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Newcastle University ePrints | eprint.ncl.ac.uk Chansombat S, Pongcharoen P, Hicks C. A mixed-integer linear programming model for integrated production and preventive maintenance scheduling in the capital goods industry. AbstractThe scheduling literature is extensive, but much of this work is theoretical and does not capture the complexity of real world systems. Capital goods companies produce products with deep and complex product structures, each of which requires the coordination of jobbing, batch, flow and assembly processes. Many components require numerous operations on multiple machines. Integrated scheduling problems simultaneously consider two or more simultaneous decisions. Previous production scheduling research in the capital goods industry has neglected maintenance scheduling and used metaheuristics with stochastic search that cannot guarantee an optimal solution. This paper presents a novel mixed integer linear programming (MILP) model for simultaneously solving the integrated production and preventive maintenance scheduling problem in the capital goods industry, which was tested using data from a collaborating company. The objective was to minimise total costs including: tardiness and earliness penalty costs; component and assembly holding costs; preventive maintenance costs; and setup, production, transfer and production idle time costs. Thus, the objective function and problem formulation were more extensive than previous research. The tool was successfully tested using data obtained from a collaborating company. It was found that the company's total cost could be reduced by up to 63.5%.

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The timetabling of lecturers, seminars, practical sessions and examinations is a core business process for academic institutions. A feasible timetable must satisfy hard constraints, an optimum timetable will additionally satisfy soft constraints, which are not absolutely essential. An Ant Colony based Timetabling Tool (ANCOTT) has been developed for solving timetabling problems. New variants of Ant Colony Optimisation (ACO) called Best-Worst Ant System (BWAS) and Best-Worst Ant Colony System (BWACS) were embedded in the ANCOTT program. Local Search (LS) strategies were developed and embedded into BWAS and BWACS to enhance their efficiency and to help find the best timetable with the lowest number of soft constraint violations. Statistical tools for experimental design and analysis were adopted to investigate the factors affecting the BWAS performance. Eight benchmarking instant problems were used for benchmarking the performance. The proposed LS enhanced both BWAS and BWACS performances by up to 70% but required longer execution time.