In today’s dynamic business environment, the product mix and demand may vary from period to period. Therefore, the same configuration of cellular systems will not be optimal in different periods. In this paper, a new mixed integer non-linear mathematical programming model is presented to design dynamic cellular manufacturing systems and combines several design features including multi-period production planning, alternate routings, system reconfiguration, duplicate machines, lot splitting, workload balance among machines and cells, cell size limits, and material flow between machines. The required capacity for parts production is modeled based on flow shop perspective and the aim of mixed integer model is to find optimal independent cells, the quantity of machine types in each cell, and production quantity of the parts during each period of the time horizon. Since this problem belongs to NP-hard class, a three-phase approach is developed to solve the model for practical purposes. Phase 1 finds a feasible solution, phase 2 finds the neighbor solutions, and phase 3 improves a feasible solution. To analyze the computational efficiency, eight test problems with different sizes are considered and the optimal and near-optimal solutions are compared. The efficiency of the algorithm in terms of the objective function values and computational times is shown by the obtained results.

Vertex and p-center problems are two well-known types of the center problem. In this paper, a p-center problem with uncertain demand-weighted distance will be introduced in which the demands are considered as fuzzy random variables (FRVs) and the objective of the problem is to minimize the maximum distance between a node and its nearest facility. Then, by introducing new methods, the proposed problem is converted to deterministic integer programming (IP) problems where these methods will be obtained through the implementation of the possibility theory and fuzzy random chance-constrained programming (FRCCP). Finally, the proposed methods are applied for locating bicycle stations in the city of Tabriz in Iran as a real case study. The computational results of our study show that these methods can be implemented for the center problem with uncertain frameworks.

Bidding price decision is a key issue for the contractors and construction companies. The success/failure of the contractors in competitive biddings is directly dependent on their bidding strategy. This paper aims to develop a hybrid statistical and mathematical modeling approach for determining the optimum bidding price in construction projects. By statistical analysis of historical data, some uncertain parameters like the number of competitors and the cost of the project are estimated. Then, a scenario-based mathematical model for bidding price decision is proposed. In order to present a model in more accordance with the real-world situations, factors like risk, minimum acceptable rate of return (MARR) and opportunistic behavior are taken into account. In order to achieve an insensitive solution to the change in the realization of the input data from the scenarios, a robust mathematical model is used. The performance of the model is evaluated through some numerical problems. Furthermore, sensitivity analysis of the key parameters and robustness evaluation of the model against uncertain parameters are conducted. To evaluate the model's effectiveness in real-world situations, a case study is analyzed by the proposed approach. Numerical results show that the proposed approach reduces the cost estimation errors and increases the average expected profit, which validates the applicability of the model in a real-world situation.

Modeling the simple assembly line balancing (SALB) problem has covered a wide range of real-world applications. The recent advances in optimization problems have created the opportunities to tackle more challenging problems. This paper presents a multi-objective decision making problem to consider two objectives, cost and cycle time, for simple assembly line balancing. The problem is formulated as a mixed integer nonlinear optimization and the proposed study of this paper uses two metaheuristics to solve the resulted problem on some benchmark problems. The preliminary results have indicated that multi objective particle swarm optimization (MOPSO) has provided better quality solutions while the hybrid method based on MOPSO and simulated annealing has yielded more non-dominated Pareto solutions.

The generalized exponential distribution could be a good option to analyse lifetime data, as an alternative for the use of standard existing lifetime distributions as exponential, Weibull or gamma distributions. Assuming different non-informative prior distributions for the parameters of the model, we introduce a Bayesian analysis using Markov Chain Monte Carlo (MCMC) methods. Some numerical illustrations considering simulated and real lifetime data are presented to illustrate the proposed methodology, especially the effects of different priors on the posterior summaries of interest.

