This research studies resource constrained PROJECT scheduling problem in a part of a refinery construction PROJECT in real world. In real world, most of the activities are new and we have problems such as activity duration uncertainty. This problem causes a change in a PROJECT makespan. The RCPSP is NP-hard. Hence, we proposed an optimization method based on simulated annealing to solve the RCPS Problem. In this paper, Fuzzy sets theory is used to represent this activity duration uncertainty. Used schedule generation scheme, in the proposed simulated annealing ALGORITHM, is a fuzzy parallel scheduling generation method. Proposed ALGORITHM generates the minimum PROJECT makespan while considers renewable resource-constrained and activity precedence and also has ability to perform with fuzzy numbers for presenting PROJECT details such as start and final time of activities and PROJECT whole time with fuzzy numbers. Finally, the results of the ALGORITHM will be evaluated and it will be represented that the proposed ALGORITHM is very efficient and can be used by managers and scheduling programmers in real PROJECTs.

Scheduling PROJECTs incorporated with materials ordering results in a more realistic problem. This paper deals with the combined problem of PROJECT scheduling and material ordering. The purpose of this paper is to minimize the total cost of this problem by determining the optimal values of activity duration, activity finish time and the material ordering schedule subject to constraints. We employ a genetic ALGORITHM approach to solve it. Elements of the ALGORITHM, such as chromosome structure, unfitness function, crossover, mutation and local search operations are explained. The results of the experimentation are quite satisfactory.

In this paper we consider the non-preemptive variant of a multi-mode resource constrained PROJECT scheduling problem (MRCPSP) with mode identity, in which a set of PROJECT activities is partitioned into disjoint subsets while all activities forming one subset have to be processed in the same mode. We present a depth-first branch and bound ALGORITHM for the resource constrained PROJECT scheduling problem with mode identity. The proposed ALGORITHM is extended with some bounding rules to reduce the size of branch and bound tree. Finally, some test problems are solved and their computational results are reported.

Resources scarcity, available capabilities and cost-benefit point of view, make it essential to select the best PROJECT(s) from available PROJECT portfolio. PROJECT selection process has a significant role in the success. Here the main problem is what PROJECTs must be selected and how manage simultaneous PROJECTs. Used approach to answer these questions must be real, fast, global, flexible, economic and easy to use. It is clear that choosing a good approach for PROJECT selection problem with economic and non-economic criteria can be vital for a PROJECT manager to success within constraints. The complexity of this problem increases as the number of PROJECTs and the number of objectives increase. Therefore, in this research we aim to present a heuristic based on genetic and simulates annealing to select and prioritize available PROJECTs based on economic and non-economic criteria. Considered issues are benefit, credit and risk (technical and financial). Presented method starts from multi population of generated solutions and moves toward the final solution. Comparison studies between our method with other recently method in the literature demonstrates the capability of it to find a good basket of PROJECTs. Experimental results demonstrate that this method can be used for all kinds of PROJECTs basket.

We consider criticality in PROJECT networks having imprecise activity duration times. It is well known that finding all possibly critical paths of an imprecise PROJECT network is an NP-hard problem. Here, based on a method for finding critical paths of crisp networks by using only the forward recursion of critical path method, for the first time an ALGORITHM is proposed which can find all possibly critical paths of interval-valued PROJECT networks. The proposed ALGORITHM considers interactivity among paths which has not been yet considered in the fuzzy PROJECT scheduling literature. The extension of the proposed ALGORITHM to the fuzzy network calculates criticality degrees of activities and paths of PROJECTs without any need to enumerate all PROJECT paths. Although ALGORITHMs for calculating criticality degrees in fuzzy networks have been previously proposed, despite the fact that they mostly consider a specific type of fuzzy numbers as activity duration times, the exiting ALGORITHMs do not discriminate possibly critical paths before calculating the criticality degrees. The computational experience on a series of wellknown PROJECT samples confirms the ALGORITHM to be remarkably more efficient than similar ALGORITHMs for fuzzy networks.

In this paper, a Fully Informed Particle Swarm (FIPS) ALGORITHM is proposed for solving the Multi-mode Resource- Constrained PROJECT Scheduling Problem (MRCPSP) with minimization of PROJECT makespan as the objective subject to resource and precedence constraints. In the proposed FIPS, A random key and the related mode list (ML) representation scheme are used as encoding schemes and the multi-mode serial schedule generation scheme (MSSGS) is considered as the decoding procedure. In particular, a new fitness function which reduces the average deviation from optimality and CPU- time is presented. Comparing the results of the proposed FIPS with other approaches using the well- known benchmark sets in PSPLIB validate the effectiveness of the proposed ALGORITHM to solve the MRCPSP.

Resource leveling has a important role in PROJECT management. Most of researches has been done in this field by considering only one PROJECT, while in most of organizations more than a PROJECT have been done simultaneity. In this paper we model the problem under the multi PROJECT construction and consider the objective function as the minimize of the difference between resource usages. This problem is shown to be NP-hard and can not be solve with ordinary optimizations methods. So we offer a genetic ALGORITHM for obtaining near optimum. The computational section shows the accuracy of proposed method.