فهرست مطالب ahmad sadeghieh

This study aims to develop a capacitated hub location-routing model to design a rapid transit network under uncertainty. The mathematical model is formulated by making decisions about the location of the hub and spoke (non-hub) nodes, the selection of the hub and spoke edges, the allocation of the spoke nodes to the hub nodes, the determination of the hub and spoke lines, the determination of the percentage of satisfied origin-destination demands, and the routing of satisfied demand flows through the lines. Capacity constraints are considered in the hub and spoke nodes and also the hub and spoke edges. Uncertainty is assumed for the demands and transportation costs, represented by a finite set of scenarios. The aim is to maximize the total expected profit, where transfers between the lines are penalized by including their costs in the objective function. The performance of the proposed model is evaluated by computational tests and some managerial insights are also provided through the analysis of the resulting networks under various parameter settings.

The competitive environment in the global market makes most countries look for better ways to solve problems in order to earn more money. One of the strategies proposed as a competitive one is to use a stable closed loop to improve performance. The present study, which has not reported any research in this field, proposes a multi-level sustainable chain-loop supply chain (SCLSC) network for pomegranate fruit. The mathematical model has been designed with the aim of offering the lowest price, the amount of response received and the reduction of costs. Our study distinguishes itself from other studies by considering the costs of using artificial intelligence in the production chain and in the reverse logistics sector, converting pomegranate waste into recycled products including ethanol for car fuel and organic fertilizer production. In order to examine the research gap and approach real-world applications, an applied example in Iran has been studied. Also, NSGA-II and MOPSO algorithms are used to solve the model, and in the new solution method, the HSA&TS multi-objective hybrid algorithm is proposed. In addition, in the comparison of algorithms, indicators in the one-way variance analysis table, the best time is . Therefore, the practical result show that the combined development algorithm of HSA&TS is a suitable technique and it is superior to other selected methods, it is also recommended, usable and implementable for the development of the logistics network.

Supply chain network design is one of the key issues in strategic chain planning that refers to the supply chain network configuration and as an infrastructure issue in its management, will have lasting effects on other tactical and operational decisions. In other words, the proper design of the supply chain network leads to the achievement of an optimal structure, which makes effective and competitive supply chain management possible. In this study, a problem of selecting a green supplier in terms of sustainability, under uncertainty based on three economic, environmental and social responsibility dimensions are studied by a case study of Sazeh Gostar Saipa Photovoltaic Company active in the pv industry. One of the environmental dimensions of the problem is the design of an efficient and flexible model for evaluating and selecting suppliers based on environmental indicators. One of the features of the proposed model is the use of environmental criteria in the process of selecting a green supplier and also the flexibility of the model in the number of sub-criteria. In this research, the theory of Rough sets has been used to find the weight of sub-environmental criteria. The obtained results confirm the efficiency of the multi-objective mathematical planning model in this research in evaluating suppliers and also using the theory of Rough sets to weight environmental indicators to achieve the above objectives.

In this paper, a bi-objective mixed-integer model for energy-aware production scheduling of continuous slurry ball mills is proposed. Slurry ball mills are considered as the main consumer of electrical energy and impose high energy costs on tile factories. Hence, minimizing the energy costs associated with slurry production through implementation of peak-load minimization strategy and optimal assignment of orders to ball mills is the main goal which is considered in this study. On the other hand, the quality of slurry has a significant effect on the quality of produced tiles. An increase in time which slurry has left in rotating slip tanks, i.e. stirring time, helps improve its quality. Thus, the second goal which is pursued in this scheduling is compliance with the stirring time standards. The effectiveness of the proposed model is illustrated on a small-scale case study with a saving of above 15% in energy costs.

The purpose of this article is to model and solve an integrated location, routing and inventory problem (LRIP) in cash-in-transit (CIT) sector. In real operation of cash transportation, to decrease total cost and to reduce risk of robbery of such high-value commodity. There must be substantial variation, making problem difficult to formulate. In this paper, to better fit real life applications and to make the problem more practical, a bi-objective multiple periods, capacitated facilities with time windows under uncertain demand (BO-PCLRIP-TW-FD) in the LRIP, motivated by the replenishment of automated teller machines, is proposed. Then, using the chance constrained fuzzy programming to deal with uncertain parameters, the comprehensive model is formulated as a crisp mixed-integer linear programming. At last, to validate the mathematical formulation and to solve the problem, the latest version of ε-constraint method (i.e., AUGMECON2) is used. The proposed solution approach is tested on a realistic instance in CIT sector. Numerical results demonstrate the suitability of the model and the formulation. The ability of the model to be useful references for security carriers in real-world cases.

