This thesis provides tools for decision support systems for perishable goods logistics. It also provides an approach for enterprises to estimate the benefit of adopting the proposed logistic systems. Perishable products are produced, transported, and consumed all over the world. Thanks to perishable goods supply chains, we can enjoy safe, fresh, and affordable products. Nevertheless, it is estimated that one third of the agricultural products produced globally for human consumption end up wasted, which amounts to 1.3 billion tonnes per year. This also means that one third of the resources and greenhouse gas emissions for producing and transporting these products are in vein. The wastage of these fresh products happens throughout the supply chains, which are often caused by the perishing nature and inefficiencies of supply chain planning. For instance, congestions at a certain location or over supply at a retailer may result in spoilage. Disruptions such as malfunctioning of cooling equipment can also contribute to wastage in supply chains. Recent technological developments provide new insights into supply chain management, allowing further waste reduction. With sensors and communication technologies, information of products such as location and freshness can be made known to supply chain planners in real-time. Thus the research question of this thesis is given real-time information of perishable goods logistics, in what ways can perishable goods supply chain players better control and coordinate logistic processes to reduce loss of perishable products? Traditional mathematical methods cannot capture enough details when describing perishable goods in their supply chains. Therefore, this thesis proposes a general framework in a system and control fashion to describe and control logistic operations. This general framework consists of a quality-aware modeling method and a model predictive control strategy. The quality-aware modeling method considers the perishable goods in the supply chain as a system, with quality and logistic features. The model predictive control strategy observes the system and steers the system in a manner that the wastage can be minimized. In this thesis, the proposed method is used in case studies of supply chains with three different commodities, namely bananas, starch potatoes, and cut roses. In each case study, because each commodity is unique in its physiological nature, it perishes in a unique way. Therefore, the supply chain takes care of the commodity in a way that may not suit other commodities. As a result, the logistic features of the supply chains are also different from each other. This requires the systems to be described differently, and control architectures can also vary. Results from the case studies show that the general framework can improve the effectiveness of supply chain logistic planning, and to reduce wastage. The improvements are quantified to illustrate the benefit of making full use of the information on perishable goods in supply chains.
|Award date||24 Jan 2019|
|Publication status||Published - 2019|
- perishable goods
- quality-aware modeling
- model predictive control