International Journal of Industrial Engineering: Theory, Applications and Practice
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DIGITAL AGRICULTURE: ANALYSIS OF VIBRATION TRANSMISSION FROM SEAT TO BACK OF TRACTOR DRIVERS UNDER MULTI-DIRECTIONAL VIBRATION CONDITIONS
Get PDFThe present research examines the impact of vibrations on seat-to-back transmissibility in tractor drivers. This study utilized a smart device for real-time data transmission to improve the experimentation by eliminating potential sources of error. Data was assessed using metrics such as weighted acceleration, daily exposure, power spectral density, and seat-to-back transmissibility. The seat pan and backrest were found to have high vibration levels on the vertical axis. Daily exposure response exceeded the exposure action limit of 0.5 m/s2, as specified in Directive 2002/44/EU. Power spectral densities at the seat pan and the backrest revealed dominant frequencies in the low-frequency range. Seat-to-back transmissibility demonstrated primary and secondary resonance within the 4.1-7.2 Hz and 8.2-11.8 Hz frequency ranges. Tractor manufacturers and designers could utilize the findings of this study to decrease the excessive vibration intensities and crucial resonating frequencies and thus enhance the operator's ride comfort
TWO-OBJECTIVE OPTIMIZATION FOR INTEGRATING PARTS ORDERING, TWO-STAGE ASSEMBLY FLOW-SHOP SCHEDULING, AND DISTRIBUTION THROUGH ROUTING
No full textThis paper presents a two-objective mixed integer linear programming model with conflicting objectives of minimizing the total cost of distribution and holding of products and minimizing the earliness and tardiness penalties. The main innovation of this study is the integration of two different levels of supply chain decision-making, including tactical and operational levels. On this basis, the problems of parts ordering, two-stage assembly flow-shop scheduling and distribution of products through routing were integrated. The Epsilon constraint method was used to solve the model. Given that each of these issues is NP-hard, and their simultaneous integration increases the complexity of the problem, a multi-objective gray wolf optimization (MOGWO) algorithm was used to find optimal Pareto fronts for large-scale problems. The statistical analysis of the MOGWO algorithm and the Epsilon constraint method showed that this algorithm had a significant difference in terms of the number of Pareto solutions and the solving time from the Epsilon constraint method
A GENETIC ALGORITHM FOR THE INTEGRATED WAREHOUSE LOCATION, ALLOCATION AND VEHICLE ROUTING PROBLEM IN A POOLED TRANSPORTATION SYSTEM
Get PDFIn this paper, we address the integrated location, allocation, and routing problem in the framework of a pooled transportation system. We assume that many enterprises with familiar customers aim to share their logistical means. Two collaborative scenarios are proposed and solved. A genetic algorithm based on Clarke and Wright’s savings heuristic is proposed to solve the different considered scenarios. A comparison is established between collaborative and noncollaborative scenarios to assess the impact of the proposed pooled transportation system. The obtained computational results indicate that the collaborative scenarios outperform the noncollaborative scenario. The total annual transportation cost is reduced by approximately 28% to 54% in the collaborative scenarios. Furthermore, the collaborative scenarios may reduce the number of required vehicles and increase the average fill rate of the used vehicles. It is worth noting that the proposed genetic algorithm solves efficiently adapted benchmark instances from the literature
SOLVING UNBALANCED INTUITIONISTIC FUZZY TRANSPORTATION PROBLEM BY USING THE ROW-COLUMN MAXIMA METHOD
No full textIn this paper, a new method is proposed for finding the optimum solution for an intuitionistic fuzzy transportation problem, entitled the Row-Column Maxima Method [RCMM]. The main goal of this method is to avoid a large number of iterations and to transport the item at the lowest cost from the point of origin to the point of destination. In addition to finding the best solution for an unbalanced transportation problem by turning it into a balanced transportation problem without the need for a fake source or destination, the suggested technique is used to solve both balanced and unbalanced intuitionistic fuzzy transportation problems. All parameters are represented as triangular intuitionistic fuzzy numbers. The proposed method has been illustrated by an example, and the result obtained by the proposed method shows superior performance compared with some existing methods available in the literature. Finally, the proposed technique is demonstrated using a numerical example and a graphical representation of the results. It is the most straightforward approach to resolving real-world transportation problems
DESIGN AND PERFORMANCE EVALUATION OF A NOVEL ULTRASONIC WELDING SONOTRODE FOR LANGEVIN TRANSDUCER USING FINITE ELEMENT APPROACH
No full textUltrasonic welding (USW) is recognized as a sustainable and green manufacturing process due to its high energy efficiency, cleanliness, and excellent welding attributes. The performance of USW majorly depends on small end vibration amplitude (SEVA), achieved through a sonotrode, whose design has been the focus of many researchers. A high SEVA is essential for achieving excellent welding attributes, minimizing wastage, and ensuring a clean environment. In this study, a novel USW sonotrode was designed to enhance the output amplitude of the Langevin ultrasonic transducer (LUT). The sonotrode was evaluated for eigenfrequency characterization and harmonic excitation response through finite element analysis. The performance of this novel ultrasonic transducer was evaluated in terms of SEVA and various types of stresses at an operating frequency close to but higher than the LUT’s axial modal frequency. Variations in axial displacement and Von Mises (VM), axial, tangential, radial, principal, and shear stresses were examined along the length of the tool for titanium, aluminum, and steel sonotrodes. The SEVA of the aluminum sonotrode was 136.72% and 5.87% higher than that of the titanium and steel sonotrodes, respectively. The equivalent VM stress in the steel sonotrode was 159.2% and 379.2% greater than their aluminum and titanium counterparts. Selected cases from the present study were compared with prior research, revealing a reasonable agreement. This work provides insights into the performance of sonotrodes in USW, offering potential paths for improving the effectiveness of this sustainable manufacturing process
