Journal of Operational Research in its Applications
Year:2020 | Volume:16 | Issue:4 (63) |Papers Count:10

In a network that has the potential to block some paths, choosing a reliable path, so that its survival probability is high, is an important and practical issue. The importance of this issue is very considerable in critical situations such as natural disasters, floods and earthquakes. In the case of the reliable path, survival or blocking of each arc on a network in critical situations is an uncertain parameter that is estimated and among all the paths that connect two distinct points, the route that is most likely to be survived is selected. Since decisions about choosing routes are dependent on other factors, such as distance, cost, or duration of the route, each of these indicators can be added to the problem as some constraints. For modeling the problem, considering the probability of survival of any arc, a reliable path is defined as a path that the product of probability of its arcs is as close as possible to one. Then the logarithm function is used to linearize the probability multiplication and the problem model is converted to the constrained shortest path model form. Finally, an algorithm is provided to solve the proposed model, which in any iteration using logical cuts, eliminates the obtained suboptimal paths and approaches the optimal solution. The results of applying this method on some networks with different structure and size show that the proposed algorithm is able to achieve a path that can be expected with high probability of surviving in critical conditions and its distant does not exceed the specified limit.

The location-routing problem is one of the combined problems in the area of supply chain management that simultaneously make decisions related to location of depots and routing of the vehicles. In this paper, the single-depot capacitated location-routing problem under uncertainty is presented. The problem aims to fi nd the optimal location of a single depot and the routing of vehicles to serve the customers when the parameters may change under different circumstances. To get closer to real-world situations, travel time of vehicles, the fi xed cost of using vehicles and customers’ demand are considered as a source of uncertainty. A combined approach including robust optimization and stochastic programming was presented to deal with the uncertainty in the problem at hand. For this purpose, a mixed integer programming model is developed and a numerical example is provided to illustrate the solution procedure on the related network. To solve the problem, Variable Neighborhood Search is also proposed. The results obtained from solving sample problems using an exact and heuristic algorithm represent the acceptable performance of the proposed algorithm.

In this paper we propose a model for coordinated planning of power and natural gas systems, as a part of electricity supply chain. This model takes into account costs and constraints of both systems, and with hiring simplifications and linearization methods transforms initially nonlinear formulation to a mixed integer linear programming (MILP) problem. Natural gas would be assumed steady state, and the nonlinear Weymouth equation is linearized using a piecewise linear function. The assumed electricity network model, as a linearization of AC power flow, would be DC power flow. As it will be shown in the paper, these assumptions will combine computational simplicity with an acceptable level of accuracy, this quality makes this model applicable to, and suitable for large real size problems of this kind.

Providing a Mathematical Model for Evaluating Resilient Suppliers and Order Allocation in Automotive Related Industries

In the current decade, determining the most appropriate supplier as a strategic factor in the supply chain has attracted lots of consideration. On the other hand, organizations do necessary measures to implement green supply chain management in order to improve environmental and economic performance. An important way to implement green supply chain management, could be revising the method of purchase. Existing Vendor managed inventory is a business initiative that providers can maintain the existing units of purchased product. In this study, a Multi-objective mathematical model has been developed to solve a VMI problem in two echelon supply chain with suppliers and a buyer. To establish a green supply chain, this model minimizes the cost of VMI system and maximizes the value of the goal. Also, the two objectives problem has been one-objective using Global criteria method and a numeric example has been provided to illustrate adequacy of this strategy. The algorithm implemented and shows good results in reasonable time complexity. It also helps decision makers and managers in the optimal, efficient and low-cost solution (number of products and number of retailers and recapitalization cycles, etc. ) in VMI discussion.

Due to the many risks involved in the supply chain, and the high costs associated with damage to the supply chain, risk identification and evaluation should be a top priority in risk management programs in organizations. Risk assessment and ratings determine the superiority of each risk based on the relevant indicators and thus provide an appropriate response to each risk. In this regard, this research has been addressed a two-objective mathematical model with a multi-source supply policy and lateral transshipment approach to the assessment of the risk of supply chain disruptions. A mathematical model, with emphasis on cost reduction and shortage value, is designed by considering the disturbance parameter on the chain. The model is solved using meta-heuristic algorithms, genetic algorithm and simulated annealing. . Next, we investigate the impact of four disturbances on the costs of the supply chain and the disruptions are evaluated and ranked based on the costs involved in the chain. The results showed that disruption of natural disasters was recognized as the most critical disruption. The findings indicate that the proposed approach is an effective framework for identifying effective parameters and prioritizing the risk of supply chain disruptions.

Cost efficiency evaluation is a very important and applicable issue in Data Envelopment Analysis (DEA). In this paper, the classical cost efficiency model in which all the input prices are known and fixed for each decision making unit is developed via undesirable outputs with the weak disposability axiom. The proposed model is a nonlinear model under the variable returns to scale condition, which is linearized for the purposes of easy solving. In order to simulate the proposed model and demonstrate its advantages and application capabilities, 56 electricity producing thermal power plants were studied based on data set that is presented in 2015. According to the simulation results based on the present method, the combined cycle and steam power plants had the highest cost efficiencies under the both assumed conditions, i. e., the constant returns to scale and the variable returns to scale. Moreover, the average cost efficiencies of the power plants were 36% and 54% under the assumed conditions of constant returns to scale and variable returns to scale, respectively.

Technology development project is a type of investment project and it is important to identify the performance indicators and planning for the correct investment. The purpose of this research is the development of indicators of portfolio success, accurate analysis of the effects of indicators on each other and the achievement of a proper investment model. In this research, the success criteria of technology development projects with a new approach from the point of view of project, plan and portfolio differentiation have been identified and categorized, their importance has been measured through statistical tests and the relationship between success criteria in each The project, plan and portfolio levels are analyzed using the DEMATEL method. The ANFIS algorithm is used to achieve optimal decision making for investment. For this purpose, investment scenarios based on their criteria and weights are considered as network inputs. Finally, based on 9 samples of 7 technology development projects, using MATLAB software and with the method Grid Partitioning, FCM, Subtractive Clustering The utility function is drawn up. From the results of this research, we can identify and categorize the success criteria of technology development projects and identify the most influential and most important criteria in each of the project, plan, and portfolio groups. Also, the effect and relationship of success metrics with each other are presented through "network mapping" charts and the results of the comparative neuro-fuzzy inference system provide the utility function of investment as a criterion for decision-making in the projects. This methodology can be generalized to the plan and the portfolio.