Integrated supply chain includes different components of order, production and distribution and it plays an important role on reducing the cost of manufacturing system. In this paper, an integrated supply chain in a form of multi-objective decision-making problem is presented. The proposed model of this paper considers different parameters with uncertainty using trapezoid numbers. We first implement a ranking method to covert the fuzzy model into a crisp one and using multi-objective particle swarm optimization, we solve the resulted model. The results are compared with the performance of NSGA-II for some randomly generated problems and the preliminary results indicate that the proposed model of the paper performs better than the alternative method.

Road construction projects are considered as the most important governmental issues since there are normally heavy investments required in such projects. There is also shortage of financial resources in governmental budget, which makes the asset allocation more challenging. One primary step in reducing the cost is to determine different risks associated with execution of such project activities. In this study, we present some important risk factors associated with road construction in two levels for a real-world case study of rail-road industry located between two cities of Esfahan and Deligan. The first group of risk factors includes the probability and the effects for various attributes including cost, time, quality and performance. The second group of risk factors includes socio-economical factors as well as political and managerial aspects. The study finds 21 main risk factors as well as 193 sub risk factors. The factors are ranked using groups decision-making method called linear assignment. The preliminary results indicate that the road construction projects could finish faster with better outcome should we carefully consider risk factors and attempt to reduce their impacts.

The generalized assignment problem (GAP) is a unique extended form of the Knapsack problem, which is tremendously practical in optimization fields. For instance, resource allocation, sequencing, supply chain management, etc. This paper tackles the GAP in uncertain environment in which the assignment costs and capacity of agents are fuzzy numbers. Two models are presented for this problem and a novel hybrid algorithm is offered using simulated annealing (SA) method and max-min fuzzy in order to obtain near optimal solution. Computational experiments validate the efficiency of proposed method.

The study examined the market entry strategies of multinational services companies into Ghana’s service sector and the linkages to firm level performance after entry. Literature was reviewed on market entry strategies, internationalisation, globalisation of service firms and resource-based theory. The study adopted a combination of both quantitative and qualitative research approaches in this study. The qualitative approach was for deeper enquiry and quantitative for empirical testing. The study found that firm specific factors affect the market entry strategy while the entry strategy also affects performance after it enters the market. Home country factors and the features of services are also seen to moderate on the effects mentioned. Respondents also indicated that the features of services were an industry wide issue not so much consideration is given to it.

Kouroush Jenab, Donald Staub, Mark Rajai PDF (233 K)
Abstract: Students at risk of dropping out of Science, Technology, Engineering, and Mathematics (STEM) programs often display signs that indicate they are at risk. A need exists to identify at risk STEM students early and to develop and implement effective intervention strategies that utilize the Total Quality Management (TQM) approach. Most of all, a database system is needed to track this early intervention process, if retention rates are to be improved. To address this need at a small community college in North Carolina, a system was developed and underwent a pilot study in Fall 2009 and Spring 2010. The two pilot groups were compared to the two control groups to identify differences in retention, course credit completion rates, and grade point averages (GPA). The first pilot group displayed no significant differences, while the second pilot group displayed significant differences in most of the areas analyzed in the study, indicating a database system can be used to improve STEM student retention. While the second of the two pilot groups displayed promising results, managerial and logistical issues, such as less than optimal instructor involvement, impeded success were identified. This paper will describe the design, implementation, and the preliminary results of this study and outlines the need for further research that confirms these preliminary findings.

Successful organizations share some identical factors that pave the way for their success. Among these factors, strategic management is the key to success for organizations to contribute more to the competitive world market of today. In this respect, the pivotal role of outsourcing cannot be denied. This research parallelizes the criteria affecting the outsourcing success as presented in Elmuti model with the Balanced score card method in the Tose & apos; e Ta & apos; avon Bank. In this research, questionnaires and interviews with experts helped determine the strategic goals at four perspectives of balanced score card method (at Tose & apos; e Ta & apos; avon Bank) and the relative weights were computed for each of balance score card (BSC) perspectives by using AHP method. As the next step, the indexes were prioritized by applying the quality function development(QFD) technique and considering strategic goals at four perspectives in section "WHAT" and the outsourcing success criteria of Elmuti model in section "HOW". At the end of algorithm, the results are compared with the Elmuti method. Based on the results, the hybrid proposed technique seems to perform better than Elmuti.

Parameter design or robust parameter design (RPD) is a statistical methodology used mostly in engineering fields as a cost-effective approach for improving the quality of products and processes. The primary goal of parameter design is to choose the levels of the control variables, which optimizes a defined quality characteristic. Modeling both the mean and variance is commonly referred to as dual modeling. In parametric dual modeling, estimations of the mean and variance parameters are interrelated. When one or both of the models (the mean or variance model) are mis-specified, parametric dual modeling can lead to faulty inferences. An alternative to parametric dual modeling is nonparametric dual modeling. However, nonparametric techniques often result in estimates characterized by high variability, which leads us to ignore important knowledge. We develop a dual modeling approach called dual model robust regression (DMRR), which is robust against user misspecification of the mean and/or variance models. Numerical and asymptotic results illustrate the advantages of DMRR over several other dual model procedures. The proposed method will be illustrated with simulations.

This paper deals with a single-machine scheduling problem with maintenance activities. Our purpose is to provide a near optimal solution using metaheuristics approach. In this problem, there are n jobs and m machines (m?n), each job must be assigned to one and only one machine, where the processing time of job (j) is (p_j). Furthermore there are M_G groups where each group has a fix periodic interval T and for each group, the maximum number of jobs processed in the machines available time interval (T) is K, (M_G=m/K). For finding the near optimal solution, we consider optimizing total cost scheduling problem. This problem has two types of costs, group cost and gap cost. In this study, first, proposed problem is formulated in a mathematical model. Next, a heuristic genetic algorithm is used to obtain the proposed problem and on example is presented to verify the efficiency of the algorithm.

multi-skilled project scheduling problem (MSPSP) has been generally presented to schedule a project with staff members as resources. Each activity in project network requires different skills and also staff members have different skills, too. This causes the MSPSP becomes a special type of a multi-mode resource-constrained project scheduling problem (MM-RCPSP) with a huge number of modes. Given the importance of this issue, in this paper, a mixed integer linear programming for the MSPSP is presented. Due to the complexity of the problem, a meta-heuristic algorithm is proposed in order to find near optimal solutions. To validate performance of the algorithm, results are compared against exact solutions solved by the LINGO solver. The results are promising and show that optimal or near-optimal solutions are derived for small instances and good solutions for larger instances in reasonable time.

C4ISR framework describes architecture in three views of information architecture and defines a set of products, which are main outputs of enterprise architecture design. Formats and templates, which are presented for C4ISR products, cannot describe uncertainty in process or data. Meanwhile, uncertainty in many information systems is unavoidable and using the concept of fuzzy numbers in architecture design helps architects handle uncertainty in process and data of organization. In this paper, we present a new template based on fuzzy-UML concept for some of C4ISR products such as Logical Data Model (OV-7), Operational Event/Trace Description (OV-6c) and Systems Event/Trace Description (SV-10c). To explain further, fictional Fast Pass system used at OilCo gas stations is used to demonstrate details of our proposed model.