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Given an instance of the minimum cost flow problem, a version of the corresponding inverse problem, called the capacity inverse problem, is to modify the upper and lower bounds on arc flows as little as possible so that a given feasible flow becomes optimal to the modified minimum cost flow problem. The modifications can be measured by different distances. In this article, we consider the capacity inverse problem under the bottleneck-type and the sum-type weighted Hamming distances. In the bottleneck-type case, the binary search technique is applied to present an algorithm for solving the problem in O(nm log n) time. In the sum-type case, it is shown that the inverse problem is strongly NP-hard even on bipartite networks

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Over five decades have passed since the first wave of robust optimization studies conducted by Soyster and Falk. It is outstanding that real-life applications of robust optimization are still swept aside; there is much more potential for investigating the exact nature of uncertainties to obtain intelligent robust models. For this purpose, in this study, we investigate a more refined description of the uncertain events including (1) event-driven and (2) attribute-driven. Classical methods transform convex programming classes of uncertainty sets. The structural properties of uncertain events are analyzed to obtain a more refined description of the uncertainty polytopes. Hence, tractable robust models with a decent degree of conservatism are introduced to avoid the over-protection induced by classical uncertainty sets.

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The present study aims to identify the factors affecting the behavior of customers’ use of ebanking services of Tejarat Bank in Tehran. A qualitative method and an in-depth interview have been applied to achieve the research goal. The information and data collected from the interviews have been analyzed using open coding and axial coding. Then, the initial indicators of the model of using e-banking services were identified. In the next step, e-banking experts and specialists were asked to comment on the indicators obtained from the interviews, using the Delphi technique. The final results demonstrated that trust in banks, perceived security, ease of use, perceived utility, the impact of society, and perceived risk are considered as indicators affecting users’ behavioral intentions and ultimately their use behavior.
 

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Following the increasing growth of urbanization in recent decades in Iran, housing has become one of the most critical issues in the country. In this regard, mass production of housing has received more attention, and residential complexes can be considered a physical manifestation of the idea of mass housing in cities. Operational efficiency in residential construction production systems is evaluated based on average house completion time, the number of houses under construction, and processing time of activities. However, these systems are prone to non-uniformity problems and suspensions resulting from different variables, such as adverse weather conditions, workplace accidents, fluctuations in house demand, and rework. The purpose of this research is to show the effect of reprocessing on the manufacturing process.  In this study, the rework parameter and the variables of frequency, duration, and time of call-back have been considered. Also, the effects of these parameters on tangible performance criteria have been investigated. In this regard, we apply the combined approach of discrete-event simulation and computational modeling; then, we compare the results. Measurements show that the systems fragmented by repeated and short repetitions while referring to early are in optimal performance.

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The traditional inverse data envelopment analysis (IDEA) models assess specific performance metrics in relation to changes in others, without taking into consideration the existence of random and undesirable outputs. This study presents a novel inverse DEA model with random and undesirable outputs, enabling the estimation of some random performance measures for changes of other random measures. The proposed chance-constrained inverse DEA model integrates both managerial and natural disposability constraints. By using the introduced approach, the estimation of natural disposable random inputs is presented for changes in random desirable outputs. Also, undesirable outputs are assessed for the perturbation of managerial disposable random inputs while the stochastic efficiency is maintained. The models are solved as  linear problems, with a numerical example provided to illustrate their application. The findings indicate that this approach is effective for evaluating efficiency and performance metrics in scenarios involving random and undesirable outputs.