New consumer awareness is shifting industry towards more sustainable practices, creating a virtuous cycle between producers and consumers enabled by eco-labelling. Eco-labelling informs consumers of specific characteristics of products and has been used to market greener products. Eco-labelling in the food industry has yet been mostly focused on promoting organic farming, limiting the scope to the agricultural stage of the supply chain, while carbon labelling informs on the carbon footprint throughout the life cycle of the product. These labelling strategies help value products in the eyes of the consumer. Because of this, decision makers are motivated to adopt more sustainable models. In the food industry, this has led to important environmental impact improvements at the agricultural stage, while most other stages in the Food Supply Chain (FSC) have continued to be designed inefficiently. The objective of this work is to define a framework showing how carbon labelling can be integrated into the design process of the FSC. For this purpose, the concept of Green Supply Chain Network Design (GSCND) focusing on the strategic decision making for location and allocation of resources and production capacity is developed considering operational, financial and environmental (CO2 emissions) issues along key stages in the product life cycle. A multi-objective optimization strategy implemented by use of a genetic algorithm is applied to a case study on orange juice production. The results show that the consideration of CO2 emission minimization as an objective function during the GSCND process together with techno-economic criteria produces improved FSC environmental performance compared to both organic and conventional orange juice production. Typical results thus highlight the importance that carbon emissions optimization and labelling may have to improve FSC beyond organic labelling. Finally, CO2 emission-oriented labelling could be an important tool to improve the effects eco-labelling has on food product environmental impact going forward.
The importance of water and energy accessibility and use has become more important as new insight into their role for sustainable development goals has become mainstream. The inclusion of water and energy in strategic decision-making is thus key. Supply chain network design (SCND) in the food industry is an interesting case study for the incorporation of water and energy utilization during the design process of global production systems. In the current green SCND research, frequently, single indicators are used such as carbon emissions to measure environmental impact. This paper presents a case study applied to an orange juice supply chain, formulated as a multi-objective optimization model. A single environmental impact indicator optimization approach is paired against one that includes water and energy use explicitly in the objective function set. Mixed conclusions are shown from the results pairing the two strategies side by side.
Humanitarian logistics is regarded as a key area for improved disaster management efficiency and effectiveness. In this study, a multi-objective integrated logistic model is proposed to locate disaster relief centers while taking into account network costs and responsiveness. Because this location problem is NP-hard, we present a genetic approach to solve the proposed model.
The demand for mobile agents in industrial environments to perform various tasks is growing tremendously in recent years. However, changing environments, security considerations and robustness against failure are major persistent challenges autonomous agents have to face when operating alongside other mobile agents. Currently, such problems remain largely unsolved. Collaborative multi-platform Cyber- Physical-Systems (CPSs) in which different agents flexibly contribute with their relative equipment and capabilities forming a symbiotic network solving multiple objectives simultaneously are highly desirable. Our proposed SMART-AGENTS platform will enable flexibility and modularity providing multi-objective solutions, demonstrated in two industrial domains: logistics (cycle-counting in warehouses) and agriculture (pest and disease identification in greenhouses). Aerial vehicles are limited in their computational power due to weight limitations but offer large mobility to provide access to otherwise unreachable places and an “eagle eye” to inform about terrain, obstacles by taking pictures and videos. Specialized autonomous agents carrying optical sensors will enable disease classification and product recognition improving green- and warehouse productivity. Newly developed micro-electromechanical systems (MEMS) sensor arrays will create 3D flow-based images of surroundings even in dark and hazy conditions contributing to the multi-sensor system, including cameras, wireless signatures and magnetic field information shared among the symbiotic fleet. Integration of mobile systems, such as smart phones, which are not explicitly controlled, will provide valuable information about human as well as equipment movement in the environment by generating data from relative positioning sensors, such as wireless and magnetic signatures. Newly developed algorithms will enable robust autonomous navigation and control of the fleet in dynamic environments incorporating the multi-sensor data generated by the variety of mobile actors. The proposed SMART-AGENTS platform will use real-time 5G communication and edge computing providing new organizational structures to cope with scalability and integration of multiple devices/agents. It will enable a symbiosis of the complementary CPSs using a combination of equipment yielding efficiency and versatility of operation.
