1,720,976 research outputs found
Energy-aware optimization for solving distributed flexible job shop scheduling problems
No full textPresented to the 19th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 14, 2023.Research completed in the Department of Industrial, Systems, and Manufacturing Engineering, College of Engineering.INTRODUCTION: The significance of distributed manufacturing and energy costs in today's continuously increasing globalizing world has been recognized by scholars and researchers and efforts have been taken to improve process scheduling efficiency/reduce manufacturing costs over the years. A Distributed Flexible Job-Shop Scheduling Problem (DFJSP) is a distributed scheduling problem where each factory represents a single Flexible Job-Shop Scheduling Problem where a particular job cannot be assigned to more than one factory. PURPOSE: This research aims to address the distributed flexible job shop scheduling problem (DFJSP) by minimizing the maximum completion time (makespan time) where setup times of available machines for current operation depends on the previous operation processed by it (sequence dependent setup times). METHODS: This research proposed and formulated a mixed integer linear programming (MILP) model with an early optimization termination criterion to solve the DFJSP. Furthermore, this model is extended to consider electricity consumption by introducing the objective function of energy usage in the form of processing energy, setup energy & idle energy consumption and a strategy to shutdown machines when the machines are in idle state. A constraint programming model and a meta-heuristic algorithm in the form of a combination of Genetic and NEH algorithm (NEH-GA) is proposed and compared with the MILP model. RESULTS: The results shows that the MILP model achieves optimality within reasonable time and performs better than constraint programming model when problem complexity is small whereas constraint programming tends to do better when the problem complexity is large. The proposed meta-heuristic algorithm performs the best in terms of computation time when the problem complexity is large albeit not guaranteeing an optimal solution. CONCLUSION: All three models were successful in finding solutions which reduce the total energy consumption in five out of the total fourteen problem instances. For future research studies, the current DFJSP base model can be modified to other objectives such as earliness/tardiness. Similarly, optimization models can be formulated for DFJSP considering machine breakdowns with sequence dependent setup times.Graduate School, Academic Affairs, University Librarie
Optimization methods for tardiness/earliness scheduling problem in job shop environment
No full textThesis (Ph.D.)-- Wichita State University, College of Engineering, Dept. of Industrial, Systems and Manufacturing EngineeringMotivated by the practical scheduling problem of job shops, this research aims to evaluate unrelated parallel machine scheduling problem with the objective of minimizing tardiness and earliness costs in both deterministic and uncertain job shop environments. In the first section, this study proposes an enhanced model considering effects of maximum allowable tardiness. It compares the total cost provided by the proposed model with the case in which there is no limitation on tardiness. In addition, the existing model in the literature is simplified to reduce computational time and enable corporate scheduling staff to use the model efficiently. The results show the effectiveness of proposed model since it reduces the total cost and computational time in most of the studied scenarios. To account for real-life cases representing uncertainty, in the second step of the research, processing time, setup time, and tooling cost are considered as uncertain input parameters. Robust optimization method is used to deal with uncertainty. The robust counterpart formulation is provided to solve the optimization problem with uncertainty and bring the tradeoff between optimality and robustness. This research considers two performance measures as robust price and hedge value to indicate importance of robust scheduling. Design of experiment (DOE) analysis is used to analyze effect of robust optimization parameters on the cost and computational time. Working experience in job shop environment indicates that there are multiple frequent real-time changes (e.g. processing times, due dates, etc.). In the third step of the research, an automated and adaptive model is proposed to dynamically reschedule the initial schedule based on the real-time data. Considering the rescheduling cost, the model also proposed a rescheduling criteria using project management concept.
