فهرست مطالب

Scientia Iranica - Volume:23 Issue: 3, 2016

Scientia Iranica
Volume:23 Issue: 3, 2016

  • Transactions E: Industrial Engineering
  • تاریخ انتشار: 1395/04/18
  • تعداد عناوین: 10
|
  • Reza Kamranrad, Amirhossein Amiri Page 1345
    Profile monitoring is a useful technique in statistical process control used when the product or process quality is represented by a function over a time period. This function represents the relationship between a response variable and one or more explanatory variables. Most existing control charts for monitoring profiles are based on the assumption that the observations within each profile are independent of each other which is often violated in practice. Sometimes there are one or more outliers in each profile, which leads to poor statistical performance of the control chart. This paper focuses on Phase II monitoring of a simple linear profile with autocorrelation within profile data in the presence of outliers. In this paper, we propose a new combined control chart based on the robust Holt-Winter model to decrease the effect of outliers. We first evaluate the effect of outliers on the performance of the proposed combined control chart. Then, we apply robust Holt-Winter and design a robust combined control chart to overcome the effect of outliers. The performance of the proposed robust Holt-Winter control chart is evaluated through extensive simulation studies. The results show that the proposed robust control chart performs well.
    Keywords: Robust control chart, profile monitoring, autocorrelated profile, Holt, Winter method
  • Faqir Muhammad, Muhammad Riaz Page 1355
    The article presents an approach to multivariate linear calibration based on the best linear predictor. The bias and mean squared error for the suggested predictor are derived in order to examine its properties. It has been examined that Bias/ and MSE/ are function of five invariant quantities. A simulation study is made for different values of response variables and sample sizes assuming different distributions for the explanatory variable. It is observed that the proposed estimator performs quite well. Some approximations to mean squared error have been suggested and the pivotal functions based on these approximations have been defined. Lower and upper tail probabilities have been calculated and it is examined that these are quite reasonable. These probabilities suggest that the relevant intervals have sensible confidence coefficient. Moreover, it is also shown that the multivariate classical and inverse estimators are special cases of the proposed estimator.
    Keywords: Best Linear Predictor, Bias, Intervals, Mean Squared Error
  • T.S. Su, C.C. Wu, C.H. Lin Page 1370
    This work presents a novel fuzzy multi-objective programming (FMOP) model with a modified S-curve membership function capable of solving integrated multi-component, multi-supplier, and multi-time-period production planning problems by using fuzzy objectives for the mobile phone manufacturing sector. The proposed model attempts to minimize total manufacturing, total inventory holding and total penalty costs simultaneously in relation to manufacturer/supplier capacity and warehouse space. Additionally, the proposed model provides a systematic means of facilitating the fuzzy decision-making process, enabling decision makers to interactively adjust the search direction during the solution procedure in order to obtain the preferred satisfactory solution of a decision maker (DM). Moreover, adequacy of the proposed model is demonstrated, based on an implementation design involving several scenarios of manufacturing production system for mobile phones. Analytical results provide a valuable reference for decision managers attempting to more thoroughly understand the systematic analysis and potential of cost-effective production planning.
    Keywords: Mobile phone, Fuzzy multi, objective programming, S, curve membership function, Cost effectiveness, Production planning, Manufacturer, supplier
  • Congjun Rao, Junjun Zheng, Zhuo Hu, Mark Goh Page 1384
    In this paper, the decision making problem ofelectricity coal procurement in power industry is investigated, and a two-stage compound mechanism based on auction and negotiation is designed for multi-attribute and multi-source procurement of electricity coal. In the first stage of this compound mechanism, a multi-attribute auction mechanism of electricity coal is designed. Concretely, the buyer’s utility function and the supplier’s utility function are defined, and the scoring rules and bidding rules are given. Moreover, aiming at maximizing the buyer’s expected utility, an optimization model of selecting winners in multi-attribute auction of electricity coal is established, and then the suppliers’ optimal bidding strategies are discussed, and the feasibility of auction mechanism is proved. Based on the winners’ scheme and corresponding pre-allocation results of electricity coal supply in the auction stage,a negotiation mechanism which can further improve the allocation efficiency and optimize the attribute combination is designed in the second stage of this compound mechanism, and the bidding efficiency is calculated by using the method of Data Envelopment Analysis (DEA) for each winner. Finally, the specific implementation steps are given to show how to apply this two-stage compound mechanism in the actual procurement of electricity coal.
    Keywords: Electricity coal procurement, Multi, attribute, multi, source procurement, Compound mechanism, Multi, attribute auction, Negotiation mechanism
  • Yin, Xiang Ma, Jing Wang, Jian, Qiang Wang, Xiao, Hong Chen Page 1399
    Many 2-tuple linguistic aggregation operators and linguistic multi-criteria group decision-making (MCGDM) approaches have been successfully applied to numerous problems, they are difficult to reflect the different semantics of linguistic terms, distances between adjacent linguistic terms, and the subjective sensations of decision-makers in diverse decision-making problems. In this paper, some 2-tuple linguistic aggregation operators are proposed, which are based on the subjective sensation scale and objective numerical scale, and a method is developed, which is based on the proposed operators, to overcome the aforementioned limitations. Firstly, the subjective sensation scale based on linguistic term sets, the subjective sensation scale and objective numerical scale based on 2-tuples are presented. Then some 2-tuple linguistic operators based on the two scales are developed; namely the generated extended 2-tuple weighted averaging (GE2T-WA) operator, generated extended 2-tuple ordered weighted averaging (GE2T-OWA) operator, generated extended 2-tuple weighted geometric (GE2T-WG) operator and generated extended 2-tuple ordered weighted geometric (GE2T-OWG) operator. Subsequently, based on the GE2T-WA and GE2T-OWA operators, or on the GE2T-WG and GE2T-OWA operators, a MCGDM method is developed. Finally, an example is provided and the proposed method is compared with some existing approaches, using the same illustrative example, for confirming its feasibility and rationality.
