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A novel framework of hyper-heuristic approach and its application in location-routing problem with simultaneous pickup and delivery

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Abstract

This paper addresses a new variant of location-routing problem (LRP), namely the LRP with simultaneous pickup and delivery (LRPSPD). A hyper-heuristic approach based on iterated local search (ILS-HH) is introduced to automatically optimize the LRPSPD. On basis of the novel proposed framework of hyper-heuristic, four selections mechanisms and five activation strategies are developed to examine the performance of the proposed framework. Three types computational evaluations were carried out and several conclusions can be drawn: (1) the proposed framework performs better than the classical one with performing several heavy-duty combinations of strategies in terms of solution quality and computing time; (2) different activated strategies have slight (not significant) effect on exploiting best solutions; (3) FRR-MAB-TS (fitness ratio rank based on multi-armed bandit with tabu search) works best among all selection methods. Moreover, the proposed approach could provide competitive, even better results compared to fine-tuned bespoke state-of-the-art approaches.

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References

  • Adriaensen S, Brys T, Nowe A (2014) Fair-Share ILS: a simple state-of-the-art iterated local search hyperheuristic. In: 16th Genetic and evolutionary computation conference, pp 1303–1310

  • Angelelli E, Mansini R (2002) The vehicle routing problem with time windows and simultaneous pick-up and delivery. In: Klose A, Speranza MG, VanWassenhove LN (eds) Quantitative approaches to distribution logistics and supply chain management. Springer, Berlin, pp 249–267

    Google Scholar 

  • Baldacci R, Mingozzi A, Calvo RW (2011) An exact method for the capacitated location-routing problem. Oper Res 59(5):1284–1296

    Google Scholar 

  • Barreto S, Ferreira C, Paixão J, Santos BS (2007) Using clustering analysis in a capacitated location-routing problem. Eur J Oper Res 179(3):968–977

    Google Scholar 

  • Belenguer JM, Benavent E, Prins C, Prodhon C (2011) A branch-and-cut method for the capacitated location-routing problem. Comput Oper Res 38(6):931–941

    Google Scholar 

  • Boudagri F, Aggoune-Mtalaa W, Bennerkrouf M (2013) Application of a clustering based location-routing model to a real agri-food supply chain redesign. In: Nuguyen N, Trawiński B, Katarzyniak R, Jo GS (eds) Advanced methods for computational collective intelligence. Springer, Berlin, pp 323–331

    Google Scholar 

  • Burke EK, Kendall G, Soubeiga E (2003) A Tabu-search hyperheuristic for timetabling and rostering. J Heuristics 9(6):451–470

    Google Scholar 

  • Burke EK, Mccollum B, Meisels A, Petrovic S, Qu R (2007) A graph-based hyper-heuristic for educational timetabling problems. Eur J Oper Res 176(1):177–192

    Google Scholar 

  • Burke EK, Hude M, Kendall G, Ochoa G, Ozcan E, Woodward JR (2010) A classification of hyper-heuristic approaches. In: Gendreau M, Potvin JY (eds) International series in operations research and management science. Springer, Boston, pp 449–468

    Google Scholar 

  • Burke EK, Gendreau M, Hyde M, Kendall G, Ochoa G, Ozcan E, Qu R (2013) Hyper-heuristics: a survey of the state of the art. J Oper Res Soc 64(12):1695–1724

    Google Scholar 

  • Ceselli A, Righini G, Tresoldi E (2014) Combined location and routing problems for drug distribution. Discrete Appl Math 165(2):113–117

    Google Scholar 

  • Chakhlevitch K, Cowling P (2008) Hyperheuristics: recent developments. In: Cotta C, Sevaux M, Sorensen K (eds) Adaptive and multilevel metaheuristics. Springer, Berlin, pp 3–29

    Google Scholar 

  • Contardo C, Cordeau JF, Gendron B (2013) A computational comparison of flow formulations for the capacitated location-routing problem. Discrete Optim 10(4):263–295

    Google Scholar 

  • Contardo C, Cordeau JF, Gendron B (2014) An exact algorithm based on cut-and-column generation for the capacitated location-routing problem. Informs J Comput 26(1):88–102

    Google Scholar 

  • Cowling P, Kendall G, Soubeiga E (2000) A hyper-heuristic approach to scheduling a sales summit. In: Practice and theory of automated timetabling III: third international conference, pp 176–190

