Scientia Iranica
Volume:22 Issue: 3, 2015

In this paper, a new model is developed considering diversity of service in service centers location problem. It is assumed that different services can be provided at each service center. The model has three objective functions including: minimizing the sum of customers’ travel time and waiting time in service centers, balancing service loads among the given centers, and minimizing the total establishment costs of service centers and assignment costs of servers. Different number of servers can be assigned to each service center. Regarding the allocation of customers to the service centers, each customer patronizes with respect to the distance to the center, the attractiveness of each service center’s site for the customer and the number of located servers at the service center. Since the proposed model is of nonlinear integer programming type and is of high complexity in solving, two meta-heuristic based heuristics using particle swarm optimization (PSO) and variable neighborhood search (VNS) are proposed inorder to solve the problem. Different sizes of numerical examples are designed and solved in order to compare the efficiency of the heuristics.

The main purpose of this paper is the optimization of multiple categorical correlated responses. So, a heuristic approach and log-linear model has been used to simultaneous estimation of responses surface parameters. Parameters estimation has been performed with the aim of maximizing the number of concordance. The concordance means that the joint probability for the occurrence of dependent responses in each treatment is more than the otherprobabilities ​​inthe same treatment. The second step of this research is the optimization of multi correlated responses for categorical data using some practical Meta heuristic algorithms such as Simulated Annealing, Tabu Search and Genetic Algorithm. Using each Meta heuristic algorithm, best controllable factors are selectedto maximizing the joint probability of success. Three simulated numerical examples with different sizes have been used to describe the proposed algorithms. Results show the superiority of the joint success probability values in the Tabu Search algorithm comparing to the other approaches.

In this research, supplier order allocation problem is investigated. The problem is that one buyer wants to allocate required products to pre-selected suppliers. Allocation is considered under some constraints such as capacity, delivery rate, linear discount and volume discount. Objectives of the model are maximizing the total value of purchase, minimizing the total cost of purchase and minimizing the total number of defective products purchased. We propose a Multi-Objective Mixed Integer Non-Linear (MOMINL) model, for multi-period suppliers order allocation, in situation where suppliers offer discount. In practice, some information such as buyer demand and suppliers delivery rate is uncertain, so fuzzy sets are applied for handling uncertainty. Since PSO and GA are one of the most effective methods to find a good solution to a difficult Multi-Objective Problem (MOP), a multi-objective optimization algorithm based on PSO and GA (MOPSOGA) is developed to solve the model and give a set of Pareto optimal solutions. The efficiency of the Pareto Archive obtained from the algorithm is evaluated based on spacing and diversity metrics.

Recently two important methods ([1],[2]) [Wang. Zh.X, Liu. Y.J, and Feng. B, “Ranking L–R fuzzy number based on deviation degree”. information science(2009). pp 2070-2077.],and [Wang.Y.M, and Luo. Y, “Area ranking of fuzzy numbers based on positive and negative ideal points.’’ Computers and Mathematics with Applications(2009). pp 1769-1779.] proposed for ranking fuzzy numbers. But we found that they both have a same basic disadvantage. In this paper after a short review on different proposed fuzzy number ranking methods, we explain the drawback on deviation degree and the area ranking methods and provide an improvement method to overcome this shortage. Our approach is based on the maximization set and minimization set methods concepts. The results show the superiority of the proposed method in comparison with other ranking methods, especially when the ranking of the inverse and the symmetry of the fuzzy numbers is of interest.

Convergent product is an assembly shape concept integrating functions and sub-functions to form a final product. To conceptualize the convergent product problem, a web-based network is considered in which a collection of base functions and sub-functions configure the nodes and each arc in the network is considered to be a link between two nodes. The aim is to find an optimal tree of functionalities in the network adding value to the product in the web environment. First, an algorithm is proposed to assign the links among bases and sub-functions. Then, numerical values as benefits and costs are determined for arcs and nodes, respectively, using a mathematical approach. Also, a customer’s value corresponding to the benefits is considered. Finally, the Steiner tree methodology is adapted to a multi-objective model optimized by an augmented ε-constraint method.Anexample is worked out to illustrate the proposed approach.

The main idea behind this study is introduce fractional order grey relational degree to analyze the relationship between sci-tech input and economic growth of China. Based on fractional order di erence operator, fractional order grey relational analysis (FGRA) is de ned. The e ect of di erent orders on grey relational analysis is discussed. Two examples show the process and eciency of its application.

