This paper presents an empirical investigation to identify and rank the factors of information market development systems influencing on the market share development. The population of this survey includes all managers who work for SMEs in city of Tehran, Iran. The study selects a sample of 230 people randomly and a questionnaire is distributed among them in Likert scale. Cronbach alpha has been calculated as 0.814, which is well above the minimum desirable level. Using structural equation modeling the study has determined seven factors including valid data, information, strategic information, organizational information, supportive information, customer information, development information and data analysis, which influence market share development.

Enterprise Resource Planning System is an important investment for organizations and determines future competitiveness and performance of a firm, which could be substantially effective. The primary objective of enterprise resource planning systems is to consolidate operational procedures among various departments, management information systems and resource allocation of the companies. Given the fact that the selection process for enterprise resource planning system is a multi-criteria decision problem and there are some ambiguities with various criteria, the use of fuzzy logic is inevitable, therefore, in this study, the main indices and standardized evaluation software enterprise resource planning system identification were used to rank six software packages including Oracle, SAP, SSA Global Solution, Sage Group, Microsoft Business Solution, People Soft focus. The results indicate that the highest priority is associated with the SAP software.

In decision making when multiple criteria are determined, the best choice depends on having complete information and proper decision-making technique. The permutation method is one of the popular techniques used in the context of multiple criteria decision making (MCDM). In this paper, a method is presented where there is more than one vector of weights for the criteria and there are uncertainties associated with criteria weights or there are multiple decision makers. We first take different weight vectors to create a multi-objective problem and then we solve them simultaneously to achieve appropriate Pareto solutions of the permutation method. Therefore, MOPSO and NSGA-II algorithms are utilized to find non-dominated solutions. Some examples in different sizes are considered to compare the efficiency of the proposed methods. Results show that by increasing the number of options and considering the computational time, the proposed methods perform better compared with the exact method. Moreover, NSGA-II is more efficient than MOPSO for the considered problem.

An integrated information system based DSS is developed for Open and Distance Learning (ODL) institutions in India. The system has been web structured with the most suitable newly developed modules. A DSS model has been developed for solving semi-structured and unstructured problems including decision making with regard to various programmes and activities operating in the ODLIs. The DSS model designed for problem solving is generally based on quantitative formulas, whereas for problems involving imprecision and uncertainty, a fuzzy theory based DSS is employed. The computer operated system thus developed would help the ODLI management to quickly identify programmes and activities that require immediate attention. It shall also provide guidance for obtaining the most appropriate managerial decisions without any loss of time. As a result, the various subsystems operating in the ODLI are able to administer its activities more efficiently and effectively to enhance the overall performance of the concerned ODL institution to a new level.

Decision making problem is the process of finding the best option out of all feasible alternatives. There are some methods for solving Multiple Criteria Decision-Making problems and Simple Additive Weighting (SAW) is one of the most popular ones. In this paper, among multi-criteria models in making complex decisions and multiple attribute models for the most preferable choice, SAW technique is extended using interval numbers. For this purpose, we first propose a method for extending Entropy method for dealing with interval data, and then the extended SAW method with interval data is proposed by using the interval weights derived by the proposed interval Entropy method. The extended SAW method is an algorithm to determine the most preferable choice out of all possible choices, when the input data are stated in interval.

In this paper, a new multi-item inventory system is considered with random demand and random lead time including working capital and space constraints with three decision variables: order quantity, safety factor and backorder rate. The demand rate during lead time is stochastic with unknown distribution function and known mean and variance. Random constraints are transformed to crisp constraints with using the chance-constrained method. The Minimax distribution free procedure has been used to lead proposed model to the optimal solution. The shortage is allowed and the backlogging rate is dependent on the expected shortage quantity at the end of cycle. Two numerical examples are presented to illustrate the proposed solution method.

It is necessary for companies and industries to select the most appropriate maintenance strategy to increase the reliability and safety level with reasonable cost. The primary objective of this paper is to assess different maintenance strategies and to select the best and the most appropriate alternatives for Saipa vehicle industry in Tehran, Iran. For this purpose, we simultaneously consider numerous conflicting objectives and constraints. In this study to counter with this conflicting and to consider the dependency among the qualitative and quantitative criteria and sub-criteria, an integration of Analytic Network Process (ANP) and fuzzy set theory are considered. Therefore, factors playing important role in selecting the best maintenance strategy are determined by reviewing the research literature and interviewing with the experts by Delphi technique. Considering the relations among different factors, a network with 4 criteria and 28 sub-criteria are proposed. In the next step, ANP technique is applied for ranking effective factors in evolution of appropriate maintenance strategy. Results reveal that the best maintenance strategy for fixture body of pride (setter) is corrective maintenance.

In this paper, we study a supply chain problem where a whole seller/producer distributes goods among different retailers. The proposed model of this paper is formulated as a more general and realistic form of traditional vehicle routing problem (VRP). The main advantages of the new proposed model are twofold. First, the time window does not consider any lower bound and second, it treats setup time as separate cost components. The resulted problem is solved using a hybrid of particle swarm optimization and simulated annealing (PSO-SA). The results are compared with other hybrid method, which is a combination of Ant colony and Tabu search. We use some well-known benchmark problems to compare the results of our proposed model with other method. The preliminary results indicate that the proposed model of this paper performs reasonably well.

In the contexture of a customer-driven goods or service design process, a well-timed update of customer’s requirements may not only serve as a necessity indicator to observe how things change over time, but also it incorporates the firms a better ground to interoperate different strategies to meet the future needs of its customer. This paper proposes a systematic methodology to deal with the customer needs’ dynamics, in terms of their relative weights, in the QFD. Compared with previous research, the contribution of this paper is fourfold. First, it applies some linguistic variables to get preferences of customers and experts to determine the relative importance of customer requirements (CRs) and the relationships between customer requirements and engineering characteristics (ECs). Second, it proposes the implementation of a forecasting technique. Third, it describes more comprehensively on how future uncertainty in the weights of customer’s needs could be estimated and transmitted into the design attributes. Fourth, it proposes the implementation of a quantitative approach, which takes into account the decision maker’s attitude towards risk to optimize the QFD decision making analysis. Finally, a real-world application of QFD is provided to demonstrate the practical applicability of the proposed methodology.

This paper presents a mathematical model for the problem of minimizing the maximum lateness on a single machine when the deteriorated jobs are delivered to each customer in various size batches. In reality, this issue may happen within a supply chain in which delivering goods to customers entails cost. Under such situation, keeping completed jobs to deliver in batches may result in reducing delivery costs. In literature review of batch scheduling, minimizing the maximum lateness is known as NP-Hard problem; therefore the present issue aiming at minimizing the costs of delivering, in addition to the aforementioned objective function, remains an NP-Hard problem. In order to solve the proposed model, a Simulation annealing meta-heuristic is used, where the parameters are calibrated by Taguchi approach and the results are compared to the global optimal values generated by Lingo 10 software. Furthermore, in order to check the efficiency of proposed method to solve larger scales of problem, a lower bound is generated. The results are also analyzed based on the effective factors of the problem. Computational study validates the efficiency and the accuracy of the presented model.