Analyzing alternatives in reverse logistics for end-of-life computers: ANP and balanced scorecard approach

Activities in reverse logistics activities are extensively practiced by computer hardware industries. One of the important problems faced by the top management in the computer hardware industries is the evaluation of various alternatives for end-of-life (EOL) computers. Analytic network process (ANP) based decision model presented in this paper structures the problem related to options in reverse logistics for EOL computers in a hierarchical form and links the determinants, dimensions, and enablers of the reverse logistics with alternatives available to the decision maker. In the proposed model, the dimensions of reverse logistics for the EOL computers have been taken from four perspectives derived from balanced scorecard approach, viz. customer, internal business, innovation and learning, and finance. The proposed approach, therefore, links the financial and non-financial, tangible and intangible, internal and external factors, thus providing a holistic framework for the selection of an alternative for the reverse logistics operations for EOL computers. Many criteria, sub-criteria, determinants, etc. for the selection of reverse logistics options are interrelated. The ability of ANP to consider interdependencies among and between levels of decision attributes makes it an attractive multi-criteria decision-making tool. Thus, a combination of balanced scorecard and ANP-based approach proposed in this paper provides a more realistic and accurate representation of the problem for conducting reverse logistics operations for EOL computers.     

Real world disassembly modeling and sequencing problem: Optimization by Algorithm of Self-Guided Ants (ASGA)

Decrease in product life along with the advent of stringent regulations and environmental consciousness have led to increased concern for methodological product recovery through disassembly operations. This research proposes a fuzzy disassembly optimization model (FDOM) and is aimed at determining the optimal disassembly sequence as well as the optimal depth of disassembly to maximize the net revenue at the end-of-life (EOL) disposal of the product in the real world situations. In order to account for the uncertainty inherent in quality of the returned products, fuzzy control theory is incorporated in the problem environment for modeling the expected value of the recovered modules. Considering the computational complexity of the problem at hand, an innovative approach of Algorithm of Self-Guided Ants (ASGAs) is proposed for the same. The performance of the proposed methodology is benchmarked against a set of test instances that were generated using design of experiment techniques and analysis of variance is performed to determine the impact of various factors on the objective. The robustness of proposed algorithm is authenticated against Ant Colony Optimization and Genetic Algorithm over which it always demonstrated better results thereby proving its superiority on the concerned problem.     

A data-driven based decomposition–integration method for remanufacturing cost prediction of end-of-life products

Remanufacturing cost prediction is conducive to visually judging the remanufacturability of end-of-life (EOL) products from economic perspective. However, due to the randomness, non-linearity of remanufacturing cost and the lack of sufficient data samples. The general method for predicting the remanufacturing cost of EOL products is very low precision. To this end, a data-driven based decomposition–integration method is proposed to predict remanufacturing cost of EOL products. The approach is based on historical remanufacturing cost data to build a model for prediction. First of all, the remanufacturing cost of individual EOL product is arranged as a time series in reprocessing order. The Improved Local Mean Decomposition (ILMD) is employed to decompose remanufacturing cost time series data into several components with smooth, periodic fluctuation and use this as input. BP neural network based on Particle Swarm Optimization (PSO-BP) algorithm is utilized to predict the cost of each component. Finally, the predicted components are added to obtain the final prediction result. To illustrate and verify the feasibility of the proposed method, the remanufacturing cost of DH220 excavator is applied as the sample data, and empirical results show that the proposed model is statistically superior to other benchmark models owing to its high prediction accuracy and less computation time. And proposed method can be utilized as an effective tool to analyze and predict remanufacturing cost of EOL products.     

