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Abstract Green and sustainability issues are being considered extensively in industry and in the literature. Most of the green-related effects in the manufacturing sector can be modeled by the costs associated with the production machines due to their resource consumption and/or pollutant emission, as a part of the operation cost. Accordingly, taking into account the machine processing costs alongside the other production criteria is one of the main approaches to consider green issues in the production management problems. In the same way, scheduling as one of the major operational problems in the factories can benefit this point of view to involve sustainability aspects. In this paper, the aforementioned modeling approach is taken one more time to establish a formulation for a green scheduling problem in uniform parallel machine environments. Here, it is assumed that the machines have different processing cost rates, i.e. they are different with regard to sustainability. The considered problem objectives are the total green cost and the makespan which are minimized simultaneously with the aim of earning Pareto optimal solutions. To this end, the ε -constraint method is first used to convert the problem into single objective problems. Then an existing heuristic is improved to solve these problems and estimate the Pareto solutions that show the trade-offs between the green cost objective and the ordinary time objective. Moreover, having a rigorous analyze in a theorem, some useful upper bounds for the gap of the green cost of the heuristic solution from optimality is derived. Finally, conducting numerical experiments at the end of the paper, the superiority of the improved heuristic to the previous one and its effectiveness in approximating the Pareto optimal solutions is demonstrated. Graphical abstract Image 1096644 Highlights • A green scheduling model in uniform parallel machine environments is developed. • The machines are assumed to have different sustainability costs. • The aim is to minimize both the total costs and the makespan, simultaneously. • The ε-constraint method is first used to convert the problem into single objective problems. • An improved version of an existing heuristic is utilized to solve the problems. [ABSTRACT FROM AUTHOR]

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]

Abstract Regarding to energy efficiency retrofit investment to numerous buildings, which buildings should be invested and which of the retrofit measures should be implemented for investable buildings are challenging tasks. The current literature have studied on choosing energy efficiency retrofit measures in single building, relatively little attention has been paid to the retrofit investment decision-making in multiple buildings. In addition, the existing studies almost put the retrofit cost as an objective that need to be minimized, and the retrofit capital budget is not taken into consideration. This paper proposes a decision-making optimization framework for energy efficiency retrofit investment in numerous buildings under financing budgetary restraint. A multi-objective optimization model with the economic goals being the net present value and time of return, and the environmental goals being the energy saving and emission reduction is presented, and then the intelligent optimization method combing particle swarm optimization and genetic algorithm is designed to search the retrofit investment strategy. The obtained investment strategy could determine which of the buildings should be invested to retrofit, and the combination of retrofitting measures for every investable building. An empirical study is conducted on 27 buildings of non-governmental organization in the state of Delaware in the United States, and the results indicate that the validity of the proposed framework. The findings indicate that the framework is an effective approach to assist the sustainability goal at regional level. Graphical abstract Image 1 Highlights • A decision-making optimization framework for energy efficiency retrofit is proposed. • Retrofit investment in numerous buildings under the capital restraint is studied. • An intelligent optimization method is designed to obtain the investment strategy. • An empirical study is conducted on 27 NGO buildings in the state of Delaware of USA. • The results could well balance economic and environmental goals of sustainability. [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]

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]