This paper seeks to identify the priority of factors affecting the quality of banking services in Bank Saderat Iran for better allocation of resources to enhance the quality of its banking services. The study develops a fuzzy method to handle uncertainty associated with the data and using analytical network process (ANP) ranks different factors influencing on service quality. The results have indicated that the quality of e-services (ESQ) is the most important factor followed by the quality of banking services agility (ASQ), the service system quality (SSQ), and the behavioral service qualities (BSQ). Moreover, the employees’ competence and skills, the reliability of the electronic system and the reliability of the service system, an impeccability banking system integrity and accountability instruments are among other effective factors influencing on the quality of banking services.
A key indicator to evaluate the success of an organization is the degree of meeting specific civil project goals based on a predetermined schedule. Therefore, the main purpose of this paper is to evaluate the performance of governmental administration agencies based on realization of civil project goals. In this paper, the information published by the President Deputy of Strategic Planning and Control, that publishes an annual report of evaluation indicators for national civil development projects, are used to evaluate and prioritize the major and non-major governmental agencies. Also, the Gray Relational Analysis (GRA) and the TOPSIS method are employed to analyze the data. The results indicate that using the GRA method, Supreme Council of Seminary and using the TOPSIS method, The Ministry of Labor and Social Affaires have gained the highest ranking.
The issue of resource over-allocating is a big concern for project engineers in the process of scheduling project activities. Resource over-allocating is frequently seen after initial scheduling of a project in practice and causes significant amount of efforts to modify the initial schedules. In this research, a new method is developed for modifying over-allocated schedules in a multi-mode resource constrained project scheduling problems (MRCPSPs) with positive cash flows (MRCPSP-PCF). The aim is to maximize profit of the MRCPSPs or logically minimizing costs. The proposed method can be used as a macro in Microsoft Office Project® Software to modify resource over-allocated days after scheduling a project. This research considers progress payment method and preemptive resources. The proposed approach maximizes profit by scheduling activities through the resource calendar respecting to the available level of preemptive resources and activity numbers. To examine the performance of the proposed method a number of experiments derived from the literature are solved. The results are then compared with the circumstances where resource constraints are relaxed. The outcomes show that in all studied cases, the proposed algorithm can provide modified schedules with no over-allocated days. Afterward the method is applied to modify a manufacturing project in practice.
In most industrial environments, it is usually considered that machines are accessible throughout the planning horizon, but in real situation, machines may be unavailable due to a scheduled preventive maintenance where the periods of unavailability are known in advance. The main idea of this paper is to consider different preventive maintenance policies on machines regarding open shop scheduling problem (OSSP) with sequence dependent setup times (SDST) using immune algorithm. The preventive maintenance (PM) policies are planned for maximizing availability of machines or keeping minimum level of reliability through the production horizon. The objective function of the paper is to minimize makespan. In total, the proposed algorithm extensively is compared with six adaptations of existing heuristic and meta-heuristic methods for the problem through data sets from benchmarks based on Taillard’s instances with some adjustments. The results show that the proposed algorithm outperforms other algorithms for this problem.
In this study an integrated model is proposed for the location inventory routing problem under uncertainty. This problem involves determining the location of distribution centers (DCs) in a three echelon supply chain. The DCs receive orders from the customer and according to a continuous review inventory replenishment policy place orders to the supplier. The products are directly shipped from the supplier to the DCs. The vehicles start from the DCs to fulfill the demands of the customers. Determining the routing of the vehicles is one of the decisions involved in this problem. The demands of customers are stochastically distributed and the capacity of DCs are limited. If one of the DCs undergo a disruption and is unable to fulfill the demands of the customers, shortage may occur. Moreover in the proposed model the shortage is considered as partial backlogging. This means that if shortage occurs, some of the orders result in lost sales and other orders are fulfilled in the next period. In order to optimally solve the proposed model a nonlinear integer programming (INLP) model is developed. However, since the problem is NP-hard, the mathematical formulation cannot be efficiently solved for large sized instances of the problem. Therefore an outer approximation method is developed to solve the problem more efficiently. The computational results show the efficiency of the proposed method.
Agricultural tractor selection is vital for farms, farmers or other agricultural companies in terms of success and competitiveness in the global market. This selection may be assumed as a MCDM (Multi Criteria Decision Making) problem involving qualitative and quantitative factors that must be simultaneously integrated into the selection process. At the same time there are many agricultural tractor alternatives in the market when purchasing an agricultural tractor. This paper deals with the agricultural tractor selection problem using TOPSIS method. This problem is also solved with two other MCDM methods; COPRAS (COmplex PRoportional ASsessment) and EVAMIX (EVAluation of MIXed Data) to rank the tractors alternatives. Lastly Borda and Copeland methods are used to aggregate all three ranking results.
Machining of hardened work materials with appropriate levels of process parameters is still a burning issue in manufacturing sectors and challenging. It is because of pressing demand of surface quality which adversely affected by evolution of tool wear. Therefore the present investigation is undertaken to make a decision on parametric optimization of multi-responses such as flank wear and surface roughness during machining hardened AISI 52100 steel (55±1) steel using mixed ceramic insert under dry environment through grey relational analysis combined with Taguchi approach. Also predicted mathematical models of 1st and 2nd order have been developed for responses and checked for its accuracy. Second order mathematical model presented higher R2 value and represents best fit of the model and adequate compared to first order model. Model indicates good correlations between the experimental and predicted results. The proposed grey-based Taguchi methodology has been proved to be efficient for solving multi-attribute decision making problem as a case study in hard machining environment.