International Journal of Industrial Engineering: Theory, Applications and Practice
Not a member yet
943 research outputs found
Sort by
A MULTI-OBJECTIVE MODEL FOR TIME–COST–QUALITY–RISK TRADE-OFF PROBLEMS IN PROJECT MANAGEMENT
Get PDFThis study presents a weighted four-dimensional time-cost-quality-risk trade-off problem to assist decision-makers in planning the best possible use of resources. The proposed model aims to minimize time and cost while maximizing quality and safety and to ensure that the project is completed as required. The critical path method was used to calculate the completion time, the analytical hierarchy process method was used to determine the weights of the quality parameters, and the 3T risk assessment method was used to calculate the risk values. The algorithm was coded in GAMS and optimized using CPLEX. A construction project with a deadline of 310 days, a budget of 5,250,000 ₺, 88% quality and a safety index (SI) of 77% was selected to analyze the accuracy of the model. The model achieved a solution with a completion time of 310 days, costs amounting to 5,247,775 ₺, 88.036% quality, and 77.338% SI
IMPROVING DISTANCE LEARNING PROCESS IN ENGINEERING EDUCATION USING DESIGN OF EXPERIMENTS: RE-DESIGN OF AN ONLINE INDUSTRIAL ENGINEERING COURSE DURING AND BEYOND THE PANDEMIC COVID-19
Get PDFAbstract: This study aims to improve the quality of the learning process in engineering education. The COVID-19 health crisis pushed the scientific community to review teaching practices and reconsider their effectiveness. Engineering education and learning were not an exception to that. This article introduces a case study using the Design of Experiments method to improve engineering education quality, especially in the distance learning process. In this case study, we focused on designing the process of distance learning and its quality by working on the case of two industrial engineering classes (2021 and 2022 classes) in a Moroccan public engineering school. The collaboration between the teacher and these two engineering students’ classes in their third year of industrial engineering enabled us to identify factors influencing learning quality. Then, we determined the optimal combinations of these factors for better quality by analyzing the results of the experiments. The Design of Experiments successfully implemented in manufacturing can also be applied to engineering education settings. The result of this study would help teachers and decision-makers understand the factors that influence the quality of learning to improve the distance learning process.
Scrap Reduction By Using Total Quality Management Tools
Get PDFA case study was carried out in one of the leading Indian industries manufacturing pre-stressed concrete steel strands (PC wire). It has major applications in bridges and construction industry. During study, lot of scrap was observed. Reasons for scrap was found out by using total quality management tools (TQM). Such as brainstorming, cause & effect diagram and pareto analysis. Main reasons were left over rings, non-conformity, chheda and weld/wire breakage. In the present study, scraps due to left over rings were reduced gradually by taking suitable action.
A Three-Phase Multicriteria Method to the Supplier Selection Problem
Get PDFThis paper describes a new multi-criteria method to solve the general supplier selection problem. The supplier selection problem is complicated and risky, owing to a variety of qualitative and quantitative factors affecting the decision-making process. For this matter, we present a unique three-phase methodology to reduce the base of potential suppliers to a manageable number and optimize the allocation of orders by means of multi-criteria techniques, namely ideal solution approach, Analytical Hierarchy Process (AHP) and Goal Programming (GP). Finally, a real-life example is provided to illustrate how the method can be used in practice
Modeling Supply Chain Diagnostics with Fuzzy Dynamic Bayesian Networks
No full textBayesian networks have been widely used as knowledge bases under uncertainty. However, in previous works, the uncertainty measure in Bayesian networks are usually probability distributions for crisp variables, which restricts the practical usefulness when incomplete knowledge or linguistic vagueness is involved in reasoning systems. This study develops a fuzzy dynamic Bayesian network (FDBN) in which fuzzy variables as well as crisp variables are considered. The proposed fuzzy dynamic Bayesian network is applied to supply chain modeling and reasoning. The simulation algorithms are designed to answer various diagnostic queries from supply chains
MEASURING PRODUCTION PERFORMANCE OF DIFFERENT PRODUCT MIXES IN SEMICONDUCTOR FABRICATION
No full textIn a competitive market. a company needs to fully utilize its current capacity in order to acquire higher profit. The purpose of this paper is to present an effective approach to fmd a set of product' mix efficient fOT the company to achieve the optimal production. Simulation is flIst used to generate the data of performance measures of different product mixes in a semiconductor fabricator. Data Envelopment Analysis (DEA) is applied next to measure multiple inputs and outputs, without pre-assigning weights, far product mixes, and an efficiency score for producing each product mix relative to other mixes can be obtained. The results provide guidance for a fab in accepting orders when its capacity cannot fully satisfy all the product demand
