Using a TSP heuristic for routing order pickers in warehouses

Theys, Christophe; Bräysy, Olli; Dullaert, Wout; Raa, Birger

doi:10.1016/j.ejor.2009.01.036

Cited by 172 publications

(123 citation statements)

References 21 publications

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“…The results proved, where PLF is relevant, the possible sequencing combinations of order picking lists that have an exponential growth. This fact proves the necessity of the heuristic optimisation method for OPRP-PLF, for example Foldesi et al defined for the road transport travelling salesman problem or Theyset al and Chen et al defined for warehouse routing problem [13,23,9]. The order picking routing optimisation algorithm has to support the operational decisions, like customer order separation for ULs and the longer distance versus more UL reconstruction time trade-off.…”

Section: Resultsmentioning

confidence: 99%

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Necessity and complexity of order picking routing optimisation based on pallet loading features

Bódis

Botzheim

Földesi

2017

Acta Universitatis Sapientiae, Informatica

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Abstract. Order picking is the most labour-intensive and costly activity of warehouses. The main challenges of its improvement are the synchronisation of warehouse layout, storage assignment policy, routing, zoning, and batching. Furthermore, the competitiveness of the warehouse depends on how it adapts to the unique customer demands and product parameters and the changes. The operators usually have to manage the picking sequence based on best practices taking into consideration the product stacking factors and minimising the lead time. It is usually necessary to support the operators by making effective decisions. Researchers of the pallet loading problem, bin packing problem, and order picking optimisation provide a wide horizon of solutions but their results are rarely synchronised. Necessity and complexity of OPRO based on PLF 163The research defines the order picking routing problem based on Pallet Loading Feature (PLF). It describes measurement and product stacking rule evaluation methods to highlight when the PLF based optimisation is necessary. The paper shows that in order picking problems based on PLF, the number of combinations a brute-force search algorithm has to examine grows exponentially, which highlights the importance of meta-heuristic optimisation. The study describes a Simulated Annealing algorithm for order picking based on PLF.

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Section: Resultsmentioning

confidence: 99%

“…Many researchers work on the different segments of the OPP, Bin Packing or PLP development. A huge amount of research has been done in the field of routing optimisation in warehouses (e.g., [23,9]). Many solutions have been defined for harmonising SLA and routing to decrease the routing distances and times (e.g., [19,8]).…”

Section: Introductionmentioning

confidence: 99%

Necessity and complexity of order picking routing optimisation based on pallet loading features

Bódis

Botzheim

Földesi

2017

Acta Universitatis Sapientiae, Informatica

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“…The solution depends on several parameters, namely the size of the vehicle fleet, the degree of heterogeneity and capacity of the vehicles, the number of depots, the degree of randomness of the demand, the time windows of the service, etc. Each selection yields a different setting, of which the most basic one is the Traveling Salesman Problem (TSP), which provides a ground on which more complicated and practical instances can be stated (Theys et al, 2010). In the TSP a sole vehicle has to visit each customer on a single, minimal cost route.…”

Section: The Vehicle Routing Problemmentioning

confidence: 99%

Integrating packing and distribution problems and optimization through mathematical programming

Miguel¹,

Frutos²,

Tohmé³

et al. 2016

10.5267/j.dsl

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This paper analyzes the integration of two combinatorial problems that frequently arise in production and distribution systems. One is the Bin Packing Problem (BPP) problem, which involves finding an ordering of some objects of different volumes to be packed into the minimal number of containers of the same or different size. An optimal solution to this NP-Hard problem can be approximated by means of meta-heuristic methods. On the other hand, we consider the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW), which is a variant of the Travelling Salesman Problem (again a NP-Hard problem) with extra constraints. Here we model those two problems in a single framework and use an evolutionary meta-heuristics to solve them jointly. Furthermore, we use data from a real world company as a test-bed for the method introduced here.

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“…Such kind of sequencing problems can be considered as the traveling salesman problem (TSP), which is among the most popular NP-hard combinatorial problems [6]. In fact, several existing investigations into these sequencing problems have been formulated via the TSP [7,11].…”

Section: Introductionmentioning

confidence: 99%

“…For example, in [11], the authors use TSP heuristic algorithms to address the sequencing problems of order pickers in conventional multi-parallel-aisle warehouse systems. Another example is the use of TSP-based k-interchange method for solving the problem of routing order pickers in single-block warehouses [7].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Transportation Sequence in Warehouse with Genetic Algorithms

Tran

Kim

2014

AETA 2013: Recent Advances in Electrical Engineering and Related Sciences

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Abstract. Optimizing transportation sequence is crucial to reduce material handling costs in warehouse operations and thus in total logistic costs. Transportation sequence is the ordering of storage and retrieval jobs that a material handling device has to perform to finish an order list. In many studies, the optimization of transportation sequence has been simplified as an order-picking problem, and accordingly solved as a classical traveling salesman problem. However, transportation sequence is a double-cycle storage and retrieval problem (DCSRP) in itself, meaning that the combination of storage and retrieval jobs into double cycles has to be considered simultaneously. In this paper, we propose formulating the DCSRP as a permutation problem and applying several genetic algorithms to solve the formulated problem. Extensive computational experiments were performed to demonstrate the capability of the approach. The experimental analysis confirms that our approach could solve the problem efficiently on the one hand, and addresses the question of which genetic operators are best applied to the formulated DCSRP on the other hand.

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Using a TSP heuristic for routing order pickers in warehouses

Cited by 172 publications

References 21 publications

Necessity and complexity of order picking routing optimisation based on pallet loading features

Necessity and complexity of order picking routing optimisation based on pallet loading features

Integrating packing and distribution problems and optimization through mathematical programming

Optimizing Transportation Sequence in Warehouse with Genetic Algorithms

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