A Two-Phase Approach for an Integrated Order Batching and Picker Routing Problem

Bué, Martin; Cattaruzza, Diego; Ogier, Maxime; Semet, Frédéric

doi:10.1007/978-3-030-25842-9_1

Cited by 11 publications

(10 citation statements)

References 7 publications

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“…The rationale of our approach is however to consider that the PRP alone is easy enough in practice, due to nowadays warehouses structure, to be solved exactly. This assumption is a cornerstone of the proposed algorithm and is supported by the existing literature on the PRP as well as the previous work and industrial experience of the authors (Bué et al, 2018;Pansart et al, 2018;. In the HappyChic industrial case presented in Section 4.2, the graph that represents the warehouse is acyclic and therefore computing picking routes boils down to an easy path problem.…”

Section: Introductionmentioning

confidence: 67%

“…These two instances are not included for the average and maximum gaps reported in Table 6. The IMPR(%) columns respectively report the average, the minimum and the maximum deviation in percentage with the results obtained with the dynamic programming procedure proposed in Bué et al (2018) 31 413.5 31.9 4111.9 0.6 -0.5 23.5 Table 6: Global results of the algorithm with a 600s time limit on the Hap-pyChic benchmark.…”

Section: Results Of Happychicmentioning

confidence: 99%

“…Feasible primal solution Each time a column k is found by the pricing algorithm, the corresponding order lines are excluded from the demand, and the number of available boxes for the orders are updated according to the boxes used in column k. A set of routes to collect the remaining order lines is generated using a dynamic programming procedure, based on the one proposed in Bué et al (2018) where the constraint on the minimum volume of boxes is removed. Each route corresponds to a column which is added into the restricted master problem.…”

Section: Rich Column Setmentioning

confidence: 99%

“…Because of the maximum number of boxes per order, these columns do not provide a feasible solution. Hence, we have adapted the dynamic programming procedure proposed in Bué et al (2018) in order to generate a feasible solution. The adaptation consists in removing the penalty for the minimum volume of boxes, and adding a high penalty for boxes if the proposed solution for an order has more boxes than the maximum number allowed.…”

Section: Master Problem Implementation Detailsmentioning

confidence: 99%

“…The benchmark originated from HappyChic is described in details in Bué et al (2018). It is based on the warehouse activity of HappyChic for 17 working days between January and November 2017.…”

Section: Benchmarkmentioning

confidence: 99%

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An efficient and general approach for the joint order batching and picker routing problem

Briant

Cambazard

Cattaruzza

et al. 2020

European Journal of Operational Research

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Picking is the process of retrieving products from inventory. It is mostly done manually by dedicated employees called pickers and is considered the most expensive of warehouse operations. To reduce the picking cost, customer orders can be grouped into batches that are then collected by traveling the shortest possible distance.This work presents an exponential linear programming formulation to tackle the joint order batching and picker routing problem. Variables, or columns, are related to the picking routes in the warehouse. Computing such routes is generally an intractable routing problem and relates to the well known traveling salesman problem (TSP). Nonetheless, the rectangular warehouse's layouts can be used to eciently solve the corresponding TSP and take into account in the development of an ecient subroutine, called oracle. We therefore investigate whether such an oracle allows for an eective exponential formulation.Experimented on a publicly available benchmark, the algorithm proves to be very eective. It improves many of the best known solutions and provides very strong lower bounds. Finally, this approach is applied to another industrial case to demonstrate its interest for this eld of application.

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Section: Introductionmentioning

confidence: 67%

Section: Results Of Happychicmentioning

confidence: 99%

Section: Rich Column Setmentioning

confidence: 99%

Section: Master Problem Implementation Detailsmentioning

confidence: 99%

Section: Benchmarkmentioning

confidence: 99%

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