Isabel Gomes

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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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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.

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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.

In this work the design of a reverse distribution network is studied. Most of the proposed models on the subject are case based and, for that reason, they lack generality. In this paper we try to overcome this limitation and a generalized model is proposed. It contemplates the design of a generic reverse logistics network where capacity limits, multi-product management and uncertainty on product demands and returns are considered. A mixed integer formulation is developed which is solved using standard B&B techniques. The model is applied to an illustrative case.

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.

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.

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.

The present work aims to support tactical and operational planning decisions of reverse logistics systems while considering economical, environmental and social objectives. In the literature, when addressing such systems economical aspects have been often used, while environmental concerns have been only recently emerging. The social component is the one less studied, and rarely the combination of the three concerns has been analyzed. This work address the three objectives and was motivated by the challenge of supporting decisions makers when managing a real case study of a recyclable waste collection system, where strategic decisions on the number and location of depots, vehicles and containers were taken beforehand. Tactical and operational decisions are studied involving the establishment of service areas for each depot and the definition and scheduling of collection routes for each vehicle. Such decisions should represent a compromise solution between the three objectives considered and support a sustainable reverse logistics plan. A multi-objective solution approach based on mixed-integer linear programming models is developed. Trade-offs between the objectives are discussed. Moreover the solutions obtained when each objective is tackled individually are compared between themselves and with the balanced solution.

This research has been motivated by a real-life problem of a waste cooking oil collection system characterized by the existence of multiple depots with an outsourced vehicle fleet, where the collection routes have to be plan. The routing problem addressed allows open routes between depots, i.e., all routes start at one depot but can end at the same or at a different one, depending on what minimizes the objective function considered. Such problem is referred as a Multi-Depot Vehicle Routing Problem with Mixed Closed and Open Inter-Depot Routes and is, in this paper, modeled through a Mixed Integer Linear Programming (MILP) formulation where capacity and duration constraints are taken into account. The model developed is applied to the real case study providing, as final results, the vehicle routes planning where a decrease of 13% on mileage and 11% on fleet hiring cost are achieved, when comparing with the current company solution.

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.

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.