Mapping optimization techniques in project management

An important function of the project management is to optimize the project in various phases and at different levels. From sourcing and allocation to scheduling and even dealing with uncertainties, the science of operation research (OR) has played an important role in this area. So far, many papers have been published using the optimization science to make various decisions regarding the project management. This study aims to investigate all papers published on the application of optimization in the project management from 1940 to 2019 and shows: a) how the trend has changed over this 79 years period, b) to what direction the trend has changed, c) determines the interesting topics of the recent years, and d) which subjects are more attractive as future studies as the applications of the optimization techniques in the project management

Locating distribution/service centers based on multi objective decision making using set covering and proximity to stock market

In the present competitive world, facility location is an important aspect of the supply chain (sc) optimization. It involves selecting specific locations for facility construction and allocation of the distribution channel among different SC levels. In fact, it is a strategic issue which directly affects many operational/tactical decisions. Besides the accessibility, which results in customer satisfaction, the present paper optimizes the establishment costs of a number of distribution channels by considering their proximity to the stock market of the goods they distribute, and proposes mathematical models for two objective functions using the set covering problem. Then, two objective functions are proposed into one through the ε-constraint method and solved by the metaheuristic Genetic Algorithm (GA). To test the resulted model, a smaller scale problem is solved. Results from running the algorithm with different ε-values show that, on average, a 10% increase in ε, which increases the value of the second objective function -distance covered by customers will cause a 2% decrease in the value of the first objective function including the costs of establishing distribution centers). The repeatability and solution convergence of the twoobjective model presented by the GA are other results obtained in this study

Vehicle Routing Problem with Uncertain Demands: An Advanced Particle Swarm Algorithm

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.Moghaddam, BF.; Ruiz García, R.; Sadjadic, SJ. (2012). Vehicle Routing Problem with Uncertain Demands: An Advanced Particle Swarm Algorithm. Computers and Industrial Engineering. 62(1):306-317. doi:10.1016/j.cie.2011.10.001S30631762