نشریه بین المللی مهندسی صنایع و مدیریت تولید
سال بیست و چهارم شماره 1 (بهار 1392)

Due to the fact that the determination of an efficient scheduling solution in the sequence of single machine operation for multiple objective programming is important, especially in production planning, we are considering a single machine sequencing problem with minimizing the number of the tasks with lateness and weighted tardiness. In this article, the application of new optimization methods in sequencing problem and scheduling are in order. We propose the mathematical model for the problem under consideration first and then by introducing simulated annealing and genetic algorithm, as solution approaches, we test their efficiency for solving the proposed problem. At the end, to increase the efficiency of the proposed model a hybrid/meta-heuristic algorithm based upon the genetic algorithm is proposed. This method identifies a collection of efficient sequencing tools for objectives minimization.

Dynamic facility layout problem (DFLP) is locating departments in different time zones where the location of each department may be varied at the beginning of each period. Traditionally, this problem is constructed based on the minimization of total handling cost, while more objectives could be considered in designing an efficient layout, like activity relationship, department closeness and etc. In this article DFLP is considered by two minimization of the total handling cost and maximization of total activity relationships. This problem is then solved by a new hybrid heuristic algorithm using local search supplemented by simulated annealing. This algorithm is so powerful to generate pareto solutions in such a way that both of convergence and diversity properties are evident.

The knowledge experienced in practice, however it basically corresponds to an area of a science in theory, is referred to as know-how. Indeed, know-how is provided by the people skills inferred from the facts of the real cases. Know-how as an action-based knowledge embraces a set of valuable technical information experienced practically by the people worked in the process of product design or manufacturing lines during the time. Hence, the know-how is basically referred to the arena of the tacit knowledge and then explicating the knowledge to the explicit type along with sharing the knowledge is referred to a critical target for pioneer organizations. Codifying the tacit knowledge in a production organization allows the management to replace the individual know-how asset with the organizational know-how asset. Then codifying know-how leading to share the knowledge is known as one of the most important target for the organizations focused on knowledge management. Although several models proposed in literature to manage the knowledge, one is not addressed to mange the know-how experienced by the operational levels of a company. In this paper after a comprehensive literature review focusing on the key factors of success of knowledge management, a practical model is proposed which can effectively help the operational levels of the organization to separate and codify the types of the know-how. Modularity approached in this model allows one managing the explicit knowledge effectively. The quality chain as the core of the model leads the management to share the knowledge using pulling approach. To help the proposed model validation, the capabilities of the proposed model are analyzed for three practical cases.

Although incorporating complexities and flexibilities of real world manufacturing systems into classic scheduling problems results in problems with greater complexity, it has immense theoretical and practical importance due to its impressive effect on system performance. In this research, three basic assumptions of a job shop scheduling problem have been revised to develop a model with three types of flexibility which fits into many cases of real manufacturing environments;sequencing flexibility, machine flexibility, and processing time flexibility. In this paper, after describing the problem and formulating it as a mathematical program, we have proposed an efficient genetic local search algorithm to solve the problem. In the proposed algorithm first, problem-specific elements including population initialization, crossover, and mutation are designed for genetic algorithm. Then, a novel local search procedure has been developed and incorporated into genetic algorithm to enhance its search ability. In experimental study, the performance of the proposed algorithm to find optimal and near-optimal solutions has been demonstrated based on some test problems which are built by incorporating processing time flexibility into a number of problems adopted from the literature.

In this article, we integrate production and maintenance to two stage lot sizing models with a central warehouse and a manufacturer by adding a new constraint in which the demand is depend on the average percent of product shortage. The central warehouse faces stochastic demand and is controlled by continuous review (R,Q) policy. Additionally, Warehouse ordering cost can be reduced through further investment. In manufacturer system, assembly line needs two types of raw materials before converting them in to the finished product. One of them requires preprocessing inside the facility before the assembly operation and the other comes directly from outside supplier in assembly line. To analyze, we formulate a nonlinear cost function to aggregate all the costs. For doing this, we use Branch and Bound and nonlinear optimization technique– Generalized Reduced Gradient methods and compare the optimal value of these methods. The model is illustrated through numerical examples and sensitivity analyses on cost functions are presented.

The main goal of FMS facility layout decision is to arrange the layout of the work stations to minimize the whole system cost. To meet this goal, in this paper a novel two phase method is presented For FMS problem with the consideration of unequal stations. In the first phase, a computer program is developed based on a Fuzzy AHP approach, for considering the quantitative and qualitative factors among workstations, stored on a knowledge base, to obtain the amount of the material flow between each two stations. Thereafter in the second phase, we optimize the FMS facility layout by utilizing the amount of material flow between stations (obtained in the first phase) by GA and ICA algorithms. Since the transportation system has the most effect on the FMS design, an AGV algorithm is used in order to increase the FMS efficiency and flexibility. Comparing the results of the ICA (first used in facility layout) to the GA algorithm, shows that the two algorithms are similar regarding power, however the ICA algorithm improves much faster.

Tuning parameters has the most important effect in performance of Neural Network. Main problem in using of ANN is parameter tuning because there is no definite and explicit method to select optimal parameters for the ANN parameters. In this study, three artificial neural network performance criteria and also three important factors which affect the selected criteria have been introduced. Moreover, Box-Behnken design has been applied to analyze the ANN structure parameters and its performance. The proposed approach has been implemented for a simulated process according to a complex mathematical function. After extracting relation between controllable factors and performance criteria, fuzzy programming is used for finding the optimal combination of controllable factors in order the best performance of Neural Network. Results attained from the numerical example show efficiency of the proposed method. Generally, proposed approach can be used for tuning of neural network’s parameters in other problems.

During the recent years extensive researchs have been done on fuzzy time series. Since length of intervals affect the forecasting results in these models, doing research in this area became an interesting topic for time series researchers, there are some studies on this issue but their results are not good enough.In this study, we propose a novel simulated annealing heuristic algorithm is used to promote the accuracy of forecasting. The algorithm enjoys two new neighborhood search operators called “subtitle” and “adjust". A Taguchi method as an optimization technique is also employed to comprehensively tune different parameters and operators of proposed model. The experimental results show that proposed models (SAFTS(is more accurate than existing models on forecasting Alabama university enrollments. At the final step, Tehran’s bourse price index (TEPIX) was used as a case study for forecasting and results indicate a good forecasting performance on this problem.

Energy has significant role in socio-economic development of counties any decision on energy sector may affect the broad area in societies. System approach in energy modeling as an efficient method is known by many researchers in management and planning in a country particularly in energy sector. Also System Dynamics is one of the efficient methods, because it has unique abilities. Hence, in this paper applied this method to analyzing effects of energy price changes at industrial section. At first, effective factors on energy consumption were identified consider related papers with industrial energy demand in literature, that these are such as energy price, energy intensity, added value, production and applied technology at production appliances. Then effects of these factors are have described by related equations, that major equation are explained to clarifying. At following, for validation the results of simulation for energy consumption are compared with the actual data for years 2000-2006. After, different scenarios in energy price have defined and for energy consumption and intensity energy are simulated and analyzed until 2020 for different scenarios.