We investigate a manufacturer’s three possible online strategies while the manufacturer also distributes its product through an independent retailer in a traditional channel. Under direct selling, the manufacturer sells its products to online consumers directly. Under reselling, the manufacturer sells its product through an e-tailer. Under agency selling, the manufacturer sells its products to consumers through an online agent’s website and pays the agent a commission fee. We show that it is optimal for the manufacturer to choose direct selling if the operating cost for the online channel under direct selling is sufficiently low relative to that under reselling as well as the online agent’s commission fee. Similarly, reselling through an e-tailer is optimal to the manufacturer if the operating cost for the online channel under reselling is sufficiently low. Furthermore, agency selling should be chosen if the online agent’s commission fee is sufficiently low. However, a strategy with a low online cost (including the operating costs under direct selling and reselling, and the commission fee under agency selling) may lead to a low profit for the traditional retailer, which eventually causes a low total profit for the supply chain. Nonetheless, consumers generally benefit from the manufacturer’s optimal online distribution strategy because of low prices and high quantities.

The rapid development of the Internet and the transformation of consumption patterns have prompted consumers to purchase fresh products online. For fresh e-commerce enterprises, logistics is an important aspect of customer satisfaction. Therefore, this study focused on online review information and used a convolutional neural network text mining model for its analysis. Logistics service elements concerned with customer satisfaction are convenience, communication, integrity, responsiveness, and reliability. Thereafter, comment information was converted to digital information using sentiment analysis. Finally, a correlation analysis was carried out to compare the significance of various influencing factors. The results confirm that convenience, communication, reliability, and responsiveness had a significant impact on customer satisfaction, whereas integrity had none. Fresh e-commerce logistic services need to improve for the development of the companies.

Currently, manufacturing enterprises attach great importance to improving processing quality and customer satisfaction. Hybrid flow shops have widespread applications in real-world manufacturing systems such as steel production and chemical industry. In a practical production process, uncertainty commonly arises due to the difficulty of knowing exact information of facilities and jobs beforehand. In order to improve processing quality and customer satisfaction of manufacturing systems in uncertain environments, this article proposes a stochastic multiobjective hybrid flow shop scheduling problem aiming at maximizing processing quality and minimizing total tardiness, where the processing time of jobs obeys a known random distribution. To describe jobs’ processing quality mathematically, a quality-based cost function is presented, and further a chance-constrained programming approach is used to formulate this problem. Then, a multiobjective artificial bee colony algorithm incorporating a stochastic simulation approach is designed by considering its characteristics. Simulation experiments are performed on a set of instances and several state-of-the-art multiobjective optimization algorithms are chosen as peer approaches. Experiment results confirm that the proposed algorithm has an excellent performance in handling this problem.

Many manufacturers and retailers have cooperated for low-carbon production in various industries. This study examines the role of consumers’ low-carbon preference in this cooperation. We construct four scenarios to investigate the effects of consumers’ low-carbon preference on the market equilibrium of supply chains’ product selection strategy. Based on the game theoretic models, optimal solutions for the two supply chains are derived with different consumers’ preference for low-carbon products. Through the discussion, we uncovered the influence of consumers’ preference on price and demand and the relationship between the influence coefficient of retailers’ promotional effort on consumers’ utility and retailer profits. In addition, given the increase of government’s low-carbon production subsidy, two supply chains will both more likely choose low-carbon production. Interestingly, under the government subsidy, the profit of manufacturer will increase or decrease more than its retailer and the market structure will not change if the two supply chains have chosen low-carbon production.

This paper addresses the problem of green manufacturing decision making for a green dual-channel supply chain (SC). In the investigated SC, the manufacturer will decide whether to adopt green manufacturing under the influence of the retailer’s fairness concern-based dual-channel. Thus, we discuss two decision scenarios: the no green manufacturing strategy with retailer fairness (NM model), and green manufacturing with retailer fairness (GM model). Our study has several findings: Firstly, adopting a green manufacturing strategy is not always beneficial to supply-chain members when a retailer has fairness. In particular, when fairness is at a relatively high level, the manufacturer will not adopt green manufacturing. Secondly, under green manufacturing, the product’s green degree and subsidies have a positive impact on the price and demand and the members’ profit and utility. Besides, the subsidies and retailer fairness have a counter effect on the optimal decision. Thirdly, comparing the two scenarios (NM & GM), we found that the channel price of the GM model is lower than the NM model. Finally, from the perspective of the supply chain system, the system tends toward the manufacturer adopting green manufacturing and maintaining retailer fairness concerns at a lower level.