The manufacturing organizations are adopting the environmentally friendlier practices to sustain in the competitive business environment. Automotive industries are adopting the environmental management standards to comply with government norms. Life Cycle Assessment (LCA) enables the evaluation of environmental impacts associated with the processes. Life Cycle Costing (LCC) enables the attainment of economic aspect of sustainability. This article presents an integrated approach of LCA- Activity Based LCC to minimize the environmental impact across the life cycle as well as to identify the costs associated with life cycle activities. Different scenarios are being analyzed from the sustainability view point and critical activities are also being identified so as to improve sustainability.

The cost parameters in economic-statistical models of control charts are usually assumed to be deterministic in the literature. Considering uncertainty in the cost parameters of control charts is very common in application. So, several researchers used scenario-based approach for robust economic-statistical design of control charts. In this paper, we specifically concentrate on the multivariate exponentially weighted moving average (MEWMA) control chart and consider interval uncertainty in the cost parameters of the MEWMA control chart and develop a robust economic-statistical design of the MEWMA control chart by using interval robust optimization technique. Meanwhile, the Lorenzen and Vance cost function is used and to calculate the average run length criterion, the Markov chain approach is applied. Then, genetic algorithm for obtaining optimal solution of the proposed robust model is used and effectiveness ofthis model is illustratedthrough a numerical example. Also, a comparison with certain situation of the cost parameters is performed. Finally, a sensitivity analysis is done to investigate the effect of changing the intervals of cost parameters of the Lorenzen and Vance model on the optimal solutions. Furthermore, a sensitivity analysis on the other certain cost parameters of the Lorenzen and Vance model is done.

Hub location problem (HLP) has been an attractive area of research for more than four decades. A recently proposed problem in the area of hub location is the hierarchical single-allocation hub median problem (SA-H-MP) which is associated with finding the location of a number of hubs and central hubs, so that the total routing cost is minimized. Owing to the problem’s complexity and intractability, this paper puts forward two metaheuristics, simulated annealing (SA) and iterated local search (ILS), and compares their performances. Results show that while both algorithms are able to reach optimal solutions on the standard CAB dataset, their runtimes are negligible and considerably lower compared to the runtimes of exact methods.

The stochastic nature of price volatility, as an important issue in stock markets, significantly affects decision makers’ decisions. In this paper, a new multivariate fractionally integratedgeneralized autoregressive conditional heteroscedasticity (MVFIGARCH) model is proposed. Being more comprehensive in comparison with the models in the literature, the proposed model considers long term parameter which is estimated simultaneously with other parameters. One of the well-known methods of MVFIGARCH estimation is the Gaussian quasi-maximum likelihood method. The Gaussian quasi-maximum likelihood estimator of MVFIGARCH model is known to be sensitive to data outliers. To correct the vulnerability of this method to outliers in data, robust M-estimators are introduced for MVFIGARCH models. Volatility models with bounded innovation propagation property are introduced to increase the robustness of the estimations. The applicability of the proposed model is justified by the volatility transmission among Tehran stock index, Dubai stock index and oil global price index using MVFIGARCH model within the time span from December 5, 2006 to January 30, 2012 is investigated. The result of estimation in different models generally shows volatility transmission from oil global market to Tehran and Dubai markets. Volatility transmission from Dubai market to Tehran was meaningfully observed as well. However, the effect of transmission was not observed in reverse direction.

We investigate a one-buyer-multi-vendor co-ordination model with vendor selection problemin a centralized supply chain. In the proposed model, the buyer selects one or more vendorsand orders an appropriate quantity. The quantity discount mechanism is used by all vendors with the aim of coordinating the supply chain. We formulate the problem as a multi objective mixed integer nonlinear mathematical model. Using the Global Criterion method, the proposed model is transformed into a single objective optimization problem. Since, the problem is NP-hard, we propose four meta-heuristic algorithms: Particle Swarm Optimization (PSO), Scatter Search (SS), Population based Harmony Search (HS-pop) and Harmony Search based Cultural Algorithm (HS-CA). The Taguchi’s robust tuning method is applied in order to estimate the optimum values of parameters. Then, the solution quality and computational time of algorithms are compared.