OPTIMIZATION OF SUSTAINABLE AUTOMOTIVE MANUFACTURING SUPPLY CHAIN WITH DEMAND PREDICTION: A COMBINATORIAL FRAMEWORK
No full textThis research proposes a sustainable automotive manufacturing supply chain design model that integrates sustainable development into decision-making and balances the triple bottom line. A mixed integer linear model with multiple echelons, product types and energy modes is proposed. The model coordinates economic efficiency, environmental pollution, and social responsibility, and a method for standardizing and integrating the multiple optimization objectives is introduced. To address demand uncertainty, a deep neural network-based forecasting method is developed. It accurately predicts market demand by combining multidimensional influencing factors and historical demand. To solve the proposed model, a multinomial tree representation-based variable neighborhood search algorithm is designed. An adaptive mechanism is adopted to improve its search efficiency in the solution space. Experiments based on real data from the automotive manufacturing industry verify the proposed framework. The results show that the framework efficiently and effectively solves the sustainable automotive manufacturing supply chain design decisions with high solution quality. The framework provides useful managerial insights for decision-makers
PROMOTION OPTIMIZATION IN COMPETITIVE ENVIRONMENTS BY CONSIDERING THE CANNIBALIZATION EFFECT
Get PDFThis study proposes a new model to optimize sales promotion in competitive markets and examines the impact of competition on sales promotion planning and business performance in retail chains. The model can be used to determine the best promotional discount for different products with a cannibalization effect when competitors are present in the retail market and offer the same products with different discounts. An integer nonlinear programming problem is proposed to model the above issue. To solve the model, it is reformulated as a mixed-integer linear programming problem. Consequently, a MIP solver can be used to solve the model in a reasonable CPU time. Several examples are solved and a sensitivity analysis of the model parameters is performed. The results of our numerical study show interesting findings that considering different competitors is very important in promotion planning and optimization. Failure to take them into account can lead to loss of profits
A METHODOLOGY FOR THE BIDDERS EVALUATION AND SELECTION IN THE PUBLIC PROCUREMENT PROCESS BASED ON HETEROGENEOUS INFORMATION AND ADAPTIVE CONSENSUS APPROACHES
Get PDFThe public procurement problem is a special problem of supplier selection that requires strict adherence to the principles of non-discrimination, free competition, and transparency in the contract awarding procedures. It is a very complex multi-criteria problem, which requires the engagement of several decision-makers (experts). The public procurement problem requires the usage of different types of conflicting criteria, the combination of different models (methods and techniques) of decision-making, as well as the modeling of different forms of uncertainty, inaccuracy, and subjectivity of decision-makers, which can represent a rather complex, difficult, and lengthy decision-making process. Therefore, the paper proposes a methodology for improving the tender process that focuses on heterogeneous preference structures of information (preference ordering, utility values, fuzzy (additive) preference relations, multiplicative preference relations, and linguistic preference relations) and an adaptive consensus approach for subjectively determining the weight of criteria and evaluation and selection of alternative bids. The Simple Additive Weighting (SAW) method is used for the final ranking of bidders. The proposed methodology enables obtaining a more objective and measurable value during subjective decision-making as well as minimizing the risk of unscrupulous, incompetent, and irresponsible decision-making, which is shown in the given example
EXPERIENCE-ORIENTED MODEL OF BUDGET ALLOCATION AND COST CONTROL FOR ENGINEERING CONSULTING PROJECTS
Get PDFThis paper presents an experience-oriented model of budget allocation and cost control for engineering consulting projects. The proposed model comprised two modules: a work item module and a work duration module. Regarding the work item module, a project manager employed the analytic hierarchy process (AHP) to determine the budget percentage allocated to each work item. Regarding the work duration module, this study compiled all S-curves appearing in each budget percentage range in past projects. A project manager then selected the optimal curve shape for each work item to determine the daily budget allocation and cost control limits throughout the work duration of each work item. Testing revealed that the proposed model facilitates project managers’ budget allocation decision-making, determines budget control limits for the overall project and for each work item and identifies work items that may be out of control at an early stage
AUTOENCODER BASED GENERATOR FOR CREDIT INFORMATION RECOVERY OF RURAL BANKS
Get PDFBy using machine learning algorithms, banks and other lending institutions can construct intelligent risk control models for loan businesses, which helps to overcome the disadvantages of traditional evaluation methods, such as low efficiency and excessive reliance on the subjective judgment of auditors. However, in the practical evaluation process, it is inevitable to encounter data with missing credit characteristics. Therefore, filling in the missing characteristics is crucial for the training process of those machine learning algorithms, especially when applied to rural banks with little credit data. In this work, we proposed an autoencoder-based algorithm that can use the correlation between data to restore the missing data items in the features. Also, we selected several open-source datasets (German Credit Data, Give Me Some Credit on the Kaggle platform, etc.) as the training and test dataset to verify the algorithm. The comparison results show that our model outperforms the others, although the performance of the autoencoder-based feature restorer decreases significantly when the feature missing ratio exceeds 70%