Reverse supply chain of residual wood biomass

Sustainability and environmental consciousness is becoming an increasingly important issue, and questions of recycling and reuse are getting more and more attention lately. While biomass is an important renewable resource and can take many forms (ranging from agricultural residues to food waste, forestry residues and construction/wood processing residues), biomass wastes are predominantly considered for the production of bioenergy. However, depending on the exact type of biomass residual, alternatives might also exist for its use that are significantly less studied. For example, the management of residual wood biomass is an important field, and not many studies have dealt with it besides its use for bioenergy. There are multiple different problem classes that can be studied in the reverse logistics processes of wood. Optimizing the activities of this supply chain and identifying their special characteristics is a relevant issue both for reducing environmental impact and for increasing the efficiency and profit of companies. The aim of this project is to study the possible alternative reverse logistics processes of biomass residuals, concentrating on woody residuals as a use-case. We plan to identify the crucial optimization problems connected to the collection, sorting, transportation, warehousing and processing of this biomass, and provide efficient models and algorithms for their solution. We will explore new approaches based on hybrid algorithms and machine learning.

Sustainability and environmental consciousness are among the most important issues in today's society, and the topics of recycling and reuse have been receiving increasing attention in recent years. Focusing on supply chains, effective approaches include closed loop supply chains, in which the reverse logistics is combined with the conventional forward supply chain. Such networks concentrate not only on the minimization of the total costs of the operational network, but also strive to mitigate environmental impacts. The facility location problem is a key problem in the design of closed loop supply chains. This problem arises in many variants and its practical applications go beyond the planning of supply chains. Typically, constraints that appear in the facility location problems cover the satisfaction of customers' demand, the limitations of goods or services from facilities, the presence of customer incompatibilities, the use of multiple sources for fulfilling the overall customer demand, among others. In most cases, the objective function involves the minimization of total network costs. The provision of efficient algorithms is essential in this context, as delivering near-optimal solutions for location problems is notably challenging. For this purpose, during this project we focused on two different location problems: the capacitated p-median problem and the capacitated facility location problem with customer incompatibilities. During this project we investigated and provided different solution methodologies for the addressed problems, including exact methods, hybrid approaches, as well as problem-independent techniques. Our proposed methods advanced the state of the art for location problems. Additionally, we extended the problem instances with a large dataset. Further work carried out during this project involves the formulation of a facility location problem for closed-loop supply chains and the aircraft maintenance routing problem with a distribution objective. On the whole, this project resulted in important contributions concerning the solutions methods for facility location problems. State-of-the-art algorithms were developed that could also be extended to other variants of location problems. The provided instances can be used for the evaluation of new algorithms.