International Journal of Industrial Engineering: Theory, Applications and Practice
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Short Sea Shipping and River-Sea Shipping in the Multi-modal Transport of Containers
No full textAbstract: The constantly increasing quantitative and qualitative requirements for the terrestrial container and Ro/Ro transport can not only be dealt with in road and rail freight transport and from transportation on inland waterways in the upcoming years. Therefore, suitable solutions have to be found which include other modes of transport, whereas both economic and ecological factors as well as macroeconomic considerations are of importance. One possible approach is to increase the use of Short Sea Shipping and River-Sea Shipping so far less applied. The underlying structures here are presented and reviewed for their advantages and disadvantages. Potential demand structures are identified and illustrated by various examples. The paper concludes with analysis and evaluation of these concepts and the summary of necessary measures for their implementation
MANUFACTURING PROCESSES MODELING USING MULTIVARIATE INFORMATION CRITERIA FOR RADIAL BASIS FUNCTION SELECTION
No full textNowadays, the advances in manufacturing technologies need to improve processes every day. For this reason, it is useful to have models for planning, optimization, simulation, and decision-making in the process. A widely used method to improve manufacturing processes is the Radial Basis Function Neural Network, which models the manufacturing process using a nonlinear radial function. There are several types of radial basis functions, but the question is: which specific function generates a better representation? This work proposes the application of Information Criteria based on Akaike’s criteria to select the radial basis function that best describes the process behavior. The present paper proposes the design of an RBF Network to predict the behavior in processes with several responses, applying the multivariate Akaike Information Criteria (AIC) as a fitness function in a Genetic Algorithm to select the Radial Basis Function to improve the prediction of the Radial Basis Function neural network
INVESTIGATION OF THE EFFECTS OF KOREAN POLICIES TO PROMOTE A GRID-CONNECTED MICROGRID
No full textMicrogrids have been considered a promising concept for integrating distributed energy resources (DERs) into the electricity grid. As such, to promote DERs in a grid-connected microgrid, we first develop an optimization model for the economic scheduling of DER generation to minimize the operation cost, including the cost of buying power from the utility grid. Despite optimal scheduling, utility-scale generation is still cost-effective. To overcome this barrier, a financial subsidy or mandatory policies are necessary promotion policies. We thus develop a framework to evaluate how the promotion policies perform based on the optimization model. Based on a case study in South Korea, we show that subsidizing the use of energy storage is an effective subsidy policy, with an appropriate autonomy level imposed by the government. In conclusion, our study suggests that a subsidy policy should be carefully designed by considering the energy independence of the grid-connected microgrid, cost reduction by utilizing DERs, and load shedding effects
ANALYSIS OF THE CENTRALIZED SUPPLY CHAIN DYNAMICS BY SETTING OPERATIONAL PARAMETERS
No full textThis study aimed to determine the importance of ordering parameters, measure their impact on supply chain dynamics in terms of total cost and the bullwhip effect when these parameters change simultaneously in the supply chain and provide a model for effective supply chain management. Response surface methodology was used for data collection in the context of the dynamic system model to determine the importance and effects of ordering parameters on supply chain dynamics. In this way, after designing a centralized supply chain model (all supply chain members followed the same ordering pattern) using a system dynamics approach, different combinations of ordering parameter levels were implemented using the response surface methodology in the supply chain model. The results were analyzed using ANOVA to determine the significance and intensity of the effects of parameters on supply chain dynamics. The results showed that the expected time for receiving the goods at the specified levels had no significant effect on total cost and bullwhip effect; i.e., when ordering, this parameter could be used more flexibly with the most negligible effect on supply chain dynamics. On the other hand, to achieve a stable supply chain (simultaneously reducing total cost and bullwhip effect), adjusting the amount of safety stock and the work-in-process was necessary to its maximum level
INVIGILATORS ASSIGNMENT IN PRACTICAL EXAMINATION TIMETABLING PROBLEMS
No full textThis paper addresses an invigilator assignment problem. The problem deals with a set of exams, each of which requires a given number of invigilators. The aim is to prepare a conflict-free schedule where all invigilator requirements of the exams are met. In this study, the conflict-free schedule is determined by a mixed-integer linear programming model and a heuristic algorithm that accounts for the following real-life concerns; assignment of the invigilators responsible for an exam, reduction of the number of successive invigilation duties, fair distribution of total workload and unfavorable workload among invigilators and, prioritization of the assignments based on invigilators’ profession. The applicability of the proposed model and the heuristic algorithm has been shown on eight different real-life problems of leading public universities in Turkey and further eight larger-sized examinations set up based on the real settings. In universities, the real schedules are manually prepared by a faculty team. Compared to the assignment of the faculty team responsible for the examination of timetabling in which balancing only the numbers of duties, we achieved to 86% decrease in the total positive error of invigilation hours by fairly distributing the invigilation duties in the model results. Besides, the following improvements are achieved by applying the proposed model; a 56% decrease in the total number of successor assignments, a 44% decrease in the total unfavorable time, and a 23% increase in the total number of department-based assignments. The heuristic algorithm improves the team schedules by 4% in terms of the total positive error of total invigilation hours and 57% in terms of the total number of successive exam assignments. Accordingly, the proposed model and the heuristic algorithm can be used as a decision support tool by the faculty team
