Hiwa Farughi; Sobhan Mostafayi; Ahmadreza Afrasiabi
Abstract
In this paper, a bi-objective mixed-integer mathematical model is presented for configuration of a dynamic cellular manufacturing system. In this model, dynamic changes and uncertainty in parts demand and machines reliability are considered. The first objective function minimizes total costs and the ...
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In this paper, a bi-objective mixed-integer mathematical model is presented for configuration of a dynamic cellular manufacturing system. In this model, dynamic changes and uncertainty in parts demand and machines reliability are considered. The first objective function minimizes total costs and the second one maximizes the machines reliability through minimizing machines failure. In addition, some routes are considered to produce each part based on operational requirements. An appropriate route is selected respect to the costs and operational time. Some parameters are considered under uncertainty in two categories. The first category such as demand is dependent on market condition and the uncontrolled competitive environment. The second one includes some parameters for production system and machines that are directly related to plans organized by production management. A robust optimization approach is used to deal with parameters uncertainty to produce feasible and optimal solutions. Furthermore, for validation and implementation of results in real world, a case study is investigated. Computational results show that the robust model reports better values for objective functions compared to the scenario-based model. In fact, Pareto-front which are resulted by robust model are dominated by scenario-based models’ Pareto front. Sensitivity analyses on main parameters of the problem are performed to drive some managerial insights that help corresponding decision makers to provide suitable and homogenous decisions in a production system.
Ali Salmasnia; Ali Tavakoli; Maryam Noroozi; Behnam Abdzadeh
Abstract
Control charts are powerful tools to monitor quality characteristics of services or production processes. However, in some processes, the performance of process or product cannot be controlled by monitoring a characteristic; instead, they require to be controlled by a function that usually refers as ...
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Control charts are powerful tools to monitor quality characteristics of services or production processes. However, in some processes, the performance of process or product cannot be controlled by monitoring a characteristic; instead, they require to be controlled by a function that usually refers as a profile. This study suggests employing exponentially weighted moving average (EWMA) and range (R) control charts for profile monitoring, simultaneously. For this purpose, the parameters of these control charts should be determined in a way that the expected total cost is minimized. In order to evaluate the statistical performance of the proposed model, the in-control and out-of-control average run lengths are applied. Moreover, the existence of uncertain parameters in many processes is a barrier to attain the best design of control charts in practice. In this paper, the economic-statistical design of control charts for linear profile monitoring under uncertain conditions is investigated. A genetic algorithm is used for solving the proposed robust model, and the Taguchi experimental design is employed for tuning its parameters. Furthermore, the effectiveness of the developed model is illustrated through a numerical example.
M. Rabbani; N. Heidari; H. Farrokhi Asl
Volume 3, Issue 2 , December 2016, , Pages 107-122
Abstract
Data envelopment analysis (DEA) is one of non-parametric methods for evaluating efficiency of each unit. Limited resources in healthcare economy is the main reason in measuring efficiency of hospitals. In this study, a bootstrap interval data envelopment analysis (BIRDEA) is proposed for measuring the ...
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Data envelopment analysis (DEA) is one of non-parametric methods for evaluating efficiency of each unit. Limited resources in healthcare economy is the main reason in measuring efficiency of hospitals. In this study, a bootstrap interval data envelopment analysis (BIRDEA) is proposed for measuring the efficiency of hospitals affiliated with the Hamedan University of Medical Sciences. The proposed method is capable to consider uncertainty and sampling errors. The inputs of this model include total number of personals, number of medical equipment, and number of operational beds. Also, outputs consist of number of inpatients, number of outpatients, number of special patients, bed-day, and bed occupancy rate. First, we estimate the efficiency by applying original DEA that does not consider any uncertainty and sampling error; then we utilize RDEA that considers uncertainty and after that we use BRDEA that consider both uncertainty and sampling error with an adaptation of bootstrapped robust data envelopment analysis and could be more reliable for efficiency estimating strategies.
E. Babaee Tirkolaee; M. Alinaghian; M. Bakhshi Sasi; M. M. Seyyed Esfahani
Volume 3, Issue 1 , June 2016, , Pages 61-76
Abstract
The urban waste collection is one of the major municipal activities that involves large expenditures and difficult operational problems. Also, waste collection and disposal have high expenses such as investment cost (i.e. vehicles fleet) and high operational cost (i.e. fuel, maintenance). In fact, making ...
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The urban waste collection is one of the major municipal activities that involves large expenditures and difficult operational problems. Also, waste collection and disposal have high expenses such as investment cost (i.e. vehicles fleet) and high operational cost (i.e. fuel, maintenance). In fact, making slight improvements in this issue lead to a huge saving in municipal consumption. Some incidents such as altering the pattern of waste collection and abrupt occurrence of events can cause uncertainty in the precise amount of waste easily and consequently, data uncertainty arises. In this paper, a novel mathematical model is developed for robust capacitated arc routing problem (CARP). The objective function of the proposed model aims to minimize the traversed distance according to the demand uncertainty of the edges. To solve the problem, a hybrid metaheuristic algorithm is developed based on a simulated annealing algorithm and a heuristic algorithm. Moreover, the results obtained from the proposed algorithm are compared with the results of exact method in order to evaluate the algorithm efficiency. The results have shown that the performance of the proposed hybrid metaheuristic is acceptable.
H. Golpira; M. Zandieh; E. Najafi; S. Sadi Nezhade
Volume 2, Issue 2 , December 2015, , Pages 43-54
Abstract
Many supply chain problems involve optimization of various conflicting objectives. This paper formulates a green supply chain network throughout a two-stage mixed integer linear problem with uncertain demand and stochastic environmental respects level. The first objective function of the proposed model ...
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Many supply chain problems involve optimization of various conflicting objectives. This paper formulates a green supply chain network throughout a two-stage mixed integer linear problem with uncertain demand and stochastic environmental respects level. The first objective function of the proposed model considers minimization of supply chain costs while the second objective function minimizes CO2 emission level. The Conditional Value at Risk (CVaR) approach is used to deal with the demand uncertainty in supply chain network in addition to the scenario based approach that is employed to deal with the stochastic level of CO2 emission. The implementation of the proposed model has been demonstrated using some randomly selected numbers and the results are analyzed accordingly.