Systems where both machines and workers are treated as constraints are termed dual- resource constrained (DRC) systems. In the last few decades, DRC scheduling has attracted much attention from researchers. This paper addresses the dual-resource constrained flexible job-shop scheduling problem (DRCFJSP) to minimize makespan. This problem is NP-hard and mainly includes three sub-problems: (1) assigning each operation to a machine out of a set of compatible machines, (2) determining a worker among a set of skilled workers for operating each operation on the selected machine, and (3) sequencing the operations on the machines considering workers in order to optimize the performance measure. This paper presents two meta-heuristic algorithms, namely simulated annealing (SA) and vibration damping optimization (VDO), to solve the DRCFJSP. The proposed algorithms make use of various neighborhood structures to search in the solution space. The Taguchi experimental design method as an optimization technique is employed to tune different parameters and operators of the presented algorithms. Numerical experiments with randomly generated test problems are used to evaluate performance of the developed algorithms. A lower bound is used to obtain the minimum value of makespan for the test problems. The computational study confirms the proper quality of results of the proposed algorithms.

The merged automobile maintenance part delivery problem will attract interests from the merged company due to the reduced delivery cost by collaborative delivery among several automobile part depots. Since the delivery problem is a very complex problem, Voronoi diagram is adopted to simplify this delivery problem by splitting customers into several sets. Then, this paper attempts to solve this delivery problem by using of artificial bee colony algorithm. To improve the performance of the artificial bee colony algorithm, an adaptive strategy is used to control the proportion of scouts and leaders. At last, the computational results for 23 benchmark problems indicate that the proposed algorithm is an effective method to solve the multi-depot vehicle routing problem. Furthermore, the results of a merged automobile maintenance part delivery problem also indicate that the improved artificial bee colony algorithm with Voronoi diagram is feasible for solving this kind of delivery problem.

We propose an efficient estimator for population median under two-phase sampling when using two auxiliary variables on the lines of Diana [Diana, G. “A class of estimators of the population mean in stratified random sampling”, Statistica, 1, pp. 59-66 (1993)] and Jhajj and Walia [Jhajj, H. S. and Walia, G. S. “A generalized difference-cum-ratio type estimator for the population variance in double sampling”, Communications in Statistics-Simulations and Computation, 41, 58-64. (2012)]. The expressions for mean squared errors are presented, correct to first order of approximation. Both theoretical and numerical comparisons reveal that the proposed estimator performs better than the unbiased sample median estimator, ratio estimator, and estimators by Srivastava et al. [Srivastava, S. K., Rani, S., Khare, B. B., and Srivastava, S. R. “A generalized chain ratio estimator for mean of finite population”, Journal of the Indian Society of Agricultural Statistics, 42(1), pp. 108-117 (1990)] and Gupta et al. [Gupta, S., Shabbir, J., Ahmad, S. “Estimation of median in two phase sampling using two auxiliary variables”,

This paper presents a bi-objective logistic design problem integrating the financial and physical flows of a closed-loop supply chain in which the uncertainty of demand and the return rate described by a finite set of possible scenarios. The main idea of this paper consists of the joint integration of enterprise finance with the company operations model, where financial aspects are explicitly considered as exogenous variables. The model addressesthe company operationsdecisions as well as the finance decisions. Moreover, the change in equity is considered as objective function along with the profit to evaluate the business aspects.Since the logistic network design is a strategic problem and the change of configuration is not easy in the future,a bi-objective robust optimization with the max-min versionis extended to cope with the uncertainty of parameters. In addition, to obtain solutions with a better time, the scenario relaxation algorithm is adapted for the proposed approach. The numerical examples are presented to show the applicability of the model along with a sensitivity analysis on financial parameters. The obtained results illustrate the importance of such modelling systems leading to more overall earnings and expressingfurther insights on the interactions between operations and finances.

In this paper, we compare four ordering policies in a lost sales inventory model with zero ordering cost, constant lead time, and Poisson demand process. These ordering policies are 1) base stock policy, 2) full delay policy, 3) simple delay policy and 4) a recently developed ordering policy called (1, T) or one for one period policy. Our work can be considered as an expansion of a previous research which compared the first three policies. We show that, for any fixed value of the ratio of unit lost sales cost over unit holding cost, there is a specific value of lead time demand beyond which the cost of (1, T) policy is lower than the costs of other three policies. Furthermore, the superiority of (1, T) policy is more significant for low values of the above ratio and becomes more pronounced as the lead time demand increases.