    Keywords: multi, criteria group decision, making, 2, tuple, subjective sensation scale, objective numerical scale, aggregation operators
  • Mahdi Bashiri, Mohammad Hasan Bakhtiarifar Page 1418
    Dealing with more than one response in the process optimization is a great issue in these recent years, so multiple response optimization studies have been grown in the published works. In the common problems, there are some input variables which can affect output responses but optimization can be more complex and more real when the responses have correlation with each other. In such problems analyst should consider the correlation structure in addition to input variables effects. In some cases, responses variables may emerge from another distributions rather than normal in which can be analyzed by the proposed method. Moreover, in some problems, response variables may have different importance for the decision maker. In this study, we try to propose an efficient method to find the best treatment in an experimental design which its correlated responses have different weights, either cardinal or ordinal ones. Also a heuristic method was proposed to deal with problems that have considerable number of correlated responses or treatments. The results of some numerical examples confirm the validity of the proposed method. Moreover a real case about Tehran air pollution is studied to show theapplicability of the proposed method in the real problems.
    Keywords: Cardinal weight, Ordinal weight, Multiple response optimization, Correlation, Transformation
  • Ali Mahmoodirad, Masoud Sanei Page 1429
    This paper presents an effective optimization method based on meta-heuristics algorithms for the design of a multi-stage, multi-product solid supply chain network design problem. First, a mixed integer linear programming model is proposed. Second, because the problem is a NP-hard, three meta-heuristics algorithms, namely Differential evolution (DE), Particle swarm optimization (PSO) and Gravitational search algorithm (GSA) are developed for the first time of this kind of problem. To the best of our knowledge, neither DE and PSO nor GSA has been considered for the multi-stage solid supply chain network design problems. Furthermore, the Taguchi experimental design method is used to adjust the parameters and operators of the proposed algorithms. Finally, to evaluate the impact of increasing the problem size on the performance of our proposed algorithms, different problem sizes are applied and the associated results are compared with each other.
    Keywords: Supply chain network design, Differential evolution, Particle swarm optimization algorithm, Gravitational search algorithm, Taguchi experimental Design
  • F. Forouzanfar, R. Tavakkoli, Moghaddam, M. Bashiri, A. Baboli Page 1441
    This paper considers a closed-loop supply chain design problem including several producers, distributors, customers, collecting centers, recycle centers, revival centers, and raw materials customers considering several periods, existing inventory and shortage in distribution centers, transportation cost and time. This problem is formulated as a bi-objective integer nonlinear programming model. The aim of this model is to determine numbers and locations of supply chain elements, their capacity levels, allocation structure,mode of transportation between them, amount of transported products between them, amount of existing inventory and shortage in distribution centers in each period to minimize the sum of system costs and transportation time in the network. To validate this model and show the applicability of it for small-sized problems, GAMS software is used. Because this given problem is NP-hard, a bee colony optimization (BCO) algorithm is proposed to solve medium and large-sized problems. Furthermore, to examine the efficiency of the proposed BCO algorithm, the associated results are compared with the results obtained by the genetic algorithm (GA). Finally, the conclusion is provided.
    Keywords: Closed, loop supply chain, Integer nonlinear programming, Transportation, Inventory, Bee algorithm
  • Ali Naimi Sadigh, S. Kamal Chaharsooghi, Majid Sheikhmohammady Page 1459
    Supply chain coordination aims at improving supply chain performance by aligning the decisions and the objectives of individual firms. Supply chain participants can coordinate on different decisions such as pricing, inventory management, and marketing decisions to gain more profit. The current research considers coordination of these decisions in a multi-product multi-echelon supply chain composed of multiple suppliers, single manufacturer, and multiple retailers. It is assumed that the demand of each product is non-linearly influenced by retailing price and marketing expenditure. Since all the supply chain members possess equal power in the market and make their decisions simultaneously, the three echelon supply chain problem is a non-cooperative Nash game. In order to find the Nash equilibrium, we formulate a Non-linear Complementarity Problem (NCP) based on the optimality conditions, and also an iterative algorithm is proposed to solve large size instances. Finally, a numerical example is presented, and a comprehensive sensitivity analysis is conducted to discuss important managerial insights.
    Keywords: Pricing, Marketing, Inventory management, Game theory, Multi, product, Multi, echelonsupply chain
  • M. Khojaste Sarakhsi, S.M.T. Fatemi Ghomi, B. Karimi Page 1474
    This paper studies joint economic lot-sizing problem (JELS) for a single vendor-single buyer system while demand is dependent on selling price. This problem is modeled for geometric shipment policy and a solution procedure is developed to find a well approximation of the global optimal solution of the problem. Since the equal size shipment policy or geometric shipment policy may yield more joint profit compared to each other, the most important factor that affects the break-even-point of geometric and equal-size policies is determined. The JELS problem for dependent demand is also modeled for geometric-then-equal size and optimal shipment policies. Solution procedures to find a well approximation of the global optimum of each problem are also developed for these models. The models and solution procedures for geometric, geometric-then-equal size and shipment policies are novel in the literature. Numerical results of the models show considerable improvement in the joint profit of the chain compared to lot-for-lot and equal-size shipment policies for chains with price-sensitive demand and could be very interesting for supply chain coordinators and practitioners.
    Keywords: Joint economic lot, sizing problem, Price, sensitive demand, Geometric shipment policy, Geometric, then, equal size shipment policy, Optimal shipment policy