Production scheduling is a decision-making process that is applied in the manufacturing and service industries to achieve efficiency, minimise production costs and maximise the profit. Production process planning and scheduling are critical functions for the sustainable development of manufacturing processes that not only minimise the time or cost, but also improve adaptability, responsiveness and robustness. Therefore, effective production process planning and scheduling is imperative in order to achieve sustainable manufacturing. This study presents a production scheduling problem and its optimal solution, for a typical real-life micro- brewery production process, based in Coventry, UK. In the brewery, various orders of product types arrive dynamically to form a queue for production in a variety of vessels with limited capacity. The operation of brewery production is determined by the processing time, the setting up time, the changeover time of each product type and the cleaning time of each vessel. The due date for delivery the product to customers is another important factor. For the brewery production system, a multi-objective optimisation problem of minimising the overall production time in a job shop is considered in this research. A novel optimisation approach for the sustainable process and scheduling is presented. The objective of the study is to formulate a mathematical model of a scheduling problem and to develop a Simulink model to simulate the scenario of the brewery production system. Subsequently, the primary focus of this thesis is the design and application of meta-heuristics methods, namely, genetic algorithm (GA), simulated annealing (SA) and ant colony optimisation (ACO), to optimise the brewery production system. In addition, it proposes a hybrid method to solve the production problem, which is comprised of an improved GA with the improved SA to minimise the total production time. The advantage of the hybrid method is not only to achieve the combination of the global search capability of GA and the local search capability of SA, but also an effective avoidance of the premature convergence and strengthen the global optimal solution at a higher temperature; at a lower temperature, the hill climbing of the SA can speed up the convergence. The proposed hybrid method is effectively applied to the brewery production system. The result demonstrates that the proposed method provides better performance and effectiveness when it is compared with other heuristic algorithms that include traditional GA, SA, ACO, the improved GA and improved SA.
|Date of Award||Dec 2015|
|Supervisor||Keith Burnham (Supervisor), Leonid Smalov (Supervisor) & Saad Amin (Supervisor)|
- Micro brewery
- Production process planning
- Production scheduling