Bioenergy supply chains can offer social benefits. In most related research, the total number of created jobs is used as the indicator of social benefits. Only a few of them quantify social benefits considering the different impact of economic activities in different locations. In this paper, a new method of measuring the social benefits of bioethanol supply chains is proposed that considers job creation, biomass purchase, and the different impacts of economic activities in different locations. A multi-objective mixed integer linear programming (MILP) model is developed to address the optimal design of a bioethanol supply chain that maximizes both economic and social benefits. The ε-constraint method is employed to solve the model and a set of Pareto-optimal solutions is obtained that shows the relationship between the two objectives. The developed model is applied to case studies in Liaoning Province in Northeast China. Actual data are collected as practical as possible for the feasibility and effectiveness of the results. The results show that the bioethanol supply chain can bring about both economic and social benefits in the given area and offers governments a better and more efficient way to create social benefits. The effect of the government subsidy on enterprises’ decisions about economic and social benefits is discussed.

In this paper we develop a new hybrid Mixed Integer Linear Programming/Constraint Programming (MILP/CP) decomposition method to solve a parallel Continuous Galvanizing Line (CGL) scheduling problem. The objective of the problem is to minimize the total production cost. Unlike common parallel scheduling problems, in this problem sequencing on each CGL is difficult because of the complex production rules of CGLs. Furthermore, all coils are required to be delivered on time, which means an assignment between coils and CGLs may not result into feasible scheduling. The problem is formulated as an integer programming model, but its structure is not suitable for optimization software package to solve. That is the reason why a new hybrid MILP/CP decomposition method is developed. To accelerate the method and reduce the computational time, a heuristic which tries to find the nature of infeasible assignments and obtain more effective cuts is embedded into the framework of the hybrid method. Some properties and a heuristic are employed to tell why the assignment is unfeasible and to derive more effective cuts. 60 instances are randomly generated to simulate actual production data. The new hybrid method is compared with the basic hybrid method without any features proposed in this paper. Numerical results show that the new hybrid method solves all instances in much less computation time than the basic one, especially for large scale instances.