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Showing 9 results for Subject: Mathematical Modeling and Applications of OR
S. Rahimi, M.m. Lotfi, M.h. Abooie,
Volume 4, Issue 2 (10-2013)
Quality function deployment is a well-known customer-oriented design procedure for translating the voice of customers into a final production. This is a way that higher customer satisfaction is achieved while the other goals of company may also be met. This method, at the first stage, attempts to determine the best fulfillment levels of design requirements which are emanated by customer needs. In real-world applications, product design processes are performed in an uncertain and imprecise environment, more than one objective should be considered to identify the target levels of design requirements, and the values of design requirements are often discrete. Regarding these issues, a fuzzy mixed-integer linear goal programming model with a flexible goal hierarchy is proposed to achieve the optimized compromise solution from a given number of design requirement alternatives .To determine relative importance of customer needs, as an important input data, we apply the well-known fuzzy AHP method. Inspired by a numerical problem, the efficiency of our proposed approach is demonstrated by several experiments. Notably, the approach can easily and efficiently be matched with QFD problems.
N. Shirvani, S. Shadrokh,
Volume 4, Issue 2 (10-2013)
We focus on a three-stage supply chain problem for fast moving consumer goods including a supplier, a manufacturer and customers. There are different orders over identical cycles, to be processed in production site. The problem is to find a joint cyclic schedule of raw material procurement and job scheduling minimized the total cost comprised of raw material ordering cost and holding cost, production cost, holding cost of finished products, tardiness cost and rejection cost. An integrated mixed integer programing model is proposed and optimal solution of some instances are provided by solving the model.
A Forghani, F Dehghanian,
Volume 5, Issue 2 (10-2014)
In the face of budgetary limitations in organizations, identifying critical facilities for investing in quality improvement plans could be a sensible approach. In this paper, hierarchical facilities with specified covering radius are considered. If disruption happens to a facility, its covering radius will be decreased. For this problem, a bi-objective mathematical formulation is proposed. Critical facilities are equivalent to the facilities which attacking them causes the most reduction in the quality of the system performance. Consequently, this problem is studied in the interdiction problem framework. To solve the multi-objective model the weighting-sum approaches are applied. The first interdictor's objective function helps decision makers to identify the vulnerability of the system. Moreover, the second objective function may assist in minimizing the cost of applied quality improvement plans.
Ms. Maryam Akbari-Jafarabadi, Prof. Reza Tavakkoli-Moghaddam, Mr. Mehdi Mahmoodjanloo, Mr. Yaser Rahimi,
Volume 6, Issue 2 (9-2015)
In general, any system may be at risk in a case of losing the critical facilities by natural disasters or terrorist attacks. This paper focuses on identifying the critical facilities and planning to reduce the effect of this event. A three-level model is suggested in the form of a defender-attacker-defender. It is assumed that the facilities are hierarchical and capable of nesting. Also, the attacker budget for the interdiction and defender budget for fortification is limited. At the first level, a defender locates facilities in order to enhance the system capability with the lowest possible cost and full covering customer demand before any interdiction. The worst-case scenario losses are modeled in the second-level. At the third level, a defender is responsible for satisfying the demand of all customers while minimizing the total transportation and outsourcing costs. We use two different approaches to solve this model. In the first approach, the third level of the presented model is coded in Gams software, its second level is solved by an explicit enumeration method, and the first level is solved by tabu search (TS). In the second approach the first level is solved by the bat algorithm (BA). Finally, the conclusion is provided.
Dr Saiedeh Gholami, Mr. Mahdi Jalalian, Dr Reza Ramezanian,
Volume 7, Issue 1 (4-2016)
In the past decade, fuel consumption and CO2 emission have increased in the airline industry. Large CO2 footprint has a damaging effect on the environment. Global concerns over this issue has made the airline industry to be greener. Most efforts of the green airline industry are improving the fuel consumption to reduce the CO2 emission and its environmental damage. Here, we use cruise speed control to control the fuel consumption and CO2 emission. Each aircraft has a different speed level needing a different fuel consumption. Service quality is studied besides the energy consumption. We investigate two objectives including total energy consumption (TEC) and passenger service level (PSL). TEC and PSL are conflicting in nature. We develop a mixed-integer nonlinear programming model to integrate schedule design, aircraft assignment and maintenance routing problems. We make use of the augmented ε-constraint method to solve the problem. To evaluate the model, a real data based on the Emirates airline flights is used. The results are compared using four different scenarios
Mr. Mirmohammad Musavi, Dr. Reza Tavakkoli-Moghaddam, Ms. Farnaz Rayat,
Volume 8, Issue 1 (4-2017)
We present a bi-objective model for a green truck scheduling and routing problem at a cross-docking system. This model determines three key decisions at the cross dock: (1) defining a sequence and schedule of inbound trucks at the receiving door, (2) specifying a sequence and a schedule of outbound trucks at the shipping door, and (3) determining the routes of the outbound truck while serving customers. The first objective function is related to responsiveness of the network that minimizes time window violations and the second objective function minimizes total fuel consumption of trucks in order to consider the environmental factor of the network. Also, a learning effect is considered in loading and unloading process times. To solve the bi-objective model, an archived multi-objective simulated annealing (AMOSA) is used and modified. Finally, a number of test problems are solved and the efficiency of the proposed AMOSA is compared with the e-constraint method.
Prof. Cornelis Roos,
Volume 8, Issue 2 (5-2017)
Many regions in the world are protected against flooding by a dike, which may be either natural or artificial. We deal with a model for finding the optimal heights of such a dike in the future. It minimizes the sum of the investments costs for upgrading the dike in the future and the expected costs due to flooding. The model is highly nonlinear, nonconvex, and infinite-dimensional. Despite this, the model can be solved analytically if there is no backlog in maintenance. If there is a backlog in maintenance, then the optimal solution can be found by minimizing a convex function over a finite interval. However, if the backlog becomes extremely large we show that the model breaks down. Our model has been used in The Netherlands to define legal safety standards for the coming decades.
Dr. Chefi Triki, Dr. Abdulwahab Al-Maimani, Dr. Jamila Akil,
Volume 8, Issue 2 (5-2017)
We deal with developing a Decision Support System (DSS) to promote the ridesharing among both students and staff of a big organization. The DSS includes a set of functions that allow the management of the riders’ requests and drivers’ availability and embeds a novel two-phase optimization approach that helps in defining the optimal riders-drivers matching. The first phase consists of solving a constraint programming model that generates all the feasible routes. Then, the second phase a bin packing based model is solved to find the optimal route for every driver in order to serve the set of riders assigned to her vehicle. We conclude by an illustrative example that shows the validity of our DSS and, finally, by a discussion on the possible commercialization of such a platform.
Dr Abolfazl Fathollahzadeh,
Volume 9, Issue 2 (6-2018)
This paper is directed to the question of how to model and design an efficient tool for the intelligent mapping which is based on both dynamic and efficient storage of data and soft computing. The former is performed by our method that learns how to store, search and delete the data. After pointing out the limitation of the crisp evaluation of the distance between two points, we argue in favor of soft computing which is based on the extension of metric space to
interval one and then to the fuzzy metric. A-Star algorithm is used to illustrate our model along with the injection of competitive data structures.