A DECOMPOSITION METHOD FOR PRODUCTION PLANNING UNDER DEMAND UNCERTAINTY IN WOOD REMANUFACTURING
No full textWood remanufacturers grapple with several challenging characteristics, such as divergent co-production, alternative processes, short order cycles, dynamic market, and imperfect raw materials. Production planning is a difficult task with such complex characteristics, especially when demand is random. In this paper, the production planning problem in wood remanufacturing mills is formulated as two-stage stochastic programming with recourse to the randomness arising from the exact required products volume resulting from the different market segments. A Benders’ decomposition algorithm is proposed as a solution method. Some enhancement techniques, namely, lower-bound inequalities, multi-cut framework, and Pareto-optimality cuts, are applied to accelerate the convergence of the Benders’ decomposition algorithm. The computational results indicate that the Benders’ decomposition algorithm is efficient in solving small problems; however, the accelerated Benders’ decomposition outperforms the Benders’ decomposition on larger problems with a large number of scenarios
DIVIDE-AND-CONQUER: A SYSTEMATIC APPROACH FOR SUBCONTRACTOR SELECTION IN DEFENSE INDUSTRY PROJECTS
No full textThe defense industry projects' sizes are generally large and may be broken down into subparts of different granularity levels, where each subpart may be assigned to a different subcontractor. On the other hand, the problem of subcontractor selection to each subpart is a complex decision-making problem that requires evaluating several criteria and the characteristics of each subpart. This study aims to model the problem of subcontractor selection in a defense industry project decomposed to multiple subprojects by combining the Analytic Hierarchy Process (AHP) and Integer Linear Programming (ILP). A project carried out at a defense industry company in Turkey has been used as a case study. An extensive set of criteria specific to the defense industry have been identified, and AHP has been applied to the relevant criteria and alternative subcontractors for each subpart. Finally, ILP has been used to include a set of constraints regarding the project specifications
INTEGRATED PRODUCTION-DISTRIBUTION PLANNING OPTIMIZATION USING NEUTROSOPHIC PROGRAMMING
No full textConsideration and management of uncertainties are critical in effective decision-making in production-distribution planning problems. Further, these decisions often involve multiple objectives which are conflicting in nature. More importantly, the decisions represented by these objective functions are often based on imprecise or uncertain data either due to unavailability or lack of objectivity of information, therefore cannot be solved by classical deterministic modeling techniques. To that end, this paper presents a neutrosophic programming-based approach to solve an integrated production-distribution planning problem in a two-echelon supply chain by considering uncertainties and indeterminateness in the data. The problem is formulated as a tri-objective mixed-integer linear programming model considering important features of production-distribution planning decisions. The three objectives considered are to minimize: total cost, delivery time, and backorder level. These objectives are represented by membership functions of the neutrosophic set, i.e., truth, indeterminacy, and falsity. The efficacy of the proposed methodology is illustrated by considering problem instances inspired by a real-world case in the automotive industry. A Pareto optimality test performed on the proposed neutrosophic model shows the existence of a strong optimal solution of the proposed neutrosophic model
MACHINE VISION-BASED, DIGITAL DISPLAY INSTRUMENT POSITIONING AND RECOGNITION
No full textHerein, an accurate and efficient algorithm for digital-display instrument positioning and recognition is proposed. The isolated forest algorithm and Otsu watershed threshold algorithm were used to distinguish digital-display instruments from nondigital-display instrument areas and separate the foreground from the background, respectively. The histogram of oriented gradient–support vector machine classification algorithm was used to distinguish instrument and non-instrument regions, which considerably improved the accuracy of digital-display instrument region positioning, avoided the interference of non-digital tube character regions, and reduced the search time of the digital tube region. A convolutional neural network was used for character recognition. Global characteristics of the character region were fully utilized, and partial digital character issues and scenarios where the decimal point is not obvious were mitigated. The proposed method can adapt to angle deviation, partial character missing, and image noise and exhibits excellent robustness and adaptability to the location and recognition of the digital tube