Critical transistors nexus based circuit-level aging assessment and prediction

Accurate age modeling, and fast, yet robust reliability sign-off emerged as mandatory constraints in Integrated Circuits (ICs) design for advanced process technology nodes. In this paper we introduce a novel method to assess and predict the circuit reliability at design time as well as at run-time. The main goal of our proposal is to allow for: (i) design time reliability optimization; (ii) fine tuning of the run-time reliability assessment infrastructure, and (iii) run-time aging assessment. To this end, we propose to select a minimum-size kernel of critical transistors and based on them to assess and predict an IC End-Of-Life (EOL) via two methods: (i) as the sum of the critical transistors end-of-life values, weighted by fixed topology-dependent coefficients, and (ii) by a Markovian framework applied to the critical transistors, which takes into account the joint effects of process, environmental, and temporal variations. The former model exploits the aging dependence on the circuit topology to enable fast run-time reliability assessment with minimum aging sensors requirements. By allowing the performance boundary to vary in time such that both remnant and nonremnant variations are encompassed, and imposing a Markovian evolution, the probabilistic model can be better fitted to various real conditions, thus enabling at design-time appropriate guardbands selection and effective aging mitigation/compensation techniques. The proposed framework has been validated for different stress conditions, under process variations and aging effects, for the ISCAS-85 c499 circuit, in PTM 45 nm technology. From the total of 1526 transistors, we obtained a kernel of 15 critical transistors, for which the set of topology dependent weights were derived. Our simulation results for 15 critical transistors kernel indicate a small approximation error (i.e., mean smaller than 15% and standard deviation smaller than 6%) for the considered circuit estimated end-of-life (EOL), when comparing to the end-of-life values obtained from Cadence simulation, which quantitatively confirm the accuracy of the IC lifetime evaluation. Moreover, as the number of critical transistors determines the area overhead, we also investigated the implications of reducing their number on the reliability assessment accuracy. When only 5 transistors are included into the critical set instead of 15, which results in a 66% area overhead reduction, the EOL estimation accuracy diminished with 18%. This indicates that area vs. accuracy trade-offs are possible, while maintaining the aging prediction accuracy within reasonable bounds.     

Comparison of economic benefits of sensor embedded products and conventional products in a multi-product disassembly line

Sensors embedded into products during the production process are important data acquisition tools for after-sale products’ condition monitoring. By facilitating data collection from critical components in a product, these sensors help companies predict components and/or products failures during product usage. They are also very useful at the end-of-life (EOL) of products. Using sensor information, the conditions, types and remaining lives of components in an EOL product can be determined prior to actual disassembly. In this study, we assess the use of sensors in determining the steps in EOL processing of products. In particular, we evaluate the impact of sensor embedded products (SEPs) on various performance measures of an appliance disassembly line controlled by a multi-kanban system. First, separate design of experiments studies based on orthogonal arrays are carried out for conventional products (CPs) and SEPs. In order to calculate the response values for each experiment, detailed discrete event simulation models of both cases are developed considering the precedence relationships among the components together with the routing of different appliance types through the disassembly line. Then, pair-wise t-tests are conducted to compare the two cases based on different performance measures. The test results show the superiority of SEPs over CPs with respect to all costs (viz., disassembly, disposal, testing, backorder, transportation, holding) as well as revenue and profit.     

Collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem using improved discrete Bees algorithm in remanufacturing✰

Remanufacturing helps to reduce manufacturing cost and environmental pollution by reusing end-of-life products. Disassembly is an inevitable process of remanufacturing and it is always finished by manual labor which is high cost and low efficiency while robotic disassembly helps to cover these shortages. Before the execution of disassembly, well-designed disassembly sequence and disassembly line balancing solution help to improve disassembly efficiency. However, most of the research used for disassembly sequence planning and disassembly line balancing problem is only applicable to manual disassembly. Also, disassembly sequence planning and disassembly line balancing problem are separately studied. In this paper, an improved discrete Bees algorithm is developed to solve the collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem. Robotic workstation assignment method is used to generate robotic disassembly line solutions based on feasible disassembly solutions obtained by the space interference matrices. Optimization objectives of the collaborative optimization problem are described, and the analytic network process is used to assign suitable weights to different indicators. With the help of variable neighborhood search, an improved discrete Bees algorithm is developed to find the optimal solution. Finally, based on a gear pump and a camera, case studies are used to verify the effectiveness of the proposed method. The results under different cycle time of robotic disassembly line are analyzed. Under the best cycle time, the performance of the improved discrete Bees algorithm under different populations and iterations are analyzed and compared with the other three optimization algorithms. The results under different assessment methods and scenarios are also analyzed.     

Application of Bayesian decision networks to life cycle engineering in Green design and manufacturing

Environmental impact assessment of design and manufacturing decisions have received significant attention in the recent years. Researchers have not only focused on industrial waste minimization and chemical substitution in processes or products, but also on the effect of product design decisions on the environment during the manufacturing, in-use and end-of-life stages of the product. This research investigates the applicability of Bayesian decision networks to study the impact of design decisions on the life cycle performance, including environmental friendliness, of a product. Bayesian decision theory provides a normative framework for representing and reasoning about decision problems under uncertainty. A framework for integrated analysis of the product life cycle is presented. We discuss the specification of domain models for wide range of processes, such as manufacturing, recycling and disposal, an action model, and an utility model.     

Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

This study presents a simulation optimization approach for a hybrid flow shop scheduling problem in a real-world semiconductor back-end assembly facility. The complexity of the problem is determined based on demand and supply characteristics. Demand varies with orders characterized by different quantities, product types, and release times. Supply varies with the number of flexible manufacturing routes but is constrained in a multi-line/multi-stage production system that contains certain types and numbers of identical and unrelated parallel machines. An order is typically split into separate jobs for parallel processing and subsequently merged for completion to reduce flow time. Split jobs that apply the same qualified machine type per order are compiled for quality and traceability. The objective is to achieve the feasible minimal flow time by determining the optimal assignment of the production line and machine type at each stage for each order. A simulation optimization approach is adopted due to the complex and stochastic nature of the problem. The approach includes a simulation model for performance evaluation, an optimization strategy with application of a genetic algorithm, and an acceleration technique via an optimal computing budget allocation. Furthermore, scenario analyses of the different levels of demand, product mix, and lot sizing are performed to reveal the advantage of simulation. This study demonstrates the value of the simulation optimization approach for practical applications and provides directions for future research on the stochastic hybrid flow shop scheduling problem.     

The construction of a collaborative-design platform to support waste electrical and electronic equipment recycling

Recycling of waste electrical and electronic equipment (WEEE) is a very important subject not only from the viewpoint of waste treatment but also from the viewpoint of recovery of valuable materials. In the past, some obstacles make recycling challenging for today's manufactured products. First, it is difficult to gain all the information necessary to plan for the recycling evaluation, as most design information is owned and kept by suppliers. Another problem in recycling end-of-life (EOL) products is a lack of technologies to handle the very complex products that are being discarded today, because the knowledge of how to do so is owned by the recycler.This research demonstrates how to support WEEE recycling analysis by environmental information with the part of bill of material. A collaborative-design platform is further constructed and collected all the needed information using computer-aided design (CAD), enterprise resource planning (ERP), and product life-cycle management (PLM) systems. Through this platform, suppliers are required to provide component information to enable the manufacturer's design for disassembly and recycling analysis. The results demonstrate that designers can obtain disassembly and recycling information through the model, so that desirable changes can be made in the early stages of a design. An industrial case study from Taiwan is also provided to demonstrate the use of this model.     

A cost-driven process planning method for hybrid additive–subtractive remanufacturing

Hybrid manufacturing combines additive manufacturing’s advantages of building complex geometries and subtractive manufacturing’s benefits of dimensional precision and surface quality. This technology shows great potential to support repairing and remanufacturing processes. Hybrid manufacturing is used to repair end-of-life parts or remanufacture them to new features and functionalities. However, process planning for hybrid remanufacturing is still a challenging research topic. This is because current methods require extensive human intervention for feature recognition and knowledge interpretation, and the quality of the derived process plans are hard to quantify. To fill this gap, a cost-driven process planning method for hybrid additive–subtractive remanufacturing is proposed in this paper. An automated additive–subtractive feature extraction method is developed and the process planning task is formulated into a cost-minimization optimization problem to guarantee a high-quality solution. Specifically, an implicit level-set function-based feature extraction method is proposed. Precedence constraints and cost models are also formulated to construct the hybrid process planning task as a mixed-integer programming model. Numerical examples demonstrate the efficacy of the proposed method.     

A grammatical optimization approach for integrated process planning and scheduling

Rapid developments in computerized manufacturing environments and increasing overlapping in the capability of manufacturing resources provoked integration of many manufacturing functions including process planning scheduling. Several approaches have been developed in the literature in order to integrate process planning and scheduling. In this paper a novel approach which makes use of grammatical representation of generic process plans is used within a multiple objective tabu search framework in order to integrate process planning and scheduling effectively. Detailed explanations along with an example problem are presented in the paper. Proposed approach is tested on literature problems and also hypothetically generated flexible job shop scheduling problems with alternative process plans to analyze its performance and efficiency.     

