سال:1391 | دوره: | شماره: |تعداد مقالات:3

The Economic Production Quantity (EPQ) model is often used in the manufacturing sector to assist firms in determining the optimal production lot size that minimizes overall production-inventory costs. There are some assumptions in the EPQ model that restrict this model for real-world applications. Some of these assumptions are (1) infinite space of warehouse, (2) all of the produced items are perfect, and (3) only one type of goods is produced. In this paper, we develop the EPQ model by assuming that each produced lot contains some imperfect items and scraps. In addition, we have more than one kind of products along with warehouse space limitations.Under these conditions, we formulate the problem as a non-linear programming model and propose a genetic algorithm to solve it. At the end, we present a numerical example to illustrate the applications of the proposed methodology and identify the optimal value of the parameters of the genetic algorithm.

Examination timetabling problem (ETP) is one of the most important issues in universities. An improper timetable may result in students' dissatisfaction as it may not let them study enough between two sequential exams. In addition, the many exams to be scheduled, the large number of students who have taken different courses, the limited number of rooms, and some constraints such as no conflict in a single student's exams make it very difficult to schedule experimentally. A mathematical programming model is required to formulate such a sophisticated problem. In this paper, a new binary model is developed for ETP. The novelty of the paper can be discussed in two directions. The first one is that a course can be offered more than once in a semester. If a course is requested by a few students, then it is enough to be offered once. If the number of students requesting a course is more than the maximum number of students who are allowed to attend a single class, then the course is multi-offered. The second novelty is that sharing a room for two simultaneous exams is allowed. Also, the model considers some hard and soft constraints, and the objective function is set in such a way that soft constraints are satisfied as much as possible. Finally, the model is applied in a sample department and is solved by GAMS.

This paper considers open-shop scheduling with no intermediate buffer to minimize total tardiness. This problem occurs in many production settings, in the plastic molding, chemical, and food processing industries. The paper mathematically formulates the problem by a mixed integer linear program. The problem can be optimally solved by the model. The paper also develops a novel metaheuristic based on an electromagnetism algorithm to solve the large-sized problems. The paper conducts two computational experiments. The first includes small-sized instances by which the mathematical model and general performance of the proposed metaheuristic are evaluated. The second evaluates the metaheuristic for its performance to solve some large-sized instances. The results show that the model and algorithm are effective to deal with the problem.