Isabel Gomes

n/a

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 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.

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 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 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.

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.

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.

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.

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.

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.

A Mixed Integer Linear Programming model for the design and planning of green supply chains is developed. Strategic and tactical decisions are taken, namely on facility location and capacity installation, supplier selection, technology selection, transportation network definition, supply planning, and product recovery. The aim of this work is to study the use of environmental indicators in these decisions while accounting for profit objectives. Different objective functions concerning environmental aspects are implemented. ReCiPe quantifies the environmental performance of the supply chain and combinations of ReCiPe’s midpoint categories allow a deeper analysis of the impact of these categories in strategic and tactical decisions. The goal is to understand if focusing on selected categories affects supply chain decisions and overall supply chain environmental impact. Net Present Value quantifies the economic performance and is used for lexicographic optimization. The model is applied to a case-study and important managerial insights are obtained. From a holistic point of view, it answers the question: how should supply chain environmental impact be assessed? From a case-study perspective, insights are obtained regarding what type of improvements should be implemented to reduce the environmental impact and how this would affect supply chain strategic and tactical decisions, along with economic performance.

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.

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 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.

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.