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      • Open Access Article

        1 - An Integrated Model of Project Scheduling and Material Ordering: A Hybrid Simulated Annealing and Genetic Algorithm
        Nima Zoraghi Amir Abbas Najafi سید تقی اخوان نیاکی
        This study aims to deal with a more realistic combined problem of project scheduling and material ordering. The goal is to minimize thetotal material holding and ordering costs by determining the starting time of activities along with material ordering schedules subject More
        This study aims to deal with a more realistic combined problem of project scheduling and material ordering. The goal is to minimize thetotal material holding and ordering costs by determining the starting time of activities along with material ordering schedules subject tosome constraints. The problem is first mathematically modelled. Then a hybrid simulated annealing and genetic algorithm is proposed tosolve it. In addition, some experiments are designed and the Taguchi method is employed to both tune the parameters of the proposedalgorithm and to evaluate its performance. The results of the performance analysis show the efficiency of the proposed methodology. Manuscript profile
      • Open Access Article

        2 - Project Portfolio Selection with the Maximization of Net Present Value
        Mostafa Nikkhah Nasab Amir Abbas Najafi
        Projects scheduling by the project portfolio selection, something that has its own complexity and its flexibility, can create different composition of the project portfolio. An integer programming model is formulated for the project portfolio selection and scheduling.Tw More
        Projects scheduling by the project portfolio selection, something that has its own complexity and its flexibility, can create different composition of the project portfolio. An integer programming model is formulated for the project portfolio selection and scheduling.Two heuristic algorithms, genetic algorithm (GA) and simulated annealing (SA), are presented to solve the problem. Results of calculations show that the algorithm performance of GA is better than SA in project portfolio selection to maximize the NPV of the project portfolio. Manuscript profile
      • Open Access Article

        3 - Modeling and Solution Procedure for a Preemptive Multi-Objective Multi-Mode Project Scheduling Model in Resource Investment Problems
        Mostafa Salimi Amir Abbas Najafi
        In this paper, a preemptive multi-objective multi-mode project scheduling model for resource investment problem is proposed. The first objective function is to minimize the completion time of project (makespan);the second objective function is to minimize the cost of us More
        In this paper, a preemptive multi-objective multi-mode project scheduling model for resource investment problem is proposed. The first objective function is to minimize the completion time of project (makespan);the second objective function is to minimize the cost of using renewable resources. Non-renewable resources are also considered as parameters in this model. The preemption of activities is allowed at any integer time units, and for each activity, the best execution mode is selected according to the duration and resource. Since this bi-objective problem is the extension of the resource-constrained project scheduling problem (RCPSP), it is NP-hard problem, and therefore, heuristic and metaheuristic methods are required to solve it. In this study, Non-dominated Sorting Genetic AlgorithmII (NSGA-II) and Non-dominated Ranking Genetic Algorithm (NRGA) are used based on results of Pareto solution set.We also present a heuristic method for two approaches of serial schedule generation scheme (S-SGS) and parallel schedule generation scheme (P-SGS) in the developed algorithm in order to optimize the scheduling of the activities.The input parameters of the algorithm are tuned with Response Surface Methodology (RSM). Finally, the algorithms are implemented on some numerical test problems, and their effectiveness is evaluated. Manuscript profile
      • Open Access Article

        4 - Solving Bi-objective Model of Hotel Revenue Management Considering Customer Choice Behavior Using Meta-heuristic Algorithms
        Surur Yaghobi Harzandi Amir Abbas Najafi
        The problem of maximizing the benefit from a specified number of a particular product with respect to the behavior of customer choices is regarded as revenue management. This managerial technique was first adopted by the airline industries before being widely used by ma More
        The problem of maximizing the benefit from a specified number of a particular product with respect to the behavior of customer choices is regarded as revenue management. This managerial technique was first adopted by the airline industries before being widely used by many others such as hotel industries. The scope of this research is mainly focused on hotel revenue management, regarding which a bi-objective model is proposed. The suggested method aims at increasing the revenue of hotels by assigning the same rooms to different customers. Maximization of hotel revenue is a network management problem aiming to manage several resources simultaneously. Accordingly, a model is proposed in this paper based on the customer choice behavior in which the customers are divided into two groups of business and leisure. Customers of the business group prefer products with full price, whereas products with discounts are most desirable for leisure customers. The model consists of two objectives, the first one of which maximizes the means of revenue, and the second one minimizes the dispersion of revenue. Since the problem under consideration is Non-deterministic Polynomial-time hard (NP-hard), two meta-heuristic algorithms of Non-dominated Sorting Genetic Algorithm-II (NSGA-II) and Multiple Objective Particle Swarm Optimization (MOPSO) are proposed to solve the problem. Moreover, the tuned algorithms are compared via the statistical analysis method. The results show that the NSGA-II is more efficient in comparison with MOPSO. Manuscript profile