Process Planning, Scheduling, and Layout Optimization for Multi-Unit Mass-Customized Products in Sustainable Reconfigurable Manufacturing System

Currently, manufacturers seek to provide customized and sustainable products, requiring flexible manufacturing systems and advanced production management to cope with customization complexity and improve environmental performance. The reconfigurable manufacturing system (RMS) is expected to provide cost-effective customization in high responsiveness. However, reconfiguration optimization to produce sustainable mass-customized products in RMS is a complex problem requiring multi-criteria decision making. It is related to three problems, process planning, scheduling, and layout optimization, which should be integrated to optimize the RMS performance. This paper aims at integrating the above three problems and developing an effective approach to solving them concurrently. It formulates a multi-objective mathematical model simultaneously optimizing process planning, job-shop scheduling, and open-field layout problem to improve RMS sustainability. The penalty for product tardiness, the total manufacturing cost, the hazardous waste, and the greenhouse gases emissions are minimized. Economic and environmental indicators are defined to modify the Pareto efficiency when searching the Pareto-optimal solutions. Exact Pareto-optimal solutions are obtained by brute-force search and compared with those of the non-environmental indicator model. NSGA-III is adopted to obtain the approximate Pareto-optimal solutions in high effectiveness and efficiency. A small numerical example is applied to validate the mathematical model and resolution methods.

Fuzzy optimization based Water Distribution Network design using Self-Adaptive Cuckoo Search Algorithm

Water Distribution Network(s) (WDN) design is gaining prominence in the urban planning context. Several factors that play a significant role in design are uncertainty in data, non-linear relation of head loss & discharge, combinatorial nature of the problem, and high computational requirements. In addition, many conflicting objectives are possible and required for effective WDN design, such as cost, resilience, and leakage. Most of the research work published has used multiobjective evolutionary optimization in solving such complex WDN. However, the challenge of such population based evolutionary approaches is that they provide multiple trade-off Pareto optimal solutions to the decision-maker who will have to choose another set of techniques to arrive at a single optimal solution. The present study employs a fuzzy optimization approach that would provide a single optimal WDN design for Hanoi and Pamapur, India. Maximization of network resilience (NR) and minimization of network cost (NC) are employed in a multiobjective context. Later, minimization of network leakages (NL) is also incorporated, leading to three objective problems. Hyperbolic Membership Function (HMF), Exponential Membership Function (EMF), and Non-linear Membership Function (NMF) are employed in Self-Adaptive Cuckoo Search Algorithm based fuzzy optimization. HMF is found suitable to determine the best possible WDN design for chosen case studies based on the highest degree of satisfaction. HIGHLIGHT Most of the research conducted till now have used evolutionary multiobjective optimization in solving WDNs. But, the challenge of such evolutionary approaches is that they provide multiple trade-off pareto optimal solutions to the decision maker who will have to further choose another methodology to converge to a single optimal solution. The proposed methodology would simplify the decision-making process for an engineer.

Combined optimization of a linear PM actuator with trapezoidal PMs and coils considering fringing effect for precision engineering

For precision engineering, a linear PM-moving actuator with trapezoidal PMs and trapezoidal coils considering the fringing effect is proposed. To take into account the effect of the finite-long trapezoidal PM array, an improved Fourier series expansion is developed to calculate the fringing-included magnetic field. Then the full-stroke thrust excited by the trapezoidal coils is accurately predicted and validated by the finite element method. Sensitivity of the trapezoidal parameters of PMs and coils is analyzed, and combined optimization is implemented by the genetic algorithm. Through the Pareto optimal solutions of thrust, the relation of the PM-coil parameter combination is described and formulated by curve fitting. Compared with the traditional rectangular PM actuators or other trapezoidal-typed actuators, the proposed actuator with trapezoidal PMs and coils further decreases the thrust ripple and largely increases the thrust magnitude simultaneously, and reaches an utmost-close effective stroke as well.

Optimization of Water-Alternating-CO2 Injection Field Operations Using a Machine-Learning-Assisted Workflow

This paper will present a robust workflow to address multi-objective optimization (MOO) of CO2-EOR-sequestration projects with a large number of operational control parameters. Farnsworth Unit (FWU) field, a mature oil reservoir undergoing CO2 alternating water injection (CO2-WAG) enhanced oil recovery (EOR), will be used as a field case to validate the proposed optimization protocol. The expected outcome of this work would be a repository of Pareto-optimal solutions of multiple objective functions, including oil recovery, carbon storage volume, and project economics. FWU's numerical model is employed to demonstrate the proposed optimization workflow. Since using MOO requires computationally intensive procedures, machine-learning-based proxies are introduced to substitute for the high-fidelity model, thus reducing the total computation overhead. The vector machine regression combined with the Gaussian kernel (Gaussian -SVR) is utilized to construct proxies. An iterative self-adjusting process prepares the training knowledgebase to develop robust proxies and minimizes computational time. The proxies’ hyperparameters will be optimally designed using Bayesian Optimization to achieve better generalization performance. Trained proxies will be coupled with Multi-objective Particle Swarm Optimization (MOPSO) protocol to construct the Pareto-front solution repository. The outcomes of this workflow will be a repository containing Pareto-optimal solutions of multiple objectives considered in the CO2-WAG project. The proposed optimization workflow will be compared with another established methodology employing a multi-layer neural network to validate its feasibility in handling MOO with a large number of parameters to control. Optimization parameters used include operational variables that might be used to control the CO2-WAG process, such as the duration of the water/gas injection period, producer bottomhole pressure (BHP) control, and water injection rate of each well included in the numerical model. It is proven that the workflow coupling Gaussian -SVR proxies and the iterative self-adjusting protocol is more computationally efficient. The MOO process is made more rapid by squeezing the size of the required training knowledgebase while maintaining the high accuracy of the optimized results. The outcomes of the optimization study show promising results in successfully establishing the solution repository considering multiple objective functions. Results are also verified by validating the Pareto fronts with simulation results using obtained optimized control parameters. The outcome from this work could provide field operators an opportunity to design a CO2-WAG project using as many inputs as possible from the reservoir models. The proposed work introduces a novel concept that couples Gaussian -SVR proxies with a self-adjusting protocol to increase the computational efficiency of the proposed workflow and to guarantee the high accuracy of the obtained optimized results. More importantly, the workflow can optimize a large number of control parameters used in a complex CO2-WAG process, which greatly extends its utility in solving large-scale multi-objective optimization problems in various projects with similar desired outcomes.

