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A Novel Multi-Objective Model for Data-Driven Scattered Storage Assignment in Warehouses
No full textA Novel Multi-Objective Model for Data-Driven Scattered Storage Assignment
Today’s competitive retail market is faced with high expectations from customers on both delivery (time) and price (cost). This situation has led retailers such as Amazon and Zalando to improve their replenishment and picking strategies at their distribution centers to achieve a higher degree of efficiency [1]. One of these replenishment strategies is Scattered Storage Assignment (SSA). The underlying idea behind SSA is to unbundle each received Stock-Keeping Unit (SKU) and spread it within different positions in the warehouse. As the distance traveled by pickers to retrieve items is a crucial issue in warehouse operations management, SSA increases the average adjacency of pickers to SKUs, irrespective of his/her actual position, which leads to less travel distance in the warehouse [2].
To the extent of our review, the prevalent definition and measure used in literature for scatteredness is based on [3]. We present a new data-driven scatteredness measure which extends the concept of scatteredness to include more real-world requirements and takes customer order data into account. To do so, a novel multi-objective mathematical model is proposed in this study. This model aims to (a) maximize the scatteredness; (b) minimize splitting order-lines by aiming to collect all items of an order-line from the same location; (c) maximize order correlation of items close to each other by reducing the distance between items which are frequently ordered together and (d) maximize adjacency of frequently-ordered items to the depot. This proposed model has several benefits.
Firstly, as order frequency of items is not identical, their degree of scatteredness is weighted differently. Therefore, association rules which describe customer order behavior, are included in the proposed scatteredness measure. Secondly, as mentioned by [4], balanced dispersion of each SKU through the warehouse is important. In other words, if 18 units of an SKU are located in three different locations with the inventory of (6, 6, 6), it is more balanced than (5, 1, 12). Consequently, this characteristic is also embedded in the proposed measure. Thirdly, pairwise distance between various locations of an SKU is taken into account. Namely, if 2 units of an SKU are located in two different locations where their pairwise distance is 30 meters, they are more scattered than the situation where their pairwise distance is 5 meters. Fourthly, in contrast to existing SSA measures that allow a single SKU in each position, the proposed measure in this study allows multiple SKUs in a position which can have different spatial capacities. Fifthly, previous research requires a pre-defined degree of scatteredness [5], while our proposed multi-objective SSA model determines the optimal storage location without needing pre-calculation of a fitted scatteredness degree. Like this, the provided approach in this study not only scatters the inventory fit to the context of corresponding business but also tries to keep the inventory level of each SKU in each location in a way that minimizes splitting order-lines.
In order to avoid having to find the Pareto frontier which affects solution time, lower and upper bounds of each criterion are calculated. Second, a data-driven method for assigning the weight of each objective is introduced in this study. Finally, as the proposed model is both non-linear and non-differentiable, a meta-heuristic solution algorithm based on Differential Evolution [6] is developed. It is notable that the authors are working on testing the model and the algorithm on problems of different scale.
References
1. Boysen, N., R. De Koster, and F. Weidinger, Warehousing in the e-commerce era: A survey. European Journal of Operational Research, 2019. 277(2): p. 396-411.
2. Weidinger, F. A precious mess: on the scattered storage assignment problem. in Operations Research Proceedings 2016: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Helmut Schmidt University Hamburg, Germany, August 30-September 2, 2016. 2018. Springer.
3. Weidinger, F., Picker routing in rectangular mixed shelves warehouses. Computers & Operations Research, 2018. 95: p. 139-150.
4. Pawar, N.S., S.S. Rao, and G.K. Adil, A New Measure for Scattering of Stocks in E-commerce Warehouses. IFAC-PapersOnLine, 2022. 55(10): p. 1357-1362.
5. Albán, H.M.G., T. Cornelissens, and K. Sörensen, Scattered storage assignment: Mathematical model and valid inequalities to optimize the intra-order item distances. Computers & Operations Research, 2023. 149: p. 106022.
