payam chiniforooshan

This paper deals with the single machine scheduling problem with sequence-dependent setup time and learning effect on processing time, where the objective is to minimize total earliness and tardiness of the jobs. A Mixed Integer Linear Programming (MILP) model capable of solving small-sized problems is proposed to formulate this problem. In view of the NP-hard nature of the problem, the Hybrid Particle Swarm Optimization (HPSO) algorithm is proposed to solve the large-sized problems. In order to utilize Particle Swarm Optimization (PSO) to solve the scheduling problems, the proposed HPSO approach uses a random key representation to encode solutions, which can convert the job sequences to continuous position values. Also, the local search procedure is included within the HPSO to enhance the exploitation of the algorithm. The performance of the proposed HPSO is verified for small and medium-sized problems by comparing its results with the best solution obtained by the LINGO. In order to test the applicability of the proposed algorithm to solve large-sized problems, 120 instances are generated, and the results are compared with a Random Key Genetic Algorithm (RKGA). The results show the effectiveness of the proposed model and algorithm.

In recent years, new theories have emphasized the importance of the role of structure and the real sector of the economy in inequality. The economic complexity approach considers this as the cause of income inequality in the production of high-tech and complex products. In this regard, the present study examines the impact of economic complexity and ranking the products and technologies affecting inequality in selected countries and Iran during the period of 2010-2017 using the econometric approach of panel data and Granger causality test. The results of the research indicate a strong and positive correlation between the two indicators of economic complexity and the Inequality-adjusted Human Development Index. Also, based on the results for the set of studied countries, the existence of a two-way causal relationship between the technology in products and revenue distribution was accepted. On the other hand, the results showed that among the top 10 export products of Iran in 2017, with the exception of unclassified transactions and polyethylene, the rest have low complexity and unequal income distribution. Finally, some policy proposals were made to move towards the production and export of products with higher technology and more income distribution in Iran.

In this paper, a mathematical model is proposed to solve cell formation problem considering alternative process routings in which more than one process route for each part can be selected. The model attempts to minimize intercellular movements and incorporates several real-life production factors and practical constraints. In order to increase the flexibility provided by the multiplicity of routings, the model distributes production volume of each part among alternative routes. Also, a constraint enforcing work load balancing among machines is included in the model. Due to the complexity and combinatorial nature of this model, an enhanced algorithm comprised of a genetic algorithm and a linear programming is proposed for solving the model. The proposed algorithm is tested by a range of test problems and compared with two algorithms from the literature. The computational results show that the proposed algorithm is effective and the proposed approach offers better solution.