Cognitive Robotic Disassembly Sequencing For Electromechanical End-Of-Life Products Via Decision-Maker-Centered Heuristic Optimization Algorithm

End-of-life (EOL) disassembly has developed into a major research area within the sustainability paradigm, resulting in the emergence of several algorithms and models to solve related problems. End-of-life disassembly focuses on regaining the value added into products which are considered to have completed their useful lives due to a variety of reasons such as lack of technical functionality and/or lack of demand. Disassembly is known to possess unique characteristics due to possible changes in the EOL product structure and hence, cannot be considered as the reverse of assembly operations. With the same logic, obtaining a near-optimal/optimal disassembly sequence requires intelligent decision making during the disassembly when the sequence need to be regenerated to accommodate these unforeseeable changes. That is, if one or more components which were included in the original bill-of-material (BOM) of the product is missing and/or if one or more joint types are different than the ones that are listed in the original BOM, the sequencer needs to be able to adapt and generate a new and accurate alternative for disassembly. These considerations require disassembly sequencing to be solved by highly adaptive methodologies justifying the utilization of image detection technologies for online real-time disassembly. These methodologies should also be capable of handling efficient search techniques which would provide equally reliable but faster solutions compared to their exhaustive search counterparts. Therefore, EOL disassembly sequencing literature offers a variety of heuristics techniques such as Genetic Algorithm (GA), Tabu Search (TS), Ant Colony Optimization (ACO), Simulated Annealing (SA) and Neural Networks (NN). As with any data driven technique, the performance of the proposed methodologies is heavily reliant on the accuracy and the flexibility of the algorithms and their abilities to accommodate several special considerations such as preserving the precedence relationships during disassembly while obtaining near-optimal or optimal solutions. This research proposes three approaches to the EOL disassembly sequencing problem. The first approach builds on previous disassembly sequencing research and proposes a Tabu Search based methodology to solve the problem. The objectives of this proposed algorithm are to minimize: (1) the traveled distance by the robotic arm, (2) the number of disassembly method changes, and (3) the number of robotic arm travels by combining the identical-material components together and hence eliminating unnecessary disassembly operations. In addition to improving the quality of optimum sequence generation, a comprehensive statistical analysis comparing the results of the previous Genetic Algorithm with the proposed Tabu Search Algorithm is also included. Following this, the disassembly sequencing problem is further investigated by introducing an automated disassembly framework for end-of-life electronic products. This proposed model is able to incorporate decision makers’ (DMs’) preferences into the problem environment for efficient material and component recovery. The proposed disassembly sequencing approach is composed of two steps. The first step involves the detection of objects and deals with the identification of precedence relationships among components. This stage utilizes the BOMs of the EOL products as the primary data source. The second step identifies the most appropriate disassembly operation alternative for each component. This is often a challenging task requiring expert opinion since the decision is based on several factors such as the purpose of disassembly, the disassembly method to be used, and the component availability in the product. Given that there are several factors to be considered, the problem is modeled using a multi-criteria decision making (MCDM) method. In this regard, an Analytic Hierarchy Process (AHP) model is created to incorporate DMs’ verbal expressions into the decision problem while validating the consistency of findings. These results are then fed into a metaheuristic algorithm to obtain the optimum or near-optimum disassembly sequence. In this step, a metaheuristic technique, Simulated Annealing (SA) algorithm, is used. In order to test the robustness of the proposed Simulated Annealing algorithm an experiment is designed using an Orthogonal Array (OA) and a comparison with an exhaustive search is conducted. In addition to testing the robustness of SA, a third approach is simultaneously proposed to include multiple stations using task allocation. Task allocation is utilized to find the optimum or near-optimum solution to distribute the tasks over all the available stations using SA. The research concludes with proposing a serverless architecture to solve the resource allocation problem. The architecture also supports non-conventional solutions and machine learning which aligns with the problems investigated in this research. Numerical examples are provided to demonstrate the functionality of the proposed approaches

A Scatter Search Approach for Multiobjective Selective Disassembly Sequence Problem