  • Cowling P, Kendall G, Soubeiga E (2002) Hyperheuristics: a tool for rapid prototyping in scheduling and optimization. In: Cagnoni S, Gottlieb J, Hart E, Middendorf M, Raidl GR (eds) Application of evolutionary computing. Springer, Berlin, pp 1–10

    Google Scholar 

  • Denzinger J, Fuchs M, (1997). High performance ATP systems by combining several AI methods. In: International joint conference on artifical intelligence, pp 102–107

  • Drexl M, Schneider M (2015) A survey of variants and extensions of the location-routing problem. Eur J Oper Res 241(2):283–308

    Google Scholar 

  • Duhamel C, Lacomme P, Prins C, Prodhon C (2010) A GRASP × ELS approach for the capacitated location-routing problem. Comput Oper Res 37(11):1912–1923

    Google Scholar 

  • Escobar JW, Linfati R, Toth P (2013) A two-phase hybrid heuristic algorithm for the capacitated location-routing problem. Comput Oper Res 40(1):70–79

    Google Scholar 

  • Escobar JW, Linfati R, Baldoquin MG, Toth P (2014) A granular variable Tabu neighbourhood search for the capacitated location-routing problem. Transp Res Part B Methodol 67(9):344–356

    Google Scholar 

  • Escobar JW, Linfati R, Adarme JW (2015) The capacitated location routing problem: review of literature. Facultad De Ingeniería 24(39):85–98

    Google Scholar 

  • Fiahlo A, Costa LD, Schoenauer M, Sebag M (2009) Dynamic mutil-armed bandits and extreme value-based rewards for adaptive operator selection in evolutionary algorithms. In: Stützle T (ed) Learning and intelligent optimization. Springer, Berlin, pp 176–190

    Google Scholar 

  • Glover F, Rafael M (2006) Tabu search. In: Alba E, Marti R (eds) Metaheuristic procedures for training neutral networks. Springer, Boston, pp 53–69

    Google Scholar 

  • Han L, Kendall G (2003) An investigation of a Tabu assisted hyper-heuristic genetic algorithm. In: CEC: 2003 congress on evolutionary computation, pp 2230–2237

  • Hemmelmayr VC, Cordeau JF, Crainic TG (2012) An adaptive large neighbourhood search heuristic for two-echelon vehicle routing problems arising in city logistics. Comput Oper Res 39(12):3215–3228

    Google Scholar 

  • Jacobsen SK, Madsen OBG (1980) A comparative study of heuristics for a two-level routing-location problem. Eur J Oper Res 5(6):378–387

    Google Scholar 

  • Kalender M, Kheiri A, Ozcan E, Burke EK (2013) A greedy gradient-simulated annealing selection hyper-heuristic. Soft Comput 17(12):2279–2292

    Google Scholar 

  • Karaoglan I, Altiparmak F, Kara I, Dengiz B (2011) A branch and cut algorithm for the location-routing problem with simultaneous pickup and delivery. Eur J Oper Res 211(2):318–332

    Google Scholar 

  • Karaoglan I, Altiparmak F, Kara I, Dengiz B (2012) The location-routing problem with simultaneous pickup and delivery: formulations and a heuristic approach. Omega 40(4):465–477

    Google Scholar 

  • Kendall G, Hussin NM (2004) A Tabu search hyper-heuristic approach to the examination timetabling problem at the MARA university of technology. In: Burke E, Trick M (eds) Practice and theory of automated timetable V. Springer, Berlin, pp 270–293

    Google Scholar 

  • Koulinas GK, Anagnostopoulos KP (2013) A new Tabu search–based hyper-heuristic algorithm for solving construction levelling problems with limited resource availabilities. Autom Constr 31(3):169–175

    Google Scholar 

  • Li K, Fialho A, Kwong S, Zhang QF (2014) Adaptive operator selection with bandits for a multiobjective evolutionary algorithm based on decomposition. IEEE Trans Evolut Comput 18(1):114–130

    Google Scholar 

  • Lin S (1965) Computer solutions for the travelling salesman problem. Bell Syst Tech J 10(10):2245–2269

    Google Scholar 

  • Lin S, Kernighan BW (1973) An effective heuristic algorithm for the travelling salesman problem. Oper Res 21(2):498–516

    Google Scholar 

  • Lopes RB, Ferreora C, Santo BS, Barreto S (2013) A taxonomical analysis, current methods and objectives on location-routing problems. Int Trans Oper Res 20(6):795–822

