<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Supply chains are one of the key tools in optimizing production and distribution simultaneously. However, information uncertainty is always a challenge in production and distribution management. The main purpose of this paper is to design a two-echelon supply chain in a multi-cycle state and in conditions of demand uncertainty. The task includes determining the number and location of distribution centers, planning capacity for active distribution centers, and determining the amount of shipments between different levels so that the total costs of the chain are minimized. Uncertainty is applied through discrete scenarios in the model and the problem is formulated by multi-stage stochastic programming method in the form of a mixed integer linear model. The results acquired using two indicators called VMS and VSS demonstrated that modeling the supply chain design problem with the multi-stage stochastic approach can result in significant costs reduction. Plus, utilizing mathematical expectation can generate misleading results, therefore resulting in the development of supply chain designs incapable of satisfying demand due to its overlooked limitations.</p>
</abstract>

Supply chains are one of the key tools in optimizing production and distribution simultaneously. However, information uncertainty is always a challenge in production and distribution management. The main purpose of this paper is to design a two-echelon supply chain in a multi-cycle state and in conditions of demand uncertainty. The task includes determining the number and location of distribution centers, planning capacity for active distribution centers, and determining the amount of shipments between different levels so that the total costs of the chain are minimized. Uncertainty is applied through discrete scenarios in the model and the problem is formulated by multi-stage stochastic programming method in the form of a mixed integer linear model. The results acquired using two indicators called VMS and VSS demonstrated that modeling the supply chain design problem with the multi-stage stochastic approach can result in significant costs reduction. Plus, utilizing mathematical expectation can generate misleading results, therefore resulting in the development of supply chain designs incapable of satisfying demand due to its overlooked limitations.

Design of a Supply Chain-Based Production and Distribution System Based on Multi-Stage Stochastic Programming

SKM, MARS, Public Health Department, Faculty of Health Science

University of Pembangunan Nasional Veteran Jakarta

National Research Ogarev Mordovia State University

Dentistry Department, Kut University College, Kut, Wasit, Iraq College of technical engineering

Department of Pharmacology, saveetha dental College and hospital

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Supply chains are one of the key tools in optimizing production and distribution simultaneously. However, information uncertainty is always a challenge in...

Design of a Supply Chain-Based Production and Distribution System Based on Multi-Stage Stochastic Programming

Pubblicato online: 05 ott 2023

Pagine: 357 - 370

Ricevuto: 10 lug 2022

Accettato: 14 mag 2023

DOI: https://doi.org/10.2478/fcds-2023-0015

Parole chiave
demand uncertainty, location, capacity planning, multi-stage stochastic programming, production and distribution

© 2023 A. Heri Iswanto et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Design of a Supply Chain-Based Production and Distribution System Based on Multi-Stage Stochastic Programming

Pubblicato online: 05 ott 2023

Pagine: 357 - 370

Ricevuto: 10 lug 2022

Accettato: 14 mag 2023

DOI: https://doi.org/10.2478/fcds-2023-0015

Parole chiavedemand uncertainty, location, capacity planning, multi-stage stochastic programming, production and distribution

© 2023 A. Heri Iswanto et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Parole chiave
demand uncertainty, location, capacity planning, multi-stage stochastic programming, production and distribution