Journal of Operational Research in its Applications
Year:2019 | Volume:16 | Issue:1 (60) |Papers Count:9

One of the issues that has been considered by the researchers in terms of theory and practice is the problem of finding minimum norm solution. In fact, in general, absolute value equation may have infinitely many solutions. In such cases, the best and most natural choice is the solution with the minimum norm. In this paper, the minimum norm-1 solution of absolute value equation is investigated. By applying the augmented Lagrangian method, this problem can be reduced to an unconstrained optimization problem with once differentiable objective function. To use Newton method, we apply the smoothing techniques. Computational results show that convergence to high accuracy often occurs in just a few iterations.

Nowadays communication networks have become an essential and inevitable part of human life. Hence, there is an ever-increasing need for expanding bandwidth, decreasing delay and data transfer costs. These needs necessitate the efficient use of network facilities. Network coding is a new paradigm that allows the intermediate nodes in a network to create new packets by combining the packets received on their incoming edges. Also, in communication network, the extensive use of high capacity physical media like fiber increases the potential damage to network services due to failures in links or nodes (cable cuts, electronic failures on switching centers, etc. ). Since quality of service has become a competitive advantage for services in the industry, planners are looking for end-to-end survivable designs that are robust with respect to failure in network components. Survivability is considered as a fraction of the demand that can still be satisfied after each failure scenario. In this paper, attempt has been made to select minimum-cost coding sub-graphs in a single or multiple multicasts over coded packet networks under survivability constraint. First, mathematical optimization models are presented for the problem. Then, the proposed model is solved by using Lagrangian relaxation method. Finally, the efficiency of the proposed model is evaluated through simulation results.

The closed loop supply chain is becoming one of the industry's most important areas of business, due to environmental and business factors. Planning and implementing a closed loop supply chain network provide more profit, customer satisfaction, and a good social image to the company. While most supply chain networks are not equipped with back-up channels, this paper presents a mixed integer nonlinear scheduling model for minimizing shipping costs, operating costs, purchasing costs, and fixed costs for construction in a multiproduct potential supply chain network. The proposed model is a multi-product supply chain supply model with the possibility of discounting the purchase of goods from the supplier. Since the design of the network is in the category of NP-hard problems, in this paper, the firefly algorithm is decoded with priority based on the optimal solution. In order to evaluate the performance of the proposed algorithm, it is compared with five algorithms (particle swarm algorithm, forest algorithm, ant colony algorithm, genetic algorithm and honey bee algorithm) in seven criteria. Comparing related numerical results with exact solutions, precise solution, as well as five proposed algorithms in a set of sample issues, indicates the provision of high-performance with high-frequency firefly algorithm with priority decoding.

Traditional DEA method considered decision making units (DMUs) as a black box, regardless of their internal structure and appraisal performance with respect to the final inputs and outputs of the units. However, in many real systems we have internal structure. For this reason, network DEA models have been developed. Parallel network DEA models are a special variation which inputs of unit allocated to several sub-processes, and the total output of these processes are the output of the relevant unit. Parallel network DEA model have been developed based on certain and confidence data and they do not have the ability to deal with uncertainty about inputs and outputs data. In this paper we are going to provide a robust parallel network DEA model and attempt to describe and analyze the new model with a real case study. The proposed model can evaluate the performance of parallel structure and it has the ability to deal with uncertainty in the data. The results show that with increasing levels of uncertainty and decreasing of reliability, efficiency of decision-making units will be further reduced. Increasing of discrimination power is another advantage of the proposed model. Also traditional network models show more deviations in the results in compare with robust network model when the data are changed.

Cross docking is a logistics strategy that is now used by a large number of firms in the various industries. Cross docking effectively a significant reduction in transport costs, without increasing inventory and at the same time maintain the level of service to customers. It also can cross dock order to reduce cycle time, improve flexibility and responsiveness lead distribution network. In this study, there are considers a truck scheduling problem in a multiple cross docks while there is temporary storage in front of the shipping docks with a limited capacity and delay times. Receiving trucks can intermittently move in and out of the docks during the time intervals between their task execution, in which trucks can enter to any of the cross docks. Thus, a mixed-integer programming (MIP) model for multiple cross docks scheduling is developed inspired by models in the body of the respective literature. Its objective is to minimize the sum of tardiness or maximize the throughput of the crossdocking system. Moreover, additional concepts considered in the new method is multiple cross docks with a limited capacity and two types of delay times. In this study, first the software GAMS is used to achieve the optimal solution, then according to the complexity of the model and increase the search space to achieve precise answer on the big issues, meta heuristic algorithm for as Imperialist Competitive Algorithm (ICA) is used to solve model and Numerical results are analyzed.

This research seeks to develop resource and goals allocation planning models in a focused decision-making environment with a parallel multi-component network structure in a case study. In such an environment, the problem of resource and goals allocation planning, the determination of the input and output of each of the decision-making units in achieving the goals of the system is such that the size of the units is taken into account and the efficiency of the components and the whole system are improving. To design such a model, Islamic Azad University provincial units were studied. Finally, using a multi-objective linear programming based on data envelopment analysis, a model was designed that, while increasing the efficiency of provincial units and Islamic Azad University with six inputs and eight outputs, two uncontrolled inputs and undesired outputs, result in resource allocations and quantitative targets proportional to the size of units.

The standard data envelopment analysis (DEA) method assumes that the values for inputs and outputs are exact. While DEA assumes exact data, the existing imprecise DEA (IDEA) assumes that the values for some inputs and outputs are only known to lie within bounded intervals, and other data are known only up to an order. In many real applications of DEA, there are cases in which some of the input and output variables belong to bounded and discrete sets and the others are known to lie within bounded intervals. In this paper a new variety of imprecise data in the DEA has been faced. The proposed approach transforms a nonlinear DEA model to an equivalent linear programming problem. Upper and lower bounds of efficiency scores of operational units are defined. A real case of commercial banks is provided to illustrate the applicability of the approach.

In today’ s competitive markets, customer satisfaction is the main goal for all managers, and at a hospital what the most adapted bed allocation policy is, a research subject is for many researchers to gain more customer satisfaction. Researchers have presented a variety of methods on allocating beds to the patients, based on their type of customers and problems. In this paper, goal programming method was proposed for optimal allocation of hospital beds. The proposed method was applied to the case study of Emergency Department of Shahid Madani Hospital in Tabriz, Iran. In this study in order to optimize the allocation of beds in the department, four goals are targeted: minimize the time taken of the bed by the patient, minimize the waiting time, maximize the use of human potential energy, and minimize the number of empty beds. The obtained results from case study were analyzed, finally.