Proceedings ArticleDOI
Optimal retail shelf space allocation with dynamic programming using bounds
Hasmukh K. Gajjar,Gajendra Kumar Adil +1 more
- pp 1068-1072
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TLDR
It is found from experimental studies that NDP using bound was much more efficient to solve large problems as compared to original dynamic programming (ODP) without using bound.Abstract:
Efficient shelf space allocation increases profitability of a retail store and thus provides competitive advantage to the retailer. Several shelf space allocation models exist in literature. However, these models are generally solved using heuristic approaches due to NP-hard nature and there is a need to develop exact methods. In this paper, we present a non-linear shelf-space allocation model (NLSSAM) and optimally solve it with a new dynamic programming (NDP) using bounds which fathoms unpromising states. It is found from experimental studies that NDP using bound was much more efficient to solve large problems as compared to original dynamic programming (ODP) without using bound. ODP could not solve all problem instances of problem sizes (number of products, n = 30 and 40) within specified CPU time limit of 400 seconds while NDP could solved problem instances of size (n = 200) with average CPU time of 7.89 seconds.read more
References
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Book
Dynamic Programming
TL;DR: The more the authors study the information processing aspects of the mind, the more perplexed and impressed they become, and it will be a very long time before they understand these processes sufficiently to reproduce them.
Journal ArticleDOI
Shelf management and space elasticity
TL;DR: It is found that location had a large impact on sales, whereas changes in the number of facings allocated to a brand had much less impact as long as a minimum threshold (to avoid out-of-stocks) was maintained.
Journal ArticleDOI
A Model for Optimizing Retail Space Allocations
Marcel Corstjens,Peter Doyle +1 more
TL;DR: In this article, a case study is used to estimate the parameters and the problem is solved within a geometrical programming framework, and an extensive comparison with alternative procedures suggests this general model leads to significantly different allocation rules and superior profit performance.
Journal ArticleDOI
A minimal algorithm for the Multiple-choice Knapsack Problem
TL;DR: A simple O(n) partitioning algorithm for deriving the optimal linear solution to the Multiple-Choice Knapsack Problem is presented, and it is shown how it may be incorporated in a dynamic programming algorithm such that a minimal number of classes are enumerated, sorted and reduced.
Journal ArticleDOI
An inventory-theoretic approach to product assortment and shelf-space allocation
TL;DR: In this article, a greedy heuristic and a genetic algorithm are proposed for the solution to the integrated problem of inventory-level-dependent demand inventory model and product assortment and shelf-space allocation.
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