In order to interstigate the factorial structure and Psychometry features of social PROBLEM- SOLVING questionnaire (long revised form). 242 male students and 248 female students of Islamic University Roudehen Branch were tested. Analysis of the main components were approved 5 theoretical factors of Social PROBLEM- SOLVING (including positive ovientation toward PROBLEM, negative orientation toward PROBLEM, logical SOLVING of PROBLEM, avoidance Style and impulsion/ Carelessness Style) and the resuit of approval factorial Structure was Supported. Correlation coefficients between factorsof social PROBLEM- SOLVING questionnaire and 24- question Scale PROBLEM- SOLVING methods questionnaire of Kasidi and Long Shows the convergent reliability of- this questionnaire. (Cronbach reliability coefficients equaled 0.80, 0.90, 0.84, 0.71 for factors of positive orientation toward PROBLEM, logical SOLVING of PROBLEM, avoidance style and impulsion/ Carelessness style.) The results of this research show the consistency of factorial structure of Social PROBLEM- SOLVING measurement among Iranian students.

The study aims at the preliminary normalization of Social PROBLEM–SOLVING INVENTORY. To this end, 242 boy and 248 girl students from Islamic Azad University, Roudehen branch were administered Social PROBLEM–SOLVING INVENTORY using a 24-item scale by D’zurilla, Nezu, and Maydeu-Olivares. In the analysis of main components, five theoretical factors of social PROBLEM-SOLVING (positive orientation toward PROBLEM, negative orientation toward PROBLEM, rational solution of a PROBLEM, avoidance and inaccuracy style) were confirmed and the results of confirmatory factorial analysis from the obtained factorial structure were supported. Correlation coefficients are indicative of a convergence validity of this questionnaire. The coefficients of Cronbach’s Alpha for the factors of positive orientation toward PROBLEM, negative orientation toward PROBLEM, rational solution of a PROBLEM, avoidance and inaccuracy style were 0.80, 0.86, 0.90, 0.84 and 0.71, respectively. The results show the stability of social PROBLEM–SOLVING INVENTORY and its validity for measuring its social PROBLEM–SOLVING questionnaire among Iranian students.

T he present study investigated the reliability and validity of the Social PROBLEM SOLVING INVENTORY-Short form (SPSI; D’Zurilla et al., 2002). Four hundred and three students (245 females and 158 males) from Islamic Azad University (age range 18-48 years) an- swered Beck's Depression INVENTORY (Beck et al., 1961), the Short Depression-Happiness Scale (Joseph et al., 2004), Satisfaction With Life Scale (Diener et al., 1985), and Social Phobia INVENTORY (Connor et al., 2000). SPSI in- ternal consistency and test-retest coefficients were 0.86 and 0.65 respectively and its correlation coefficients with the following were: Beck's Depression INVENTORY (r=-0.48), Depression-Happiness (r=0.38), Satisfaction With Life Scale (r=0.18) and Social Pho- bia (r=-0.39). The findings confirmed the construct and concurrent validity of SPSI and indicated the need for further studies.

In this paper, we present an integrated version of the Ng model and Zhou and Fan model [W. L. Ng, A simple classifier for multiple criteria ABC analysis, European Journal of Operation Research, 177 (2007) 344-353; P. Zhou & L. Fan, A note on multi-criteria ABC INVENTORY classification using weighted linear optimization, European Journal of Operation Research, 182 (2007) 148-18491]. The model that Ng [1] offered, hereafter called the Ng-model, in spite of its advantages may lead to a situation in which the weights of some criteria in relation to an item would not play any role in determining overall score that item. Also, the scale transformation that he applied for transforming the measures of items under criteria into interval 0-1 is not suitable for the small-scale measures. On the other hand, for the R INVENTORY item, the Zhou and Fan model [2], hereafter called the ZF-model, should be solved through a linear optimizer 2R times in which an INVENTORY manager might has no any background in regard with optimizer. Furthermore, when number of items is large, the computing time would increase. Therefore, in order to remove drawbacks of both the approaches, an integrated model is presented in which objective functions are the same ZF-method but its constraints is similar to Ng model. At last, results obtained from applying the proposed model in an illustrative example are compared with Ng and ZF-models.

