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Authors: Ning Xue 1 ; Dario Landa-Silva 1 ; Grazziela Figueredo 2 and Isaac Triguero 1

Affiliations: 1 ASAP Research Group, School of Computer Science, University of Nottingham and U.K. ; 2 IMA Research Group, School of Computer Science, University of Nottingham and U.K.

ISBN: 978-989-758-352-0

Keyword(s): Highly Perishable Food Inventory, Discrete Event Simulation, Particle Swarm Optimisation.

Related Ontology Subjects/Areas/Topics: Agents ; Applications ; Artificial Intelligence ; Bioinformatics ; Biomedical Engineering ; Enterprise Information Systems ; Information Systems Analysis and Specification ; Inventory Theory ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Methodologies and Technologies ; Operational Research ; Optimization ; Simulation ; Supply Chain Management ; Symbolic Systems

Abstract: The taste and freshness of perishable foods decrease dramatically with time. Effective inventory management requires understanding of market demand as well as balancing customers needs and preferences with products’ shelf life. The objective is to avoid food overproduction as this leads to waste and value loss. In addition, product depletion has to be minimised, as it can result in customers reneging. This study tackles the production planning of highly perishable foods (such as freshly prepared dishes, sandwiches and desserts with shelf life varying from 6 to 12 hours), in an environment with highly variable customers demand. In the scenario considered here, the planning horizon is longer than the products’ shelf life. Therefore, food needs to be replenished several times at different intervals. Furthermore, customers demand varies significantly during the planning period. We tackle the problem by combining discrete-event simulation and particle swarm optimisation (PSO). The simulati on model focuses on the behaviour of the system as parameters (i.e. replenishment time and quantity) change. PSO is employed to determine the best combination of parameter values for the simulations. The effectiveness of the proposed approach is applied to some real-world scenario corresponding to a local food shop. Experimental results show that the proposed methodology combining discrete event simulation and particle swarm optimisation is effective for inventory management of highly perishable foods with variable customers demand. (More)

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Paper citation in several formats:
Xue, N.; Landa-Silva, D.; Figueredo, G. and Triguero, I. (2019). A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food.In Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES, ISBN 978-989-758-352-0, pages 406-413. DOI: 10.5220/0007401304060413

@conference{icores19,
author={Ning Xue. and Dario Landa{-}Silva. and Grazziela P. Figueredo. and Isaac Triguero.},
title={A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food},
booktitle={Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,},
year={2019},
pages={406-413},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007401304060413},
isbn={978-989-758-352-0},
}

TY - CONF

JO - Proceedings of the 8th International Conference on Operations Research and Enterprise Systems - Volume 1: ICORES,
TI - A Simulation-based Optimisation Approach for Inventory Management of Highly Perishable Food
SN - 978-989-758-352-0
AU - Xue, N.
AU - Landa-Silva, D.
AU - Figueredo, G.
AU - Triguero, I.
PY - 2019
SP - 406
EP - 413
DO - 10.5220/0007401304060413

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