In recent years, the concerns on energy efficiency in manufacturing systems have been growing rapidly due to the pursuit of sustainable development. Production scheduling plays a vital role in saving energy and promoting profitability for the manufacturing industry. In this paper, we are concerned with a just-in-time (JIT) single machine scheduling problem which considers the deterioration effect and the energy consumption of job processing operations. The aim is to determine an optimal sequence for processing jobs under the objective of minimizing the total earliness/tardiness cost and the total energy consumption. Since the problem is NP -hard, an improved multi-objective particle swarm optimization algorithm enhanced by a local search strategy (MOPSO-LS) is proposed. We draw on the idea of k-opt neighborhoods and modify the neighborhood operations adaptively for the production scheduling problem. We consider two types of k-opt operations and implement the one without overlap in our local search. Three different values of k have been tested. We compare the performance of MOPSO-LS and MOPSO (excluding the local search function completely). Besides, we also compare MOPSO-LS with the well-known multi-objective optimization algorithm NSGA-II. The experimental results have verified the effectiveness of the proposed algorithm. The work of this paper will shed some light on the fast-growing research related to sustainable production scheduling.

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A single-machine scheduling problem that minimizes the total weighted tardiness with energy consumption constraints in the actual production environment is studied in this paper. Based on the properties of the problem, an improved particle swarm optimization (PSO) algorithm embedded with a local search strategy (PSO-LS) is designed to solve this problem. To evaluate the algorithm, some computational experiments are carried out using PSO-LS, basic PSO, and a genetic algorithm (GA). Before the comparison experiment, the Taguchi method is used to select appropriate parameter values for these three algorithms since heuristic algorithms rely heavily on their parameters. The experimental results show that the improved PSO-LS algorithm has considerable advantages over the basic PSO and GA, especially for large-scale problems.

With the hyper-speed development of the networks, online streaming is the future trend of watching films and TV series. Netflix, as one of the earliest streaming service companies, is indeed successful in the market. As the competition among online streaming companies is getting tougher, it is important to understand the market and marketing strategies. Against this background, this study aims to explore 1) the importance of developing and exploring the teenage drama market, 2) the benefits and possible problems of existing teenage drama, and 3) online and offline marketing methods used by Netflix targeting teenage audiences. Through literature search and case studies of existing teenage drama, it is indicated that teenagers learn from drama, which may imitate the behaviors of characters in the drama. Furthermore, there will be positive influences that lead to benefits for the growth of teenagers as they can learn to deal with things they may someday encounter. The findings also suggest that social media, which most teenagers use every day, is one of the best ways drama producers can access to those teenage audiences. Offline marketing is helpful in making direct interactions with potential audiences and can make audiences experience scenarios which make them feel more realistic. The findings may be helpful for companies in the industry to understand the market and audiences, also the marketing strategies to use.

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