    Google Scholar 

  • Lopes RB, Ferreira C, Santos BS (2016) A simple and effective evolutionary algorithm for the capacitated location-routing problem. Comput Oper Res 70:155–162

    Google Scholar 

  • Madsen OBG (1983) Methods for solving combined two level location-routing problems of realistic dimensions. Eur J Oper Res 12(3):295–301

    Google Scholar 

  • Marshall RJ, Johnsto M, Zhang MJ (2015) Hyper-heuristic operator selection and acceptance criteria. In: Ochoa G, Chicano F (eds) Evolutionary computation in combinatorial optimization. Springer, Berlin, pp 99–113

    Google Scholar 

  • Nagy G, Salhi S (1998) The many-to-many location-routing problem. TOP 6(2):261–275

    Google Scholar 

  • Nagy G, Salhi S (2007) Location-routing: issues, models and methods. Eur J Oper Res 177(2):649–672

    Google Scholar 

  • Nareyek A (2003) Choosing search heuristics by non-stationary reinforcement learning. In: Resende MGC, DeSousa JP (eds) Metaheuristics: computer decision-making applied optimization. Springer, Boston, pp 523–544

    Google Scholar 

  • Osman IH (1993) Metastrategy simulated annealing and Tabu search algorithms for the vehicle routing problem. Ann Oper Res 41(4):421–451

    Google Scholar 

  • Ozcan E, Bilgin B, Kormaz EE (2008) A comprehensive analysis of hyper-heuristics. Intell Data Anal 12(1):3–23

    Google Scholar 

  • Popke S, Pisinger D (2006) An adaptive large neighbourhood search heuristic for pickup and delivery with time windows. Transp Sci 40(4):455–472

    Google Scholar 

  • Prins C, Prodhon C, Wolfler CR (2006) Solving the capacitated location-rrouting problem by a GRASP complemented by a learning process and a path relinking. 4OR 4(3):221–238

    Google Scholar 

  • Prins C, Prodhon C, Ruiz A, Soriano P, Calvo RW (2007) Solving the capacitated location-routing problem by a cooperative Lagrangean relaxation-granular Tabu search heuristic. Transp Sci 41(4):470–483

    Google Scholar 

  • Prodhon C, Prins C (2014) A survey of recent research on location-routing problems. Eur J Oper Res 238(1):1–17

    Google Scholar 

  • Rahim F, Sepil C (2014) A location-routing problem in glass recycling. Ann Oper Res 233(1):329–353

    Google Scholar 

  • Sabar N, Ayob M, Kendall G, Qu R (2015) The automatic design of a hyper-heuristic framework with gene expression programming for combinatorial optimization problems. IEEE Trans Evolut Comput 19(3):309–325

    Google Scholar 

  • Salhi S, Nagy G (1999) A cluster insertion heuristic for single and multiple depot vehicle routing problems with backhauling. J Oper Res Soc 50(10):1034–1042

    Google Scholar 

  • Savelsbergh M (1992) The vehicle routing problem with time widows: minimizing route duration. Informs J Comput 4(2):146–154

    Google Scholar 

  • Shaw P (1997) A new local search algorithm providing high quality solutions to vehicle routing problem. Technical report, department of Computer Science, University of Strathclyde, Scotland

  • Shaw P (1998) Using constraint programming and local search methods to solve vehicle routing problems. In: Maher M, Puget JF (eds) Principles and practice of constraint programming. Springer, Berlin, pp 417–431

    Google Scholar 

  • Strickler A, Lima JAP, Vergilio SR, Pozo ATR (2016) Deriving products for variability test of feature models with a hyper-heuristic approach. Appl Soft Comput 49:1232–1242

    Google Scholar 

  • Thierens D (2005) An adaptive pursuit strategy for allocating operator probabilities. In: Genetic and evolutionary computation conference, pp 1539–1546

  • Ting CJ, Chen CH (2013) A multiple ant colony optimization algorithm for the capacitated location routing problem. Int J Prod Econ 15(4):333–346

    Google Scholar 

  • Tuzun D, Burke LI (1999) A two-phase Tabu search approach to the location routing problem. Eur J Oper Res 116(1):87–99

    Google Scholar 

  • Walker JD, Ochoa G, Gendreau M, Burke EK (2012) Vehicle routing and adaptive iterated local search within the hyflex hyper-heuristic framework. In: International conference on learning and intelligent optimization, pp 265–276

