Scientia Iranica
Volume:25 Issue: 1, Jan - Feb 2018

A flexible cell scheduling problem (CSP) under time-of-use (TOU) electricity tariffs are developed in this study. To apply a kind of energy-conscious policy, over cost of on-peak period electricity consumption, limitations on total energy consumption by all facilities, set up time available on each cell, part defect (pert) percentage and the total number of automated guided vehicles (AGV) are considered. Additionally, an ant colony optimization (ACO) algorithm is employed to find a near-optimum solution of proposed mixed integer linear programming (MILP) model with the objective of minimizing the total cost of CSP model. Since no benchmark is available in the literature, a lower bound is implemented as well to validate the result achieved. Moreover, to improve the quality of the results obtained by meta-heuristic algorithms, two hybrid algorithms (HGA and HACO) was proposed to solve the model. For parameter tuning of algorithms, Taguchi experimental design method is applied. Then, numerical examples are presented to prove the application of the proposed methodology. Our results compared with the lower bound and as a result it confirmed that HACO was capable to find better and nearer optimal solutions.

In a notable change from the position in the past, the Greek government is committed to greening the economy and has assumed determined policies and actions to boost the utilization of renewable energy. The aim of the paper is, firstly, to present the latest developments of the renewable energy policy in Greece, the current achievements and impediments in the implementation of planned reforms in the accomplishment of its 2020 targets, and the specific policy measures introduced; second, to discuss the pace of respective developments in other EU-28 member countries; and, third, through a questionnaire survey and stratified interviews with market participants, to verify the achievements of the government towards reversing previous bureaucratic and prone to corruption procedures. Respectively, research survey results from our survey and interviews conducted in the second semester of 2014 are presented. The majority of respondents expect that the targets set in the National Renewable Energy Action Plan will be reached by 2020. The paper and the questionnaire survey have been conducted under the auspices of the European research program THALES, which intends to measure various aspects of the shadow economy in Greece, also including the areas of renewable energy trade and finance.

In many realistic production environments, jobs will take longer time if they begin later. This phenomenon is known as deteriorating jobs which have widely been studied. In this paper, the piecewise linear deterioration is discussed in a single machine scheduling problem of minimizing the maximum tardiness. After proving the NP-hardness of problem, a Branch and Bound and a heuristic algorithm with O(n2) are proposed for solving the large scale problems to near optimal solutions. The heuristic approach is also used to determine an upper bound on the solution of B&B algorithm. The computational results for evaluating performance of the two algorithms confirm the excellent performance of B&B algorithm as it is able to solve the problems with at least 32 jobs within a reasonable time. Notably, the heuristic approach is quite accurate and efficient with an average error percentage of less than 0.3%.

Researchers in applied sciences are often concerned with multivariate random vari9
ables. In particular, multivariate discrete data often arise in many fields (statistical
10 quality control, biostatistics, failure and reliability analysis, etc.) and modeling such
11 data is a relevant task, as well as simulating correlated discrete data satisfying some spe12 cific constraints. Here we consider the discrete Weibull distribution as an alternative to
13 the popular Poisson random variable and propose a procedure for simulating correlated
14 discrete Weibull random variables, with marginal distributions and correlation matrix as15 signed by the user. The procedure indeed relies upon the Gaussian copula model and an
16 iterative algorithm for recovering the proper correlation matrix for the copula ensuring
17 the desired correlation matrix on the discrete margins. A simulation study is presented,
18 which empirically assesses the performance of the procedure in terms of accuracy and
19 computational burden, also in relation to the necessary (but temporary) truncation of
20 the support of the discrete Weibull random variable. Inferential issues for the proposed
21 model are also discussed and are eventually applied to a dataset taken from the literature,
22 which shows that the proposed multivariate model can satisfactorily fit real-life correlated
23 counts even better than the most popular or recent existing ones.

In this study, the multi-objective programming (MOP) method was used to solve network DEA (NDEA) models with assumption that, negative data is considered for the proposed NDEA model which consists of semi-negative and semi-positive input and output. At first, two stage and then k stage production models were formulated with consideration of negative data. In the multi-objective programming, two separate objective functions including the divisional efficiencies and the overall efficiency of the organization are modeled. In comparison to conventional DEA with negative data, the advantage of the proposed NDEA models is consideration of intermediate processes and products, in order to calculate the organization's overall efficiency. However, in conventional DEA, sub-stages of the organizations are neglected. To measure the efficiencies of an organization regarding interactive internal process, two case studies were investigated by application of the NDEA-MOP method with negative data. Case study 1 is focused on units with two stages having semi-negative and semi-positive indexes. In case study 2, units with three stages are evaluated. These units also have semi-negative and semi-positive indexes. The overall efficiency of each unit is calculated using the proposed models. Fuzzy approach as a solution procedure is applied.

