The project scheduling problem is both practically and theoretically of paramount importance. From the practical perspective, improvement of project scheduling as a critical part of project management process can lead to successful project completion and significantly decrease of the relevant costs. From the theoretical perspective, project scheduling is regarded as one of the interesting optimization issues, which has attracted the attention of many researchers in the area of operations research. Therefore, the project scheduling issue has been significantly evaluated over time and has been developed from various aspects. In this research, the topics related to Resource-Constrained Project Scheduling Problem (RCPSP) are reviewed, recent developments in this field are evaluated, and the results are presented for future studies. In this regard, first, the standard problem of RCPSP is expressed and related developments are presented from four aspects of resources, characteristics of activities, type of objective functions, and availability level of information. Following that, details about 216 articles conducted on RCPSP during 1980-2017 are expressed. At the end, in line with the statistics obtained from the evaluation of previous articles, suggestions are made for the future studies in order to help the development of new issues in this area.
Due to the importance of relief operations in disasters, this paper aims to contribute humanitarian logistics under uncertainty. In this paper, a three-level relief chain model consisting of suppliers, relief distribution centers, and affected areas is considered. The uncertainty associated with demand, supply, and all of the cost parameters is addressed by employing robust optimization, where the uncertain parameters are independent and bounded random variables. While the proposed model attempts to minimize the total costs of the relief chain, it implicitly maximizes people's satisfaction level in the affected areas through applying a penalty to shortages of relief commodities. Additionally, a data set derived from a real disaster case study in the Alborz area, which is vulnerable to earthquakes, is applied to test the efficiency of the proposed robust relief chain model compared to its deterministic form. The study analyzes the degree to which each uncertain parameter affects the solution of the relief chain model and consequently helps the decision maker to tune the parameter values more accurately.
The Vehicle Routing Problem (VRP) has been thoroughly studied in the last decades. However, the main focus has been on the deterministic version where customer demands are fixed and known in advance. Uncertainty in demand has not received enough consideration. When demands are uncertain, several problems arise in the VRP. For example, there might be unmet customers' demands, which eventually lead to profit loss. A reliable plan and set of routes, after solving the VRP, can significantly reduce the unmet demand costs, helping in obtaining customer satisfaction. This paper investigates a variant of an uncertain VRP in which the customers' demands are supposed to be uncertain with unknown distributions. An advanced Particle Swarm Optimization (PSO) algorithm has been proposed to solve such a VRP. A novel decoding scheme has also been developed to increase the PSO efficiency. Comprehensive computational experiments, along with comparisons with other existing algorithms, have been provided to validate the proposed algorithms.
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