{"id":{"repo_id":"nus","oai_identifier":"oai:scholarbank.nus.edu.sg:10635/18013"},"canonical_url":"https://search.dev.ndltd.org/etd/nus/oai:scholarbank.nus.edu.sg:10635/18013","repository":{"repo_id":"nus","name":"National University of Singapore","base_url":"https://scholarbank.nus.edu.sg/oai/request"},"display":{"title":"Job shop scheduling to minimize work-in-process, earliness and tardiness costs","abstract":"Our research is motivated by a scenario of a manufacturing company receiving highly customized orders from different customers. A good production schedule is required to complete the orders on time with the limited resources and minimize the relevant costs. Such a scenario is modelled as a job shop problem with non-regular performance measure (shorted as JIT-JSP). The objective of JIT-JSP is to minimize three inventory related costs: Work in process (WIP) holding, earliness and tardiness cost.Schedule generation procedures including idle time insertion are studied to generate feasible schedule for JIT-JSP. A modified tabu search algorithm (MTS) is developed to improve the schedule quality by searching the neighborhood for better schedules iteratively. A dispatching rule selector based on neural network is developed to solve the performance fluctuation of dispatching rules. Computational results show that the proposed procedures significantly improve the schedule quality.","abstract_html":"Our research is motivated by a scenario of a manufacturing company receiving highly customized orders from different customers. A good production schedule is required to complete the orders on time with the limited resources and minimize the relevant costs. Such a scenario is modelled as a job shop problem with non-regular performance measure (shorted as JIT-JSP). The objective of JIT-JSP is to minimize three inventory related costs: Work in process (WIP) holding, earliness and tardiness cost.Schedule generation procedures including idle time insertion are studied to generate feasible schedule for JIT-JSP. A modified tabu search algorithm (MTS) is developed to improve the schedule quality by searching the neighborhood for better schedules iteratively. A dispatching rule selector based on neural network is developed to solve the performance fluctuation of dispatching rules. Computational results show that the proposed procedures significantly improve the schedule quality.","abstract_has_math":false,"creators":["ZHU ZHECHENG"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":[],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2009,"date_issued":"2009-01-06","date_published":"2009-01-06","updated_at":"2026-07-24T03:31:38Z","subjects":["job shop scheduling, just in time, schedule generation procedure, idle time insertion, tabu search, dispatching rule selector"],"languages":[],"rights":[],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":null,"outbound_label":null,"outbound_source":null},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:creator","label":"Author","values":["ZHU ZHECHENG"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date.issued","label":"Date","values":["2009-01-06"]},{"key":"dc:relation.isreferencedby","label":"Dc Relation Isreferencedby","values":["https://scholarbank.nus.edu.sg/handle/10635/18013"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["job shop scheduling, just in time, schedule generation procedure, idle time insertion, tabu search, dispatching rule selector"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["https://scholarbank.nus.edu.sg/bitstreams/1ee46d78-ce64-46aa-9c99-94407b9eb737/download"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["Our research is motivated by a scenario of a manufacturing company receiving highly customized orders from different customers. 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The objective of JIT-JSP is to minimize three inventory related costs: Work in process (WIP) holding, earliness and tardiness cost.Schedule generation procedures including idle time insertion are studied to generate feasible schedule for JIT-JSP. A modified tabu search algorithm (MTS) is developed to improve the schedule quality by searching the neighborhood for better schedules iteratively. A dispatching rule selector based on neural network is developed to solve the performance fluctuation of dispatching rules. 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