Review of conventional metaheuristic techniques for resource-constrained project scheduling problem

This paper is concerned with an overview of the Resource-Constrained Project Scheduling Problem (RCPSP) and the conventional meta-heuristic solution techniques that have attracted the attention of many researchers in the field. Therefore, researchers have developed algorithms and methods to solve the problem. This paper addresses the single-mode RCPSP where the objective is to optimize and minimize the project duration while the quantities of resources are constrained during the project execution. In this problem, resource constraints and precedence relationships between activities are known to be the most important constraints for project scheduling. In this context, the standard RCPSP is presented. Then, the classifications of the collected papers according to the year of publication and the different meta-heuristic approaches applied are presented. Five weighted articles and their meta-heuristic techniques developed for RCPSP are described in detail and their results are summarized in the corresponding tables. In addition, researchers have developed various conventional meta-heuristic algorithms such as genetic algorithms, particle swarm optimization, ant colony optimization, bee colony optimization, simulated annealing, evolutionary algorithms, and so on. It is stated that genetic algorithms are more popular among researchers than other meta-heuristics. For this reason, the various conventional meta-heuristics and their corresponding articles are also presented to give an overview of the conventional meta-heuristic optimizing techniques. Finally, the challenges of the conventional meta-heuristics are explored, which may be helpful for future studies to apply new suitable techniques to solve the Resource-Constrained Project Scheduling Problem (RCPSP).

Project scheduling and buffer management: A comprehensive review and future directions

In the project management, buffers are considered to handle uncertainties that lead to changes in project scheduling which in turn causes project delivery delay. The purpose of this survey is to discuss the state of the art on models and methods for project buffer management and time optimization of construction projects and manufacturing industries. There are not literally any surveys which review the literature of project buffer management and time optimization. This research adds to the previous literature surveys and focuses mainly on papers after 2014 but with a quick review on previous works. This research investigates the literature from project buffer sizing, project buffer consumption monitoring and project time/resource optimization perspectives.

Impacts of synergies on software project scheduling

The adequate allocation of human resources is one of the most important success factors in software projects. Although project teams can be regarded as complex systems in which a team’s performance is highly influenced by the interdependencies among team members, the allocation methods applied to date have focused only on individual skills and consider project teams as units of isolated workers. The existing software project scheduling problem (SPSP) is extended to (1) consider different skills and efficiencies of employees and (2) examine the pairwise synergies between them, as well as to (3) handle the flexible structure of the project that is used in flexible management, such as agile project management. To better understand the impact of synergies on the project’s cost, the solutions of the traditional and extended SPSP versions are analyzed and compared on the generated project networks. The results show not only that this factor has a highly significant impact but also that the project cost strongly depends on the structural parameters of the synergy network (e.g., topology, network size and degree centrality). Among these parameters, a low degree of centrality and some topologies, most notably star and circular networks, obtained the highest reduction in the projects’ total cost.

The R-PSO algorithm solving multi-skill resource-constrained project scheduling problem

The Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP) is a combinational optimization problem with many applications in science and practical areas. This problem aims to find out the feasible schedule for the completion of projects and workflows that is minimal duration or cost (or both of them - multi objectives). The researches show that MS-RCPSP is classified into NP-Hard classification, which could not get the optimal solution in polynomial time. Therefore, we usually use approximate methods to carry out the feasible schedule. There are many publication results for that problem based on evolutionary methods such as GA, Greedy, Ant, etc. However, the evolutionary algorithms usually have a limitation that is fallen into local extremes after a number of generations. This paper will study a new method to solve the MS-RCPSP problem based on the Particle Swarm Optimization (PSO) algorithm that is called R-PSO. The new improvement of R-PSO is re-assigning the resource to execute solution tasks. To evaluate the new algorithm's effectiveness, the paper conducts experiments on iMOPSE datasets. Experimental results on simulated data show that the proposed algorithm finds a better schedule than related works.

Resource-constrained Time-cost-quality-energy-environment Tradeoff in Project Scheduling by Considering Blockchain Technology: A Case Study of Healthcare Project

Blockchain Technology (BCT) is expanding day by day and is used in all pillars of life and projects. In this research, we survey applicable of BCT in project management for the first time. We presented a Resource-Constrained Time-Cost-Quality-Energy-Environment tradeoff problem in project scheduling by considering BCT (RCTCQEEBCT). We utilize hybrid robust stochastic programming, worst case and Conditional Value at Risk (CVaR) to cope with uncertainty and risks. This type of robustification and risk-averse is presented in this research. A real case study is presented in a healthcare project. We utilize GAMS-CPLEX to solve the model. Finally, we analyze finish time, conservative coefficient, the confidence level of CVaR and the number of scenarios. The most important research result is that applying BCT decreases cost, energy, and pollution and increases quality. Moreover, the total gap between RCTCQEEBCT and without BCT is approximately 2.6%. When compacting finish time happens or if the conservative coefficient increases to 100%, costs, energy, and pollution environment increase, but quality decreases. If the confidence level of CVaR increase, the cost, energy and environment function functions grow up and quality is approximately not changed.