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An approach for the solution to order batching and sequencing in picking systems

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Abstract

This article discusses the use of Genetic Algorithms to solve a specific variation of the Order Batching and Sequencing Problem (OBSP) called Optimized Picking Sequence (OPS). Essentially, OPS is an optimization problem of the order picking in a typical Warehouse (WA) that employs a low-level picker-to-parts system with a pick-and-sort process. The OPS solution premise is to minimize the time and total cost of the picking processes in order to better adjust the trade-off between the level of customer service and the efficiency of this WA. To solve the OPS, we propose a computational tool called GA–OPS which is formulated by the iteration of two Genetic Algorithms (GABATCH and GATSP). GABATCH groups products from different picking orders into picking batches to prevent earliness and tardiness in order fulfilment and to minimize the number of picking travels. GATSP finds the best possible picking routes to reduce distance and picking total time for each batch. The OPS approach is an extension of the Optimal Billing Sequencing (OBS) problem and deals with practical dilemmas not addressed by the OBSP yet. The objective is to evaluate the GA–OPS performance in face of the conditions needed to adapt it to the real conditions of WAs. Experiments with problems of different complexity levels showed that the GA–OPS provides satisfactory quality solutions to any OPS instance. The iteration approach proposed to the OPS fills a gap in the literature and makes innovative contributions to advances towards developing and applying picking optimization methods which are more suitable to the reality of the mentioned WAs.

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References

  1. De Koster RBM, Le-Duc T, Roodbergen KJ (2007) Design and control of warehouse order picking: a literature review. Euro J Oper Res 182(2):481–501

    Article  MATH  Google Scholar 

  2. Bottani E, Cecconi M, Vignali G et al (2012) Optimisation of storage allocation in order picking operations through a genetic algorithm. Int J Logist Res Apps 15(2):127–146

    Article  Google Scholar 

  3. Tompkins JA, White JA, Bozer YA et al (2010) Facilities planning. Wiley, New York

    Google Scholar 

  4. Bartholdi III JJ, Hackman ST. Warehouse & distribution science: release 0.96. The Supply Chain and Logistics Institute, School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, GA, http://www2.isye.gatech.edu/~jjb/wh/book/editions/wh-sci-0.96.pdf 2014, Accessed 10 Aug 2018

  5. De Koster RBM, Johnson AL, Roy D (2017) Warehouse design and management. Int J Prod Res 55(21):6327–6330

    Article  Google Scholar 

  6. Chabot T, Lahyani R, Coelho LC et al (2017) Order picking problems under weight, fragility and category constraints. Int J Prod Res 55(21):6361–6379

    Article  Google Scholar 

  7. Scholz A, Schubert D, Wäscher G (2017) Order picking with multiple pickers and due dates—simultaneous solution of order batching, batch assignment and sequencing, and picker routing problems. Euro J Oper Res 263(2):461–478

    Article  MathSciNet  MATH  Google Scholar 

  8. Gils TV, Ramaekers K, Caris A et al (2018) Designing efficient order picking systems by combining planning problems: state-of-the-art classification and review. Euro J Oper Res 267(1):1–15

    Article  MathSciNet  MATH  Google Scholar 

  9. Marchet G, Melacini M, Perotti S (2015) Investigating order picking system adoption: a case-study-based approach. Int J Logist Res Apps 18(1):82–98

    Article  Google Scholar 

  10. Gu J, Goetschalckx M, McGinnis LF (2010) Research on warehouse design and performance evaluation: a comprehensive review. Euro J Oper Res 203(3):539–549

    Article  MATH  Google Scholar 

  11. Grosse EH, Glock CH, Neumann WP (2017) Human factors in order picking: a content analysis of the literature. Int J Prod Res 55(5):1260–1276

    Article  Google Scholar 

  12. Henn S, Schmid V (2013) Metaheuristics for order batching and sequencing in manual order picking systems. Comput Ind Eng 66(2):338–351

    Article  Google Scholar 

  13. Van Nieuwenhuyse I, De Koster RB (2009) Evaluating order throughput time in 2-block warehouses with time window batching. Int J Prod Econ 121(2):654–664

    Article  Google Scholar 

  14. Pinto ARF, Crepaldi AF, Nagano MS (2018) A genetic algorithm applied to pick sequencing for billing. J Intell Manuf 29(2):405–422

    Article  Google Scholar 

  15. Chen TL, Cheng CY, Chen YY et al (2015) An efficient hybrid algorithm for integrated order batching, sequencing and routing problem. Int J Prod Econ 159:158–167

    Article  Google Scholar 

  16. Petersen CG, Aase G (2004) A comparison of picking, storage, and routing policies in manual order picking. Int J Prod Econ 92(1):11–19

    Article  Google Scholar 

  17. Chang FL, Liu ZX, Zheng X et al (2007) Research on order picking optimization problem of automated warehouse. Syst Eng Theory Pract 27(2):139–143