A Decision Maker-Centered End-of-Life Product Recovery System for Robot Task Sequencing

End-of-life (EOL) disassembly focuses on regaining the value embodied in 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 similar reasoning, obtaining a near-optimal/optimal disassembly sequence requires intelligent decision making during the disassembly when the sequence needs 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 or if one or more joint types are different than the ones that are listed in the original BOM, the sequence needs to be able to adapt and generate a new and accurate alternative for disassembly. These considerations require disassembly sequencing to be solved by more powerful and versatile methodologies justifying the utilization of image detection technologies for online real-time disassembly while imposing search techniques which would provide more efficient solutions than their exhaustive search counterparts. Therefore, EOL disassembly sequencing literature offers a variety of heuristic techniques. 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 study builds on previous disassembly sequencing research and introduces an automated robotic disassembly framework for EOL electronic products. The model incorporates decision makers’ (DMs’) preferences into the problem environment for efficient material and component recovery. A numerical example is provided to demonstrate the functionality of the proposed approach.     

Bilevel model for production–distribution planning solved by using ant colony optimization

This paper addresses a hierarchical production–distribution planning problem. There are two different decision makers controlling the production and the distribution processes, respectively, that do not cooperate because of different optimization strategies. The distribution company, which is the leader of the hierarchical process, controls the allocation of retailers to each depot and the routes which serve them. In order to supply items to retailers, the distribution company orders from the manufacturing company the items which have to be available at the depots. The manufacturing company, which is the follower of the hierarchical process, reacts to these orders deciding which manufacturing plants will produce them. A bilevel program is proposed to model the problem and an ant colony optimization based approach is developed to solve the bilevel model. In order to construct a feasible solution, the procedure uses ants to compute the routes of a feasible solution of the associated multi-depot vehicle route problem. Then, under the given data on depot needs, the corresponding production problem of the manufacturing company is solved. Global pheromone trail updating is based on the leader objective function, which involves costs of sending items from depots to retailers and costs of acquiring items from manufacturing plants and unloading them into depots. A computational experiment is carried out to analyze the performance of the algorithm.     

Optimization framework for DFG-based automated process discovery approaches

The problem of automatically discovering business process models from event logs has been intensely investigated in the past two decades, leading to a wide range of approaches that strike various trade-offs between accuracy, model complexity, and execution time. A few studies have suggested that the accuracy of automated process discovery approaches can be enhanced by means of metaheuristic optimization techniques. However, these studies have remained at the level of proposals without validation on real-life datasets or they have only considered one metaheuristic in isolation. This article presents a metaheuristic optimization framework for automated process discovery. The key idea of the framework is to construct a directly-follows graph (DFG) from the event log, to perturb this DFG so as to generate new candidate solutions, and to apply a DFG-based automated process discovery approach in order to derive a process model from each DFG. The framework can be instantiated by linking it to an automated process discovery approach, an optimization metaheuristic, and the quality measure to be optimized (e.g., fitness, precision, F-score). The article considers several instantiations of the framework corresponding to four optimization metaheuristics, three automated process discovery approaches (Inductive Miner—directly-follows, Fodina, and Split Miner), and one accuracy measure (Markovian F-score). These framework instances are compared using a set of 20 real-life event logs. The evaluation shows that metaheuristic optimization consistently yields visible improvements in F-score for all the three automated process discovery approaches, at the cost of execution times in the order of minutes, versus seconds for the baseline approaches.

     

Solving facilities location problem in the presence of alternative processing routes using a genetic algorithm

Facilities location problem deals with the optimization of location of manufacturing facilities like machines, departments, etc. in the shop floor. This problem greatly affects performance of a manufacturing system. It is assumed in this paper that there are multiple products to be produced on several machines. Alternative processing routes are considered for each product and the problem is to determine the processing route of each product and the location of each machine to minimize the total distance traveled by the materials within the shop floor. This paper presents a mixed-integer non-linear mathematical programming formulation to find optimal solution of this problem. A technique is used to linearize the formulated non-linear model. However, due to the NP-hardness of this problem, even the linearized model cannot be optimally solved by the conventional mathematical programming methods in a reasonable time. Therefore, a genetic algorithm is proposed to solve the linearized model. The effectiveness of the GA approach is evaluated with numerical examples. The results show that the proposed GA is both effective and efficient in solving the attempted problem.     

A hybrid heuristic to solve the parallel machines job-shop scheduling problem

This paper presents an advanced software system for solving the flexible manufacturing systems (FMS) scheduling in a job-shop environment with routing flexibility, where the assignment of operations to identical parallel machines has to be managed, in addition to the traditional sequencing problem. Two of the most promising heuristics from nature for a wide class of combinatorial optimization problems, genetic algorithms (GA) and ant colony optimization (ACO), share data structures and co-evolve in parallel in order to improve the performance of the constituent algorithms. A modular approach is also adopted in order to obtain an easy scalable parallel evolutionary-ant colony framework. The performance of the proposed framework on properly designed benchmark problems is compared with effective GA and ACO approaches taken as algorithm components.     

