Big data have been widely studied by numerous scholars and enterprises due to its great power in making highly reliable decisions for various complex systems. Remanufacturing systems have recently received much attention, because they play significant roles in end-of-life product recovery, environment protection and resource conservation. Disassembly is treated as a critical step in remanufacturing systems. In practice, it is difficult to know the accurate data of end-of-life products such as disassembly time because of their various usage processes, leading to the great difficulty of making effective and reliable decisions. Thus, it is necessary to model the disassembly process with stochastic programming method where the past collected data are fitted into stochastic distributions of parameters by applying big data technology. Additionally, designing and applying highly efficient intelligent optimization algorithms to handle a variety of complex problems in the disassembly process are urgently needed. To achieve the global optimization of disassembling multiple products simultaneously, this work studies a stochastic multi-product disassembly line balancing problem with maximal disassembly profit while meeting disassembly time requirements. Moreover, a chance-constrained programming model is correspondingly formulated, and then, an enhanced group teaching optimization algorithm incorporating a stochastic simulation method is developed by considering this model’s features. Via performing simulation experiments on real-life cases and comparing it with five popularly known approaches, we verify the excellent performance of the designed method in solving the studied problem.

The potential broad market of green consumption has encouraged an increasing number of enterprises to carry out green technology innovation activities. This paper examines a two-stage supply chain of e-commerce sales channels under different cooperative models. We find that consumers’ green preferences are the main factor that affects green product market demand. The manufacturer and the retailer can raise the levels of green technology innovation and extend green promotional services to expand product market demand in online and offline channels. However, consumers’ e-commerce preferences and online free-riding behaviors affect the manufacturer’s sales channel choice. The retailer can improve the level of green promotional services to hold offline channel market demand, while promotional behaviors have a positive/negative spillover effect on online market demand if the level of free riding falls above/below consumers’ e-commerce preferences. The higher the cooperative level is, the later the manufacturer will open the online channel and close the offline channel to ensure a high level of green promotional service from the cooperative retailer. The results show that the stronger the level of cooperation among all members is, the better the economic, ecological, and social benefits will be. Therefore, we design a revenue-cost sharing contract that can effectively motivate green technology innovation and green promotional services and afford all members win-win profits.

We investigate a closed-loop supply chain (CLSC) where the retailer and the third-party recycler compete against each other to collect waste electrical and electronic equipment (WEEE) given that collection effort is their private information. Using the principle-agent theory, we develop a CLSC model with dual collection channel without the government’s reward–penalty mechanism (RPM). An information screening contract is designed for the manufacturer to attain real information on collection effort levels; meanwhile, the optimal decision-making results of other decision variables are derived. Next, we take RPM into account to further examine the efficacy of the government’s guidance mechanism in improving collection rate and profits of CLSC members. Our results indicate that (i) the collection competition reduces the total collection quantity and the expected profits of all the CLSC members without RPM; (ii) all CLSC members’ expected profits are improved if both two collection agents select a high collection effort level without and with RPM; (iii) RPM increases buyback price, collection price, collection quantity, and franchise fee but decreases wholesale price and retail price; with the reward–penalty intensity increasing, the manufacturer’s expected profit first decreases and then increases, while the expected profits of H-type retailer and H-type third-party recycler continue to increase. We find that RPM may ultimately stimulate the collection agents to collect more WEEEs, while the intense collection competition reduces the profits of CLSC members.

This paper investigates the impacts of cap-and-trade (CAT) regulation on a three-echelon closed-loop supply chain network (CLSCN) that consists of suppliers, high-emission and low-emission manufacturers, demand markets and carbon trading centers. The presented CLSCN model includes both product trading and carbon trading subnets. Combining variational inequality theory (VI) with complementary theory, we first characterize the optimal conditions for members in each tier first, and then derive that of the entire CLSCN. In addition, we focus on the effects of carbon caps and EOL collection rate target on CLSCN performances with numerical examples. The results reveal that, in some cases, there is a consistency between carbon emission reduction target of the government and the profit target of enterprises. The government should choose reasonable and moderate carbon caps for all the enterprises to balance the CLSCN members’ economic interests, carbon emissions, as well as resources utilization rate. Moreover, the government should not blindly pursue a high collection rate target. The above conclusions can provide practical guidance for governments and enterprises in a CLSCN under CAT regulation.

This paper examines the waste of electrical and electronic equipments (WEEE) and draws on variational inequalities to model the closed-loop supply chain network. The network consists of manufacturers, retailers and consumer markets engaging in a Cournot-Nash game. Retailers are responsible for collecting WEEE in the network. It is assumed that the price of the remanufactured goods is different from that of the newly manufactured ones. The network equilibrium occurs when all players agree on volumes and prices. Several properties of the model are examined and the modified projection method is utilized to obtain the optimal solutions. Numerical examples are provided to illustrate the impact of CLSC parameters on the profits of channel members and consumer benefits, and to provide policy support for governments. We find that it is necessary to regulate a medium collection rate and a certain minimum recovery rate. This is also advantageous to manufacturers in producing new manufactured products. The impact of collection rate and recovery rate on manufacturers are greater than that on retailers. Consumers can benefit from the increase of the recovery rate as well as the collection rate.