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.

This thesis deals with the design of solid biomass terminals from a strategic operational point of view. The design of terminals is here characterized as the total equipment selection, purchase, utilization and salvage within the terminal bounds.

This dissertation investigates how to design a pipe belt conveyor system in a more effective way, considering two aspects: to ensure conveyor belt has sufficient bending stiffness to form an enclosed pipe shape without a contact loss with the idler rolls; and to reduce energy consumption of the system from the indentation rolling resistance.
A conveyor belt bending stiffness is quantified from the toughability test. To detect an appearance of a contact loss with the idler rolls, contact forces are determined using three approaches: experimental testing; a newly introduced analytical approach, constructed based on the Displacement Method of Superposition with Maxwell-Mohr Integrals, and using FEM analysis. To determine the indentation rolling resistance, a 3D Maxwell model is used with multiple Maxwell parameters and Winkler foundation.

Bulk solids handling continues to play an important role in a number of industries. One of the issues during bulk solids handling processes is equipment surface wear. Wear results in high economic loss and increases downtime. Current wear reduction methods such as optimizing transfer conditions or using wear-resistant materials, have brought notable progress. Nevertheless, the wear loss is still significant. Therefore, new solutions for reducing the surface wear must be investigated.
Because wear also occurs to the surfaces of many biological organisms, inspirations for wear reduction can be obtained from biology. In this research, the bionic design method is explored to reduce the surface wear of bulk solids handling equipment.
This thesis firstly illustrates the analytical wear models in bulks solids handling. Hence, the wear phenomena in biology are investigated. Based on the analogies between biology and bulk solids handling, a bionic design method for wear reduction of bulk solids handling equipment surfaces is developed. Furthermore, two bionic models for reducing abrasive and erosive wear respectively, are proposed for the applications of bulk solids handling equipment surfaces.
To model the effects of applying bionic models on the surface wear of bulk solids handling equipment, the discrete element method (DEM) is utilized. Using the parameter values obtained from experiments, the wear of bionic surfaces and conventional smooth surfaces is successfully modeled.
By comparing predicted wear loss from bionic surfaces and smooth surfaces, the effectiveness of reducing wear by application of bionic models are successfully demonstrated. Moreover, parametric studies on geometrical parameters of bionic models were also carried out. The results demonstrate that as biological wear reduction mechanisms are implemented, wear reduction of bulk solids handling equipment surfaces can be achieved. It is shown that abrasive wear loss can be reduced by up to 63% whilst erosive wear loss can be reduced by up to 26%.

The possible larger amount of container throughput and the limited handling capacities of existing infrastructures impose increasingly high pressure on large ports in improving competitiveness. Inside container terminals, land-side automated guided vehicles have been used extensively for decades to improve terminal operational efficiency and sustainability. Transport between terminals, i.e., Inter Terminal Transport (ITT), is currently mainly realized by manned trucks. However, road traffic has already been heavy in port areas with limited land. For geographically complex ports, e.g., the port of Rotterdam, travel distances by land can be much longer than by water between some terminals. Expanding the existing physical transportation infrastructure might be an option, at extremely high costs nonetheless. As an alternative, more efficient and sustainable ways for port logistics need to be investigated.