Disassembly sequence has received much attention in recent years. This work proposes a multiobjective optimization of model for selective disassembly sequences and maximizing disassembly profit and minimizing disassembly time. An improved scatter search (ISS) is adapted to solve proposed multiobjective optimization model, which embodies diversification generation of initial solutions, crossover combination operator, the local search strategy to improve the quality of new solutions, and reference set update method. To analyze the effect on the performance of ISS, simulation experiments are conducted on different products. The validity of ISS is verified by comparing the optimization effects of ISS and nondominated sorting genetic algorithm (NSGA-II)

Disassembly sequence planning validated thru augmented reality for a speed reducer

The lifecycle of a product is getting shorter in today’s market realities. Latest developments in the industry are heading towards achieving products that are easy to recycle, by developing further technological advances in raw materials ought to include input from End of Life (EOL) products so a reduction of natural harm could be achieved, hence reducing the overall production environmental footprint. Therefore, the approach taken as a design for environment, a key request nowadays in order to develop products that would ease the reverse manufacturing process leading to a more efficient element recycling for later use as spare parts or remanufacturing. The methodology proposed compares three probable disassembly sequences following a comparison of literature-found procedures between genetic algorithms and as a “state space search” problem, followed by a hybrid approach developed by the authors. Time and evaluation of these procedures reached to the best performing sequence. A subsequent augmented reality disassembly simulation was performed with the top-scored operation sequence with which the user is better able to familiarize himself with the assembly than a traditional paper manual, therefore enlightening the feasibility of the top performing sequence in the real world

Towards cleaner production: a roadmap for predicting product end-of-life costs at early design concept

The primary objective of the research was to investigate how disposal costs were being incurred in the domain of defence electronic systems by the Original Equipment Manufacturer (OEM) and subsequently to ascertain a novel approach to prediction of their end-of-life (EOL) costs. It is intended that the OEM could utilise this method as part of a full lifecycle cost analysis at the conceptual design stage. The cost model would also serve as a useful guide to aid decision making at the conceptual design stage, so that it may lead to the design of a more sustainable product in terms of recycling, refurbishment or remanufacture with the consideration of financial impact. The novelty of this research is that it identifies the significance of disposal costs from the viewpoint of the OEM and provides a generic basis for evaluation of all the major EOL defence electronic systems. A roadmap has been proposed and developed to facilitate the prediction of disposal costs and this will be used to determine a satisfactory solution of whether the EOL parts of a defence electronic system are viable to be remanufactured, refurbished or recycled from an early stage of a design concept. A selected defence electronic system is used as a case study. Based on the findings, the proposed method offers a manageable and realistic solution so that the OEM can estimate the cost of potential EOL recovery processes at the concept design stag

