ARTIFICIAL INTELLIGENCE APPLIED TO ASSIGNED MERCHANDISE LOCATION IN RETAIL SALES SYSTEMS

This paper presents an option for improving the process of assigning storage locations for merchandise in a warehouse. A disadvantage of policies in the literature is that the merchandise is assigned allocation only according to the volume of sales and the rotation it presents. However, in some cases it is necessary to deal with other aspects such as family group membership, the physical characteristics of the products, and their sales pattern to design an integral policy. This paper presents an alternative to the afore- mentioned process using Flexsim ® , artificial neural networks, and genetic algorithms.

• Publication year

  2016

• Venue

  South African Journal of Industrial Engineering

• Publication date

  2016-05-01

• Fields of study

  Business, Computer Science, Engineering

• Identifiers
  DOI 10.7166/27-1-1192
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

• House Rent Estimation in Dhaka City by Multi Layer Perceptions Neural Network

  2014cited by this paper
• Application of storage consolidation intervals to approximate random storage transaction times with closest open location load dispatching

  2012cited by this paper
• Storage policies and order picking strategies in U-shaped order-picking systems with a movable base

  2012cited by this paper
• Storage location assignment and order picking optimization in the automotive industry

  2012cited by this paper
• Business indicators of healthcare quality: Outlier detection in small samples

  2012cited by this paper
• Using a multiple-GA method to solve the batch picking problem: considering travel distance and order due time

  2008cited by this paper
• Efficient formation of storage classes for warehouse storage location assignment: A simulated annealing approach

  2008cited by this paper
• Introduction to genetic algorithms

  2007cited by this paper
• Design and control of warehouse order picking: A literature review

  2006cited by this paper
• The optimum design of a warehouse system on order picking efficiency

  2006cited by this paper
• Batching orders in warehouses by minimizing travel distance with genetic algorithms

  2005cited by this paper
• A comparison of picking, storage, and routing policies in manual order picking

  2004cited by this paper
• Verification and validation of simulation models

  2003cited by this paper
• Layout and routing methods for warehouses

  2001cited by this paper
• Routing policies and COI-based storage policies in picker-to-part systems

  1998cited by this paper
• Genetic Algorithms in Search Optimization and Machine Learning

  1988cited by this paper

• Revolutionizing Interactive Learning: A Comprehensive Review of Study-Buddy - An EdTech Innovation Built on the MERN Stack Prof. Ms. S.G. Pundkar

  2024cites this paper
• Study-Buddy: An EdTech Innovation with MERN Stack, Revolutionizing Interactive Learning

  2024cites this paper
• Solving the Storage Location Assignment Problem Using Reinforcement Learning

  2023cites this paper
• Machine Learning in Warehouse Management: A Survey

  2023cites this paper
• Artificial intelligence in retail – a systematic literature review

  2022cites this paper
• Establishing a Novel Algorithm for Highly Responsive Storage Space Allocation Based on NAR and Improved NSGA‐III

  2022cites this paper
• MERN Stack Web-Based Education Management Information Systems for Pacific Island Countries

  2022cites this paper
• Association between distribution centre design and contextual characteristics

  2021cites this paper
• A novel method for dried chili pepper classification using artificial intelligence

  2021cites this paper
• AN ARTIFICIAL INTELLIGENCE AND KNOWLEDGE-BASED SYSTEM TO SUPPORT THE DECISION- MAKING PROCESS IN SALES

  2019cites this paper
• Optimisation of stock keeping unit placement in a retail distribution centre

  2018cites this paper