Journal of Industrial Engineering and Management Studies

Abstract In this research, a new emerging model for the Tehran stock exchange market is considered, and a model with realistic constraints for the mentioned market is provided. Realistic constraints are incorporated in this model for applicable purposes, one of which is the transaction cost. The limited maximum number of stocks that should be invested is also considered. Additionally, constraints have been added to the classic portfolio selection model to prevent stocks from being bought in tiny quantities and avoid over-buying some stocks. The mentioned constraint can improve diversity in the selected portfolio. One of the factors considered by many financial market investors is the amount of liquidity of the stocks they have purchased. This study also considers the amount of portfolio liquidity as one of the essential objective functions affecting the selection of the portfolio. Finally, in the model presented in the present study, the investors can have a different stock portfolio according to their preferences. The proposed model is multi-objective fuzzy goal programming, which can simulate uncertainty in the Tehran stock exchange market and provide a rational framework for investors who invest in the financial markets. As the numerical instances show, the solutions when additional constraints are added to the mathematical model are close to exact results. This difference became significant when the maximum number of stocks increased. According to the results, when the number of stocks increases, GAMS software loses its functionality, and the utilization of meta-heuristics as an option is inescapable. Finally, the harmony search algorithm with added realistic constraints has provided better portfolios in such a situation. To compare the results, a genetic algorithm was used as the competing algorithm. After solving different instances and comparing the results of the algorithms, the superiority of the proposed harmony search algorithm has been proved.

Abstract This research extends a two-phase algorithm for parallel job scheduling problem by considering earliness and tardiness as multi-objective functions. Here, it is also assumed that the jobs may use more than one machine at the same time, which is known as parallel job scheduling. In the first phase, jobs are grouped into job sets according to their machine requirements. For this, here, a heuristic algorithm is proposed for coloring the associated graph. In the second phase, job sets will be sequenced as a single machine scheduling problem. In this stage, for sequencing the job sets which are obtained from the first phase, a discrete algorithm is proposed, which comprises two well-known metaheuristics. In the proposed hybrid algorithm, the genetic algorithm operators are used to discretize the particle swarm optimization algorithm. An extensive numerical study shows that the algorithm is very efficient for the instances which have different structures so that the proposed algorithm could balance exploration and exploitation and improve the quality of the solutions, especially for large-sized test problems.

Abstract Assembly lines are flow-oriented production systems that are of great importance in the industrial production of standard, high-volume products and even more recently, they have become commonplace in producing low-volume custom products. The main goal of designers of these lines is to increase the efficiency of the system and therefore, the assembly line balancing to achieve an optimal system is one of the most important steps that have to be considered in the design of assembly lines. The purpose of the assembly line balancing is to assign tasks to the workstation called the station, so that prerequisite relationships, cycle times, and other assembly line constraints to be met and a number of line performance criteria to be optimized. In this study, considering the social responsibility related objective function, a mathematical model is proposed for scheduling and balancing the cost-oriented assembly line that has resource constraints with cost uncertainty. The box set robust optimization is applied and the obtained model is solved with the augmented epsilon constraint in the GAMS and some test problems and their results are presented. Finally, the cost parameter has been changed in a robust optimization approach and the obtained results have been analyzed for different costs.