INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING AND PRODUCTION MANAGEMENT (IJIE) (INTERNATIONAL JOURNAL OF ENGINEERING SCIENCE) (PERSIAN)
Year:2013 | Volume:23 | Issue:4|Papers Count:10

In this paper, minimizing the number of tardy jobs in two-machine flowshop scheduling with non-simultaneous job entrance is discussed. It is proven that the complexity of the problem is NP_hard. Therefore, a heuristic algorithm is proposed to solve the large scale problems. Besides, an exact branch and bound algorithm with utilizing heuristic algorithm as upper bound proposed to achieve optimal solution. Computational results demonstrate that branch and bound method solves problems with 28 jobs in the set High and 20 jobs in the set Low in a reasonable time. Results show the capability of the proposed upper bound, lower bounds and dominance rules. Also, it is shown that the average ratio of optimal solution to the heuristic one with the objective S (1-Ui) is at most 1.11 which is smaller in contrast with other researches in the literature. This ratio proves efficacy of the proposed heuristic algorithm. Finally, according to efficiency of the presented approach, sample problems with large dimensions were solved and their results were displayed.

Natural disaster occurrence and its impacts enforced governments to presents their practical plans to encounter disasters and mitigate its damages and losses by suitable response. Although, disaster managers can utilize their experiences to plan the response of small disasters and manage the related activities; however, they cannot handle these activities in some large scale disaster such as earthquake or flood. To manage logistics activities effectively in a disaster situation, decision support system is a fundamental tool. For this purpose, this research attempts to develop a logistics model to scheme logistics activities in a response phase of earthquake. In other words, the presented model provides a transportation scheme for commodities and injured people and aids disaster managers to utilize their resources efficiently. Furthermore, since rapid response is the most important goal in earthquake response phase, the objective function of the developed model is to minimize total unsatisfied demand and unserved wounded people in planning horizon.

In certain statistical process control applications, quality of a process or product can be characterized by a function between response variable and one or more independent variables. This function commonly referred to as profile. Response variable can be continuous or discrete. All of the research assumes that the response variable is continuous. Whereas, some of the potential applications of profile monitoring are cases where the output can be modeled using polytomous (especially multinomial) or binary logistic regression models. Polytomous response variables, especially multinomial variables, can have various applications especially in service industry. In this paper, we propose some methods for monitoring a profile when the process output is a multinomial response variable. Multinomial logistic regression (OLR) provides the basis for our profile model. Performances of the proposed methods in terms of the signal probability for different out-of-control scenarios are compared based on simulation studies.

As a consequence of recent enormous changes in all aspects of business and production environments a new requirements has been uprising within the filmmaking environment as well. In respect of such a condition, a new framework for managing the production of films was introduced to filmmaking industries, named post- studio method of filmmaking. While this framework has been widely accepted by the pioneers in Hollywood and the other related centers, it would be in need of some new complementary tools. These tools as a core competency of the organizing approach of the post studio filmmaking projects must be capable of responsively assisting the managers so that they can be able to assign a quantified agent to a specified task in an optimum way. This research is one of many attempts that occurred within the last decade to provide a set of consistent methodologies by which managers and producers be able to optimize the procedure of assignment of skilled human resource into the different tasks and activities within different steps of the production processes.The main outcome of this study is an innovative problem solving algorithm (PSA) in a fuzzy – genetic environment which integrates the features from a number of disciplines for;hierarchical searching the choices of decision and enumerating a number of pair “task- person” as possible options,mathematical modeling of the options, responsively in a fuzzy environment,delineating an optimum collection of options by solving the mathematical model with applying an integration of a fuzzy genetic algorithm into the theory of possibility,designing a hierarchical fuzzy solutions environment in which the collection of optimum solutions are weighted based on their priority, and then, designating some necessary modules in MATLAB software environment to allow the managers to apply the problem solving algorithm (PSA)in a user friendly situation.

