Journal of Operational Research in its Applications
Year:2017 | Volume:14 | Issue:1 (52)|Papers Count:9

In this paper, an optimization method is used for solving a fractional optimal control problem with significant applications in chemical engineering. Considered optimal control is the control system of the isothermal continuous stirred tank reactors. The Riemann-Liouville fractional derivative is used to describe the mathematical model of control system. For solving the fractional optimal control problem, at first by using different order of moments we achieve an optimal control problem in space of moments and then by using discretization technique of variables we will get a semidefinite optimization problem. Finally, by solving the equivalent optimization problem we obtain the solution of the considered fractional optimal control problem.

In order to improve service quality and satisfy specific delivery requests from different kinds of customers, recently suppliers are tending to afford more efficient and convenient distribution services rather than follow traditional approaches. Customers may have changed preferred hours, and wholesalers must distribute goods in different time windows. In this article, for the first time the Time Depended Petrol Station Replenishment Problem with Time Windows (TDPSRPTW) is considered and a new mathematical programming and a column generation method is proposed to solve for it. This version of the PSRPTW is motivated by the fact that in some circumstances, traffic conditions play an important role and cannot be ignored in order to perform a realistic optimization. The TDPSRPTW consists in optimally routing a limited heterogeneous fleet of vehicles of fixed capacity during of working day when the traveling times are dependent on time, that is, the time of the day when the trip on a leg was initiated. The delivery to a customer must also satisfy the customer’s delivery time window. The optimization consists in finding the solution that minimizes the number of tours and the total travel time. The speed distributions, from which the travel times can be calculated, are supposed to be known at the beginning of the optimization. The algorithm was tested in a set of instances including 15 stations proposed by Cornillier et al. in 2009 and it has proven to be highly competitive in term of the solution quality.

worldwide rivalry of commerce surroundings lead organizations to focus on selecting the best project portfolio among available projects utilizing their scarce resources in the most effective manner. To accomplish that, organizations should consider the uncertainty intrinsic in projects on the basis of an appropriate valuation techniques with regard to flexibility in investment decisions making within an optimization framework. In this research, the problem of project selection under uncertain environment is formulated by using robust optimization model for dealing with the complexities and uncertainties regarding the construction of a project portfolio. First of all, a general mathematical formulation which can address compound real option valuation is presented which accounts for managerial flexibility, is employed to correct the deficiency of traditional discounted cash flow valuation that excludes any form of flexibility. Then, A project selection model is presented by robust optimization, which are effective for solving problems under uncertainty. Finally, by supposing budget of the organization to be rare, the model maximize the n_fold compound options formula as the objective function solve according to combinatorial robust optimization algorithm. An example is provided to illustrate the proposed decision approach.

One of the most important problems in research and applicable field of production management is a suitable scheduling for different operations. So, there are many approaches for job shop or non- job shop scheduling problems. Since job shop scheduling problems (JSP) belong to NP-Hard class, some met heuristics method such as Tabu Search, Simulated Annealing, Genetic Algorithm and Particle Swarm Optimization are used in different papers. This paper tries to solve this problem by using these algorithms and compares their conclusions. So, some problems with different sizes are used and they are analyzed by time, quantified and parametric approaches.

Relief logistics is one of the most effective tools in the crisis management. Growing number of global disasters and crisis events in Iran in the last decade promote the importance of relief logistics planning. Facility location and the resources allocation are basic Instruments in relief logistics operations. In this paper, covering radius of facilities are considered discrete variable With a step function of cost. The proposed maximal covering location model determines where, what kind and at what capacity the relief facility will be established. A multi-objective formulation is proposed for this discrete problem and a multi-objective robust programming approach is developed. Then the problem solved by the GAMS software and the model has been tested by using an emergency example. The results imply a good capability of the model in improving the population coverage and reducing life-loss during large-scale emergencies.

This study was conducted to investigate the overall efficiency of production and transmission of electricity. For this purpose, sixteen areas of production and transmission of electricity industry of country were selected as decision-making units. Based on Khalili and Shahmiri model, one mathematical model was proposed that includes two stages of the production and transmission of electricity. In the proposed model at the end stage of the production, output variables were considered which do not enter to next stage of production. In the second phase, in addition to inputs received from production stage, other input variables were considered that are not included to model from previous stage. In the first stage, variables of domestic consumption of power stations and consumed fuel as input variables maximum production load variables, special production, gross production, efficiency, nominal power of power station, and practical power of power station were considered as outpour variables. In the second stage, variables of maximum production load, especial production, gross production, capacity of power transmission posts and transmission lines were considered as input variables, delivered energy variable was considered as desired output, and energy losses were considered as undesired output variable. Results of two stages indicated that districts of Azerbaijan, Isfahan, Tehran, Khorasan, Khuzestan, Semnan, Kerman, and Fars were efficient.

In basic DEA models, one faces the challenge of zero and unequal weights for evaluating each decision-making unit (DMU). On the other hand, for measuring the efficiency in these models, the system is considered as a black box disregarding internal processes. One of the strategies applied to deal with this problem is to use common weights of each input/output in all DMUs. In practice, most decision-making units evaluated with each other including internal processes with different structures referred to as network structure. Similarly, there exists such challenge for the network structures. Kao et al. proposed the common set of weights in a two-stage network structure. This paper aims at proposing the same strategy for network structures in the general case to deal with the above problem. The modelsare applied on kao’s examples to illustrate the results.

In many industries, manufacturers for various reasons, have to collect products are used by customers. Then, depending on the situation returns, required to process the decision taken on the product. In this paper the problem of optimizing inventory control and product planning in the environment has been integrated reverse logistics. Logistics network to consist of two stages. In the first stage returns using the qualitative thresholds defined, separated and sent to the appropriate lines recovery or disposal, are subject to quality inspection. In the second stage having different amounts sent to the line, a mixed integer optimization algorithm (MILP) to lower the total cost of our network. Model with a view to minimizing the costs, the type of problems that are NP-Hard, the problem increases exponentially. Therefore, in this study, Genetic Algorithm is used to solve the model.