6. Storn, R. and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 1997. 11: p. 341-359
A Novel Multi-Objective Model for Data-Driven Scattered Storage Assignment in Warehouses
No full textA Novel Multi-Objective Model for Data-Driven Scattered Storage Assignment
Today’s competitive retail market is faced with high expectations from customers on both delivery (time) and price (cost). This situation has led retailers such as Amazon and Zalando to improve their replenishment and picking strategies at their distribution centers to achieve a higher degree of efficiency [1]. One of these replenishment strategies is Scattered Storage Assignment (SSA). The underlying idea behind SSA is to unbundle each received Stock-Keeping Unit (SKU) and spread it within different positions in the warehouse. As the distance traveled by pickers to retrieve items is a crucial issue in warehouse operations management, SSA increases the average adjacency of pickers to SKUs, irrespective of his/her actual position, which leads to less travel distance in the warehouse [2].
To the extent of our review, the prevalent definition and measure used in literature for scatteredness is based on [3]. We present a new data-driven scatteredness measure which extends the concept of scatteredness to include more real-world requirements and takes customer order data into account. To do so, a novel multi-objective mathematical model is proposed in this study. This model aims to (a) maximize the scatteredness; (b) minimize splitting order-lines by aiming to collect all items of an order-line from the same location; (c) maximize order correlation of items close to each other by reducing the distance between items which are frequently ordered together and (d) maximize adjacency of frequently-ordered items to the depot. This proposed model has several benefits.
Firstly, as order frequency of items is not identical, their degree of scatteredness is weighted differently. Therefore, association rules which describe customer order behavior, are included in the proposed scatteredness measure. Secondly, as mentioned by [4], balanced dispersion of each SKU through the warehouse is important. In other words, if 18 units of an SKU are located in three different locations with the inventory of (6, 6, 6), it is more balanced than (5, 1, 12). Consequently, this characteristic is also embedded in the proposed measure. Thirdly, pairwise distance between various locations of an SKU is taken into account. Namely, if 2 units of an SKU are located in two different locations where their pairwise distance is 30 meters, they are more scattered than the situation where their pairwise distance is 5 meters. Fourthly, in contrast to existing SSA measures that allow a single SKU in each position, the proposed measure in this study allows multiple SKUs in a position which can have different spatial capacities. Fifthly, previous research requires a pre-defined degree of scatteredness [5], while our proposed multi-objective SSA model determines the optimal storage location without needing pre-calculation of a fitted scatteredness degree. Like this, the provided approach in this study not only scatters the inventory fit to the context of corresponding business but also tries to keep the inventory level of each SKU in each location in a way that minimizes splitting order-lines.
In order to avoid having to find the Pareto frontier which affects solution time, lower and upper bounds of each criterion are calculated. Second, a data-driven method for assigning the weight of each objective is introduced in this study. Finally, as the proposed model is both non-linear and non-differentiable, a meta-heuristic solution algorithm based on Differential Evolution [6] is developed. It is notable that the authors are working on testing the model and the algorithm on problems of different scale.
References
1. Boysen, N., R. De Koster, and F. Weidinger, Warehousing in the e-commerce era: A survey. European Journal of Operational Research, 2019. 277(2): p. 396-411.
2. Weidinger, F. A precious mess: on the scattered storage assignment problem. in Operations Research Proceedings 2016: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Helmut Schmidt University Hamburg, Germany, August 30-September 2, 2016. 2018. Springer.
3. Weidinger, F., Picker routing in rectangular mixed shelves warehouses. Computers & Operations Research, 2018. 95: p. 139-150.
4. Pawar, N.S., S.S. Rao, and G.K. Adil, A New Measure for Scattering of Stocks in E-commerce Warehouses. IFAC-PapersOnLine, 2022. 55(10): p. 1357-1362.
5. Albán, H.M.G., T. Cornelissens, and K. Sörensen, Scattered storage assignment: Mathematical model and valid inequalities to optimize the intra-order item distances. Computers & Operations Research, 2023. 149: p. 106022.