    Article  Google Scholar 

  18. Li J, Huang R, Dai JB (2017) Joint optimisation of order batching and picker routing in the online retailer’s warehouse in China. Int J Prod Res 55(2):447–461

    Article  Google Scholar 

  19. Scholz A, Wäscher G (2017) Order batching and picker routing in manual order picking systems: the benefits of integrated routing. Cent Eur J Oper Res 25(2):491–520

    Article  MathSciNet  MATH  Google Scholar 

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

    Google Scholar 

  21. Bertrand JWM, Fransoo JC (2002) Modelling and simulation: operations management research methodologies using quantitative modeling. Int J Oper Prod Manage 22(2):241–264

    Article  Google Scholar 

  22. Gibson DR, Sharp GP (1992) Order batching procedures. Euro J Oper Res 58(1):57–67

    Article  Google Scholar 

  23. Hsu CM, Chen KY, Chen MC (2005) Batching orders in warehouses by minimizing travel distance with genetic algorithms. Comput Ind 56(2):169–178

    Article  Google Scholar 

  24. Clarke GU, Wright JW (1964) Scheduling of vehicles from a central depot to a number of delivery points. Oper Res 12(4):568–581

    Article  Google Scholar 

  25. Vinod HD (1969) Integer programming and the theory of grouping. J Am Stat Assoc 64(326):506–519

    Article  MATH  Google Scholar 

  26. Armstrong RD, Cook WD, Saipe AL (1979) Optimal batching in a semi-automated order picking system. J Oper Res Soc 30(8):711–720

    Article  MATH  Google Scholar 

  27. Elsayed EA, Dube R, Proctor CL (1980) Heuristic algorithms for handling orders within a warehouse. Units and Bulk Materials Handling. ASME Publication, San Francisco, pp 225–233

    Google Scholar 

  28. Elsayed EA (1981) Algorithms for optimal material handling in automatic warehousing systems. Int J Prod Res 19(5):525–535

    Article  Google Scholar 

  29. Goetschalckx M (1983) Storage and retrieval policies for efficient order picking operations. PhD Thesis, Georgia Institute of Technology, GA

  30. Ratliff HD, Rosenthal AS (1983) Order-picking in a rectangular warehouse: a solvable case of the traveling salesman problem. Operations Research 31(3):507–521

    Article  MATH  Google Scholar 

  31. Gademann N, Van de Velde S (2005) Order batching to minimize total travel time in a parallel-aisle warehouse. IIE Trans 37(1):63–75

    Article  Google Scholar 

  32. Bozer YA, Kile JW (2008) Order batching in walk-and-pick order picking systems. Int J Prod Res 46(7):1887–1909

    Article  MATH  Google Scholar 

  33. Ho YC, Tseng YY (2006) A study on order-batching methods of order-picking in a distribution centre with two cross-aisles. Int J Prod Res 44(17):3391–3417

    Article  MATH  Google Scholar 

  34. Ho YC, Su TS, Shi ZB (2008) Order-batching methods for an order-picking warehouse with two cross aisles. Comput Industrial Eng 55(2):321–347

    Article  Google Scholar 

  35. Hsieh LF, Huang YC (2011) New batch construction heuristics to optimise the performance of order picking systems. Int J Prod Econ 131(2):618–630

    Article  Google Scholar 

  36. Wäscher G (2004) Order picking: a survey of planning problems and methods. In: Dyckhoff H, Lackes R, Reese J (eds) Supply chain management and reverse logistics. Part III. Berlin. Springer, Berlin, Heidelberg, pp 323–347

    Chapter  Google Scholar 

  37. Elsayed EA, Unal OI (1989) Order batching algorithms and travel-time estimation for automated storage/retrieval systems. Int J Prod Res 27(7):1097–1114

    Article  MATH  Google Scholar 

  38. Hwang H, Kim DG (2005) Order-batching heuristics based on cluster analysis in a low-level picker-to-part warehousing system. Int J Prod Res 43(17):3657–3670

    Article  MATH  Google Scholar 

  39. Tsai CY, Liou JJ, Huang TM (2008) Using a multiple-GA method to solve the batch picking problem: considering travel distance and order due time. Int J Prod Res 46(22):6533–6555

    Article  MATH  Google Scholar 

  40. Albareda-Sambola M, Alonso-Ayuso A, Molina E et al (2009) Variable neighborhood search for order batching in a warehouse. Asia Pac J Oper Res 26(5):655–683

    Article  MATH  Google Scholar 

  41. Henn S, Koch S, Doerner KF et al (2010) Metaheuristics for the order batching problem in manual order picking systems. Bus Res 3(1):82–105

