Selecting an appropriate project portfolio and starting work at the right time are two key decision-making problems in project and engineering management. Project portfolio selection (PPS) and scheduling become complicated when random attribute values and unknown attribute weights are simultaneously considered. To manage this complex decision-making problem, a stochastic multi-attribute acceptability analysis (SMAA)-based approach is proposed to formulate multi-attribute PPS and scheduling problem with random attribute values and unknown attribute weights. Four heuristic algorithms, namely, SMAA-based particle swarm optimisation, SMAA-based genetic algorithm, SMAA-based simulated annealing algorithm, and SMAA-based teaching–learning-based optimisation, have been developed to solve this problem. The performance of the four algorithms is also evaluated on four data sets.

This paper aims at checking the effectiveness of environmental policy pushed by the Central Government at provincial level since China’s entry into the World Trade Organization (WTO). For this purpose, the industrial system of each province is divided into industrial production sub-system and pollution treatment sub-system, and a novel slack-based measure data envelopment analysis (SBM-DEA) model with non-cooperative game is proposed to evaluate the environment efficiency of both industrial production sub-system and pollutant treatment sub-system. The results show that the proposed model can describe the environmental efficiency more precisely than the traditional DEA models. During 2003–2012, the efficiencies of industrial production sub-system and pollution treatment sub-system at the provincial level are both relatively low. Specifically, the efficiency of industrial production is not only lower than pollution treatment efficiency, but is falling generally, especially in the Eastern area. However, in the Central and Western area, the efficiency of industrial production remains relatively stable, and pollution treatment efficiency is rising steadily. The technology gap between the Central/Western area and the Eastern area is narrowing, particularly concerning pollution treatment technology. We thus conclude that though the Central Government’s environmental policies fail to solve the inner contradiction between economic and environmental systems, and they indirectly contribute to the expansion of pollutant treatment technology among the Eastern, Central, and Western areas, which is conducive to the coordinated development among different areas.

The generalized equilibrium efficient frontier data envelopment analysis (GEEFDEA) approach, an extension of the DEA method, has been widely used to solve the problem of evaluating decision making units (DMUs) producing fixed-sum outputs. It constructs a common equilibrium efficient frontier through a minimum reduction strategy for fixed-sum outputs and uses this frontier as a benchmark to achieve a complete ranking of DMUs. However, the existence of multiple feasible equilibrium efficient frontiers may lead to inconsistency in the evaluation criteria, and this possibility limits the method’s usefulness. In this paper, an integrated framework for solving this problem is proposed to rank DMUs by using stochastic multicriteria acceptability analysis (SMAA-2) method combined with the GEEFDEA approach. Instead of using a certain common equilibrium efficient frontier as in conventional GEEFDEA approaches, we explore all possible frontiers to answer various robustness questions by computing rank acceptability indices and pairwise winning indices. Furthermore, we derive the complete ranking from the dominance relationships among the DMUs. Two numerical examples are used to demonstrate the effectiveness and rationality of the proposed hybrid approach.