Scheduling problem is categorized as an NP-hard problem. Therefore, the exact model may not be effective to solve large size problems. Thus, in the last part of this research, we proposed an enhanced Genetic Algorithm (E-GA) based model by optimizing GA operators and proposing hybrid dispatch rule to provide initial solution. The results show the efficiency and effectiveness of proposed model compared to multiple existing methods in the literature
Multiple numerical experiments are presented to validate proposed models using data collected from a local job shop that manufacturers aerospace parts. Various performance measurements are evaluated to compare performance of proposed approaches
Economic development and job creation by applying optimization methods for scheduling problem in job shops
No full textPoster project completed at the Wichita State University Department of Industrial, Systems, and Manufacturing Engineering. Presented at the 16th Annual Capitol Graduate Research Summit, Topeka, KS, February 26, 2019.The manufacturing sector earned one of the top spots in 2017 Kansas economy adding over 1,000 jobs. This developing direction is predicted to continue with 0.5% growth in 2019. Within the manufacturing sector, aviation is ranked fourth in Kansas with over 30,000 workers. 44% of Kansans work for small businesses (less than 50 employees) and this percentage is projected to increase since the annual growth of small businesses in Kansas is projected as 6%. Therefore, this research aims to study typical operations in one of these small businesses in the aviation sector. The study evaluates the scheduling problem with the objective of minimizing total earliness/tardiness cost. A new model is proposed that considers the effects of maximum allowable tardiness. In addition, the existing model in the literature is simplified to reduce computational time and enable corporate scheduling staff to use the model efficiently. The model is validated using data collected from a local job shop that manufactures aerospace parts in Wichita, Kansas. The results show the effectiveness of the proposed model since it reduces the total cost and computational time in most of the studied scenarios. As a result, decreased costs and more satisfied customers are expected to bring more business to Wichita and lead to a significant increase in economic development and job creation
Cyber-physical supply chain using ranked blockchain
No full textThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Industrial, Systems, and Manufacturing EngineeringThe complex nature of modern supply chains has made the traceability of goods
throughout their lifecycles very difficult. Modern technology and the digitization of
the supply chain have created vast amounts of data that would be useful for traceability;
however, the transference of this information throughout the supply chain
as the products are moved is a difficult and costly problem to solve. Traceability
systems have been built; however, these are typically confined to single organizations
and the portions of the product’s lifecycle that are within the custody of
that organization. This thesis proposes the creation of a ranked blockchain-based
cyber-physical supply chain system. The use of blockchain technology allows for the
creation of a decentralized system that, through smart contracts, can enforce arbitrary
logic at a transactional level. Blockchain also creates an immutable ledger of
the past in which information, once written, can be guaranteed to persist forward.
Furthermore, ranking can abstract some of the informational aspects of products
and enhance traceability. Two prototypes were constructed, the former being a
simple prototype demonstrating how a Cyber-Physical Supply Chain (CPSC) system
could be created using blockchain. The latter is an expanded version and
includes integrity management. The results from these two prototypes show how
a CPSC blockchain traceability system can be created
An energy consumption model under time-of-use rates for scheduling of manufacturing shops
No full textPoster project completed at the Wichita State University Department of Industrial, Systems and Manufacturing Engineering. Presented virtually at the 18th Annual Capitol Graduate Research Summit, Topeka, KS, February 18, 2021.One of the most important contributors supporting economic prosperity in Kansas is attributed to manufacturing. It accounted for 16.30% of the output and 11.69% of the workforce (2020 Kansas Manufacturing Facts). In addition, small businesses employed 51% of the private workforce in 2016 (Kansas Small Business Profile, 2019). With such an importance of small businesses within Kansas, this research focuses on small manufacturing shops. Scheduling of manufacturing industries with the aim of minimizing makespan have been studied extensively over the last decades. Recently, green production scheduling considering energy efficiency is emerging as one of the important research areas. To address the critical issues concerning the increased energy consumption and environmental pollution, this research attempts to develop a multi-objective model for manufacturing shops, considering electric consumption cost under time of use (TOU) tariffs, worker's cost, and completion time of all products. Workforces with different performance are considered for machine tools adjustment. A multi-objective genetic algorithm (MOGA) is constructed to minimize not only completion time of all jobs, but also the total electricity costs (TEC) and worker's costs. In order to check the performance of the proposed algorithm, different scheduling test problems are designed and a comprehensive computational experiment is carried out. The results indicate that the proposed MOGA algorithm is able to constructs different optimal scheduling scenarios for manufacturing shops, by which products are completed in a shortest time interval. In addition, total cost including energy consumption and workforces' costs are reduced to a great extent
Integrated adaptive forecasting and energy aware scheduling model using genetic algorithm with dynamic stopping condition
