 74:240–256

    Google Scholar 

  • Holland J (1975) Adaptation in natural and artificial systems. University of Michigan Press, Ann Arbor

    Google Scholar 

  • Hansen P, Mladenović N (2001) Variable neighborhood search: principles and applications. Euro J Oper Res 130(3):449–467

    MathSciNet  MATH  Google Scholar 

  • Kang H, Pearn WL, Chung I, Lee A (2015) An enhanced model for the integrated production and transportation problem in a multiple vehicles environment. Soft Comput. https://doi.org/10.1007/s00500-015-1595-7

    Article  Google Scholar 

  • Karaoğlan I, Kesen SE (2016) The coordinated production and transportation scheduling problem with a time-sensitive product: a branch-and-cut algorithm. Int J Prod Res. https://doi.org/10.1080/00207543.2016.1213916

    Article  Google Scholar 

  • Kaya O, Kubalı D, Örmeci L (2013) A coordinated production and shipment model in a supply chain. Int J Prod Econ 143(1):120–131

    Google Scholar 

  • Kim SJ, Kim KS, Jang H (2003) Optimization of manufacturing parameters for a brake lining using Taguchi method. J Mater Process Technol 136:202–208

    Google Scholar 

  • Kirkpatrick S, Gelatt CD Jr, Vecchi MP (1983) Optimization by simulated annealing. Science 220(4598):671–680

    MathSciNet  MATH  Google Scholar 

  • Kolischa R, Padman R (2001) An integrated survey of deterministic project scheduling. Omega 29:249–272

    Google Scholar 

  • Lacomme P, Moukrim A, Quilliot A, Viont M (2016) The Integrated Production and Transportation Scheduling Problem based on a GRASP × ELS resolution scheme. IFAC-PapersOnline 49–12:1466–1471

    Google Scholar 

  • Lei D, Guo X (2014) Variable neighbourhood search for dual-resource constrained flexible job shop scheduling. Int J Prod Res 52(9):2519–2529

    Google Scholar 

  • Lei H, Laporte G, Guo B (2012) A generalized variable neighborhood search heuristic for the capacitated vehicle routing problem with stochastic service times. TOP 20(1):99–118

    MathSciNet  MATH  Google Scholar 

  • Li KP, Sivakumar AI, Mathirajan M, Ganesan VK (2004) Solution methodology for synchronizing assembly manufacturing and air transportation of consumer electronics supply chain. Int J Bus 9:361–380

    Google Scholar 

  • Li KP, Ganesan VK, Sivakumar AI (2005) Synchronized scheduling of assembly and multi-destination air transportation in a consumer electronics supply chain. Int J Prod Res 43:2671–2685

    Google Scholar 

  • Li KP, Ganesan VK, Sivakumar AI (2006a) Scheduling of single stage assembly with air transportation in a consumer electronic supply chain. Comput Ind Eng 51:264–278

    Google Scholar 

  • Li KP, Ganesan VK, Sivakumar AI (2006b) Methodologies for synchronized scheduling of assembly and air transportation in a consumer electronic supply chain. Int J Logist Syst Manag 2:52–67

    Google Scholar 

  • Li KP, Sivakumar AI, Fu Q, Jin X (2007) A case study for synchronized scheduling and manufacturing and air transportation in consumer electronics supply chain. In: IEEE conference on industrial engineering and engineering management (IEEM) Singapore, pp 1629–1633

  • Li KP, Sivakumar AI, Ganesan VK (2008) Complexities and algorithms for synchronized scheduling of parallel machine assembly and air transportation in consumer electronic supply chain. Eur J Oper Res 187:442–455

    MathSciNet  MATH  Google Scholar 

  • Li K, Jia Z, Leung J (2015) Integrated production and delivery on parallel batching machines. Eur J Oper Res 247(3):755–763

    MathSciNet  MATH  Google Scholar 

  • Lotfi MM, Tavakkoli-Moghaddam R (2013) A genetic algorithm using priority-based encoding with new operators for fixed charge transportation problems. Appl Soft Comput 13:2711–2726

    Google Scholar 

  • Low C, Chang C, Li R, Huang C (2014) Coordination of production scheduling and delivery problems with heterogeneous fleet. Int J Prod Econ 153:139–148

    Google Scholar 

  • Menéndez B, Bustillo M, Pardo EG, Duarte A (2017) General variable neighborhood search for the order batching and sequencing problem. Eur J Oper Res 263(1):82–93

    MathSciNet  MATH  Google Scholar 

  • Michalewicz Z, Vignaux GA, Hobbs M (1991) A nonstandard genetic algorithm for the nonlinear transportation problem. Informs J Comput 3:307–316

    MATH  Google Scholar 

  • Mohammadi S, Mirzapour S, Hashem A, Rekikc Y (2020) An integrated production scheduling and delivery route planning with multi-purpose machines: a case study from a furniture manufacturing company. Int J Prod Econ 219:347–359