JOINT OPTIMIZATION OF CAPACITATED LOT-SIZING WITH LOST SALES AND NON-CYCLICAL PREVENTIVE MAINTENANCE
No full textJoint optimization of maintenance and lot-sizing-related operations offers significant benefits for businesses. This research studies the joint optimization of non-cyclical preventive maintenance and capacitated lot-sizing with lost sales. The objective is to determine the optimal lot size in each period, decide the optimal preventive maintenance for servicing machines over a planning horizon, and minimize the total cost related to service, operation, setup, production, inventory, and lost sales. An innovative framework is proposed to solve the problem, consisting of a novel mixed-integer linear programming formulation and an approximative production-planning algorithm combined with a multi-neighborhood descendent approach. Extensive experiments based on real-world data are conducted to verify the effectiveness of the proposed framework
Proteomic Pattern Analysis Using Neural Networks
Get PDFProtein profiling of biologic samples by techniques such as surface-enhanced laser desorption/ionization (SELDI) or matrix assisted laser desorption/ionization (MALDI) yields massive amounts of data that require use of automated techniques to detect expression patterns. This paper suggests a neural network based classification and clustering technique for the analysis of proteomic data on serum samples collected from human subjects exposed to diesel exhaust fumes (DEF). Data were collected on samples from 93 subjects exposed to DEF. Proteomic patterns were analyzed using Neuralware Predict software obtained from Neuralware Inc. The cascade correlation algorithm was used as the classification algorithm and self-organizing maps (SOM) was used as the clustering algorithm. The protein peaks were identified using the Ciphergen Software. The most discriminating peaks were identified by applying a student t-test and using the p-value as the criterion for discrimination. The classification and clustering algorithms were applied to the two data sets. The use of a neural network program for analysis of proteomic patterns from serum samples obtained from human subjects exposed to DEF or not exposed to DEF showed excellent discrimination. Such an approach has potential to play an important role in determining deleterious effects of occupational exposures and discovery of biomarkers
Modeling and Solving an Integrated Supply Chain System
Get PDFWe develop and optimize an integrated supply chain model that combines strategic decision (where to locate distribution centers) with the tactical decisions (production levels and inventory levels) and operational decisions (satisfying customer demand on time and coordinating logistics network). In previous studies, the integrated models for supply chain have been applied mostly in two areas: the first is the integration of production and inventory; and the second is the integration of production and distribution. We build a mixed-integer programming model to minimize the total costs of distribution, storage, inventory and operations, with production levels high enough to satisfy customer demand. Two heuristic solution methods are presented: horizontal staging and time slicing. The results show that the horizontal staging heuristic can provide solutions very close to the best solution of the optimal model and with significant savings in run time
Clustering Techniques for Barge to Boat Assignment
Get PDFThis paper focuses on the development of clustering methods to determine effective assignments of barges to tow boats for intra-river transport. Barges are clustered such that dwell time, handling, and transit are minimized while constraints associated with pick-up and delivery requirements, physical tow sizes, and travel time are considered. The results from this paper indicate that ‘complete linkage’ and ‘partitioning around medoids’ clustering methods outperform the other grouping models considered in terms of maximizing boat utilization
ADAPTIVE SCHEDULING OF AERO-ENGINE ASSEMBLY BASED ON Q-LEARNING IN KNOWLEDGEABLE MANUFACTURE
No full textAn adaptive optimization scheduling AQ (Assembly Q-learning) algorithm of knowledgeable manufacture is proposed for an aero-engine assembly line to address the scheduling of an aero-engine assembly workshop in an uncertain manufacture system, which combines the real-time feature of Q-learning with the self-adaptation feature of knowledgeable manufacture system. An adaptive scheduling model of aero-engine assembly based on Q-learning is developed for the purpose of minimizing earliness penalty and completion time cost. New production scheduling rules are proposed to deal with the aero-engine assembly scheduling problem. Addressing the characteristics of aero-engine assembly, four system-state features are defined, and a proper reward is designed as a reward function. Coherence of the reward function and the scheduling objective is proved by theorem. Simulation tests show that the developed algorithm is superior to other scheduling rules in various situations. Particularly, in the ever-changing assembling environment, better results are guaranteed by the desirable adaptivity of the proposed algorithm