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During the past two decades, some industries have been moving towards project-centered systems in many modern countries. Therefore, managing simultaneous projects with considering the limitations in resources, equipment and manpower is very crucial. In the real world, project-based organizations are always facing with two main important features. First, the construction projects are decentralized and their distances are long, and second, there are several construction projects undertaken at different time periods. Therefore, appropriate selection of projects with regard to the capabilities of the organization may lead with increasing an expected profitability. This paper investigates the multi-period decentralized multi construction-project and scheduling problem subject to resource constraints, optimal resource pool location, deterioration and batch ordering of nonrenewable resources altogether, for the first time in the literature. In order to describe the problem under consideration in this paper and obtaining the optimal solutions, a mixed integer linear programming model is developed. Finally, the impact of decision integration on the profit profile of an organization is comprehensively investigated by solving numerical examples and through developing some heuristic methods.

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In decentralized construction projects, costs are mostly related to investment, material, holding, logistics, and other minor costs for implementation. For this reason, simultaneous planning of these items and appropriate scheduling of activities can significantly reduce the total costs of the project undertaken. This paper investigates the decentralized multiple construction projects scheduling problem with the aim of minimizing 1) the completion time of the construction projects and 2) the costs of project implementation. Initially, a bi-objective integer programming model is proposed which can solve small-size problems using the method. Then, a Priority Heuristic Algorithm (PHA), Non-dominate Sorting Artificial Bee Colony (NSABC) and Non-dominate Sorting Genetic Algorithm II (NSGA-II) are developed to handle large-size problems using a modified version of Parallel Schedule Generation Scheme (PSGS). The computational investigations significantly reveal the performance of the proposed heuristic methods over exact ones. Finally, the proposed methods are ranked using TOPSIS approach and metric definition. The results show that NSGA-II-100 (NSGA-II with 100 iterations), NSABC-100 (NSABC with 100 iterations) and PHA are ranked as the best known solution methods, respectively.