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In retail, assortment planning refers to selecting a subset of products to offer that maximizes profit. Assortments can be planned for a single store or a retailer with multiple chain stores where demand varies between stores. In this paper, we assume that a retailer with a multitude of stores wants to specify her offered assortment. To suit all local preferences, regionalization and store-level assortment optimization are widely used in practice and lead to competitive advantages. When selecting regionalized assortments, a tradeoff between expensive, customized assortments in every store and inexpensive, identical assortments in all stores that neglect demand variation is preferable.
We formulate a stylized model for the regionalized assortment planning problem (APP) with capacity constraints and given demand. In our approach, a 'common assortment' that is supplemented by regionalized products is selected. While products in the common assortment are offered in all stores, products in the local assortments are customized and vary from store to store.
Concerning the computational complexity, we show that the APP is strongly NP-complete. The core of this hardness result lies in the selection of the common assortment. We formulate the APP as an integer program and provide algorithms and methods for obtaining approximate solutions and solving large-scale instances.
Lastly, we perform computational experiments to analyze the benefits of regionalized assortment planning depending on the variation in customer demands between stores.
This paper presents a method for classifying traffic participants based on high-resolution automotive radar sensors for autonomous driving applications. The major classes of traffic participants addressed in this work are pedestrians, bicyclists and passenger cars. The preprocessed radar detections are first segmented into distinct clusters using density-based spatial clustering of applications with noise (DBSCAN) algorithm. Each cluster of detections would typically have different properties based on the respective characteristics of the object that they originated from. Therefore, sixteen distinct features based on radar detections, that are suitable for separating pedestrians, bicyclists and passenger car categories are selected and extracted for each of the cluster. A support vector machine (SVM) classifier is constructed, trained and parametrised for distinguishing the road users based on the extracted features. Experiments are conducted to analyse the classification performance of the proposed method on real data.