Isabel Gomes

In this paper, an MILP formulation is proposed for the design of a reverse logistics network based on a warehouse location–allocation model, which optimizes, simultaneously, the forward and reverse networks. A single product model with unlimited capacity is first defined. Subsequently, the model is extended to a multi-product capacitated recovery network model, where capacity limitations and a multi-product system can be considered. The proposed model is compared to published work in the field, where different model assumptions have been proposed. Two cases are described so as to gain a better insight into the model and allow a comparative analysis.

In this work the design and planning of a Closed-Loop Supply Chain (CLSC) under uncertain conditions is considered and a two-stage scenario-based modeling approach is proposed in order to deal with this multi-product, multi-period problem. The network design and tactical plan are simultaneously optimized in a given time horizon, comprehending the raw material acquisition and the processing, storage and distribution of intermediates, returns and final products. Uncertainty is associated to the returns, both in terms of their quantity, which is customer dependent, and quality, which is determined at the sorting centers. Therefore, the ensuing mathematical MILP formulation considers the simultaneous integration of two important uncertainty sources, which represents an important modeling advantage, allowing a better understanding of the reverse network structure. For each probabilistic scenario, the model estimates the amount of products to be returned by customers and of returns to be remanufactured for each quality level, as well as the storage levels. Several tests based on a real sized example of a Portuguese glass company are undertaken in order to show the applicability of the developed approach. Based on these tests, the influence of the uncertain quality and quantity of returns on the design and planning of the CLSC is assessed.

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.

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.

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.

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.

The increase focus of companies in clients’ satisfaction, independently of the market they operate in, results from a growing degree of consumers expectations. To fulfill such increase, companies have to arm themselves with the correct internal capabilities, achieving not only efficacy in the satisfaction of each customer, but also efficiency, by operating at the lower possible cost. One of those internal capabilities is the logistics network, which is representing, more and more, a great challenge for companies, given the complexity of the variables that compose it. In this paper, an optimization model is developed that contemplates simultaneously the design and planning of supply chains with reversed flows, through a multi-period and a multi-product network. The proposed model acts as a support tool for the decisions to be made in a real case of a Portuguese telecommunications company, in order to optimize its logistics network.

Sustainable supply chain design is nowadays an important topic where not only economic and environmental aspects should be accounted for, but also social aspects are to be considered. A mathematical programming model was developed and a case-study was performed considering two different social indicators: one that prefers facility location in regions of lower GDP and the other in regions of higher unemployment rate. Results show that the outcome depends on the indicator used. However, for the case presented, minimum cost solution also corresponds to a good social solution regarding GDP, which could translate into economic incentives for the company.

This paper addresses the design and planning of supply chains with product return. A graph approach is used as the modelling methodology. Commonly, the application of graph approaches to the design of supply chains, considers nodes as chain entities and arcs as connections between them. These assumptions are extended in the present work where products may also be associated with both nodes and arcs. A multi-product network formulation is obtained which is further generalised to consider the modelling of time, resulting in a dynamic multi-product network model with product return. This generalisation assumes that any node is a transformation point which allows inbound and outbound products to differ. Considering four different kinds of entities (factories, warehouses, sorting centres and customers), proper functions are defined for each one: production, postponement, usage and selection. An example, base on a Portuguese industry case, is applied in order to corroborate the model applicability and adequacy to real world problems.

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.

In this paper, a strategic location-allocation model is developed for the simultaneous design of forward and reverse supply chains. Strategic decisions such as network design are accounted for together with tactical decisions, namely, production, storage and distribution planning. The integration between strategic and tactical decisions is achieved by considering two interconnected time scales: a macro and a micro time. At macro level, the supply chain is designed in order to account for the existing demands and returns, whose satisfaction is planned simultaneously at the micro level where tactical decisions are taken. A Mixed Integer Linear Programming formulation is obtained which is solved to optimality using standard Branch & Bound techniques. Finally, the model accuracy and applicability is illustrated through the resolution of a case study.

