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This paper establishes how organizations can overcome barriers to sustain manufacturing environmental performance improvement. There is a growing body of knowledge on energy and resource efficiency, but there is a gap in how to counter barriers to enable technical and financial success. In this paper, barriers to energy efficiency adoption established from the literature are identified from 100 short-term manufacturing operations projects across 11 European plants over a three-year period. The longitudinal examination of multiple projects provides research insight absent from the literature that could be used by companies to enhance the outcomes of their improvement programs. Learning from early projects resulted in interventions to enable reduction in environmental impact and cost. The contribution of this research is the identification of enablers to sustain energy efficiency advancement of vision, objectives, operational responsibility, methods, training, people, community, equipment, and data. This paper has potential to better explain how organizational enablers mitigate barriers to environmental efficiency adoption to in turn reduce impact and to reduce cost. [ABSTRACT FROM AUTHOR]

Energy-aware scheduling in a manufacturing environment with real-time energy pricing is a challenging problem. The purpose of this paper is to study a nonpreemptive scheduling problem on a single-machine to minimize the total tardiness and total energy cost under time-of-use electricity tariffs, which is a mixed-integer multi-objective mathematical programming model. To achieve these objectives, we develop several new holistic genetic algorithms. The proposed model is solved via several methods including weighted sum method and multiobjective genetic algorithms based on dominance rank (GA-1), weighted sum aggregation (GA-2), dominance ranking procedure and crowding distance comparison (GA-3), and heuristic approach (GA-H). This paper illustrates that operations management, which is typically observed as a significant technique that allows manufacturing to operate in challenging data enabled environment in an industrial internet of things ecosystem, can also carefully optimize a production process to improve energy cost in order to manage environmental challenges. Thus, the findings provide information on when to start each job. We provide detailed experimental results evaluating the performance of the proposed algorithms. In a case study, we illustrate how the results of the multiobjective model could be utilized in decision making using the technique for order preference by similarity to ideal solution method. [ABSTRACT FROM AUTHOR]

Building a sustainable modern electricity sector is a key part of the United Nations’ 2030 Sustainable Development Goals. Smart meters are expected to contribute to the achievement of this goal by providing new demand-side management opportunities for utilities. Attempting to address existing gaps in the extant literature, this paper examines the effects of metering technologies and demand-side management program investments using panel data from 87 American electricity utilities for the period 2009–2012. Our model provides strong explanatory power with respect to energy efficiency, and shows that automated meter reading (AMR) devices, advanced metering infrastructure (AMI) devices, direct program costs, and incentive costs all have a positive influence on energy efficiency effects. In contrast, the results for load management are largely insignificant, with the exception of incentive costs that has a small, but significant, negative impact on peak load reductions. We discuss the implications and potential research directions arising from these findings, in particular, the need to consider interactions between different demand-side programs and the influence of different types of information technologies that are emerging as part of the smart grid. [ABSTRACT FROM PUBLISHER]

Energy is an expensive resource that is becoming more scarce with increasing population and demand. In this paper, a mathematical model to minimize energy consumption and reduce total completion time of a single machine is proposed, and a multiobjective genetic algorithm is utilized to obtain an approximate set of nondominated alternatives. Furthermore, dominance rules and a heuristic are proposed to increase the speed of the proposed genetic algorithm. Finally, the analytical hierarchical process is utilized to select a solution with some additional criteria. [ABSTRACT FROM PUBLISHER]

Although the literature on environmental sustainability is rapidly increasing, many studies suggest that this problem needs to be dealt with by considering a broader perspective, i.e., supply chains. In this paper, we put forward the idea that different practices for greening suppliers can be combined to shape different green supply management (GSM) strategies. More specifically, the following research questions are posed. 1) How do firms combine managerial practices to form GSM strategies? 2) What are the variables that affect this decision? 3) What are the results of the different GSM strategies? Drawing on a survey among Spanish firms, we performed a cluster analysis that identified three main GSM strategies. In the second step, we contrasted these clusters using environmental drivers, context, and performance variables. The results show that there are several approaches to “greening” the supply chain, with different results and company profiles. [ABSTRACT FROM PUBLISHER]