The interphase is a region between fibers and a matrix, which has different properties from the matrix and the fibers, but is dependent on them. Considering the interphase region has a significant effect on the accuracy of obtained residual stresses. So far, in order to obtain the micromechanical residual stresses, the interphase properties are considered as an average. In this paper, the properties of the interphase are assumed variable by using a suitable UMAT code in the ABAQUS software. The equations of previous studies that have acquired interphase properties to be variable are used to write the UMAT code. A representative volume element (RVE) in polymer composites is modeled in three phases in the ABAQUS software and the interphase properties are considered as FGM by using the UMAT code. Temperature variation during curing to environment temperature is the only loading factor in the RVE. The matrix, fiber and interphase stresses are obtained in the ABAQUS software. The achieved stresses were compared with the results of previous studies that considered interphase properties as average. Finite element and energy methods were used in previous papers but in the present study just the finite element method with variable interphase properties was use. In addition, residual stress diagrams with the variable interphase properties are plotted to study the effect of the thermal expansion coefficient. The results of this study are similar to those in previous ones, and the curves are improved.

The information of trip demand as the freight origin-destination (O-D) matrices plays a significant role in intercity transportation engineering and planning. Access to this matrix from conventional methods needs to spend time, money and human resources. During the recent years, numerous studies have been conducted in order to obtain the freight O-D matrix using freight vehicle count information in network links. Furthermore, the estimation of O-D matrix and its communicating with freight vehicles can greatly help planners improve logistic, safety and the maintenance issues. This paper presents a method based on the minimization of the deviations between the observed and estimated values for finding the freight O-D matrix. To solve the model, traffic count data of trucks in the network links as well as other related information resources are used. The model is solved for a case study in Iran using meta-heuristic Simulated Annealing Algorithm.

Recently, researchers have focused on how to minimize the negative effects of industrial activities on environment. Consequently, they work on mathematical models, which minimize the environmental issues as well as optimizing the costs. In the field of supply chain network design, most managers consider economic and environmental issues, simultaneously. This paper introduces a bi-objective supply chain network design, which uses fuzzy programming to obtain the capability of resisting uncertain conditions. The design considers production, recovery, and distribution centers. The advantage of using this model includes the optimal facilities, locating them and assigning the optimal facilities to them. It also chooses the type and the number of technologies, which must be bought. The fuzzy programming converts the multi objective model to an auxiliary crisp model by Jimenez approach and solves it with ?-constraint. For solving large size problems, the Multi Objective Differential Evolutionary algorithm (MODE) is applied.

Nowadays, the increase in competition has increased the relative importance of innovation for most firms and many managers believe a good innovation must be knowledge oriented. This paper has tried to determine different methods for creating knowledge in project oriented organizations. The study designs a questionnaire in Likert scale and distributes it among 32 experts who were well informed about different methods of knowledge creation and lessons learned. Cronbach alphas for all components of the survey were well above the desirable level. The study has detected 11 methods for knowledge creation and lessons learned. In terms of preliminary assessment, business transactions has received the highest impact while knowledge team has received the highest effect in terms of necessary assessment. The results of this survey have indicated that although there are several methods for detecting knowledge within organizations, in most cases, it is not easy to gain value added knowledge within an organization, quickly. The people who participated in our survey have indicated that organizational commitment, brainstorming, Delphi and storytelling also have played important role for creation of knowledge. The results have also shown that brainstorming, knowledge brokers, map knowledge and work experience were easier to use for knowledge creation and lessons learned compared with other forms of knowledge creation.

In this investigation, neodymium orthoferrite (NdFeO3) nanoparticles has been synthesized through ultrasonic method in the presence of octanoic acid as surfactant. This method comparing to the other methods is very fast and it does not need high temperatures during the reaction. The spherical NdFeO3 nanoparticles with an average particles size of about 40 nm can be obtained at a relatively high calcination temperature of 800 °C for 4 h. Also, product obtained by this method are uniform in both morphology and particles size. The phase composition, morphology, lattice parameters and size of particles in these product are characterized by Fourier transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD) scanning electron microscopy (SEM) and energy dispersive X-ray spectrometer (EDX). The XRD analysis reveals only the pattern corresponding to perovskite type NdFeO3 which crystallizes in the orthorhombic structure. Energy dispersive X-ray analysis confirms the elemental compositions of the synthesized material.