In this work we propose a stochastic model for the design and planning of closed-loop supply chains.
Uncertainties in demand and return volumes are modelled together with uncertain transportation
costs. A two-stage stochastic programming is developed and a sensitivity analysis to the worst-case
probability is performed in order to test the solution robustness. Finally, in order to prove the
goodness of the stochastic approach, the value of the stochastic solution and the value of perfect
information are computed. An example based on a real case shows the model applicability.

This paper is focused on the delimitation of service areas and on vehicle routes definition in recyclable waste collection systems with more than one depot. Three types of materials have to be collected in separated routes, so the problem is modelled as a multi-product, multi-depot vehicle routing problem. A hybrid method is developed where a MIP solver is embedded inside a heuristic framework. The effectiveness of this method is tested by comparing the results obtained for some test instances when solved only by an exact formulation. The hybrid method is then applied to a medium size problem based on a real recyclable waste collection system.

The increase in societal awareness towards environmental issues has accrued the responsibility of goods producers, which at present came to encompass the entire product life cycle. Recently, the efficient design and operation of supply chains with return flows have, in particular, become a major challenge for many companies, given the high number of factors involved and their intricate interactions. In this paper, a multi-period and multi-product network model for the simultaneous design and planning of supply chains with reverse flows is proposed. A graph approach based on the conventional concepts of nodes and arcs is employed to model the network, where it is assumed that any network node is a transformation point of inbound into outbound flows, which in the limit may not differ, and that related arcs describe products flows along the chain. In here the formulation of time adopts a management perspective, i.e., the strategic design of the supply chain is dealt simultaneously with the tactical planning of its operation, which covers supply, production, storage and distribution. An example based on a Portuguese industry case is studied in order to validate both the applicability and adequacy of the model to real world problems.

This paper proposes a new optimization methodology for supply chain design, using ant colony optimization. The objective of this methodology is to choose the facilities that will take part in a multi-product closed-loop supply chain, such as factories, warehouses and disassembly centers, in order to minimize the costs related to these facilities and those related to transportation costs, both in the forward and reverse chains. Considering that total production quantities for factories, expected cross-docking stocks for warehouses, and disassembly centers are determined by this methodology, it can be considered that it undertakes both strategic and tactical Supply Chain Management (SCM) problems at once. The developed algorithm, SCant-Design, is sufficiently general to solve any SCM configuration, with linear and nonlinear cost functions and constraints. The algorithm results were compared to a MILP approach for a particular case study and the obtained value for the cost function is very similar, although using less facilities.

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The present work aims to develop a decision supporting tool to help the decision making process related to the planning of waste collection systems. Such systems involve more than one depot and the objective is to define the delimitation of service areas and the vehicles routes. The problem is modelled as a multi-product, multi-depot vehicle routing problem. A hybrid method that combines exact formulations with heuristic procedures is developed and applied to a real recyclable collection system whose managers want to restructure the current service areas as well as the vehicle routes used to collect three types of recyclable materials.

In recent years much attention has been given to global supply chains. However, the emphasis has been on forward chains, with reverse flows been insufficiently studied. In this work, we study closed loop supply chains and analyse the impact caused by the introduction of new facility sites into an already existing supply chain. The model considers both strategic and tactical decisions allowing for the design of a global supply chain with reverse flows. The impact of the network redesign is analysed and the associated costs calculated. The obtained MILP is solved using a standard Branch and Bound solver. The results obtained prove that the proposed model handles satisfactorily the network redesign problem.

This paper addresses the planning of a real recyclable packaging waste collection system operating in Portugal. The company’s logistics network is characterized by the existence of multiple depots and multiple products to be collected in each site. Service areas and vehicle routes are currently defined respecting the municipal boundaries and present a need for improvement. To study this problem mixed integer linear programming formulations have been developed allowing the redesign of service areas and of collection routes under two scenarios, respectively, considering the definition of service areas by depot and by material. The scenario where the current service areas are maintained and the collection routes are optimized is also studied. Savings up to 20% in total distance are achieved by redesigning service areas and collection routes.