International Journal of Industrial Engineering & Production Research

---

In this paper, we consider scheduling of project networks under minimization of total weighted resource tardiness penalty costs. In this problem, we assume constrained resources are renewable and limited to very costly machines and tools which are also used in other projects and are not accessible in all periods of time of a project. In other words, there is a dictated ready date as well as a due date for each resource such that no resource can be available before its ready date but the resources are allowed to be used after their due dates by paying penalty cost depending on the resource type. We also assume, there is only one unit of each resource type available and no activity needs more than it for execution. The goal is to find a schedule with minimal total weighted resource tardiness penalty costs. For this purpose, we present a hybrid metaheuristic procedure based on the greedy randomized adaptive search algorithm and path-relinking algorithm. We develop reactive and non-reactive versions of the algorithm. Also, we use different bias probability functions to make our solution procedure more efficient. The computational experiments show the reactive version of the algorithm outperforms the non-reactive version. Moreover, the bias probability functions defined based on the duration and precedence relation characteristics give better results than other bias probability functions.

---

In this paper, we consider a stochastic Time-Cost Tradeoff Problem (TCTP) in PERT networks for project management, in which all activities are subjected to a linear cost function and assumed to be exponentially distributed. The aim of this problem is to maximize the project completion probability with a pre-known deadline to a predefined probability such that the required additional cost is minimized. A single path TCTP is constructed as an optimization problem with decision variables of activity mean durations. We then reformulate the single path TCTP as a cone quadratic program in order to apply polynomial time interior point methods to solve the reformulation. Finally, we develop an iterative algorithm based on Monte Carlo simulation technique and conic optimization to solve general TCTP. The proposed approach has been tested on some randomly generated test problems. The results illustrate the good performance of our new approach.

---

The concept of "complexity" is familiar to most of project's managers, but it is not comprehended in the same way. although the complexity highlights negative points, but it may bring positive advantages which support the project. Researches conducted on this field show that the understanding of "complexity" between the researchers is different and it is mainly depends on their points of view. In fact, many identified aspects of the complexity in the literature are related to the aims of the research. In this paper, an attempt was made to describe the positive / negative features of the complexity of project using three approaches research literature (manufacturing and project complexity), interviews (deep interview with 20 experts) and questionnaire. The research was conducted on the Complex product and system (CoPS) projects. in addition, WH question technique was used. In conclusion, a 5p model (Purpose, Product, Process, People, Peripheral) has been introduced as the outcome of the study.

---

Due to the particular importance of projects in human life and in organizations, proper project management has been always regarded highly by researchers and practitioners. Recent advances in technology and fundamental changes in most scientific areas have affected projects and made their nature and environmental circumstances much more complex than in the past. Fortunately, in recent years, many scholars have recognized the importance of complexity in modern project management and tried to identify its various aspects. Furthermore, one of the main factors for a project’s success is the assignment of an appropriate project manager. Many studies have been done about project managers' competencies and the selection methods of a suitable project manager. In most of these researches, the amount and type of project complexity have been explained as influential factors for determining the competent project manager. However, a specific approach for project manager selection considering the complexity of projects is not provided yet. Hence, in this paper we try to design and implement a fuzzy group decision making approach to allocate the best project manager taking into account the project complexity. Also, owing to the importance of construction projects in the development of countries' basic infrastructures, we exclusively studied this kind of projects. Finally, it should be noted that from the viewpoint of complexity theory, system complexity can exist in two forms: static and dynamic. Therefore, considering the breadth of issues related to each of these two complexity areas, just the static complexity of construction projects has been studied here.

---

The aim of this study is to present a new method to predict project time and cost under uncertainty. Assuming that what happens in projects implementation which is expressed in the form of Earned Value Management (EVM) indicators is primarily related to the nature of randomness or unreliability, in this study, by using Monte Carlo simulation, and assuming a specific distribution for the time and cost of project activities, a significant number of predicting scenarios will be simulated. According to the data, an artificial neural network is used as efficient data mining methods to estimate the project time and cost at completion.

---

The Multi-Mode Resource Constrained Project Scheduling Problem (MMRCPSP) is one of the most important problems in project scheduling context. The MMRCPSP consists of activities to be scheduled subject to precedence and resource constraints. The effort needed in order to accomplish activities in the MMRCPSP is a discrete function of job performing modes. However, MMRCPSP is a basic model with a rather too restrictive assumptions to be applied practically. Therefore, there are many extensions over basic MMRCPSP model in terms of objective functions, resource constraints, and solving procedures. This research is aiming at fulfilling tow ambitions. First, to collect researches related MMRCPSP and to classify them based on a framework consisting of six distinct classes. Second, to indicate current trends and potential areas of future research. In order to fulfill the second goal a new mathematical method is proposed and applied which identify recent trends and gaps in a systematic manner.
 

---

Project scheduling problem with resources constraint is a well-known problem in the field of project management. The applicable nature of this problem has caused the researchers’ tendency to it. In this study, project scheduling with resource constraints and the possibility of interruption of project activities as well as renewable resources constraint has been also applied along with a case study on construction projects. Construction projects involve complex levels of work. Making wrong decisions in selecting methods and how to allocate the necessary resources such as manpower and equipment can lead to the results such as increasing the predetermined cost and time. According to NP-Hard nature of the problem, it is very difficult or even impossible to obtain optimal solution using optimization software and traditional methods. In project scheduling using CPM method, critical path is widely used; however, in this method, the resource constraints is not considered. Project Scheduling seek proper sequence for doing the project activities. This study has been conducted using Bees meta-heuristic algorithm, with the aim of optimizing the project completion time. Finally, the results obtained from three algorithms and GAMS software shows that this algorithm has better performance than and the solution among the other algorithms and has achieved the accurate solutions.

---

In order to manage a project with integrity, a cohesive communication is needed between its various sections; possible risks, identification of stakeholders, providing the necessary resources on time and managing their availability, focusing on the approved budget and satisfactory quality the project can be successfully done. In the recent year BIM has as new aspects to engineering and architecture, and has become an accepted platform for planning and executing construction projects and contributed to integration of various fields and. also, project management standards, such as PMBOK, have come to aid construction managers. Through the basic capacities of BIM, and questionnaires according to aspects of PMBOK, the present study tries to identify the superior effects of BIM on project management. Moreover, it seeks to recognize the most significant aspects of BIM application on project management. by employing the FANP-AVIKOR decision making method to prioritize the parameters of the collected results, the study’s conclusion will indicate that almost all of PMBOK aspects equally benefit from using BIM; in addition, it will show that 3D BIM capacities, including clash detection, plan correction, are superior in comparison with 6D BIM and 7D BIM capacities.

---

Project scheduling is one of the most important and applicable concepts of project management. Many project-oriented companies and organizations apply variable cost reduction strategies in project implementation. Considering the current business environments, in addition to lowering their costs, many companies seek to prevent project delays. This paper presents a multi-objective fuzzy mathematical model for the problem of project scheduling with the limitation of multi-skilled resources able to change skills levels, optimizing project scheduling policy and skills recruitment. Given the multi objectivity of the model, the goal programming approach was used, and an equivalent single-objective model was obtained. Since the multi-skilled project scheduling is among the NP-Hard problems and the proposed problem is its extended state, so it is also an NP-Hard problem. Therefore, NSGA II and MOCS meta-heuristic algorithms were used to solve the large-sized model proposed using MATLAB software. The results show that the multi-objective genetic algorithm performs better than the multi-objective Cuckoo Search in the criteria of goal solution distance, spacing, and maximum performance enhancement.

---