Genetic Programming Based Identification of an Overhead Crane

Overhead cranes carry out an important function in the transportation of loads in industry. The ability to transport a payload quickly and accurately without excessive oscillations could reduce the chance of accidents as well as increase productivity. Accurate modelling of the crane system dynamics reduces the plant-model mismatch which could improve the performance of model-based controllers. In this work the simulation model to be identified is developed using the Euler-Lagrange method with friction. A 5-step ahead predictor, as well as a 10-step ahead predictor, are obtained using multi-gene genetic programming (MGGP) using input-output data. The weights of the genes are obtained by using least squares. The results of 15 different genetic programming runs are plotted on a complexity-mean square error graph with the Pareto optimal solutions shown.

Flow routing aiming load balancing and path length in multi-hop networks with different link qualities

This paper considers an exact bi-objective approach for simultaneously minimizing the total cost of flow routing and the network bottleneck when link qualities and flow weights are relevant. This is useful for wireless multi-hop networks with long-term TCP (Transmission Control Protocol) flows. The introduced proposal can generate a minimal complete set of Pareto-optimal solutions. Our proposal is evaluated through simulation in which are explored different parameter settings and metrics.

Combinatorial Optimization of Pre-Formed Hose Assemblies

Cable and hose routing is a complex and time-consuming process that often involves several conflicting objectives. Complexity increases further when routes of multiple components are to be considered through the same space. Extensive work has been done in the area of automatic routing where few proposals optimize multiple hoses together. This paper proposes a framework for the routing of multiple pre-formed hoses in an assembly using a unique permutation process where several alternatives for each hose are generated. A combinatorial optimization process is then used to find Pareto-optimal solutions for the multi-route assembly. This is coupled with a scoring model that predicts the overall fitness of a solution based on designs previously scored by the engineer as well as an evaluation system where the engineer can score new designs found through the use of the framework to update the scoring model. The framework is evaluated using a testcase from a car manufacturer showing a severalfold time reduction compared to a strictly manual process. Considering the time savings, the proposed framework has the potential to greatly reduce the overall routing processes of hoses and cables.

The Parametric Modeling and Two-Objective Optimal Design of a Downwind Blade

To cope with the future challenges to the blade that will be introduced by the development of extreme-scale wind turbines, this study focuses on the optimization design of the aerodynamic shape of a downwind blade via the inverse design method. Moreover, the genetic algorithm is used to optimize the chord, twist angle, and pre-bending parameters of the blade to maximize the energy production of the rotor and minimize the flapping bending moment of the blade root. By taking a 5-MW wind turbine as the optimization object, the two-objective optimization design of the downwind blade is carried out, and Pareto optimal solutions in line with the expectations are obtained. After analyzing four representatives of the Pareto optimal solutions, while a more ideal solution is found to sacrifice 9.41% of the energy production of the rotor, the flapping bending moment of the blade root is reduced by 42.92%, thereby achieving the lightweight optimization design of an extreme-scale wind turbine blade. Furthermore, based on the selected four sets of blades, the influence mechanisms of the chord, twist angle, and pre-bending on the optimization goal are analyzed, and it is found that the pre-bending parameter has the greatest influence on the two optimization goals.

CONFIGURING CUSTOMIZED PRODUCTS WITH DESIGN OPTIMIZATION AND VALUE-DRIVEN DESIGN

In order to efficiently design and deliver customized products, it is crucial that the process of translating customer needs to engineering characteristics and into unique products is smooth and without any misinterpretations. The paper proposes a method that combines design optimization with value-driven design to support and automate configuration of customized products. The proposed framework is applied to a case example with spiral staircases, a product that is uniquely configured for each customer from a set of both standard and customized components; a process that is complex, iterative and error-prone. In the case example, the optimization and value-driven design models are used to automate and speed-up the process of delivering quotations and design proposals that could be judged based on both engineering characteristics as well as their added value, thereby increasing the knowledge at the sales stage. Finally, a multi-objective optimization algorithm is employed to generate a set of Pareto-optimal solutions that contain four clusters of solutions that dominate the baseline design. Hence the decision-maker is given a set of optimal solutions to choose from when balancing different economical and technical characteristics.