The most important problem for investors, at the beginning stages of their works, is the way of assigning their investment to one or more different investment alternatives in such a way that with the least possible risk the maximum return become obtainable. In the economic literature this is known as the problem of portfolio selection. This article tries to introduce an efficient way for supporting decision maker in the selection of appropriate portfolio for investment purposes. The portfolio is based upon the mean-variance-skewness with the return of portfolio is considered to be fuzzy to match with the world reality more. This article proposes a hybrid intelligent algorithm for finding an optimal or new optimal solution of the problem. Here, authors use Genetic Algorithm to find the right portfolio with the help of neural network and fuzzy computer simulation knowledge. Due to the fact that trained neural network was used the computation time has reduced tremendously in comparison with the straight use of the fuzzy simulation. Authors have used two example problems to demonstrate the efficiency of the proposed algorithm in comparison with other hybrid algorithms from the literature.

In scheduling problems, it is often assumed that all machines are available during scheduling horizon. Clearly, a machine may not be available due to breakdown or performing maintenance activities. This paper considers a multiple-route job shop configuration, where each part has multiple routes to be produced. In this context, the problem of scheduling jobs together with a set of pre-defined maintenance activities is of interest. Due to the complexity of the problem, no exact mathematical approach can yield solution in reasonable amount of time and, thus, applying heuristic approach is suggested. In this paper, the problem is first formulated as a 0-1 non-linear integer program. Then an approach based on artificial immune system is proposed to tackle the complexity of the problem. In order to evaluate the performance of the proposed approach 30 cases are designed and solved using the proposed approach and Lingo software. The results show that the proposed approach can obtain good solutions in reasonable time.

In this paper, multi-class dynamic PERT network is considered as a queueing network, where the projects are similar and new projects are generated according to a Poisson process. Each activity is performed independently in its corresponding service station with exponential distribution by one server from several servers settled in the service station based on FCFS (Fist Come, First Served) discipline. Also, each project’s end result has a penalty cost that is some linear function of its due-date and its actual completion time. In this investigation, for computing the due date for multi-class dynamic PERT network, we first convert the queueing network into a stochastic network. Then, by constructing an appropriate finite-state continuous-time Markov model, a system of differential equations is created to solve and find the project completion time distribution for any particular project, analytically. Finally, the optimal due date for delivery time is obtained by using a linear function of its due-date and minimizing the expected aggregate cost per project.

In this paper we investigate the issues of data classification (as one of the branches of data mining science) in form of multi-objective mathematical programming model. The model that we present and investigate is a MODM problem. First time, based on support vector machine (SVM) idea (To maximize the margin of two groups), a multi-criteria mathematical programming model was proposed for data mining problems to classified instances in two separated group based on two goals of data discriminate (To maximize the distance between different groups and to minimize the misclassification of observations). Since then, some people have been working on developing the classification models based on mathematical programming methods. Simultaneously and also independently, individuals worked on support vector machine methods to improve them. Regarding to the same philosophy of these two groups of optimization methods, in this paper, to fill the gap between these two research paths, we use the updated and improved SVM methods to present a model for classification in data mining based on multi-objective programming.

In this paper, a mixed integer programming model for Advanced Planning and Scheduling has been presented and implemented in Mann Plastic Company with the objective of minimizing cost of tardiness and earliness of an order. The proposed mathematical model considers capacity constraints, operation sequence, due date in multi order environment and outsourcing.In order to solve the mathematical model for advanced planning and scheduling, by using data from Mann Plastic Company, Genetic Algorithm was used and better results with less penalty obtain from genetic algorithm compared to current status. Using the outputs of this model the final product assembly plan and the injection schedules for Mann Plastic Company as well as contractor were determined.

Now with the increased investment in large projects and implementation of projects with many techniques such as engineering, construction and financing so planning finance has become very important.This article has been struggling with a lack of existing history in the past for time and cost estimation to execute, design and construction of project in a minimum of financial cost. the specification of megaprojects activity related to engineering, supply and installation need to use of a fuzzy model based on expectation values and Greatest chance for a possible model variables - Fuzzy Scheduling get loans at the beginning of the project is planned to limit the whole time according to plan and lowest cost supply constraints prerequisite activities arise. Solving model simulations based on combining the concept of fuzzy random variables with using genetic algorithm. Finally solved by providing a numerical example is provided based on model assumptions.