Optimization of a proton exchange membrane fuel cell membrane electrode assembly

A computational framework for fuel cell analysis and optimization is presented as an innovative alternative to the time consuming trial-and-error process currently used for fuel cell design. The framework is based on a two-dimensional through-the-channel isothermal, isobaric and single phase membrane electrode assembly (MEA) model. The model input parameters are the manufacturing parameters used to build the MEA: platinum loading, platinum to carbon ratio, electrolyte content and gas diffusion layer porosity. The governing equations of the fuel cell model are solved using Netwon’s algorithm and an adaptive finite element method in order to achieve near quadratic convergence and a mesh independent solution respectively. The analysis module is used to solve the optimization problem of finding the optimal MEA composition for maximizing performance. To solve the optimization problem a gradient-based optimization algorithm is used in conjunction with analytical sensitivities. By using a gradient-based method and analytical sensitivities, the framework presented is capable of solving a complete MEA optimization problem with state-of-the-art electrode models in approximately 30 min, making it a viable alternative for solving large-scale fuel cell problems.     

Product selection for promotion planning

This paper addresses a very important question—how to select the right products to promote in order to maximize promotional benefit. We set up a framework to incorporate promotion decisions into the data-mining process, formulate the profit maximization problem as an optimization problem, and propose a heuristic search solution to discover the right products to promote. Moreover, we are able to get access to real supermarket data and apply our solution to help achieve higher profits. Our experimental results on both synthetic data and real supermarket data demonstrate that our framework and method are highly effective and can potentially bring huge profit gains to a marketing campaign.     

Integration of process planning and scheduling using chaotic particle swarm optimization algorithm

Process planning and scheduling are two of the most important manufacturing functions traditionally performed separately and sequentially. These functions being complementary and interrelated, their integration is essential for the optimal utilization of manufacturing resources. Such integration is also significant for improving the performance of the modern manufacturing system. A variety of alternative manufacturing resources (machine tools, cutting tools, tool access directions, etc.) causes integrated process planning and scheduling (IPPS) problem to be strongly NP-hard (non deterministic polynomial) in terms of combinatorial optimization. Therefore, an optimal solution for the problem is searched in a vast search space. In order to explore the search space comprehensively and avoid being trapped into local optima, this paper focuses on using the method based on the particle swarm optimization algorithm and chaos theory (cPSO). The initial solutions for the IPPS problem are presented in the form of the particles of cPSO algorithm. The particle encoding/decoding scheme is also proposed in this paper. Flexible process and scheduling plans are presented using AND/OR network and five flexibility types: machine, tool, tool access direction (TAD), process, and sequence flexibility. Optimal process plans are obtained by multi-objective optimization of production time and production cost. On the other hand, optimal scheduling plans are generated based on three objective functions: makespan, balanced level of machine utilization, and mean flow time. The proposed cPSO algorithm is implemented in Matlab environment and verified extensively using five experimental studies. The experimental results show that the proposed algorithm outperforms genetic algorithm (GA), simulated annealing (SA) based approach, and hybrid algorithm. Moreover, the scheduling plans obtained by the proposed methodology are additionally tested by Khepera II mobile robot using a laboratory model of manufacturing environment.     

Learning and revision in cognitive robotics disassembly automation

Disassembly is a key step for an efficient treatment of end-of-life (EOL) products. A principle of cognitive robotics is implemented to address the problem regarding uncertainties and variations in the automatic disassembly process. In this article, advanced behaviour control based on two cognitive abilities, namely learning and revision, are proposed. The knowledge related to the disassembly process of a particular model of product is learned by the cognitive robotic agent (CRA) and will be implemented when the same model has been seen again. This knowledge is able to be used as a disassembly sequence plan (DSP) and disassembly process plan (DPP). The agent autonomously learns by reasoning throughout the process. In case of an unresolved condition, human assistance is given and the corresponding knowledge will be learned by demonstration. The process can be performed more efficiently by applying a revision strategy that optimises the operation plans. As a result, the performance of the process regarding time and level of autonomy are improved. The validation was done on various models of a case-study product, Liquid Crystal Display (LCD) screen.