Trading off cost, emission, and quality in cold chain design: A simulation approach. (August 2021)

• Record Type: Journal Article
• Authors: Fan, Yun; de Kleuver, Caroline; de Leeuw, Sander; Behdani, Behzad
• Is Part Of: Computers & industrial engineering. Volume 158(2021)
• Publication Date: 2021-08
• Subjects: Cold chain management -- Reefer container -- Quality management -- Agent-based modelling -- Slow steaming strategy; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107442
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Cold chain management requires managing trade-offs among cost, emissions and quality. An agent-based simulation is presented to assess trade-offs in a multi-actor setting. Integrating quality management into logistics processes reduces cost and emissions. The slow steaming strategy reduces the cost/emissions but lowers banana quality. Abstract: A cold chain is a complex system. It must deal with the requirements of cost efficiency, timeliness, product quality, and environmental impacts as well as the specific technical challenges in handling perishable cargo. Taking an action to accomplish one of these goals might fail in achieving another goal. Hence, one of the most important challenges in cold chain management is to identify solutions balancing cost, quality, and environmental concerns. Additionally, multiple stakeholders are involved in cold chains who have different perceptions of the product quality, economic, and sustainability aspects. There can be a conflict of interests between stakeholders in the chain. Considering the complexity of cold chains, tailored solutions must be designed to trade off different objectives in cold chain design. This paper aims at presenting an agent-oriented simulation framework to support decision-making in the design and operation of cold chains to trade off cost, emission, and quality. To illustrate the simulation framework, a case study of a global banana supply chain is presented and discussed. In the numerical study, the slow streaming strategy and the in-transit quality management system are analysed. The results show the capability of the presented model to analyse different scenarios and evaluate the influence on total logistics cost, emission, and the quality of perishable products. The numerical study results show a trade-off between reducing energy consumption and preserving product quality when changing the vessel speed. Furthermore, integrating quality management with logistics activities could optimize the operational costs and emission along the chain; however, the quality might be lower than a post-transport quality management system.