Get PDFThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Industrial, Systems, and Manufacturing EngineeringAccording to Annual Energy Outlook 2022, published by the US Energy Information and Administration, manufacturing energy intensity is predicted to be reduced from 2018 to 2050. Despite near term uncertainty due to COVID-19 outbreak, this change can be attributed to the paradigm shift towards environmental sustainability and energy efficiency resulting in efforts by manufacturers to reduce costs, achieve carbon neutral status, and adhere to local, state, and international regulations. Manufacturers implement various demand side management projects for this purpose, one of them being energy efficient scheduling. The job shop scheduling model takes into consideration energy and demand costs, earliness and tardiness costs, worker costs, machine depreciation cost, and resource leveling for worker. Artificial Neural Network model is used to provide highly accurate prediction of energy prices and genetic algorithm is used to obtain job sequence and assignment in reasonable time for the job shop scheduling problem
Energy efficient scheduling with penalty costs, labor costs and volatile electricity prices
No full textThesis (M.S.)-- Wichita State University, College of Engineering, Dept. of Industrial, Systems, and Manufacturing EngineeringManufacturing sector is consuming the high amount of energy at a rapid pace which is leading to energy crisis as well as affecting the environment by the emission of carbon footprints. So, there is a practical interest and necessity to reduce the greenhouse gases and use the energy efficiently. Production scheduling techniques has been in practice from many decades in order to minimize the overall cost involved in the production and use the resources effectively. In the recent times energy efficient scheduling technique has come into use where the major goal is reducing the energy usage involved in the production which would ultimately reduce the overall production cost. Utility companies implement various policies like time of use, real time pricing, critical time pricing, seasonal pricing etc., based on the firms which could be taken advantage of in order to use the energy efficiently. In this energy efficient scheduling the jobs were scheduling on the machines during the low pricing periods with minimum effect on the due dates as well as the labor costs. A mathematical model was proposed in this research which minimize the overall production costs which includes electricity consumption charge, demand charge, penalty costs and labor costs. The validation of the proposed model is verified using various case studies and the results have proven the inclusion of the penalty costs and labors costs would reduce the overall costs incurred
Estimating and optimizing HVAC energy costs in industrial building
No full textPresented to the 18th Annual Symposium on Graduate Research and Scholarly Projects (GRASP) held at the Rhatigan Student Center, Wichita State University, April 29, 2022Research completed in the Department of Industrial, Systems and Manufacturing Engineering, College of EngineeringIn the US, industrial buildings accounts for over 33% of the total energy consumption. Heating, ventilation & air-conditioning (HVAC) system consumes second most energy after production system. In industrial buildings, heat waste from heavy machineries and large building envelope makes the HVAC system work harder when compared to commercial and residential buildings. The objective of this research is to find factors responsible for high HVAC system energy costs and optimize them to maximize energy savings. Most industrial facilities do not have sub-metering of individual energy consuming component. To establish baseline energy consumption model and disaggregate the HVAC component of energy consumption relative to changes in production and weather data, we propose the use of simple inverse linear regression models using monthly utility billing and weather data. In addition, multi-layer Long short-term memory (LSTM) neural network model is used to forecast short term weather to predict temperature. Using the forecasted weather and baseline HVAC energy consumption relation, a mixed integer programming model is used to maximize energy savings by scheduling activities within the building. The optimization model was tested using experimental data to find trends and relation between energy savings and factors affecting it by considering constraints such as resource availability, deadlines etc. The LSTM model achieves a high validation accuracy of up to 80%. This optimization model can potentially achieve up to 30% reduction in HVAC energy consumption.Graduate School, Academic Affairs, University Librarie
Blood supply chain simulation: An investigatory analysis
Get PDFPoster project completed at the Wichita State University Department of Industrial, Systems and Manufacturing Engineering. Presented at the 21st Annual Capitol Graduate Research Summit, Topeka, KS, March 21, 2024.In this study, we investigate the efficiency of blood donation processes, emphasizing Red Blood Cells (RBCs) in line with International Blood Banking Standards. Utilizing simulations, we assess donor wait times and resource management, aiming to enhance the donation experience and the efficiency of blood collection. Our analysis emphasizes the importance of managing queues and resources effectively to reduce wait times and improve donor satisfaction. Identifying discrepancies between expected and actual resource use, we find opportunities to decrease downtime and streamline the donation process. Crucially, we recommend implementing donor incentives inspired by successful global case studies to boost repeat donations. These enhancements are particularly relevant to Kansas, where optimizing blood donation processes could significantly improve healthcare outcomes and foster greater community engagement. By ensuring a steady supply of RBCs, our suggested strategies aim to strengthen the resilience of Kansas's healthcare system, highlighting the state's commitment to leading in healthcare delivery and community health initiatives. Incorporating globally inspired donor incentives into our approach not only aligns with International Blood Banking Standards but also underscores Kansas's role in pioneering efficient and community-focused healthcare solutions
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