Matthieu Godichaud

Sustainable management of end-of-life systems

In a sustainable development context, the stakes of the last stage of system life cycle, the end-of-life stage, have increased over recent years. End-of-life systems have to be de-manufactured in order to be valued so as to respond to environmental concerns. The aim of a disassembly strategy consists in issuing a solution to the whole decision problem raised during the end-of-life stage of systems. Indeed, decision makers have to select valuable components according to technical, economical and environmental criteria and then design and optimise a disassembly support system that will generate these products. The solution obtained is what we refer to in this article as a disassembly trajectory. The work presented in this article is about planning these trajectories on different horizons integrating several arrivals of end-of-life systems. The proposed approach, with Bayesian networks and influence diagrams as the underlying mathematical tools, enables dynamically defined uncertainties to be taken into account.

Disassembly process planning using Bayesian network

The management of end-of-life systems becomes more and more important due to the awareness of their environmental impact. In this context, the disassembly process requires more attention with the ultimate goal to make profit. In this paper, we propose a new approach to determine optimal disassembly plan of an end-of-life system by using bayesian network. To take advantage of some existing approaches that use Petri Net to model such process, a Petri Net model is first established and then translated to Bayesian Network in order to take into account inevitable uncertainties associated to such process.

Metaheuristic based optimization for capacitated disassembly lot sizing problem with lost sales

Disassembly operations are required for most of manufactured products at the end of their life cycle. As economic activities and environmental pressures increase, the volume of product reverse flows are more and more important and costly. In this context, we propose an optimization method to minimize cost in disassembly planning with lot sizing and lost sales. Comparing to others lot sizing problems with lost sales, this problem has some specificities that required original optimization methods. To this end, we proposed a metaheuristic based on genetic algorithm scheme that integrates some neighborhoods dedicated to this problem. The quality of the solutions is compared with those obtained from a mathematical programming solver for small instances and different configurations of the algorithm are compared. The metaheuristic allows finding good solutions in a reasonable computational time for this tactical problem for all instance sizes.

Multi-Objective Capacitated Disassembly Scheduling with Lost Sales

Disassembly scheduling is one of the important problems in reverse logistic decisions. This paper focuses on this problem with capacity restrictions on disassembly resources, lost sales, multiple products and without part commonality. A model with two objectives is developed and optimized by a multi-objective approach. The first objective is a sum of several costs to minimize: setup cost, inventory cost, and over capacity penalty cost. The second objective is a measure of the service level. Considering the complexity of this model, a genetic algorithm is developed (NSGA-II) to obtain a set of Pareto-optimal solutions, the results are compared with those calculated by a mixed integer programming model. Results of computational experiments on randomly generated test instances indicates that the genetic algorithm gives good quality solutions up to all problem sizes in a reasonable amount of computation time whereas linear programming solvers do not give solution in reasonable time.

Efficient metaheuristic for multi-product disassembly lot sizing problem with lost sales

Disassembly planning aims to determine the quantity of end-of-life products in order to satisfy the demand of leaf items over a given planning horizon while minimizing several costs. Disassembly planning problem with two levels, multi-products type and capacity constraints is treated by developing a linear programming model. The objective of this model is to minimize the sum of the fixed costs of disassembly, inventory holding cost of components and lost sales cost. In order to solve it, we propose an efficient optimization method based on genetic algorithm and Fix-and-Optimize heuristic. Our contribution is both the development of a new model allowing lost sales, and then we propose a new approach to solve it for all instances adapted from literature.

Comparing Inventory Policies for Closed-Loop Supply Chain Using Simulation-Based Optimization

Abstract Sustainable development principles lead to new challenges for supply chains management. To cope with these new challenges, one of the answers is to develop new activities related to reverse logistic which aim at recovered product from customer to valorization facilities (disassembly, recycling, remanufacturing …). These activities contribute to reduce use of new materials and negative impact of end-of-life products but they also generate more complex information and material flows. In this context, inventory control of return product and new product is a critical issue. New inventory control policies have to be developed to manage both return and new product supply. In this paper, a supply chain model based on simulation and multi-objective optimization is proposed to determine best control policies and their parameters for multi-echelon closed-loop supply chain.

Formalizing and Solving Information Collection Problems with Autonomous Sensor Systems

This paper addresses the problem of planning data collection missions for a set of Information Collection Systems (ICS) to respond to a set of information requests. This problem goes from the formalization of information needs to the optimization of ICS actions. After having formalized requests and decomposed them into elementary requests, the problem can be modeled with a graph characterizing the various aspects: coordination and assignment of ICSs, request satisfaction and ICS use optimization. Based on this graph, the problem can be solved with an A*-like search algorithm.

Modèle pour la planification des stratégies de déconstruction des systèmes en fin de vie

In a sustainable development context, stakes of the last stage of system life cycle, the end-of-life stage, have increased these last years. End-of-life systems have to be demanufactured in order to be valued answer so to some environmental requirements. The aim of disassembly strategies is to bring solutions to the whole decision problem risen during the end-of-life stage of systems. In particular, decision maker have to select valuable components according to technical, economical and environmental criteria and, then, design and optimise disassembly support system that will generate these products. The solution to this problem aim at determining what we call a disassembly trajectory. The work presented in this paper is about planning of these trajectories on different horizons that integrate several arrivals of end-of-life systems. The proposed approach allow to taking into account uncertainties defined on a temporal dimension.