6. Storn, R. and K. Price, Differential evolution–a simple and efficient heuristic for global optimization over continuous spaces. Journal of global optimization, 1997. 11: p. 341-359
Let Customers Scatter the Inventory: A Multi-Objective Storage Location Assignment in Warehouses
No full textThe rapid growth of online retailing necessitates flexible warehouse management strategies to adapt to this evolving landscape. One of the key challenges in this area is to reduce the order-picking travel distance. This travel distance is highly affected by the Storage Location Assignment (SLA) decision, which determines how products are allocated to locations in the warehouse. This study aims to develop a mixed SLA strategy which tries to adopt different SLA strategies to some degree that is tailored to the customer order pattern. To do so, four criteria are defined to assess the SLA state: (a) Scatteredness: increases the accessibility of each Stock Keeping Unit (SKU) by spreading its units through the storage locations. (b) Integrity: avoids collecting a single order-line from multiple locations by keeping sufficient number of each SKU in its storage location(s). (c) Association: stores correlated SKUs close to each other. (d) Depot adjacency: stores high-demand SKUs near the depot(s). To address the dynamic nature of business needs, a data-driven approach is introduced to weight each criterion. Then, A multi-objective mathematical model, incorporating contextual constraints and these weighted measures, is proposed to optimize the SLA. As the ultimate goal is to reduce the order-picking travel distance, the proposed model and hypothesis will be validated for this goal under various business environments
Let Customers Scatter the Inventory: A Multi-Objective Storage Location Assignment in Warehouses
No full textThe rapid growth of online retailing necessitates flexible warehouse management strategies to adapt to this evolving landscape. One of the key challenges in this area is to reduce the order-picking travel distance. This travel distance is highly affected by the Storage Location Assignment (SLA) decision, which determines how products are allocated to locations in the warehouse. This study aims to develop a mixed SLA strategy which tries to adopt different SLA strategies to some degree that is tailored to the customer order pattern. To do so, four criteria are defined to assess the SLA state: (a) Scatteredness: increases the accessibility of each Stock Keeping Unit (SKU) by spreading its units through the storage locations. (b) Integrity: avoids collecting a single order-line from multiple locations by keeping sufficient number of each SKU in its storage location(s). (c) Association: stores correlated SKUs close to each other. (d) Depot adjacency: stores high-demand SKUs near the depot(s). To address the dynamic nature of business needs, a data-driven approach is introduced to weight each criterion. Then, A multi-objective mathematical model, incorporating contextual constraints and these weighted measures, is proposed to optimize the SLA. As the ultimate goal is to reduce the order-picking travel distance, the proposed model and hypothesis will be validated for this goal under various business environments
Let Customers Scatter the Inventory: Multi-Objective Storage Location Assignment in Warehouses
No full textThe rapid growth of online retailing necessitates flexible warehouse management strategies to adapt to this evolving landscape [1]. One of the critical challenges in this area is to reduce the order-picking travel distance [2]. This travel distance is highly affected by the Storage Location Assignment (SLA) decision, which determines how products are allocated to locations in the warehouse [3].
Prevalent SLA strategies are: (a) Scattered storage assignment (SSA): increases the accessibility of each Stock Keeping Unit (SKU) by spreading its units through the storage locations [4]; (b) Correlated storage assignment (CSA): stores correlated SKUs close to each other [5]; (c) Turnover class-based (TCB): stores high-demand SKUs near the depot(s) [6].
This study proposes a mixed SLA approach that adopts each prevalent SLA strategy to some degree, tailored to the customer order pattern. To do so, three analytical measures are defined to assess the realization degree of each prevailing SLA strategy. In order to address the dynamic nature of business needs, a data-driven approach is introduced to weigh each criterion. Then, proceeding from a descriptive phase to a prescriptive one, a novel multi-objective mathematical model for SLA optimization is proposed. This model incorporates the mentioned weighted measures and the contextual constraints.
As the main goal of SLA optimization is to reduce the order-picking travel distance, the proposed model is tested in the collaborative human-robot configuration in a mixed-shelves layout. In this configuration, robots and pickers are paired and pick all items on a pick list; once all items are picked, the picker sends the robot to the depot and starts picking with another robot [7]. Finally, post hoc analyses are being performed to validate the reliability of the proposed approach.