Automatic Disassembly Task Sequence Planning of Aircrafts at their End-of-Life

RÉSUMÉ Une prise de conscience des problèmes environnementaux à l'échelle mondiale ainsi que des avantages économiques a stimulé les chercheurs à trouver les possibilités de réutiliser et de recycler les produits en fin de vie. Chaque année plusieurs centaines d'avions atteignent globalement fin de leur navigabilité et doivent être retirés du service actif. De ce fait, une attention accrue est maintenant accordée à la fin de vie des avions. Désassemblage joue un rôle important dans la prise de décision de fin de vie. La faisabilité économique du processus de démontage avec beaucoup d'incertitudes est une préoccupation majeure limitant sa mise en oeuvre dans la pratique de l'industrie. De nombreuses recherches dans le domaine de la planification et des opérations de processus de démontage a été fait, qui visent de plus en plus la faisabilité économique du démontage avec la réduction des temps de démontage de proposer des séquences de démontage optimisées. Par conséquent, ces dernières années, de nombreux chercheurs ont publié des articles sur la planification de la séquence de démontage des produits en fin de vie qui est un problème NP-complet optimisation combinatoire. Néanmoins, il y a eu un peu d'attention à la planification de la séquence de démontage d'avions en fin de vie. Cette thèse aborde la planification de séquence de démontage des pièces réutilisables d'avions en fin de vie avant le démantèlement pour le recyclage. Puisque les composants récupérés vont être utilisés à nouveau, une approche non-destructive tout en respectant les instructions fournies dans le manuel d'entretien d'avion intitulé « Aircraft Maintenance Manuel » (AMM) pour le retrait des pièces est prise en considération. Ordonnancement de désassemblage dans cette recherche ne traite pas le séquençage le démontage des pièces comme dans d'autres études, mais il planifie séquence de tâches de démontage dans l'AMM. Une tâche de démontage consiste combinaison d'opérations pour la préparation du démontage ou le procède de démontage pour un ou plusieurs pièces. Tout d'abord, un modèle de séquençage de démontage est proposé par l'examen structure des tâches de démontage dans l'AMM. Ensuite, un code Matlab est développé qui lit la base de données énuméré des tâches et sous-tâches qui sont acquises à partir de l'AMM et génère la séquence de démontage des tâches et sous-tâches automatiquement en utilisant le modèle proposé. Le code est capable de générer des séquences de désassemblage de tâches pour n’importe quelle pièce sollicitée.----------ABSTRACT An awareness of the world’s environmental problems plus economic benefits has stimulated researchers to seek the opportunities to reuse and recycle end-of-life (EOL) products. Each year hundreds of aircraft globally reach end of their airworthiness and should be withdrawn from active service. Due to this fact, increased attention is now being paid to EOL of aircrafts. Disassembly plays an important role in EOL decision making. The economic feasibility of the disassembly process with lots of uncertainties is a main concern limiting its implementation in industry practice. Many researches in the field of disassembly process planning and operations has been done that aim increasing economic feasibility of disassembly with reducing disassembly times with proposing optimized disassembly sequences. Consequently, in recent years, many scholars have published articles on disassembly sequence planning of EOL products that is a NP-complete combinatorial optimization problem. Nevertheless, there has been a scant attention towards disassembly sequence planning of EOL aircrafts. This thesis addresses disassembly sequence planning of reusable components of EOL aircrafts before dismantling it for recycling. Since retrieved components are going to be used again, a nondestructive approach with respecting all instructions provided in aircraft maintenance manual (AMM) for removal of parts is taken into consideration. Disassembly scheduling in this work does not deal with scheduling disassembly of components as in other works but it schedules sequence of removal Tasks in AMM. A removal task consists combination of operations for preparation of disassembly or process of disassembly for a part or multiple parts. At first, a disassembly sequencing model with considering structure of disassembly tasks in AMM is proposed. Afterwards a Matlab code is developed which reads from enumerated database of tasks and subtasks that are acquired from AMM and generates disassembly sequence of tasks and subtasks automatically using the proposed model. The code is capable of generating disassembly sequences of tasks for any given removal task of solicited part. Finally, a greedy and an adaptive greedy algorithm are proposed to optimize disassembly sequence of tasks with minimizing changes in visited zones of disassembly operations. Results generated in Matlab code, suggests effectiveness of proposed adaptive greedy algorithm

A review of discrete-time optimization models for tactical production planning

This is an Accepted Manuscript of an article published in International Journal of Production Research on 27 Mar 2014, available online: http://doi.org/10.1080/00207543.2014.899721[EN] This study presents a review of optimization models for tactical production planning. The objective of this research is to identify streams and future research directions in this field based on the different classification criteria proposed. The major findings indicate that: (1) the most popular production-planning area is master production scheduling with a big-bucket time-type period; (2) most of the considered limited resources correspond to productive resources and, to a lesser extent, to inventory capacities; (3) the consideration of backlogs, set-up times, parallel machines, overtime capacities and network-type multisite configuration stand out in terms of extensions; (4) the most widely used modelling approach is linear/integer/mixed integer linear programming solved with exact algorithms, such as branch-and-bound, in commercial MIP solvers; (5) CPLEX, C and its variants and Lindo/Lingo are the most popular development tools among solvers, programming languages and modelling languages, respectively; (6) most works perform numerical experiments with random created instances, while a small number of works were validated by real-world data from industrial firms, of which the most popular are sawmills, wood and furniture, automobile and semiconductors and electronic devices.This study has been funded by the Universitat Politècnica de València projects: ‘Material Requirement Planning Fourth Generation (MRPIV)’ (Ref. PAID-05-12) and ‘Quantitative Models for the Design of Socially Responsible Supply Chains under Uncertainty Conditions. Application of Solution Strategies based on Hybrid Metaheuristics’ (PAID-06-12).Díaz-Madroñero Boluda, FM.; Mula, J.; Peidro Payá, D. (2014). A review of discrete-time optimization models for tactical production planning. International Journal of Production Research. 52(17):5171-5205. doi:10.1080/00207543.2014.899721S51715205521