In this paper, a novel multi-objective model for dynamic and integrated network design of sustainable closed-loop supply chain network is proposed, which aims to optimize economic, environmental, and social concerns, simultaneously. In order to have a dynamic design, multiple strategic periods are considered during the planning horizon. Furthermore, different short-term decisions are integrated with long-term decisions related to the network design problem. Two of these short-term decisions are determining selling price of products in forward logistics and buying price of used products from customer zones in reverse logistics. Based on the complexity of proposed multi-objective model, a multi-objective imperialist competitive algorithm (MOICA) is proposed to solve the model, and the results are compared with a non-dominated sorting genetic algorithm (NSGA-II). Finally, to evaluate the performance of proposed algorithm, several numerical examples are used, which the results indicate the efficiency of the proposed algorithm.

In this paper, a fuzzy multi-objective model is presented to select and allocate the order to the suppliers in uncertainty conditions, considering multi-period, multi-source, and multi-product cases at two levels of a supply chain with pricing considerations. Objective functions considered in this study as the measures to evaluate the suppliers are the purchase, transportation, and ordering costs, timely delivering or deference shipment quality or wastages which are amongst major quality aspects. Partial and general coverage of suppliers in respect of distance and finally supplier's weights make the amounts of products orders more realistic. Deference and coverage parameters in the model are considered as uncertain and random triangular fuzzy number. Since the proposed mathematical model is NP-hard, multi-objective particle swarm optimization (MOPSO) algorithm is presented. To validate the performance of MOPSO, we applied non-dominated sorting genetic algorithm (NSGA-II). Taguchi technique is executed to tune the parameters of both algorithms. A practical case study in an agricultural industry is shown to demonstrate that the proposed algorithm applies to the real-world problems. The results are analyzed using quantitative criteria, performing parametric, and non-parametric statistical analysis.

2-dimension uncertain linguistic variables (2DULVs) are a powerful tool to express the fuzzy or uncertain information, and the weighted Bonferroni mean can not only take the attribute importance into account but also capture the interrelationship between the attributes. However, the traditional Bonferroni mean can only deal with the crisp numbers. In this paper, Bonferroni mean was extended to process the 2DULVs. Firstly, we proposed the normalized weighted geometric Bonferroni mean (NWGBM) operator and the generalized normalized weighted geometric Bonferroni mean (GNWGBM) operator, which have the characteristics of reducibility and also consider the interrelationships between two attributes. Then we introduced the computation rules, characteristics, the expected value and comparison method of the 2DULVs. Further, we developed the 2-dimension uncertain linguistic normalized weighted geometric Bonferroni mean (2DULNWGBM) and the 2-dimension uncertain linguistic generalized normalized weighted geometric Bonferroni mean (2DULGNWGBM), and explored some properties and discussed some special cases of them. Finally, we developed a new decision making method based on these operators, and an example is given to compare with the existing methods.

Soft set theory acts as a fundamental tool for handling the uncertainty in the data by adding a parameterizations factor during the process as compared to fuzzy and intuitionistic fuzzy set theory. In the present manuscript, the work has been done under the intuitionistic fuzzy soft sets (IFSSs) environment and proposed some new averaging/geometric prioritized aggregation operators in which the preferences related to attributes are taken in form of IFSSs. Desirable properties of its have also been investigated. Furthermore, based on these operators, an approach to investigate the multi- criteria decision making (MCDM) problem has been presented. The e ectiveness of these operators has been demonstrated through a case study.

Different categories of Iranian handmade carpet are produced each year. Since of resource limitation, it is so important for managers to allocate more resources to the most efficient categories. So the main purpose of this illustration is to consider most efficient types of carpet in production and sales stages. To do so, different categories of Iranian handmade carpet are considered as DMUs. This study utilizes network DEA for constructing a model to analyze total and partial efficiency of Iranian Handmade Carpet Company (IHCC) simultaneously. IHCC consists of three main departments that are working jointly to maximize productivity of the firm; therefore, the case of IHCC is a multi-stage system with shared intermediate variables, extra inputs to the second stage and undesired outputs. The novelty of this paper is the methodology used for calculating the efficiency which is based on multi-objective programming. Results of experimental data of IHCC is summarized in order to prepare some brilliant management strategies based on partial and total efficiency scores of different carpet categories. Since the lack of familiar researches in the area of carpet industry efficiency measurement, this research will provide valuable information for decision makers.