Forecasting, production planning, and control
Seife Ebeyedengel Tekletsadik
Abstract
During my travel to the research case industry to advice internship placed students of our university, I have communicated with company owners that there is a problem related with productivity measurment andimprovement. So that we have decided to assess the overall productivity of the company ...
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During my travel to the research case industry to advice internship placed students of our university, I have communicated with company owners that there is a problem related with productivity measurment andimprovement. So that we have decided to assess the overall productivity of the company with the considertaions of potential sub systems with in it. The existing system overall productivity level have been measured through Performance Objectives Productivity (PO-P) approach. In the case company six subsystems where identified: technology, production, marketing, ergonomics, values and goals and materials. Afterwards, their importances to the company have been prioritized using paired comparison analysis technique. By using Pareto analysis; technology, marketing, ergonomics and production have been identified as potential subsystems. By using PO-P approach the productivity of these four potential subsystems has been measured. As a result, productivity indices of production, marketing, technology, and ergonomics subsystems are 0.7379, 0.6661, 0.7882 and 0.7156 respectively. This resulted the existing system overall productivity of 0.652.
Forecasting, production planning, and control
A. Esmaili Dooki; p. Bolhasani; A. Alam Tabriz
Abstract
Nowadays the Resource Constrained Project Scheduling Problem (RCPSP) has triggered a substantially significant issue among scheduling problems. The purpose of RCPSP is minimizing the duration of the projects due to both limited available resources and precedence constraints. Indeed, it attempts to consume ...
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Nowadays the Resource Constrained Project Scheduling Problem (RCPSP) has triggered a substantially significant issue among scheduling problems. The purpose of RCPSP is minimizing the duration of the projects due to both limited available resources and precedence constraints. Indeed, it attempts to consume the total resources by finding the best duration for each activity. This paper proposes a new multi-objective mathematical model for multi-mode RCPSP with interruption to minimize the completion time of the project, maximize the Net Present Value (NPV) of the project, and minimize the allocating workforce’s costs to perform required skills of all activities. To solve the proposed model, an efficient method based on Me measure is used to cope with the uncertainties, and TH method is utilized to convert the multi-objective method into the single one. Furthermore, this paper presents a novel hybrid meta-heuristic algorithm based on Imperialist Competitive Algorithms (ICA) named Self-Adaptive Imperialist Competitive Algorithm (SAICA) to solve the mathematical model which has never been used to solve this type of problems before. Also, to evaluate the proposed method, its performance is investigated against some meta-heuristic algorithms: Differential Evolution (DE) and Imperialist Competitive Algorithm (ICA). Then, a numerical example, two case studies and a real case study have been carried out to embody both validity and efficiency of the presented approach. The obtained results embody that the proposed SAICA is more effective and practical in comparison with DE, ICA, and BCO in decreasing the project duration and also, the considerable effect on solutions confirms the quality of the proposed method.
Forecasting, production planning, and control
H. Nasiri; K. Taghizadeh; B. Amiri; V. Shaghaghi Shahri
Abstract
Economic cycles are referred as repeatable movement of economic indicators with different domain and duration. Detection of these cycles may help in forecasting the contraction period or expansions of important parts of the economy specifically industry. In this regard, many economist and researchers ...
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Economic cycles are referred as repeatable movement of economic indicators with different domain and duration. Detection of these cycles may help in forecasting the contraction period or expansions of important parts of the economy specifically industry. In this regard, many economist and researchers have focused on composite leading indicators which are growing in terms of diversity of econometric methods. This paper studies 13 macro economical time series in order to develop the best composite indicator, reflecting business cycles of Iran’s industry. Number of established licenses, production index of large industrial units, producer price index and import value index are the chosen variables that make the proposed composite leading indicator. The result of comparison between composite leading indicator and value added time series in the studied period (1997 up to 2012) showed a good correlation between the fluctuations of composite indicators and value added of industry. The result also showed that the proposed leading indicator has the ability to predict the business cycle for maximum 4 and minimum 1 period ahead and in average the forecasted period is equal to 3.2 seasons.
