In today’s world, due to rapid changes in the market, scheduling as one of the most fundamental issues of competitive production, plays a very important role in maintaining the competitive position and survival of manufacturing organizations, therefore, development of scheduling models in order to improve the timing criteria is of great importance. In this research, we put the development of a no-wait flow-shop scheduling model alongside with the effect of learning into consideration to minimize the cost of consumption of resources. Finding the correct sequence of two machines’ performance and optimized allocation of the resources for any performance on each machine, were considered as the main goal in this study. To solve the problem, metaheuristic genetic algorithms, particle swarm optimization, imperialist competitive algorithms, optimization of the whale and the League Champions algorithms, have been used. The statistical comparisons and also using of TOPSIS Multi-Criteria Decision Making method, indicate high level of efficiency of the League Champions algorithm with the utility weight of 0.9516.

This paper presents a mathematical model for scheduling of a single machine when there are preemptions in jobs. The primary objective of the study is to minimize different objectives such as earliness, tardiness and work in process. The proposed mathematical problem is considered as NP-Hard and the optimal solution is available for small scale problems. Therefore, a genetic algorithm (GA) is developed to solve the problem for large-scale problems. The implementation of the proposed model is compared with GA for problems with up to 50 jobs using three methods of roulette wheel sampling, random sampling and competition sampling. The results have indicated that competition sampling has reached optimal solutions for small scale problems and it could obtain better near-optimal solutions in relatively lower running time compared with other sampling methods.

In recent years, logistics development is considered as an important aspect of any country’s development. Outsourcing logistics activities to third party logistics (3PL) providers is a common way to achieve logistics development. On the other hand, globalization and increasing customers’ concern about the environmental impact of activities as well as the appearance of the issue of social responsibility have led companies employ sustainable supply chain management, which considers economic, environmental and social benefits, simultaneously. This paper proposes a multi-objective model to design logistics network for 3PL providers by considering sustainable objectives under uncertainty. Objective functions include minimizing the total cost, minimizing greenhouse gas emission and maximizing social responsibility subject to fair access to products, number of created job opportunities and local community development. It is worth mentioning that in the present paper the perishability of products is also considered. A numerical example is provided to solve and validate model using augmented Epsilon-Constraint method. The results show that three sustainable objectives were in conflict and as the one receives more desirable values, the others fall into more undesirable values. In addition, by increasing maximum perishable time periods and by considering lateral transshipment among facilities of a level one can improve sustainability indices of the problem, which indicates the necessity of such policy in improving network sustainability.

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 two-objective model presented by the GA are other results obtained in this study.

In this paper, a novel multi-objective robust possibilistic programming model is proposed, which simultaneously considers maximizing the distributive justice in relief distribution, minimizing the risk of relief distribution, and minimizing the total logistics costs. To effectively cope with the uncertainties of the after-disaster environment, the uncertain parameters of the proposed model are considered in the form of fuzzy trapezoidal numbers. The proposed model not only considers relief commodities priority and demand points priority in relief distribution, but also considers the difference between the pre-disaster and post-disaster supply abilities of the suppliers. In order to solve the proposed model, the LP-metric and the improved augmented ε-constraint methods are used. Second, a set of test problems are designed to evaluate the effectiveness of the proposed robust model against its equivalent deterministic form, which reveales the capabilities of the robust model. Finally, to illustrate the performance of the proposed robust model, a seismic region of northwestern Iran (East Azerbaijan) is selected as a case study to model its relief logistics in the face of future earthquakes. This investigation indicates the usefulness of the proposed model in the field of crisis.

In this paper, a new hybrid algorithm based on multi-objective genetic algorithm (MOGA) using simulated annealing (SA) is proposed for scheduling unrelated parallel machines with sequence-dependent setup times, varying due dates, ready times and precedence relations among jobs. Our objective is to minimize makespan (Maximum completion time of all machines), number of tardy jobs, total tardiness and total earliness at the same time which can be more advantageous in real environment than considering each of objectives separately. For obtaining an optimal solution, hybrid algorithm based on MOGA and SA has been proposed in order to gain both good global and local search abilities. Simulation results and four well-known multi-objective performance metrics, indicate that the proposed hybrid algorithm outperforms the genetic algorithm (GA) and SA in terms of each objective and significantly in minimizing the total cost of the weighted function.

In this paper, the synthesis of arylidene heterobicyclicpyrimidinones and thiones is reported by condensation of aromatic aldehydes, cyclopentanone, and urea or thiourea in the present of FePO4 as the green and recyclable catalyst. Using solvent-free conditions, non-toxic and inexpensive materials, simple and clean work-up, relatively short reaction times, and high yields of the products are the advantages of this method. Also, some new derivatives of arylidene dihydropyrimidinones and thiones were prepared by this green method.

The main aim of this paper is to present a novel multi-objective gray wolf optimization (MOGWO) by utilizing the Kriging meta-model. To this end, surrogate models are used in Multi-Objective Gray Wolf Optimizer as the fitness function. The meta-model is obtained based on exact analysis and numerical simulations. Inheritable Latin Hypercube Design (ILHD) is used as the design of experiments for generation and testing the Kriging model. Then, sensitivity analysis is done to evaluate the effect of design parameter on system responses. The sensitivity analysis leads to appropriate selection of optimization design variables. Hence, the MOGWO algorithm is applied to the problem, the set of non-dominated optimal points are obtained as Pareto Front and one optimal point is selected based on the minimum distance approach. The most important purpose of the methodology is to improve the time consuming in multi-objective optimization problems. In conclusion, for the design of hydrazine catalyst bed was utilized from the proposed methodology. In case, design variables are catalyst bed pellet diameter, loading factor, thrust chamber pressure and Reaction efficiency and objective functions are increasing performance and reducing mass and pressure drop. The results of optimal catalyst bed parameters and also corresponding value of objective functions are shown the performance of methodology in the space propulsion system applications.

Composite materials show different mechanical properties in different directions which leads to complications in the prediction of their behavior. In addition to being inhomogeneous, behavior of composite materials is highly dependent on the curing process and many studies have tried to address these issues. Residual stress can induce high errors in prediction of composite material properties and leads to a difference between theoretical and experimental results. It has been observed that the humidity of environment and the chemical properties of composite materials are also important. Fewer studies have addressed thick layer composites and no damage theory has been developed for these materials. In this paper residual stress in thick layer composites have been studied. Using numerical analysis, the curing process has been modeled and mechanical properties of a thick layer composite are obtained and the induced residual stress in these composites has been assessed. Unlike thin layer composites, the stress in each layer is not specific and a tensile and compressive stress field can exist simultaneously in thick layer composites.

Risk response planning is a widely concerned issue. Project managers usually struggle to control different kinds of risks. In project risk management, after evaluating risks, the final process relates to choosing desirable responses to the risks. In this paper, a comprehensive framework confronts the issue in three main phases. In the first phase, all the risks, responses and their relations in a geothermal drilling project are detected. These relations imply there are inner dependent and outer dependent relations. In the second phase, ANP, DEMATEL and fuzzy theory play important roles in weighting risks and responses. In the third phase, to enable a more realistic solution, a zero-one integer programming reflects a budget constraint and other required constraints. After obtaining the optimal responses, the effect of budget is analyzed. In addition, the influences of risks on each other are discussed more deeply. Collectively, this study offers a new perspective on how to handle project risks and choose their responses