This paper proposes a mathematical model for ride-sharing vehicles with a common destination. A number of cars should assign to individuals by a company to pick up other participants in their way to the common destination. Traveling time as an important parameter is considered an uncertain parameter to enhance the applicability of the model which is formulated using fuzzy programming and necessity concept. Moreover, to have a better solution with better productivity, maximizing the earliest departure time of the individuals is considered beside of minimizing total traveling time. This helps to make justice among individuals for departure time. Goal programming is employed to work with objective functions and solve the model. Furthermore, a numerical example is implemented on the model to evaluate the applicability of the model which indicates the efficiency of employing fuzzy programming and considering both of the objective functions using goal programming. Results of the numerical example indicate the importance of considering both of the objective functions together in which ignoring each of them leads to inefficient solutions.

In order to achieve the sensing, communication and processing tasks of Wireless Sensor Networks, an energy-efficient routing protocol is required to manage the dissipated energy of the network and to minimalize the traffic and the overhead during the data transmission stages. Clustering is the most common technique to balance energy consumption amongst all sensor nodes throughout the network. In this paper, a multi-objective bio-inspired algorithm based on the Firefly and the Shuffled frog-leaping algorithms is presented as a clustering-based routing protocol for Wireless Sensor Networks. The multi-objective fitness function of the proposed algorithm has been performed on different criteria such as residual energy of nodes, inter-cluster distances, cluster head distances to the sink and overlaps of clusters, to select the proper cluster heads at each round. The parameters of the proposed approach in the clustering phase can be adaptively tuned to achieve the best performance based on the network requirements. Simulation outcomes have displayed average lifetime improvements of up to 33.95%, 32.62%, 12.1%, 13.85% compared with LEACH, ERA, SIF and FSFLA respectively, in different network scenarios.

This paper presents a multi-stage model for accurate prediction of demand for dairy products (DDP) by the use of artificial intelligence tools including Multi-Layer Perceptron (MLP), Adaptive-Neural-based Fuzzy Inference System (ANFIS), and Support Vector Regression (SVR). The innovation of this work is the improvement of artificial intelligence tools with various meta-heuristic algorithms including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Invasive Weed Optimization (IWO), and Cultural Algorithm (CA). First, the best combination of factors with can affect the DDP is determined by solving a feature selection optimization problem. Then, the artificial intelligent tools are improved with the goal of making a prediction with minimal error. The results indicate that demographic behavior and inflation rate have the greatest impact on dairy consumption in Iran. Moreover, PSO still exhibits a better performance in feature selection in compare of newcomer meta-heuristic algorithms such as IWO and CA. However, IWO shows the best performance in improving the prediction tools by achieving an error of 0.008 and a coefficient of determination of 95%. The final analysis demonstrates the validity and reliability of the results of the proposed model, as it supports the simultaneous analysis and comparison of the outputs of different tools and methods.

This paper considers the multi-depot vehicle routing problem with time window in which each vehicle starts from a depot and there is no need to return to its primary depot after serving customers. The mathematical model which is developed by new approach aims to minimizing the transportation cost including the travelled distance, the latest and the earliest arrival time penalties. Furthermore, in order to reduce the problem searching space, a novel GA clustering method is developed. Finally, Experiments are run on number problems of varying depots and time window, and customer sizes. The method is compared to two other clustering techniques, fuzzy C means (FCM) and K-means algorithm. Experimental results show the robustness and effectiveness of the proposed algorithm.

Emergency is one of the most important services in every city. An event is sudden and unexpected so immediate attention is necessary for us to decide correctly. This subject becomes serious by population increase and urban development. These factors and their ranking help the urban emergency services and reduces the mortality of the humans. So this causes the people to be satisfied with of the emergency services. The purpose of this paper is to obtain the effective factors on the location of emergency centers in Shiraz city and prioritize these factors using analytical by Hierarchy method (AHP) so that we can propose the solutions for improving the locations of emergency centers. Using theoretical studies of past research and interviews specify the effective factors. They are time, distance, cost, facilities, and technological issues, quality of service, traffic and population density. Then we prioritize these factors by studying between two staff and patient population. Our result of this method shows the time as the most important factor and the most effective factors for the emergency center. At the end, the other factors were rated.

Reverse logistic has attracted a lot of attention from researchers in recent years due to government regulations، environmental problems، extension of social responsibility and customer demands. In addition، the decline in the natural resources and raw materials combined with the increase in production costs and problems of dealing with trash of the industry and consumer products makes the cycle of consumption very interesting to researchers from the production point to the last stage of recycling. This subject gave way to newer concepts like integrated، closed-loop and stable supply chain in the past decade. This paper presents a robust design for a multi-product، multi-echelon، closed-loop logistic network model in an uncertain environment. To this aim، a multi-objective mathematical programming model is developed wherein its objective functions include profit، social and environmental impacts. First، a deterministic mixed-integer linear programming model is developed for designing a closed-loop supply chain network. Then، the robust counterpart of the proposed mixed-integer linear programming model is presented by using the recent extensions in robust optimization theory. Finally، to assess the robustness of the solutions obtained by the novel robust optimization model، they are compared to those generated by the deterministic mixed-integer linear programming model in a number of realizations under different test problems.