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This paper investigates the problem of how to achieve a positive cash flow balance by multimode multiproject scheduling, in which a contractor must implement multiple projects concurrently, and activities can be performed with one of several alternative modes. First, based on formulating cash flows for the projects, we construct an optimization model that can minimize the maximum gap between accumulative cash outflow and cash inflow, thus balancing cash flow positively by arranging optimal execution modes and start times for activities. Then, we prove the NP-hardness of the studied problem and design two metaheuristic algorithms, namely, tabu search (TS) and simulated annealing (SA), which search the desirable solutions in nested and mixed ways, respectively. Finally, taking the multistart iterative improvement (MSII) as comparison algorithm, the performance of the two algorithms developed is evaluated through a computational experiment performed on a data set generated randomly. From the research results, the following conclusions are drawn. The TS and SA are more suitable for solving the smaller and larger problems, respectively, while the nested searching structure could enhance the algorithm’s efficiency. With increases in the advance payment proportion, the number of milestone activities, the client’s payment proportion, or the project deadline, the contractor’s maximal cash flow gap decreases.

Time/cost trade-off problems is a well-known project scheduling problem. These years many researchers begin to focus on the project scheduling under uncertainty, such as resource idleness, high inventory and missing deadlines. This paper addresses a time-cost-robustness trade-off project scheduling problem with multi-mode under uncertainty. A multi-objective model of three objectives is constructed and three propositions are proposed. A e-constraint method nesting genetic algorithm is designed to solve this multi-objective problem along with four improvement measures, and the computational experiment is conducted to validate the effectiveness of the proposed algorithm.

This paper addresses the resource-constrained project proactive scheduling problem with uncertain activity durations considering information handling cost. An proactive optimization model is proposed to derive the information handling cost and the proactive starting time of each activity decisions that minimize the total uncertainty cost of a proactive schedule, which includes an information handling cost for improving standard variance of activity duration, an uncertainty occupation cost of time buffer to renewable resources as well as a mean cost of multiple reactive adjustments, satisfying the project deadline, the renewable resources availability and the precedence constraints. Aiming at the NP-hardness of the problem, three heuristic algorithms are designed, including a Tabu Search, a Variable Neighborhood Search, and a combined program of the two algorithms. Ultimately, an extensive computational experiment is used to show the relative performance of these algorithms and properties. It is shown that a proactive schedule which has less total uncertain cost and certain robustness can be finally obtained by investing information handling cost to decrease the mean cost of multiple reactive adjustments.

The robustness of project schedule plays an important role for the smooth execution of project under uncertain conditions. This paper studies the proactive project scheduling problem with stochastic activity durations and flexible resource constraints. The objective is to schedule the starting times of the activities in a reasonable way so as to maximize the robustness of project schedule under the constraints of precedence, flexible resources and project deadline. The studied problem is defined at first. Then the optimization model is constructed. Based on the NP-hardness attribute of the problem and the characteristics of the model, a two-layer nested tabu search heuristic algorithm and a new heuristic algorithm are developed to obtain satisfactory solutions. Ultimately, algorithms are tested on a standard instance set generated randomly and the influences of the key parameters are analyzed.

In this paper, we investigate the resource-constrained project scheduling problem with resource transfer times under uncertain environment, aiming to generate robust baseline schedules that will be as stable as possible during project execution. In this problem, resource transfer times are taken into account, and the objective function is solution robustness maximization. As the problem is proved to be NP-hard in the strong sense, we develop a tabu search algorithm to solve the problem. After linearizing the proposed model, we use a commercial mathematical programming solver as a benchmark to solve the problem. From the computational results, we find that the developed tabu search algorithm is effective and efficient in solving the defined problem, which offers a way to deal with the proactive scheduling problem with resource transfer times. Through a sensitivity analysis, we find that robustness increases with the extension of the project due date and the decrease of resource transfer times.

This paper summarizes the recent researches on uncertain project scheduling problems. The literatures of the proactive project scheduling problems are presented firstly. Then we review the achievements of the reactive project scheduling problems. According to the methods of dealing with uncertain information, the developments of the stochastic project scheduling problems and the fuzzy project scheduling problems are described respectively. Ultimately, the paper is sumed up and the directions for further research in this field are indicated.

This paper analyzes the game process of the R&D investment decision-making of asymmetry firms. The research results indicate that under the incomplete patent system, either the large-sized knowledge-based enterprises or the small-sized knowledge-based enterprises will lose their motivation for R&D investment. Under the complete patent system, the large-sized enterprises have the fundamental motility to carry out R&D investment, while the small-sized ones have the intention of cooperation and innovation in R&D. This conclusion is of stronger policy effects.