A. Forghani; F. Dehghanian
Volume 3, Issue 1 , Spring 2014, Pages 1-10
Abstract
In this paper a partial interdiction problem on a capacitated hierarchical system is studied. We consider an attacker who can interdict facilities at different levels and each interdiction level causes a specified reduction in the capacity of a facility depending upon its service level in the hierarchy. ...
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In this paper a partial interdiction problem on a capacitated hierarchical system is studied. We consider an attacker who can interdict facilities at different levels and each interdiction level causes a specified reduction in the capacity of a facility depending upon its service level in the hierarchy. First, the interdictor identifies her interdiction strategy whose aim is to cause the most demand satisfaction cost subject to her budgetary limitation. Subsequently, the defender tries to optimize the objective function which is similar to the attacker’s one but in the opposite direction. The defender is responsible for choosing the least cost strategy in order to satisfy all customers’ demand. She can achieve this goal by two ways: (1) allocating their demand to the hierarchical facilities subject to their residual capacity, (2) benefiting from outsourcing option. This problem can be regarded as a static Stackelberg game between a malicious interdictor as the leader and a system defender as the follower. In this paper we propose a bi-level mathematical formulation in order to model the problem. To solve this problem with exhaustive enumeration, CPLEX has been used.
M. Modarres Yazdi; M. Shafiei; S.M. Sahihi Oskooyi
Volume 3, Issue 1 , Spring 2014, Pages 11-25
Abstract
Data envelopment analysis (DEA) is a non-parametric analytical methodology widely used in efficiency measurement of decision making units (DMUs). Conventionally, after identifying the efficient frontier, each DMU is compared to this frontier and classified as efficient or inefficient. This thesis introduces ...
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Data envelopment analysis (DEA) is a non-parametric analytical methodology widely used in efficiency measurement of decision making units (DMUs). Conventionally, after identifying the efficient frontier, each DMU is compared to this frontier and classified as efficient or inefficient. This thesis introduces the most productive scale size (MPSS), and anti- most productive scale size (AMPSS), and proposes several models to calculate various distances between DMUs and both frontiers. Specifically, the distances considered in this paper include: (1) both the distance to MPSS and the distance to AMPSS, where the former reveals a unit’s potential opportunity to become a best performer while the latter reveals its potential risk to become a worst performer, and (2) both the closest distance and the farthest distance to frontiers, which may proved different valuable benchmarking information for units. Subsequently, based on these distances, eight efficiency indices are introduced to rank DMUs. Due to different distances adopted in these indices, the efficiency of units can be evaluated from diverse perspectives with different indices employed. In addition, all units can be fully ranked by these indices.
R. Hosseiny; V. Amirzadeh; M. A. Yaghoobi
Volume 3, Issue 1 , Spring 2014, Pages 26-38
Abstract
Although a control chart can signal an out-of-control state in a process, but it does not always indicate when the process change has begun. Identifying the real time of the change in the process, called the change point, is very important for eliminating the source(s) of the change and assists process ...
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Although a control chart can signal an out-of-control state in a process, but it does not always indicate when the process change has begun. Identifying the real time of the change in the process, called the change point, is very important for eliminating the source(s) of the change and assists process engineers in identifying the responsible special cause and ul t imately in improving the proces s. In this paper, we first introduce an estimator for a change point with linear trend in the Poisson process, based on the likelihood function using a slope parameter. Then we apply Monte Carlo simulation to evaluate the accuracy and the precision performance of the proposed change point estimator. Finally we compare, the proposed estimator with the MLE of the Poisson process change point derived under linear trend disturbance on the basis of cumulative sum (CUSUM) and Shewhart C control charts. The results show that the proposed procedure outperforms the MLE designed for drift time with regard to variance and is more effective in detecting drift time when the magnitude of change is relatively large.
S. Molaei; K.M. Cyrus
Volume 3, Issue 1 , Spring 2014, Pages 39-48
Abstract
Preventive maintenance is a broad term that encompasses a set of activities aimed at improving the overall reliability and availability of a system. Designers of the preventive maintenance schedules attempt to minimize the overall cost of system operation. There is no substitute for perfection in maintenance ...
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Preventive maintenance is a broad term that encompasses a set of activities aimed at improving the overall reliability and availability of a system. Designers of the preventive maintenance schedules attempt to minimize the overall cost of system operation. There is no substitute for perfection in maintenance to ensure zero breakdowns in machine; therefore it is necessary to get a machinery breakdown insurance against the risks that might occur at business. Previous researches didn’t consider the effect of engineering insurance on maintenance scheduling while it affect the cost function of maintenance scheduling seriously. Engineering insurance pays for all repair costs of machinery, therefore the cost function of maintenance scheduling is affected. This paper presents a new cost function for maintenance scheduling by considering the effects of engineering insurance. Due to the uncertainty in the cost parameters related to the cost function which are very common in application, the paper proposed the application of the scenario-based approach for robust design of maintenance scheduling. Then, genetic algorithm is developed for obtaining the optimal solution of the proposed robust model and the effectiveness of this model is illustrated through a numerical example.
M. Rabbani; H. Yousefnejad; H. Rafiei
Volume 3, Issue 1 , Spring 2014, Pages 49-54
Abstract
This article attempts to cope with one of the most vital strategic decisions in the supply chain design in terms of manufacturing context. The issues of finding the best position of Customer Order Decoupling Point (CODP) in a production line have been taken into consideration by many researchers in recent ...
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This article attempts to cope with one of the most vital strategic decisions in the supply chain design in terms of manufacturing context. The issues of finding the best position of Customer Order Decoupling Point (CODP) in a production line have been taken into consideration by many researchers in recent years, but locating CODP along a supply chain has not yet been completely investigated. Here we present a novel combined DEA/AHP method to tackle the problem of positioning CODP in a supply chain. Then in order to prove the applicability of the proposed structure in a real case, the model is implemented in a food processing supply chain.
