Scientia Iranica
Volume:23 Issue: 6, 2016

Project portfolio selection is an important problem for having an ecient and e ective project management. This paper proposes a new framework to identify the optimal project portfolio. First, the in uencing criteria are derived with respect to higher priorities from the fuzzy DEMATEL method under the balanced scorecard framework. Afterwards, a utility-based multi-choice goal programming technique is applied to determine the project portfolio in regard to the chosen criteria and some other operational limitations. The synergy amongst projects and the outsourcing option are also taken into account in order to provide a more realistic selection process. Finally, applicability and validity of the proposed integrated model are tested by a case study conducted in a pharmaceutical company.

Partner selection is gradually recognized as an essential factor in gaining success in a cooperative settings. In this paper, a novel approach based on fuzzy AHP group decision-making for partner selection of joint venture projects is proposed. In this approach, rst, the in uential factors for selection of appropriate partners based on literature review and interviews with experts are identi ed. Then, using a method considering the risk, knowledge, and educational background of decision-makers, the impact of decision-makers is calculated. Pairwise comparison matrices are performed, and the weights of criteria are calculated based on two methods (multiplicative and additive). Then, the calculated weights of criteria and the potential partners have been ranked via an ecient ranking index. Finally, the application of this methodology to a real case study in National Iranian Petrochemical Company (NPC) is conducted. The contribution of this study is developing a fresh systematic approach for partner selection of international joint ventures and application in a real-life case study to present an operational guideline to the petrochemical industries. The results of this study reveal that the equipment of the partner, its nancial capacity, trusts and management skills are the most important criteria for establishing the durable partners in the international joint venture of NPC.

In most researches in the area of profile monitoring, it is assumed that observations are independent of each other. Whereas, this assumption is usually violated in practice and observations are autocorrelated. The control charts are the most important tools of the statistical process control which are used to monitor the processes over time. The control charts usually signal the out-of-control status of the process with a time delay. Whereas knowing real-time of the change (change point), one can achieve great savings on time and expenses. In this paper, the estimation of the change point in the simple linear profiles with AR (1) autocorrelation structure within each profile is considered. In the proposed method, by acquiring the joint probability density function of the autocorrelated observations, the maximum likelihood estimation method is applied to estimate the step change point. Here, we specifically focus on Phase II and compare the performance of the proposed estimator with the existing estimators in the literature through simulation studies. In addition, the application of the proposed estimator in comparison with the two estimators is illustrated through a real case. The results show the better performance of the proposed estimator.

Facility/supplier location-allocation and supplier selection-order allocation are two of the most important decisions for both designing and operation supply chains. Conventionally these two issues will be discussed separately. Due to similarity and relationship between these issues, in this paper we investigate an integrated model for supplier location-selection and order allocation problem in supply chain management (SCM). The objective function is set in such a way that the establishment costs, inventory-related costs, and transportation costs as quantitative criteria have been minimized. As regards, the costs are uncertainty, therefore we have considered them stochastic. This paper developed a bi-objective model for optimization of the mean and variance of costs. Also, the capacities of supplier are limited. This mixed integer nonlinear program solved with two meta-heuristics genetic algorithm and simulated annealing. Finally, these two methods compared in terms of both solution quality and computational time. To obtain a high degree of validity and reliability GAMS software and meta-heuristic results in small sizes compared.

The majority of the researches in the integrated vendor-buyer inventory problem assume that the shipments are equal. In this paper, shipments are considered to be non-equal. Both demand and delivery times are also assumed to be stochastic. Moreover, unsatisfied demand can be backordered and lost, as well asconsidering a service level constraint. The objective is to minimize both buyer and vendor costs at the same time. The problem is solved by an exact heuristic algorithm. To validate the algorithm’s performance, the results are compared with that of LINGO solver. Finally, a set of numerical problems is applied to compare the result in the integrated and independent forms.

In this paper, a bi-objective mathematical model is presented to optimize supply chain network with location-inventory decisions for perishable items. The goals are to minimize total cost of system including transportation cost of perishable items from centers into DCs, DCs to ultimate center, transportation cost of unusual orders, and fixed cost of centers as DCs as well as demand unresponsiveness. Considering special conditions for holding items, regional DCs, and determining average of life time items assigned to centers are other features of the proposed model. With regard to complexity of the proposed model, a Pareto-based meta-heuristic approach called multi-objective imperialist competitive algorithm (MOICA) is presented to solve the model. To demonstrate performance of proposed algorithms, two well-developed multi-objective algorithms based on genetic algorithm including non-dominated ranked genetic algorithm (NRGA) and non-dominated sorting genetic algorithm (NSGA-II) are applied. In order to analyze the results, several numerical illustrations are generated; then, the algorithms compared both statistically and graphically. The results analysis show the robustness of MOICA to find and manage Pareto solutions.

This paper addresses the problem of designing a supply chain network consisting of suppliers, manufacturers, warehouses, and customers in which all manufacturers belong to a single owner. All players in this chain are performing under uncertainty. The single product of this supply chain needs one strategic and one non-strategic part for its nal assembly. To hedge against uncertainty in supply and demand, the manufacturers tend to take part in a set of suppliers through a portfolio of contracts, and unmet demand will be satis ed by purchasing from spot market with stochastic prices. In addition, demands, supply capacities, and warehouse capacities are stochastic as well, and the problem has been modeled as a two-stage stochastic program with recourse. Then, a hybrid solution strategy based on sample average approximation and accelerated Benders decomposition is proposed to tackle the problem. The proposed strategy is able to obtain good quality solutions for a large number of scenarios and within an acceptable time interval. Computational results show the e ectiveness of the stochastic model as compared to its deterministic counterpart.

Shigeno et al.''s algorithm(2000) is a scaling method to solve the minimum cost flow problem. In each phase, they applied the most positive cut canceling idea. In this paper, we present a new approach to solve the problem, which uses the scaling method of Shigeno et al.(2000), but, in each phase, we apply the out-of-kilter idea instead of the most positive cut canceling idea. Our algorithm is inspired by Ghiyasvand(2012). The algorithm gives a geometrical explanation to the optimality concept. For a network with $n$ nodes and $m$ arcs, the algorithm performs $O(log (nU))$ phases and runs in $O(m(m鹚 n)log (nU))$ time (where $U$ is the largest absolute arc bound ), which is $O(m(m鹚 n)log n)$ under the similarity assumption. This time is the running time of the algorithms by Orlincite{O} and Vygencite{V} which are the best strongly polynomial-time algorithms to solve this problem.

Comparison of quality for products (supplies and goods) is extremely important for manufacturers and consumers. Based on correct comparisons, manufacturers and consumers can nd better suppliers to cooperate and better merchandise to purchase, respectively. Quality is often measured and compared by process capability indices, among which Cp is very e ective, simple to apply, and particularly useful for the rst round of comparison. In practice, Cp is unknown and should be estimated from observations. Let dCpi denote the maximum likelihood estimator obtained from normal process, Xi, with index value Cpi; i = 1; 2. If dCp1 >dCp2 with high probability when (1 )Cp2 Cp1. Given 0 dCp2), we demonstrate how to nd the smallest sample size needed to ensure observing dCp1 > dCp2 with probability greater.