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An integrated methodology for the optimisation of aggregate production-distribution plan in supply chains

Benham Fahimnia

 

Thesis Abstract

Supply Chain Management concerns the optimal flow of material from raw material suppliers to the end-users through procurement, production and distribution activities. Implementation of a supply-chain (SC) system has crucial impacts on a company's financial performance. Overall SC performance is influenced significantly by decisions taken in the production-distribution (P-D) network. A comprehensive P-D planning problem integrates decisions in production, transportation and warehousing as well as inventory management to minimise the overall production and distribution costs, while ensuring that the products are produced and distributed at the right quantities, to the right end-users, and at the right time. Increasing interest in evaluating the performance of SC over the last years indicates the need for the development of complex optimisation models able to solve unanswered questions in the P-D network.

Current research gaps targeted in this research are: (1) oversimplification of proposed models, mainly due to the complexities associated with the optimisation of a real-life P-D plan; (2) the need to further improve the quality and precision of the proposed solution approaches for the optimisation of integrated P-D planning problems; (3) implementation of the proposed solution approaches in real-life large-scale case studies.

• To deal with the 'oversimplification' issue in the past models, this research develops an optimisation model based on the integration of an Aggregate Production Plan and a Distribution Plan considering all production and distribution alternatives as well as detailed production cost components. The proposed model incorporates the production of multiple products during multiple time-periods in multiple manufacturing plants each comprising of multiple machine centres as well as the distribution of finished products from the stack buffers in manufacturing plants to multiple end-users both directly (plants to end-users) and indirectly (plants to warehouses to end users).
• Genetic Algorithm is chosen for the optimisation of the developed model, following a detailed analysis and evaluation on the alternative tools and techniques. The proposed model will be implemented in the form of a software package providing a visual user interface to enhance the ease of modelling for solving different P-D planning problems from different complexity levels.
• To demonstrate the applicability of the proposed approach, we implement the developed model in a real-life large-scale case study in collaboration with a manufacturing firm producing Honda and Yamaha motorcycle mudguards. The obtained results are finally evaluated in a comparative analysis comparing the outcomes with the ones from existing approaches.

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