In this work a two-stage scenario-based modeling approach is proposed in order to simultaneously deal with the design and planning decisions in supply chain networks, where both forward and reverse flows are considered (closed-loop supply chains) subject to uncertain conditions. A mixed integer linear programming (MILP) approach is developed with the underlying objective of profit maximization. Planning takes into account raw material acquisition and processing, storage and distribution of several products flowing through the network. Uncertainty is associated to the quantity and quality of the flow of products of the reverse network, which are directly affected by customers and sorting centers, respectively. The approach considers, by means of scenarios, the simultaneous integration of two important uncertainty sources, thus presenting an important modeling advantage, i.e. that of providing a better understanding of the CLSCs. The applicability of the formulation is tested with a real sized example of a Portuguese glass company.

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.

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.

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.

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A laboratory-scale reactor system is built and operated to measure the kinetic of formation for single and mixed carbon dioxide-tetrahydrofuran hydrates. The T-cycle method, which is used to collect the kinetic data, is briefly discussed. For single carbon dioxide hydrate, the induction time decreases with the increase of the initial carbon dioxide pressure up to 2.96. MPa. Beyond this pressure, the induction time is becoming relatively constant with the increase of initial carbon dioxide pressure indicating that the liquid phase is completely supersaturated with carbon dioxide. Experimental results show that the inclusion of tetrahydrofuran reduces the induction time required for hydrate formation. These observations indicate hydrate nucleation process and onset growth are more readily to occur in the presence of tetrahydrofuran. In contrast, the presence of sodium chloride prolongs the induction time due to clustering of water molecules with the ions and the salting-out effects. It is also shown that the degree of subcooling required for hydrate formation is affected by the presence of tetrahydrofuran and sodium chloride in the hydrate forming system. The presence of tetrahydrofuran in the hydrate system significantly reduces the amount of carbon dioxide uptake. The apparent rate constant, k, for those systems are reported. © 2010.

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