In this paper a multi-commodity multi-period INVENTORY routing PROBLEM in a two-echelon supply chain consisting of a manufacturer and a set of retailers has been studied. In addition to INVENTORY management and distribution planning, production planning has also been considered in the above PROBLEM. The objective is to minimize total system cost that consists of production setup, INVENTORY holding and distribution costs. The commodities are delivered to the retailers by an identical fleet of limited capacity vehicles through direct shipment strategy. Also it is assumed that production and storage capacity is limited and stockout is not allowed. Since similar PROBLEMs without distribution planning are known as NP-hard, this is also an NP-hard PROBLEM. Therefore, in this paper, a new improved particle swarm optimization algorithm has been developed consisting of two distinguished phases for PROBLEM SOLVING. First, the values of binary variables are determined using the proposed algorithm and then, the continuous variables are calculated by SOLVING a linear programming model. Performance of the proposed algorithm has been compared with genetic and original particle swarm optimization algorithms using various samples of random PROBLEMs. The findings imply significant performance of the proposed algorithm.

One of the fundamental human cognitive processes is PROBLEM SOLVING. As a higher-layer cognitive process, PROBLEM SOLVING interacts with many other cognitive processes such as learning, decision making and analysis. The purpose of this research was to prepare norm scores and determine the validity and reliability of parker's PROBLEM SOLVING INVENTORY (PPSI) in B.A. college students. The samples were 360 persons that were selected in Guilan Payame Noor University by cluster random sampling method and completed PPSI and Cassidy & Long PROBLEM SOLVING Style Questionnaire. The results of the factor analysis revealed that the INVENTORY with four factor explained 59.18% of the total variance. Also, Results showed that Cronbach Alpha coefficients 0/84, test–retest reliability 0.81 and split half was 0.63 and Coefficient of correlation between with Cassidy & Long PROBLEM SOLVING Style Questionnaire was 0.65. The research emphasize that PPSI is a reliable and suitable instrument for measuring PROBLEM SOLVING among Iranians.

In order to exploring the factor structure and psychometric properties of the Social PROBLEM SOLVING INVENTORY (Revised Short-Form) 253 male and 275 female students of the Islamic Azad University province were administered the social PROBLEM SOLVING INVENTORY (2002) and Big Five INVENTORY (1991). Principal component analysis confirmed the theoretical five factors of the social PROBLEM SOLVING (including positive PROBLEM orientation, negative PROBLEM orientation, rational PROBLEM SOLVING, avoidance style and impulsive/carelessness style) and findings of confirmatory factor analysis provided support for derived factor structure. Correlations between the factors of social PROBLEM SOLVING INVENTORY and Big Five Factors INVENTORY provided empirical support for the convergent validity of the social PROBLEM SOLVING INVENTORY. Cronbach's coefficient for positive PROBLEM orientation, negative PROBLEM orientation, rational PROBLEM SOLVING, avoidance style and impulsive/carelessness style were 0.68, 0.77, 0.71, 0.80 and 73 respectively.The findings support the consistency of the factor structure of the social PROBLEM SOLVING INVENTORY, and its validity in measuring social PROBLEM SOLVING in Iranian students.

In this paper, a supplier-retailer transportation system is investigated as a two-echelon environment. There is a single location in each echelon; the unique supplier at the first echelon has to replenish the retailer's warehouse at the second echelon. By the way, the shortage situation should be avoided. For this situation, a model is provided based on the traditional EOQ model. The INVENTORY costs, ordering costs, transportation cost, etc. are considered in this model. Multistage shipment during each ordering period with a specific number of vehicles is allowed in the proposed model. The model's decisions involved in managing the system include design decision (i.e., optimized number of required vehicles), as well as operation decision (i.e., optimized order quantity and number of trips and transportation stages). A solution algorithm is proposed for the proposed model and implemented with C#.Net which is available and applicable on the website, namely www.PedramSahba.com. A numerical example and sensitivity analysis are presented for exposing the model and algorithm capability and then verifying and validating the model.

This paper considers a multi-period, multi-product INVENTORY-routing PROBLEM in a two-level supply chain consisting of a distributor and a set of customers. This PROBLEM is modeled with the aim of minimizing bi-objectives, namely the total system cost (including startup, distribution and maintenance costs) and risk-based transportation. Products are delivered to customers by some heterogeneous vehicles with specific capacities through a direct delivery strategy. Additionally, storage capacities are considered limited and the shortage is assumed to be impermissible. To validate this new bi-objective model, the ε-constraint method is used for SOLVING PROBLEMs. The ε-constraint method is an exact method for SOLVING multi-objective PROBLEMs, which offers Pareto's solutions, such as meta-heuristic algorithms. Since PROBLEMs without distribution planning are very complex to solve optimally, the PROBLEM considered in this paper also belongs to a class of NP-hard ones. Therefore, a non-dominated sorting genetic algorithm (NSGA-II) as a well-known multi-objective evolutionary algorithm is used and developed to solve a number of test PROBLEMs. In this paper, 20 sample PROBLEMs with the -constraint method and NSGA-II are solved and compared in different dimensions based on Pareto's solutions and the time of resolution. Furthermore, the computational results showed the better performance of the NSGA-II.