Isabel Gomes

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.

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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.

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, 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.

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This paper proposes a systematic methodology for supply chain planning optimization and assessment of different scenarios. This methodology will help companies throughout the difficult process of strategic decision-making in what concerns the design of an efficient supply chain structure. The proposed methodology allows the analysis of complex systems as it integrates a decision support tool developed to agile the process of scenario comparison, when designing network structures. This tool allows practitioners to take advantage of the optimization models without the need of learning modelling languages. An example based on a real case study is presented, showing the methodology/tool applicability.

In this work, the design of supply chains with reverse flows is studied in a multi-objective context. A MILP model is developed for the optimization of the supply chain structures where both environmental impacts and costs are considered. An optimal Pareto front is obtained that plays an important role in evaluating the best locations of all the entities in the supply chain while accounting for the global supply chain environmental impacts. A study based on a Portuguese glass industry is presented to corroborate both the applicability and the adequacy of the model.

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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Optimized supply chains, both economically as well as environmentally, are significant instruments to be adopted by companies if they want to succeed in the current competitive world. However, without the support of decision tools to help, at least approximately, the integration of these two aspects in the company’s decision making such aim is difficult to reach. The purpose of this study is to develop a mixed integer linear programming model (MILP), which allows the optimization of closed loop supply chains, considering not only economical aspects, but also environmental ones. The model developed was applied to a Portuguese company where their logistic network structure was redesigned.

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 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 European Union directive for electric and electronic waste, published in 2003, enforced all European countries to meet some targets concerning the recycling and recovery of these products. This directive was transposed to the Portuguese legislation in 2004. Following this, a group of EEE producers set up an organization (Amb3e) whose mission was to design and manage a nationwide recovery network for WEEE, which will be the subject matter of this work. A generic MILP model is proposed to represent this network, which is applied to its design and planning, where the best locations for collection and sorting centres are chosen simultaneously with the definition of a tactical network planning. Several analyses are performed to provide further insights regarding the selection of these alternative locations. The results gave support to the company strategic expansion plans for a high number of centres to be opened and to their location near the major WEEE sources.

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.