  • Wasner M, Zapfel G (2004) An integrated multi-depot hub-location vehicle routing model for network planning of parcel service. Int J Prod Econ 90(3):403–419

    Google Scholar 

  • Wu XL, Pietro C, Leandro M, Gabriela O, Yao X (2016) An evolutionary hyper-heuristic for the software project scheduling problem. In: Handl J, Hart E, Lewis P, López-Ibáñez M, Ochoa G, Paechter B (eds) Parallel problem solving from nature—PPSN XIV. Springer, Cham, pp 37–47

    Google Scholar 

  • Yildiz EAD, Karaoglan I, Altiparmak F (2016) Two echelon location routing problem with simultaneous pickup and delivery: mixed integer programming formulations and comparative analysis. Springer, Berlin, pp 275–289

    Google Scholar 

  • Yildz H, Johnson MP, Roehring S (2013) Planning for meals-on-wheels: algorithms and application. J Oper Res Soc 64(10):1540–1550

    Google Scholar 

  • Yu VF, Lin SW (2014) Multi-start simulated annealing heuristic for the location routing problem with simultaneous pickup and delivery. Appl Soft Comput 24:284–290

    Google Scholar 

  • Yu VF, Lin SY (2016) Solving the location-routing problem with simultaneous pickup and delivery by simulated annealing. Int J Prod Res 54(2):526–549

    Google Scholar 

  • Yu VF, Lin SW, Lee W, Ting CJ (2010) A simulated annealing heuristic for the capacitated location routing problem. Comput Ind Eng 58(2):288–299

    Google Scholar 

  • Zamli K, Kamal Z, Basem Y, Kendall G (2016) A Tabu search hyper-heuristic strategy for T-way test suite generation. Appl Soft Comput 44:57–74

    Google Scholar 

  • Zamli K, Din F, Kendall G, Ahmed BS (2017) An experimental study of hyper-heuristic selection and acceptance mechanism for combinatorial T-way test suite generation. Inf Sci 399:121–153

    Google Scholar 

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Acknowledgements

Authors thank Professor Yu. V.F. and Lin. S.Y. for providing assistance during the experiment, and anonymous referees and editors for their constructive comments, time and patience devoted to the review of this paper. The presented research was supported by the National Natural Science Foundation of China (No. 61572438, 61873240, 61402409), Science Technology plan project of Zhejiang (No. 2017C33224) and National Natural Science Foundation of Zhejiang (No. Y19F030052).

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Authors and Affiliations

  1. College of Mechanical Engineering, Zhejiang University of Technology, Hangzhou, 310012, Zhejiang, People’s Republic of China

    Yanwei Zhao, Longlong Leng & Chunmiao Zhang

  2. Jiaxing Vocational and Technical College, Jiaxing, 314036, Zhejiang, People’s Republic of China

    Chunmiao Zhang

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  1. Yanwei Zhao
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  2. Longlong Leng
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Correspondence to Yanwei Zhao.

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Appendix 1

Appendix 1

Table of acronyms

A

 AM

All moves

 Avg.

Average

B

 BC

Brand-and-cut

 BKS

Best known solution

C

 CS

Selection of HC

E

 ELS

Evolutionary local search

 FLP

Facility location problem

 FRR-MAB

Fitness ratio rank based on multi-armed bandit

 FRR-MAB-TS

FRR-MAB combined with TS

G

 GA

Genetic algorithm

 GD

Great deluge

 GH

Greedy heuristic

H

 HC

Hill climber

 HH

Hyper-heuristic

 HLH

High-level heuristic

I

 IE

Improving and equal

 ILS

Iterated local search

L

 LLH

Low-level heuristic

 LRP

Location-routing problem

 LRPSPD

LRP with simultaneous pickup and delivery

M

 MC

Monte Carlo

 MDVRP

Multi-depot vehicle routing problem

 MH

Mutational heuristic

 MS

Selection of MH

O

 OI

Only improving

R

 RD

Random descend

 RKGH

Regret-k greedy heuristic

 RP

Random permutation

S

 SA

Simulated annealing

 SR

Simple random

T

 TS

Tabu search

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Zhao, Y., Leng, L. & Zhang, C. A novel framework of hyper-heuristic approach and its application in location-routing problem with simultaneous pickup and delivery. Oper Res Int J 21, 1299–1332 (2021). https://doi.org/10.1007/s12351-019-00480-6

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