New model of the storage location assignment problem considering demand correlation pattern

Zhang, Renqian; Wang, Meng; Pan, Xing

doi:10.1016/j.cie.2019.01.027

Cited by 59 publications

(36 citation statements)

References 45 publications

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“…Nevertheless, it is quite computationally expensive to find the shortest routes for SLAP containing multiple picking orders concurrently. Therefore, there is a large body of literature on simple and practical routing policies for SLAP [4]. Examples of such routing policies include S-shape, return, midpoint, largest gap, among others [1][13] [14].…”

Section: Problem Descriptionsmentioning

confidence: 99%

“…Examples of such routing policies include S-shape, return, midpoint, largest gap, among others [1][13] [14]. S-shape policy is the most popular routing policy [4] [15]. Under the S-shape policy, an order picker checks the aisles closer to I/O point first.…”

Section: Problem Descriptionsmentioning

confidence: 99%

“…In order to fulfill an order, an order picker visits the locations of the slots containing the required SKU, retrieves the right number of items, and transports them to a consolidation workstation or a dock. Order picking is the most important activity in a DC, as it is heavily laborintensive and accounts for a significant part of the overall operational cost [1] [4]. In this context, minimizing the travel distance of order pickers has been an important objective of operations management in DCs [5] [6].…”

Section: Introductionmentioning

confidence: 99%

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Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers

2020

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This paper considers heuristic approaches that can be used to assign stock keeping units (SKU) to individual slots in distribution center. Firstly, we propose two novel strategies, slot selection and frequent itemset grouping. The former is used to find the most suitable slot for a single SKU, while the latter is for sequencing SKUs in an appropriate order. Secondly, we develop several storage location assignment heuristics by applying the two strategies. Especially, the heuristics are designed to assign frequently ordered SKU to a slot close to I/O (input/output) point and SKUs frequently ordered together to slots close to each other. Consequently, the proposed heuristics are helpful to reduce the travel distance of order picker in distribution center. In this paper, travel distance of order picker is calculated based on a routing policy that enables order picker to move along a flexible route. For illustration, we applied the heuristics to real data collected from a large distribution center. The experiment results reveal that slot selection strategy is very helpful to reduce average travel distance of order picker, especially under greedy routing policy. Also, frequent itemset grouping strategy can provide additional reduction in average travel distance if it is applied together with slot selection strategy.

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Section: Problem Descriptionsmentioning

confidence: 99%

Section: Problem Descriptionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers

2020

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“…The paper [13] introduced the concept of the demand correlation pattern (DCP) to describe the correlation among items, based on which a new model is constructed to address the storage location assignment problem (SLAP). To solve the model, a heuristic and a simulated annealing method were developed.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…In general, the assignment problem is formulated as given in Section 4. The variants of the assignment problems presented in sources [7][8][9][10][11][12][13][14] are obtained by modifying the constraints and objective function of Table 1.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of an accelerating hungarian method for assignment problems

Munapo

2020

EEJET

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The Hungarian method is a well-known method for solving the assignment problem. This method was developed and published in 1955. It was named the Hungarian method because two theorems from two Hungarian mathematicians were used. In 1957, it was noticed that this algorithm is strongly polynomial and has a complexity of order O(n 4) This is the reason why the Hungarian method is also known as the Kuhn-Munkres algorithm. Later on, in 1971 the complexity of the method was improved to order O(n 3) A smallest uncovered element is selected to create a single zero at every iteration. This is a weakness and is alleviated by selecting more than one smallest uncovered element thus creating more than one zero at every iteration to come up with what we now call the Accelerating Hungarian (AH) method. From the numerical illustration of the Hungarian method given in this paper, we require 6 iterations to reach optimality. It can also be shown that selecting a single smallest uncovered element (e s) makes the Hungarian method inefficient when creating zeros. From the same numerical illustration of the proposed algorithm (AH) also given in this paper, it can be noted that only one iteration is required to reach optimality and that a total of six zeros are created in one iteration. Assignment model and the Hungarian method have application in addressing the Weapon Target Assignment (WTA) problem. This is the problem of assigning weapons to targets while considering the maximum probability of kill. The assignment problem is also used in the scheduling problem of physicians and medical staff in the outpatient department of large hospitals with multi-branches. The mathematical modelling of this assignment problem results in complex problems. A hybrid meta-heuristic algorithm SCA-VNS combining a Sine Cosine Algorithm (SCA) and Variable Neighbourhood Search (VNS) based on the Iterated Hungarian algorithm is normally used

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