Isabel Gomes

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.

Packaging waste collection systems are responsible to collect, within a geographic area, three types of packaging materials (paper, glass and plastic/metal) that are disposed by the final consumer into special bins. Those systems are often characterized by having a network with multiple depots that act as transfer and sorting stations, and where the vehicle fleet is based. However, each depot is often managed independently and not as a part of a unique system. In this work, four current tactical/operational practices that contribute to the independent management of each depot are analysed. The change of such practices is investigated and their impact assessed on the total collection cost. A solution methodology based on mathematical formulations is developed to plan service areas, vehicle routes and vehicle schedules taking into account new alternative solutions in managing the system as a whole. Such methodology is applied to a real case study of a company responsible for the collection of the packaging waste in 7 municipalities in mainland Portugal. New service areas, collection routes and vehicle schedules are defined and significant savings are obtained in terms of the total distance travelled as well as in terms of the number of required vehicles, resulting in a decreasing of the total system cost.

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.

This paper addresses the planning of recyclable waste collection systems while accounting for economic and environmental concerns. Service areas and vehicle routes are defined for multiple-depot logistics networks where different products have to be collected. The problem is modeled as a multi-product, multi-depot vehicle routing problem with two objective functions: distance and CO2 emissions minimization. A decomposition solution method is developed and applied to a real case-study. Six scenarios are studied regarding different service areas configuration and different objective functions. Savings up to 22% in distance and 27% in CO2 emissions are achieved, excelling economical and environmental goals.

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 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.

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.

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 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.

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.

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.

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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 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.

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 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.

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 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.

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.