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Since sustainable scheduling is arousing increasing attention from many manufacturing enterprises and energy consumption is a core problem regarding sustainability, the purpose of this paper is to develop an energy-efficient scheduling method to fulfill material delivery tasks in mixed-model assembly lines. In this research, the objective of minimizing the energy consumption is jointly integrated with the operational criterions when executing material delivery tasks. Owing to the NP-hard nature of the considered problem, a Taboo enhanced Particle Swarm Optimization (TEPSO) algorithm is developed to solve the multi-objective problem. Several improving strategies are applied to enhance the performance of the proposed TEPSO in order to obtain a stronger local search capability and faster search speed. The performance of the proposed TEPSO algorithm is evaluated by comparing with two other high-performing multi-objective optimization methods. Computational experiments are conducted in order to test and verify the effectiveness and efficiency of the proposed TEPSO algorithm. The achievements reported in this paper might be inspiring for further studies on energy-efficient production scheduling. [ABSTRACT FROM AUTHOR]

This paper aims to develop an optimal real-life energy-efficient design for a production plant within the concept of the circular economy. The problem is to install a Heat Recovery System (HRS) that utilizes the hot oil used by the compressors to heat the water for the central heating system. To achieve the desired level of energy efficiency this design problem must be formulated from both the optimization and sustainability points of view. Additionally, this design problem must also consider the investment cost. In line with this purpose, this paper formulates this design problem as an optimization problem employing a mathematical programming approach as a single objective, and as a multi-objective optimization problem through a goal programming approach. Besides, this paper uses the return on investment as a key performance indicator, since it deals with a real-life design problem with an investment cost. The related design problem is solved with the single objective and multi-objective versions of the developed mathematical programming model via a commercial solver to identify different design alternatives and hence giving the decision-maker to make a selection option. Finally, the capability of the developed mathematical programming model is tested on a set of randomly generated problems. The obtained results indicate that the developed mathematical programming model is a successful decision support system since its single and multi-objective versions are capable of identifying energy-efficient production designs within the context of the real-life problem on hand and the circular economy. • A real-life energy-efficient design problem for a production plant is undertaken. • A heat recovery system is proposed within the concept of a circular economy. • The problem is modelled from both the optimization and sustainability aspects. • The problem is formulated as multi-objective models using goal programming. • The models are solved to minimize investment costs and maximize energy efficiency. [ABSTRACT FROM AUTHOR]

In recent years, there has been a growing concern over the environmental impact of traditional manufacturing, especially in terms of energy consumption and related emissions of carbon dioxide. Besides the adoption of new equipment, production scheduling could play a key role in reducing the total energy consumption of a manufacturing plant. In this paper, we explicitly introduce the objective of minimizing energy consumption into a typical production scheduling model, i.e., the job shop scheduling problem, based on a machine speed scaling framework. To solve this bi-objective optimization problem, we propose a multi-objective genetic algorithm incorporated with two problem-specific local improvement strategies. These local improvement procedures aim to enhance the solution quality by utilizing the mathematical models of two restricted subproblems derived from the original problem. Comprehensive computational experiments have been carried out to verify the effectiveness of the proposed solution approach. The results presented in this work may be useful for future research on energy-efficient production scheduling. [ABSTRACT FROM AUTHOR]

Dielectric medium (kerosene) between the tool electrode and workpiece forms a reaction product, owing to thermal effect of discharge in the process of electrical discharge machining (EDM), which is emitted from the surface in the form of harmful gas and may get into the operator's body by breath system inhalation and contact with skin. Therefore, exhaust emissions should be considered as important performance indicators for the green machining in the process of EDM. Moreover, energy efficiency and machining productivity should be improved and the cut process must take ecological constraints into consideration. Thereafter, the definition of green manufacturing helps to evaluate different and sometimes conflicting aspects of the manufacturing process, such as machining productivity, surface quality, energy efficiency and exhaust emissions. But it is difficult to dynamically adjust the weights of different aspects according to the actual processing conditions. This paper proposes a framework for optimization of cutting parameters to minimize energy consumption and exhaust emissions during electrical discharge machining of Al 6061 and SKD 11. Three cutting process parameters (peak current, pulse period, and duty cycle) are tested against material remove rate (MRR), surface roughness (Ra), energy efficiency per volume (EEV), and exhaust emissions characteristics (EEC) for EDM of these two materials. First, the process energy and exhaust emissions are analyzed. The results show that EEV is negatively correlated with surface quality (Ra) for both Al 6061 and SKD 11 material. Second, two data analyses (Taguchi and adaptive-network-based fuzzy inference system (ANFIS) analyses) are utilized to obtain high MRR and best Ra, EEV and EEC for the desired machining performances and environmental sustainability. It is found that the relative errors between the predicted values and experimental results of MRR, Ra, EEV and EEC are all not more than 20%, and found that the maximum of mean relative errors of them is 13.04%. Finally, a framework implemented by an optimization system, based on ANFIS, enhanced cuckoo search (ECS) algorithm and desirability function, is built to obtain the desired cutting parameters. It is found that high peak current (8.94A) and pulse period (500 μ s) are recommended to obtain multi-objective optimization for machining Al 6061, and that the optimal solutions of the EEV and EEC are significantly decreased, by 87.13% and 37.09%, respectively. Similarly, high peak current (8.77A) and pulse period (261.5 μ s) are recommended to obtain multi-objective optimization for machining SKD 11, and the optimal solutions of the EEV and EEC are significantly decreased, by 93.73% and 46.85%, respectively. Thereafter, it can be concluded from the above research that the proposed technique offers significant advantages and potential for applications in the green manufacturing field. Image 1 • Energy efficiency per volume was negatively correlated with Ra. • Maximizing energy efficiency and minimizing exhaust emissions by empirical models were obtained. • Balancing the machining performances and environmental sustainability was considered. • An optimization system with manual multi-objective weights was build. • Green regions of exhaust-friendly and energy-friendly were acquired. [ABSTRACT FROM AUTHOR]

Spindle system is the major mechanical component in machine tools, and its performance is responsible for a significant portion of the total consumed energy of machine tools. Conventional design optimization of spindle system is partially focused on parameter optimization of spindle motor or transmission system, contributing to an increase of the motor efficiency. Given that concurrent interactions among them is complex, very little efforts has been done to conduct integration optimization for optimum energy efficiency. To this end, a new approach of spindle system design is presented with consideration of the above two aspects adequately, to achieve the maximum energy and material efficiency. Firstly, the energy characteristic of spindle system is explicitly modelled on the basis of energy flow analysis. Then, a multi-objective optimization model for parameter optimization of spindle motor and transmission system is developed to take the both maximum energy efficiency and minimum volume as objectives, which is subjecting to a set of constraints with related to the cutting parameters boundary, processing requirements and shifting power losses. Finally, a multi-objective improved teaching-learning based optimization (MO-ITLBO) algorithm is presented to solve the developed optimization model. The performance of the proposed design approach of lathe spindle system is demonstrated through different working conditions. The experimental results indicate that the design of energy and material efficient machine tools can be achieved. [ABSTRACT FROM AUTHOR]