    Article  Google Scholar 

  42. Henn S, Wäscher G (2012) Tabu search heuristics for the order batching problem in manual order picking systems. Euro J Oper Res 222(3):484–494

    Article  MATH  Google Scholar 

  43. Xu X, Liu T, Li K et al (2014) Evaluating order throughput time with variable time window batching. Int J Prod Res 52(8):2232–2242

    Article  Google Scholar 

  44. Henn S (2015) Order batching and sequencing for the minimization of the total tardiness in picker-to-part warehouses. Flexible Serv Manuf J 27(1):86–114

    Article  Google Scholar 

  45. Won J, Olafsson S (2005) Joint order batching and order picking in warehouse operations. Int J Prod Res 43(7):1427–1442

    Article  MATH  Google Scholar 

  46. Elsayed EA, Lee MK, Kim S et al (1993) Sequencing and batching procedures for minimizing earliness and tardiness penalty of order retrievals. Int J Prod Res 31(3):727–738

    Article  Google Scholar 

  47. Elsayed EA, Lee MK (1996) Order processing in automated storage/retrieval systems with due dates. IIE Trans 28(7):567–578

    Article  Google Scholar 

  48. Azadnia AH, Taheri S, Ghadimi P, et al. Order batching in warehouses by minimizing total tardiness: a hybrid approach of weighted association rule mining and genetic algorithms. Sci World J 2013; 1–13

  49. Chen MC, Wu HP (2005) An association-based clustering approach to order batching considering customer demand patterns. Omega 33(4):333–343

    Article  Google Scholar 

  50. Mladenović N, Hansen P (1997) Variable neighborhood search. Comput Oper Res 24(11):1097–1100

    Article  MathSciNet  MATH  Google Scholar 

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

    Article  MathSciNet  MATH  Google Scholar 

  52. Kulak O, Sahin Y, Taner ME (2012) Joint order batching and picker routing in single and multiple-cross-aisle warehouses using cluster-based tabu search algorithms. Flexible Serv Manuf J 24(1):52–80

    Article  Google Scholar 

  53. Roodbergen KJ, De Koster R (2001) Routing order pickers in a warehouse with a middle aisle. Euro J Oper Res 133(1):32–43

    Article  MathSciNet  MATH  Google Scholar 

  54. Helsgaun K (2000) An effective im plementation of the Lin-Kernighan traveling salesman heuristic. Euro J Oper Res 126(1):106–130

    Article  MathSciNet  MATH  Google Scholar 

  55. Gen M, Cheng R, Lin L (2008) Network models and optimization: multiobjective genetic algorithms approach. Springer, London

    MATH  Google Scholar 

  56. Elsayed SM, Sarker RA, Essam DL (2014) A new genetic algorithm for solving optimization problems. Eng App Artif Intell 27:57–69

    Article  Google Scholar 

  57. Gu J, Goetschalckx M, McGinnis LF (2007) Research on warehouse operation: a comprehensive review. Euro J Oper Res 177(1):1–21

    Article  MATH  Google Scholar 

  58. Henn S, Koch S, Wäscher G (2012) Order batching in order picking warehouses: a survey of solution approaches. In: Manzini R (ed) Warehousing in the global supply chain: Advanced models, tools and applications for storage systems. Springer, London, pp 105–137

    Chapter  Google Scholar 

  59. Cergibozan Ç, Tasan AS (2016) Order batching operations: an overview of classification, solution techniques, and future research. J Intell Manuf 30(1):335–349

    Article  Google Scholar 

  60. Haupt Randy L, Haupt Sue E (2004) Practical genetic algorithms, 2nd edn. Wiley, New York

    MATH  Google Scholar 

  61. De Jong K (1988) Learning with genetic algorithms: an overview. Mach Learn 3(2–3):121–138

    Google Scholar 

  62. Goldberg DE and Lingle R. Alleles, loci, and the traveling salesman problem. In: Jonh J (ed) ICGA Proceedings of the first international conference on genetic algorithms and their applications, Pittsburgh, USA, 24-26 July-1 1985, paper no. 15, ICGA, Pittsburgh, pp 154–159

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Acknowledgements

The research of the authors is partially supported by the grants numbers 306,075/2017-2 and 430,137/2018-4 from the Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq) and Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), Brazil.

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  1. São Carlos School of Engineering, Production Engineering Department, University of São Paulo, Av. Trabalhador São Carlense, 400, São Carlos, SP, 13566-590, Brazil

    Anderson Rogério Faia Pinto & Marcelo Seido Nagano

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  1. Anderson Rogério Faia Pinto
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  2. Marcelo Seido Nagano
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Correspondence to Marcelo Seido Nagano.

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Pinto, A.R.F., Nagano, M.S. An approach for the solution to order batching and sequencing in picking systems. Prod. Eng. Res. Devel. 13, 325–341 (2019). https://doi.org/10.1007/s11740-019-00904-4

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