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 bibliographic review of production line design and balancing under uncertainty

This bibliography reviews the solution methods developed for the design and balancing problems of production lines such as assembly and disassembly lines. The line design problem aims in determining the number of workstations along with the corresponding assignment of tasks to each workstation, while the line balancing problem seeks an assignment of tasks, to the existing workstations of the line, which ensures that the workloads are as equal as possible among the workstations. These two optimisation problems can be also integrated and treated as a multi-objective optimisation problem. This review considers both deterministic and stochastic formulations for disassembly lines and is limited to assembly line design and balancing under uncertainty. This bibliography covers more than 90 publications since 1976 for assembly and 1999 for disassembly

TeMA: A Tensorial Memetic Algorithm for Many-Objective Parallel Disassembly Sequence Planning in Product Refurbishment

The refurbishment market is rich in opportunities—the global refurbished smartphones market alone will be $38.9 billion by 2025. Refurbishing a product involves disassembling it to test the key parts and replacing those that are defective or worn. This restores the product to like-new conditions, so that it can be put on the market again at a lower price. Making this process quick and efficient is crucial. This paper presents a novel formulation of parallel disassembly problem that maximizes the degree of parallelism, the level of ergonomics, and how the workers' workload is balanced, while minimizing the disassembly time and the number of times the product has to be rotated. The problem is solved using the Tensorial Memetic Algorithm (TeMA), a novel two-stage many-objective (MaO) algorithm, which encodes parallel disassembly plans by using third-order tensors. TeMA first splits the objectives into primary and secondary on the basis of a decision-maker's preferences, and then finds Pareto-optimal compromises (seeds) of the primary objectives. In the second stage, TeMA performs a fine-grained local search that explores the objective space regions around the seeds, to improve the secondary objectives. TeMA was tested on two real-world refurbishment processes involving a smartphone and a washing machine. The experiments showed that, on average, TeMA is statistically more accurate than various efficient MaO algorithms in the decision-maker's area of preference

Combining a hierarchical task network planner with a constraint satisfaction solver for assembly operations involving routing problems in a multi-robot context

This work addresses the combination of a symbolic hierarchical task network planner and a constraint satisfaction solver for the vehicle routing problem in a multi-robot context for structure assembly operations. Each planner has its own problem domain and search space, and the article describes how both planners interact in a loop sharing information in order to improve the cost of the solutions. The vehicle routing problem solver gives an initial assignment of parts to robots, making the distribution based on the distance among parts and robots, trying also to maximize the parallelism of the future assembly operations evaluating during the process the dependencies among the parts assigned to each robot. Then, the hierarchical task network planner computes a scheduling for the given assignment and estimates the cost in terms of time spent on the structure assembly. This cost value is then given back to the vehicle routing problem solver as feedback to compute a better assignment, closing the loop and repeating again the whole process. This interaction scheme has been tested with different constraint satisfaction solvers for the vehicle routing problem. The article presents simulation results in a scenario with a team of aerial robots assembling a structure, comparing the results obtained with different configurations of the vehicle routing problem solver and showing the suitability of using this approach.Unión Europea ARCAS FP7-ICT-287617Unión Europea H2020-ICT-644271Unión europea H2020-ICT-73166

Intelligent systems in manufacturing: current developments and future prospects

Global competition and rapidly changing customer requirements are demanding increasing changes in manufacturing environments. Enterprises are required to constantly redesign their products and continuously reconfigure their manufacturing systems. Traditional approaches to manufacturing systems do not fully satisfy this new situation. Many authors have proposed that artificial intelligence will bring the flexibility and efficiency needed by manufacturing systems. This paper is a review of artificial intelligence techniques used in manufacturing systems. The paper first defines the components of a simplified intelligent manufacturing systems (IMS), the different Artificial Intelligence (AI) techniques to be considered and then shows how these AI techniques are used for the components of IMS

A Robust Robotic Disassembly Sequence Design Using Orthogonal Arrays and Task Allocation

Disassembly sequence planning (DSP) is a nondeterministic polynomial time (NP) complete problem, making the utilization of metaheuristic approaches a viable alternative. DSP aims at creating efficient algorithms for deriving the optimum or near-optimum disassembly sequence for a given product or a product family. The problem-specific nature of such algorithms, however, requires these solutions to be validated, proving their versatility in accommodating substantial variations in the problem environment. To achieve this goal, this paper utilizes Taguchi’s orthogonal arrays to test the robustness of a previously-proposed Simulated Annealing (SA) algorithm. A comparison with an exhaustive search is also conducted to verify the efficiency of the algorithm in generating an optimum or near-optimum disassembly sequence for a given product. In order to further improve the solution, a distributed task allocation technique is also introduced into the model environment to accommodate multiple robot arms.http://dx.doi.org/10.3390/robotics801002