Isabel Gomes

In this work, the design and operation planning of a multi-period, multi-product closed-loop supply chain is addressed. Recovered end-of-life products from customers are evaluated in disassembly centers and accordingly are sent back to factories for remanufacturing, or leave the network either by being sold to third parties or by being sent to disposal. Typical uncertain parameters are product demand, production cost, and returned product volume and evaluation, among others. So, stochastic optimization approaches should be used for problem solving, where different topology decisions on the timing, location and capacity of some entities (factories, and distribution and sorting centers) are to be considered along a time horizon. A two-stage multi-period stochastic mixed 0–1 bilinear optimization model is introduced, where the combined definition of the available entities at the periods and the products’ flow among the entities, maximizes the net present value of the expected total profit along the time horizon. A version of the mixture of chance-constrained and second order stochastic dominance risk averse measures is considered for risk management at intermediate periods of the time horizon. Given the high dimensions of the model it is unrealistic to look for the optimality of the solution in an affordable computing effort for current hardware and optimization software resources. So, a decomposition approach is considered, namely a Fix-and-Relax decomposition algorithm. For assessing the computational validation of the modeling and algorithmic proposals, pilot cases are taken from a real-life glass supply chain network whose main features are retained.

This volume focuses on the collection of waste and waste streams as an integral aspect of sustainable waste management. The authors take economic models and behavioral studies into account to go beyond just descriptions of waste collections technologies and collection route design. Models and tools for sustainable waste collection are described in detail, and the authors provide a comprehensive, integrated methodology to design waste collection systems that reduce environmental impacts, are economically viable, and achieve buy-in and participation from target populations.
Part I of the book provides fundamentals and context on waste hierarchy, including waste prevention, reduction and reuse, waste collection itself, and steps such as preparation for recycling, recycling, treatment, and landfilling. Background in environmental, social, and economic concerns surrounding waste collection is also provided here. Part II addresses tools for design, operation, and maintenance of waste collection systems. Part III focuses on how the tools presented in Part II can be used to support sustainability assessments and decisions that consider the entire life cycle of waste and the role of waste collection programs in waste prevention, reduction, reuse, recycling, treatment, and disposal. Part IV addresses the challenges of developing sustainable waste management systems and addresses the role of waste collection in sustainable waste management in the future.

Abstract This work presents ToBLoOM – Triple Bottom Line Optimization Modelling, a decision support tool for the design and planning of sustainable supply chains. It consists of a multi-objective mixed integer linear programming model which integrates several interconnected decisions: facility location and capacity determination; supplier selection and purchase levels definition; technology selection and allocation; transportation network definition including both unimodal and intermodal options; supply planning; product recovery and remanufacturing. The three pillars of sustainability are addressed as objective functions: economic, through Net Present Value; environmental through the Life Cycle Analysis methodology ReCiPe; and social through a developed GDP-based metric. Uncertainty is considered using a stochastic ToBloOM. This applied to a case of a European based company with markets in Europe and South America. This work contributes to the literature by building on several identified research gaps such as the need for an integrated approach that allows simultaneous assessment of different interacting supply chain decisions, the need to explicitly assess the environmental impact in closed-loop supply chains, the need to assess the impact of supply chains on society, and the need for a multi-objective tool that includes all the three pillars of sustainability. Strategies towards a more sustainable supply chain are also derived from this work.

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A Mixed Integer Linear Programming model for the design and planning of green supply chains is developed. Strategic and tactical decisions are taken, namely on facility location and capacity installation, supplier selection, technology selection, transportation network definition, supply planning, and product recovery. The aim of this work is to study the use of environmental indicators in these decisions while accounting for profit objectives. Different objective functions concerning environmental aspects are implemented. ReCiPe quantifies the environmental performance of the supply chain and combinations of ReCiPe’s midpoint categories allow a deeper analysis of the impact of these categories in strategic and tactical decisions. The goal is to understand if focusing on selected categories affects supply chain decisions and overall supply chain environmental impact. Net Present Value quantifies the economic performance and is used for lexicographic optimization. The model is applied to a case-study and important managerial insights are obtained. From a holistic point of view, it answers the question: how should supply chain environmental impact be assessed? From a case-study perspective, insights are obtained regarding what type of improvements should be implemented to reduce the environmental impact and how this would affect supply chain strategic and tactical decisions, along with economic performance.

Supply Chain Design problems often result into multiperiod stochastic mixed integer problems that are hard to solve. In this paper we propose a metaheuristic algorithm as a specialization for two- stage problems of the so-named Fix-and-Relax Algorithm presented previously for solving large- scale multiperiod stochastic mixed 0-1 optimization problems under a time stochastic dominance risk averse strategy, so-named TSD. Some computational experience is presented.

In this work, a multi-objective mixed integer linear programming (moMILP) model is presented for the design and planning of sustainable closed loop supply chains. It includes strategic decisions such as facility location, definition of transportation modes, technology selection and allocation, as well as tactical decisions. The model includes four objectives: 1) The economic pillar, measured through the net present value (NPV); 2) the environmental pillar, which includes Life Cycle Assessment (LCA), through the application of the ReCiPe methodology; 3 and 4) The social pillar measured using two socio-economic indicators applied by the European Union in its Sustainable Development Strategy. The applicability of the model is demonstrated through a representative supply chain case study. Results show that the different objectives influence the supply chain structure and translate in significantly different and conflicting decisions, showing the importance of such type of models to better understand the decisions implications on the different dimensions of sustainability.