This dissertation presents dynamic system models of continuous manufacturing systems based on analogies with electrical systems. The developed modeling technique has the capability to explicitly specify production control schemes including control points, material and information flow paths, and logical operations. The model provides standard graphical representations and governing equations to describe both the steady state and transient responses of continuous manufacturing systems. For deterministic systems, these equations can be solved to get closed-form solutions. For stochastic systems, numerical solutions can be obtained for any probabilistic distribution. The electrical analogs provide an excellent tool to model control signals and logical operations. This is especially important for pull control schemes where qualitative descriptions often found in the literature can be ambiguous. The proposed technique is demonstrated by modeling push and a variety of pull systems. The developed models are used to study the relative performance of push, CONWIP, and kanban control systems. The results show that the card count distribution significantly affects the performance of a kanban system, and that drawing conclusions on kanban performance requires card count optimization. The results also show that the relative performance of push and CONWIP systems varies with operational factors. The factors studied in this dissertation are the bottleneck utilization, line balance, demand rate variability, and processing rate variability. At some combinations of these factors, the push system is superior to the CONWIP system, with other combinations, the CONWIP system is the superior system. In this work, push systems tend to have better relative performance (compared to CONWIP) at high variability levels in the processing rates and low variability levels in the demand rate, while CONWIP systems tend to have better relative performance (compared to push) at high variability levels in the demand rate and low variability levels in the processing rates. CONWIP systems tend to have higher relative performance at high utilization levels and in lines where a distinct bottleneck exists.
","abstract_html":"<p>This dissertation presents dynamic system models of continuous manufacturing systems based on analogies with electrical systems. The developed modeling technique has the capability to explicitly specify production control schemes including control points, material and information flow paths, and logical operations. The model provides standard graphical representations and governing equations to describe both the steady state and transient responses of continuous manufacturing systems. For deterministic systems, these equations can be solved to get closed-form solutions. For stochastic systems, numerical solutions can be obtained for any probabilistic distribution. The electrical analogs provide an excellent tool to model control signals and logical operations. This is especially important for pull control schemes where qualitative descriptions often found in the literature can be ambiguous. The proposed technique is demonstrated by modeling push and a variety of pull systems. The developed models are used to study the relative performance of push, CONWIP, and kanban control systems. The results show that the card count distribution significantly affects the performance of a kanban system, and that drawing conclusions on kanban performance requires card count optimization. The results also show that the relative performance of push and CONWIP systems varies with operational factors. The factors studied in this dissertation are the bottleneck utilization, line balance, demand rate variability, and processing rate variability. At some combinations of these factors, the push system is superior to the CONWIP system, with other combinations, the CONWIP system is the superior system. In this work, push systems tend to have better relative performance (compared to CONWIP) at high variability levels in the processing rates and low variability levels in the demand rate, while CONWIP systems tend to have better relative performance (compared to push) at high variability levels in the demand rate and low variability levels in the processing rates. CONWIP systems tend to have higher relative performance at high utilization levels and in lines where a distinct bottleneck exists.</p>","abstract_has_math":false,"creators":["Sader, Bashar Hafez"],"institution":"Brigham Young University - Provo","degree_name":"PhD","degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":null,"date_issued":"","date_published":null,"updated_at":"2026-07-24T01:27:34Z","subjects":["manufacturing systems","modeling technique","pull systems","simulation","CONWIP relative performance","Mechanical Engineering"],"languages":["English"],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"https://scholarsarchive.byu.edu/etd/315","outbound_label":"Repository record","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["Sader, Bashar Hafez"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2005-05-12T07:00:00Z"]},{"key":"dc:publisher","label":"Institution","values":["Brigham Young University - Provo"]},{"key":"dc:type","label":"Dc Type","values":["Dissertation"]},{"key":"thesis:degree_name","label":"Degree Name","values":["PhD"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["manufacturing systems","modeling technique","pull systems","simulation","CONWIP relative performance","Mechanical Engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["English"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["https://scholarsarchive.byu.edu/etd/315","https://scholarsarchive.byu.edu/context/etd/article/1314/viewcontent/ETD_CISOPTR_335.pdf"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ira A. Fulton College of Engineering and Technology; Mechanical Engineering"]},{"key":"dc:description.abstract","label":"Abstract","values":["This dissertation presents dynamic system models of continuous manufacturing systems based on analogies with electrical systems. The developed modeling technique has the capability to explicitly specify production control schemes including control points, material and information flow paths, and logical operations. The model provides standard graphical representations and governing equations to describe both the steady state and transient responses of continuous manufacturing systems. For deterministic systems, these equations can be solved to get closed-form solutions. For stochastic systems, numerical solutions can be obtained for any probabilistic distribution. The electrical analogs provide an excellent tool to model control signals and logical operations. This is especially important for pull control schemes where qualitative descriptions often found in the literature can be ambiguous. The proposed technique is demonstrated by modeling push and a variety of pull systems. The developed models are used to study the relative performance of push, CONWIP, and kanban control systems. The results show that the card count distribution significantly affects the performance of a kanban system, and that drawing conclusions on kanban performance requires card count optimization. The results also show that the relative performance of push and CONWIP systems varies with operational factors. The factors studied in this dissertation are the bottleneck utilization, line balance, demand rate variability, and processing rate variability. At some combinations of these factors, the push system is superior to the CONWIP system, with other combinations, the CONWIP system is the superior system. In this work, push systems tend to have better relative performance (compared to CONWIP) at high variability levels in the processing rates and low variability levels in the demand rate, while CONWIP systems tend to have better relative performance (compared to push) at high variability levels in the demand rate and low variability levels in the processing rates. CONWIP systems tend to have higher relative performance at high utilization levels and in lines where a distinct bottleneck exists.
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"],"dc:format":["application:pdf"],"dc:identifier":["https://scholarsarchive.byu.edu/etd/315","https://scholarsarchive.byu.edu/context/etd/article/1314/viewcontent/ETD_CISOPTR_335.pdf"],"dc:language":["English"],"dc:publisher":["Brigham Young University - Provo"],"dc:source":["Brigham Young University - Provo"],"dc:subject":["manufacturing systems","modeling technique","pull systems","simulation","CONWIP relative performance","Mechanical Engineering"],"dc:title":["Development and Application of a New Modeling Technique for Production Control Schemes in Manufacturing Systems"],"dc:type":["Dissertation"],"thesis:degree_name":["PhD"]},"updated_at":"2026-07-24T01:27:34Z"}