This paper proposes a new mixed-integer model for the multi-skill resourceconstrained PROJECT SCHEDULING problem (MSRCPSP). The interactions between workers are represented as undirected networks. Therefore, for each required skill, an undirected network is formed which shows the relations of human resources. In this paper, community detection in networks is used to find the most compatible working groups to perform PROJECT activities. In this respect, a greedy algorithm (GRA) is proposed to detect the most compatible communities of workers. The proposed greedy algorithm maximizes modularity as a well-known objective to find high quality communities of workers. Besides, a new heuristic is developed to assign workers to activities based on the communities obtained by the GRA. The MSRCPSP is an NP-hard optimization problem with the objective of minimizing the make-span of the PROJECT. Therefore, a dandelion algorithm (DA), which is a metaheuristic, is proposed to solve the problem. The dandelion algorithm is used to solve test problems of the iMOPSE dataset. To validate the outputs of the proposed method, three other meta-heuristics including genetic algorithm (GA), harmony search (HS) algorithm, and differential evolution (DE) method are employed. The Taguchi method is hired to tune all algorithms. These algorithms are compared with each other in terms of several performance measures. The results show the superiority of the dandelion algorithm in terms of all performance measures.

In this paper, we address a new problem in the context of PROJECT payment SCHEDULING when PROJECT activities are allowed to be crashed with the purpose of maximizing the contractors net present value (NPV). We assume that the contractor is paid at some pre-specified points of time according to the volume of work performed. Upon completion of activities, the cost of their execution is paid. Two different approaches are used to determine the volume of work performed at so called review points. In the first approach, only completed activities are considered. In the second approach, any portions of the activities that are executed are considered. To increase the volume of work performed at the review points, the contractor may decide to crash some activities and as such possibly increases his NPV. As activity crashing costs the contractor money, a compromise needs to be made. Two mathematical models are developed to study each approach and hence help the contractor to make the best decision. These models offer a means of investigating whether it is advisable to crash some activities and are therefore of practical importance. It is shown that the contractor may increase his NPV, even when he pays for the activity crashing costs. The performance of the mathematical models is illustrated using a numerical example.

Despite the dynamic nature of real life SCHEDULING problems, few studies focus on stochastic resource constrained PROJECT SCHEDULING problem and its variants. In this study, we consider stochastic resource possibilities and propose a new chance constraint, piecewise-linear and mixed integer programming model. Model is tested and verified with known PROJECT instances. One of the main strengths of the proposed model is it can be used to construct baseline schedules with a predetermined confidence interval. This gives scheduler an opportunity to construct proactive actions in order to minimize disruptions.

The construction supply chain encounters with a lot of challenges that one of the most significant can refer to the higher sources wasting in PROJECT site and also the high pollution on amount of this type of supply chains. On the other hand, the most managers of supply chains need to the integration of supply chain such as PROJECT sources and time specifics, determining the production and inventory level, and also determining numbers and type of vehicles that are included, in order to its cost is calculated in an optimal form. For this purpose, in this article an integrated model has been proposed which first objective function is maximizing the profit, and the second objective amount is minimizing the emission of the carbon dioxide gas, whereas a solution way for the prevention of sources waste in PROJECT site is presented. With assumption of determining PROJECT network and also the durations and daily demand of each activity, this supply chain model pans the different periods of time. To consider this bi-objective and nonlinear model, the model has been first linearized, and by using the epsilon-constraint method and also coding in GSMS software has been solved and its results have been finally analyzed with two numerical examples.

A common problem arising in PROJECT management is the fact that the baseline schedule is often disrupted during the PROJECT execution because of uncertain parameters. As a result, PROJECT managers are often unable to meet the deadline time of the milestones. Robust PROJECT SCHEDULING is an effective approach in case of uncertainty. Upon adopting this approach, schedules are protected against possible disruptions that may occur during PROJECT execution. In order to apply robust SCHEDULING principles to real PROJECTs, one should make assumptions close to the actual conditions of the PROJECT as much as possible. In this paper, in terms of uncertainty in both activities duration and resources availability, some methods are proposed to construct the robust schedules. In addition, various numerical experiments are applied to different problem types with the aid of simulation. The main purpose of those is to assess the performance of robust SCHEDULING methods under different conditions. Finally, we formulate recommendations regarding the best method of robust SCHEDULING based on the results of these experiments.