References:
1. Lone, S., N. Harboul, and J. Weltevreden, 2021 European e-commerce report. 2021.
2. Boysen, N., R. De Koster, and F. Weidinger, Warehousing in the e-commerce era: A survey. European Journal of Operational Research, 2019. 277(2): p. 396-411.
3. Van Gils, T., et al., Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions. International Journal of Production Economics, 2018. 197: p. 243-261.
4. Weidinger, F. A precious mess: on the scattered storage assignment problem. in Operations Research Proceedings 2016: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Helmut Schmidt University Hamburg, Germany, August 30-September 2, 2016. 2018. Springer.
5. Xiao, J. and L. Zheng, A correlated storage location assignment problem in a single-block-multi-aisles warehouse considering BOM information. International Journal of Production Research, 2010. 48(5): p. 1321-1338.
6. Petersen, C.G. and R.W. Schmenner, An evaluation of routing and volume‐based storage policies in an order picking operation. Decision Sciences, 1999. 30(2): p. 481-501.
7. Azadeh, K., et al., Zoning strategies for human–robot collaborative picking. Decision Sciences, 2023. 00, p. 1-21
Let Customers Scatter the Inventory: Multi-Objective Storage Location Assignment in Warehouses
No full textThe rapid growth of online retailing necessitates flexible warehouse management strategies to adapt to this evolving landscape [1]. One of the critical challenges in this area is to reduce the order-picking travel distance [2]. This travel distance is highly affected by the Storage Location Assignment (SLA) decision, which determines how products are allocated to locations in the warehouse [3].
Prevalent SLA strategies are: (a) Scattered storage assignment (SSA): increases the accessibility of each Stock Keeping Unit (SKU) by spreading its units through the storage locations [4]; (b) Correlated storage assignment (CSA): stores correlated SKUs close to each other [5]; (c) Turnover class-based (TCB): stores high-demand SKUs near the depot(s) [6].
This study proposes a mixed SLA approach that adopts each prevalent SLA strategy to some degree, tailored to the customer order pattern. To do so, three analytical measures are defined to assess the realization degree of each prevailing SLA strategy. In order to address the dynamic nature of business needs, a data-driven approach is introduced to weigh each criterion. Then, proceeding from a descriptive phase to a prescriptive one, a novel multi-objective mathematical model for SLA optimization is proposed. This model incorporates the mentioned weighted measures and the contextual constraints.
As the main goal of SLA optimization is to reduce the order-picking travel distance, the proposed model is tested in the collaborative human-robot configuration in a mixed-shelves layout. In this configuration, robots and pickers are paired and pick all items on a pick list; once all items are picked, the picker sends the robot to the depot and starts picking with another robot [7]. Finally, post hoc analyses are being performed to validate the reliability of the proposed approach.
References:
1. Lone, S., N. Harboul, and J. Weltevreden, 2021 European e-commerce report. 2021.
2. Boysen, N., R. De Koster, and F. Weidinger, Warehousing in the e-commerce era: A survey. European Journal of Operational Research, 2019. 277(2): p. 396-411.
3. Van Gils, T., et al., Increasing order picking efficiency by integrating storage, batching, zone picking, and routing policy decisions. International Journal of Production Economics, 2018. 197: p. 243-261.
4. Weidinger, F. A precious mess: on the scattered storage assignment problem. in Operations Research Proceedings 2016: Selected Papers of the Annual International Conference of the German Operations Research Society (GOR), Helmut Schmidt University Hamburg, Germany, August 30-September 2, 2016. 2018. Springer.
5. Xiao, J. and L. Zheng, A correlated storage location assignment problem in a single-block-multi-aisles warehouse considering BOM information. International Journal of Production Research, 2010. 48(5): p. 1321-1338.
6. Petersen, C.G. and R.W. Schmenner, An evaluation of routing and volume‐based storage policies in an order picking operation. Decision Sciences, 1999. 30(2): p. 481-501.
7. Azadeh, K., et al., Zoning strategies for human–robot collaborative picking. Decision Sciences, 2023. 00, p. 1-21
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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