Isabel Gomes

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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.

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A multi-product model for the design of global supply chains with reverse flows is proposed. Two levels of decisions are considered, one strategic and one tactical. The first is modelled through a macro perspective of time where the determination of the network structure and flows is accomplished. At tactical level, a micro perspective of time is considered, where production planning and inventory management are addressed in detail. A mixed integer linear programming formulation is developed which is solved with standard Branch and Bound techniques. The model accuracy and suitability are studied using a case study.

In the last few decades there has been a massive growth in the Waste of Electric and ElectronicEquipment (WEEE). Part of these residues was already properly treated in some countries, but the lack of environmentally friendly options forced the European Union (EU) to take action. Two EU directives were created based on reduction, reutilization and recycling of WEEE. The need to properly designrecoverynetworks for such products appears as mandatory, where not only the economic aspects should be accounted for but also the environmental ones. The present paper addresses this problem and presents a generic optimization model for the design and planning of a recovery and treatment network of WEEE, minimizing both the costs and the environmental impacts that arise from the activity performed.

The model is applied to the real case of Amb3E, the Portuguese Association for the Management of Waste of Electric and ElectronicEquipment. Since its formation, in 2006, this organization has been registering an immense growth in the volume of residue collected, and faces now the necessity of reformulating its recoverynetwork. Both the actual cost structure and the best possible cost structure for Amb3E, given by the optimization of the model, are analyzed. These two scenarios are compared in order to turn clear the differences between them and to assess how the recoverynetwork of Amb3E can be improved through optimization. A similar analysis is performed from an environmental impacts perspective.

The rapid growth of electric and electronic equipment waste (WEEE) transformed this waste stream into a worldwide problem. The Directive 2002/96/EC on electrical and electronic waste aims at the reduction of the environmental impact of WEEE, encouraging end-of-life management, eco design, life cycle analyses and extended producer responsibility. However, this legislation may not produce the results the legislator aimed for. In this work we analyse the environmental impact of a WEEE recovery network in the Portuguese context. With this aim, a model, previously developed by the authors for the optimal design of this network using economic indicators (Salema, 2007), was now adapted to design the network subjected to the minimization of environmental performance indices. The original mathematical formulation was found to be flexible and easily adapted to the two types of indices and the major differences between the optimal network configurations obtained, were identified and discussed.

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The efficient design and operation of supply chains with return flows represent a major optimization challenge, given the high number of factors involved and their intricate interactions. In particular, the quality level of the return products has strong economic and societal implications and depends greatly on the type of product (glass, paper, electronic, oil, etc) and on the degree of consumers’ readiness, frequently promoted by various kinds of awareness raising campaigns. A multi-product multi-period model was previously developed by the authors [1] for the closed-loop supply chain (CLSC) design and planning, where strategic and tactical decisions were comprehensively considered. This model is now being extended to handle the uncertainty related to the quality of the returned products, which at this stage is modeled by a two-stage scenario-based stochastic approach. General strategies to solve optimization problems involving uncertainty tend to exhibit poor computational performance, due to the problem NP-hard complexity, which tends to worsen with the problem size. Therefore and, in addition, a model performance solution enhancement is also being explored. To increase the efficiency of the solution approach, an alternative representation to some of the integer variables employed in the mathematical formulation was developed, which is tested by means of computational experiments being performed on illustrative real sized examples.

The design and planning of efficient supply chains (SC) is a major challenge that increases when the return of products has to be accounted for, the so-called closed-loop supply chains (CLSC). In the present work the effect of disruptions and modifications in the operating conditions of CLSCs are investigated on the basis of a 2-stage scenario based model previously developed by the authors. Metrics derived from graph theory are used, along with more conventional economic and operational indices. A discussion on the results obtained is presented to assess how the design and planning of the CLSC respond to these challenges and how these metrics may contribute to this objective.

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This work presents a multi-objective optimization methodology that accounts for economic, environmental and social concerns in a supply chain with reverse flows. Environmental impact assessment is considered through the use of Recipe 2008. A social benefit indicator is developed where the creation of employment in less developed regions is preferred. The multi-objective approach is used to reach a solution of compromise between the three sustainability pillars. The model is applied to a case study developed in collaboration with a Portuguese company, leader in battery production.

In this paper we address the problem of uncertainty in the design and planning of a multi-period, multi-product closed loop supply chain, where the recovered products are end-of-life products that are disassembled and recycled. Uncertainty is explicitly modelled by considering customers’ demands and returns to be stochastic. A two–stage model is developed where first stage decisions concern the facility location while second stage decisions are the production planning of the supply chain. The integer L- shaped method was adopted as the solution tool and computational tests were performed on multi-period and multi-commodity networks randomly generated based on a reference case. A comparison between the proposed solution method and the straight use of the CPLEX is performed.

The Portuguese directive 230/2004 established December 2006 as the dead-line for the legal targets of EEE recovery to be achieved. The EEE producers working in Portugal set up an organization whose mission is to design and manage this recovery network. A MILP model was previously developed to optimize the network design where, by considering EEE sources and recycling facilities locations as known, sorting centre locations were determined together with the planning of collection and sorting activities. In the present paper, the network planned by the Portuguese EEE organization is compared with the results provided by this early model. In addition, and taking into account new organizational objectives, where collecting and sorting entities should independently perform the associated activities, the developed model is further extended, in order to account for a new network level related to the collection and sorting activities, and the previous network is retrofitted.

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A general model for the design and planning of closed-loop supply chains is proposed. The model is both strategic and tactical in nature, containing two levels of decisions: the location of facilities and the planning of production, storage and distribution. Its mathematical formulation is solved using a standard Branch and Bound technique. The model applicability and accuracy are studied on a modified version of an already published case study.

An European directive established that all EU countries must have, until the end of 2006, a recovery network for the electric and electronic equipment waste (WEEE). In Portugal, a group of producers formed a non-profit organization to design and manage this waste now. In this work, a model developed previously by the authors to design and plan a generic WEEE network is presented and applied to the Portuguese case. The results obtained are discussed and compared with the network that is currently operating.

All European countries are building structures to support collection and recovery of electric and electronic waste (WEEE). In this work, the study of a Portuguese recovery network is presented. A MILP model is developed in order to determine the best WEEE network structure as well as the associated optimal plan. Several different analyses are performed.

Much attention is currently given to the study of supply chains. Most studies are, however, only concerned with forward chains, rendering reverse chains a field insufficiently covered. Few optimization models addressing either the design of reverse networks or the simultaneous design of forward and reverse networks have been presented. We propose a MILP model for the design and planning of an integrated forward and reverse chain. While minimizing cost, the network structure is defined simultaneously with production and storage planning. Dynamic aspects are accounted for using a two-time scale, fully interconnected structure, where at a macro time the network design is performed and at a micro time the planning is optimised. The deterministic model is extended and a scenario approach is applied to account for uncertainties in demand and return of products. A case study is solved and a common structure to all scenarios is obtained, while scenario dependent policies are defined for distribution, production and storage. The model generality is corroborated with good performance results.

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.

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