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In many problems, it is necessary to take into account monotonic relations. Monotonic (isotonic) Regression (MR) is often involved in solving such problems. The MR solutions are of a step-shaped form with a typical sharp change of values between adjacent steps. This, in some applications, is regarded as a disadvantage. We recently introduced a Smoothed MR (SMR) problem which is obtained from the MR by adding a regularization penalty term. The SMR is aimed at smoothing the aforementioned sharp change. Moreover, its solution has a far less pronounced step-structure, if at all available. The purpose of this paper is to further improve the SMR solution by getting rid of such a structure. This is achieved by introducing a lowed bound on the slope in the SMR. We call it Smoothed Slope-Constrained MR (SSCMR) problem. It is shown here how to reduce it to the SMR which is a convex quadratic optimization problem. The Smoothed Pool Adjacent Violators (SPAV) algorithm developed in our recent publications for solving the SMR problem is adapted here to solving the SSCMR problem. This algorithm belongs to the class of dual active-set algorithms. Although the complexity of the SPAV algorithm is $𝑂(𝑛^2)$, its running time is growing in our computational experiments almost linearly with $𝑛$. We present numerical results which illustrate the predictive performance quality of our approach. They also show that the SSCMR solution is free of the undesirable features of the MR and SMR solutions.

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This is a special issue of the Iranian Journal of Operations Research composed of some of the invited talks presented at the 10th International Iranian Operations Research Society (IORS) Conference held in University of Mazandaran, Babolsar, May 3-5, 2017. The IORS conference is an annual event and is the main forum for presenting new theoretical and applied developments of OR within Iran. In recent years, international participation has been promoted to enhance cooperation among internal and external researchers. There were over 400 participants with 186 accepted talks and 138 poster presentations. The selected papers were reviewed going through the usual reviewing process and 7 papers were finally accepted for publication in the current issue.
In the first paper, Adil Bagirov and Sona Taheri develop an algorithm based on optimization for clustering data using an $𝐿_1$-norm. In doing this, they find the Clarke stationary points of the clustering problem and use the points for an effective clustering of data. Comparative test results are presented.
In the second paper, Günter Karl Franz Bärwolff, Minjie Chen and Hartmut Schwandt, concerned with an efficient planning of public transportation systems, propose a simulation of pedestrian flow behaviors by presenting both macroscopic and microscopic models of the pedestrian dynamics. The authors provide comparative test results of the proposed simulation with a real video clip.
The third paper, by Oleg Burdakov and Oleg Sysoev, presents the development of an active-set algorithm based on duality for solving a special regularized slope-constrained monotonic regression problem. The authors show competitive complexity results both theoretically and in practice, while illustrating desirable features of the obtained solutions.
The fourth paper by Fateme Kouchakinejad and Alexandra Šipošová is concerned with the notion of ordered weighted averaging operators and gives a review of their applications in decision making. The authors also give some generalizations of the operators along with illustrative examples.
The last three articles are concerned with certain applied problems in Sweden, Netherlands and Oman.
As the fifth paper, Peter Lohmander presents some results for a stochastic optimal control approach to the management of the wildlife. The author first derives general optimal control and value functions, and then makes use of relevant functions for the moose management in Sweden.
Cornelis Roos discusses a mathematical model developed for protecting the Netherlands from possible incurring flood damages. The author has been seriously involved with the development of the model in the past decade and has been shown to be successful in using the model in the Netherlands to set up legal safety standards in the country.
Finally, Chefi Triki, Abdulwahab Al-Maimani and Jamila Akil propose a ridesharing model for use in Muscat, Oman, to control the growing traffic congestion in the city. They provide a detection support system for the model. The set of feasible routes of the ridesharing is found by solving a constrained mathematical programming problem. Then, a bin packing problem is modelled and solved to find the optimal routes. Illustrative examples are worked through.

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Predictive analytics is an area of statistics that deals with extracting information from data and using
that to predict trends and behavioral patterns. Many mathematical models have been developed and
used for prediction, and in some cases, they have been found to be very strong and reliable. This
paper studies different mathematical and statistical approaches for events prediction. The main goal
of this research is to design and construct a hybrid prediction method for events prediction, based on
Logistic Regression (LR) method and Data Envelopment Analysis (DEA) technique. In this study, a
novel hybrid algorithm was developed, and considering the kind of collected data, LR method was
applied for input selection, and the capability of the additive (ADD) model of DEA was examined to
predict the occurrence or non-occurrence of the events. To apply the proposed approach, the selected
disease for the case study was a stroke. The results showed that any patient who was placed on the
frontier has had a stroke by one or more risk factors. On the other hand, the observations that were
not on the frontier had not suffered from a stroke. The overall accuracy of 88.5 percentages was
obtained for the developed method
 

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Regression is a statistical technique used in finance, investment, and several other domains to assess the magnitude and precision of the association between a dependent variable (often represented as Y) and a set of other factors (referred to as independent variables). This work introduces a linear programming approach for constructing regression models for Neutrosophic data. To achieve this objective, we use the least absolute deviation approach to transform the regression issue into a linear programming problem. Ultimately, the efficacy of the suggested approach in resolving such problems has been shown via the presentation of a concrete illustration.
 

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Background & Aim: Management style in medical care is very important in the guidance and performance of the treatment team. This is effective in improving the quality and reducing the treatment time and increasing the productivity of organizations. Using different management styles in organizations will lead to different results in employee performance and organizational productivity. The present study was conducted to determine the relationship between management styles and organizational productivity in the Children's Medical Center of Tehran University of Medical Sciences.
Methods & Materials: This study is predictive correlational in nature. It was conducted on 112 nurses and managers working in the treatment department who were selected based on the random sampling method from. The data were analyzed through conducting ANOVA, t-test, Pearson correlation, and linear regression analysis in SPSS version 16.
Results: The mean score of applying authoritarian style was 4.54, consultative style 5.54, collaborative style 5.55, relational style 5.64 and transformational style 5.59. There was a statistically significant and direct relationship between management style and organizational productivity (p<0.05), (r=0.760). The determination coefficient of linear regression modeling was used to predict the changes in organizational productivity based on the management style indicated that 87.2% of the changes in organizational productivity was explained under the influence of independent variables.
Conclusion: Due to the direct relationship between the use of management styles and organizational productivity, it is not possible to use one style in organizations and the use of different styles in different departments will improve the productivity of the organization.
Background & Aim: Management style in medical care is very important in the guidance and performance of the treatment team. This is effective in improving the quality and reducing the treatment time and increasing the productivity of organizations. Using different management styles in organizations will lead to different results in employee performance and organizational productivity. The present study was conducted to determine the relationship between management styles and organizational productivity in the Children's Medical Center of Tehran University of Medical Sciences.
Methods & Materials: This study is predictive correlational in nature. It was conducted on 112 nurses and managers working in the treatment department who were selected based on the random sampling method from. The data were analyzed through conducting ANOVA, t-test, Pearson correlation, and linear regression analysis in SPSS version 16.
Results: The mean score of applying authoritarian style was 4.54, consultative style 5.54, collaborative style 5.55, relational style 5.64 and transformational style 5.59. There was a statistically significant and direct relationship between management style and organizational productivity (p<0.05), (r=0.760). The determination coefficient of linear regression modeling was used to predict the changes in organizational productivity based on the management style indicated that 87.2% of the changes in organizational productivity was explained under the influence of independent variables.
Conclusion: Due to the direct relationship between the use of management styles and organizational productivity, it is not possible to use one style in organizations and the use of different styles in different departments will improve the productivity of the organization.