Results 181 to 190 of about 527 (217)
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Mathematical modeling and solving procedure of the planar storage location assignment problem
Computers and Industrial Engineering, 2009This paper focuses on the planar storage location assignment problem (PSLAP) that needs to be clearly defined and newly formulated. In addition, the solving procedure should be developed. The PSLAP can be defined as the assignment of the inbound and outbound objects to the storage yard with aim of minimizing the number of obstructive object moves.
Changkyu Park, Junyong Seo
exaly +2 more sources
The storage location assignment problem for outbound containers in a maritime terminal
International Journal of Production Economics, 2012Abstract This paper addresses the storage location assignment problem for outbound containers. The problem is decomposed into two stages. The yard bays and the amount of locations in each yard bay, which will be assigned to the containers bounded for different ships, are determined in the first stage.
Zhiqiang Lu
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Comparing heuristic algorithms of the planar storage location assignment problem
Transportation Research, Part E: Logistics and Transportation Review, 2010Abstract This paper discusses the newly defined planar storage location assignment problem (PSLAP). We develop a mathematical programming model and GA-based and dynamic PSLAP heuristic algorithms for the solving procedure. Using the testing set, we compare the performance of GA-based and dynamic PSLAP heuristic algorithms.
Changkyu Park, Junyong Seo
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New model of the storage location assignment problem considering demand correlation pattern
Computers and Industrial Engineering, 2019Abstract Order-picking is the most time- and labor-consuming operation in a warehouse and significantly influences supply chain efficiency. One of the basic methods for improving order-picking efficiency involves assigning storage locations to appropriate items, i.e., the storage location assignment problem (SLAP).
Ren-Qian Zhang, Xing Pan
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A genetic programming-based hyper-heuristic approach for storage location assignment problem
This study proposes a method for solving real-world warehouse Storage Location Assignment Problem (SLAP) under grouping constraints by Genetic Programming (GP). Integer Linear Programming (ILP) formulation is used to define the problem. By the proposed GP method, a subset of the items is repeatedly selected and placed into the available current best ...
Jing Xie 0007 +4 more
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Optimization of the Storage Location Assignment Problem Using Nested Annealing
The Storage Location Assignment Problem (SLAP) has a significant impact on the efficiency of warehouse operations. We propose a multi-phase optimizer for the SLAP, where the quality of an assignment is based on distance estimates of future-forecasted ...
Johan Oxenstierna +3 more
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New Benchmarks and Optimization Model for the Storage Location Assignment Problem
The Storage Location Assignment Problem (SLAP) is of primary significance to warehouse operations sincethe cost of order-picking is strongly related to where and how far vehicles have to travel.
Johan Oxenstierna +2 more
core +3 more sources
Centralised warehouses are a widespread practice in the healthcare supply chain, as they allow for the storage of large quantities of products a short distance from hospitals and pharmacies, allowing both a reduction in warehouse costs and prompt replenishment in case of shortages.
Nilson F. M. Mendes +2 more
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Efficiency and effectiveness of logistic activities, in general, and of distribution networks, in particular, are largely influenced by the way warehouses operate as nodes of these networks.
Rosa Micale +2 more
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Gravity Clustering: A Correlated Storage Location Assignment Problem Approach
2020 Winter Simulation Conference (WSC), 2020Warehouses and warehouse-related operations have long been a field of interest for researchers. One of the areas that researchers focus on is the Storage Location Assigning Problem (SLAP or Slotting). The goal in this field is to find the best location in a warehouse to store the products.
Mohammadnaser Ansari, Jeffrey S. Smith
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