    Google Scholar 

  • Mortazavi A, Arshadi Khamseh A, Naderi B (2015) A novel chaotic imperialist competitive algorithm for production and air transportation scheduling problems. Neural Comput Appl 26:1709–1723

    Google Scholar 

  • Noroozi A, Mahdavi Mazdeh M, Heydari M, Rasti-Barzoki M (2018) Coordinating order acceptance and integrated production-distribution scheduling with batch delivery considering Third Party Logistics distribution. J Manuf Syst 46:29–45

    Google Scholar 

  • Pana Q, Tasgetirenc MF, Liangd Y (2008) A discrete particle swarm optimization algorithm for the no-wait flow shop scheduling problem. Comput Oper Res 35:2807–2839

    MathSciNet  Google Scholar 

  • Phadke MS (1989) Quality engineering using robust design. Prentice-Hall, NJ

    Google Scholar 

  • Rostamian Delavar M, Hajiaghaei-Keshteli M, Molla–Alizadeh–Zavardehi S (2010) Genetic algorithms for coordinated scheduling of production and air transportation. Expert Syst Appl 37:8255–8266

    Google Scholar 

  • Samadi A, Mehranfar N, Fathollahi Fard AM, Hajiaghaei-Keshteli M (2018) Heuristic-based metaheuristics to address a sustainable supply chain network design problem. J Ind Prod Eng 35(2):102–117

    Google Scholar 

  • Schermer D, Moeini M, Wendt O (2019) A hybrid VNS/Tabu search algorithm for solving the vehicle routing problem with drones and en route operations. Comput Oper Res 109:134–158

    MathSciNet  MATH  Google Scholar 

  • Taguchi G (1986) Introduction to quality engineering. White Plains: Asian Productivity Organization/UNIPUB, New York

    Google Scholar 

  • Tahmasbi-birgani E, Najafi R, Soltani A, Mahmoodi-Rad S, Molla-Alizadeh-Zavardehi S (2012) Coordinated scheduling of production and air transportation in supply chain management. Int J Emerg Technol Adv Eng 2(10):2250–2459

    Google Scholar 

  • Tizhoosh HR (2005) Opposition-based learning: a new scheme for machine intelligence. In: Proceedings of the international conference on computational intelligence for modelling, control and automation, and international conference on intelligent agents, web technologies and internet commerce, pp 695–701

  • Ullrich CA (2013) Integrated machine scheduling and vehicle routing with time windows. Eur J Oper Res 227(1):152–165

    MathSciNet  MATH  Google Scholar 

  • Vignaux GA, Michalewicz Z (1991) A genetic algorithm for the linear transportation problem. IEEE Trans Syst Man Cybern 21:445–452

    MathSciNet  MATH  Google Scholar 

  • Wang D, Zhu J, Wei X, Cheng T, Yin Y, Wang Y (2019) Integrated production and multiple trips vehicle routing with time windows and uncertain travel times. Comput Oper Res 103:1–12

    MathSciNet  MATH  Google Scholar 

  • Zandieh M, Molla-Alizadeh-Zavardehi S (2008) Synchronized production and distribution scheduling with due window. Journal of Applied Sciences 8:2752–2757

    Google Scholar 

  • Zandieh M, Molla-Alizadeh-Zavardehi S (2009) Synchronizing production and air transportation scheduling using mathematical programming models. J Comput Appl Math 230:546–558

    MathSciNet  MATH  Google Scholar 

  • Zhang B, Pan QK, Gao L, Zhang XL (2018) A hybrid variable neighborhood search algorithm for the hot rolling batch scheduling problem in compact strip production. Comput Ind Eng 116:22–36

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to M. Hajiaghaei–Keshteli.

Ethics declarations

Conflict of interest

The authors declare that there is no any potential conflict of interest regarding the publication of this paper.

Ethical approval

The authors certify that they have no any affiliation with or involvement with human participants or animals performed by any of the authors in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this paper.

Additional information

Communicated by V. Loia.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix 1

See Table 16.

Table 16 The orthogonal array L27 for GA algorithm

Appendix 2

See Table 17.

Table 17 The orthogonal array L27 for SA algorithm

Appendix 3

See Table 18.

Table 18 The orthogonal array L9 for PB-PSO algorithm

Appendix 4

See Table 19.

Table 19 The orthogonal array L9 for DPSO algorithm

Appendix 5

See Table 20.

Table 20 The orthogonal array L9 for H-VNS–SA algorithm

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mousavi, M., Hajiaghaei–Keshteli, M. & Tavakkoli–Moghaddam, R. Two calibrated meta-heuristics to solve an integrated scheduling problem of production and air transportation with the interval due date. Soft Comput 24, 16383–16411 (2020). https://doi.org/10.1007/s00500-020-04948-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-020-04948-y

Keywords

Navigation