Heuristics for supply chain and manufacturing functions

What is it about?

This study proposes an integrated framework for incorporating essential manufacturing and supply chain functions, including process planning, scheduling, due-date assignment, and delivery optimization. The aim is to achieve several benefits, such as balanced workload distribution, enhanced company performance, generation of more realistic planning schedules, and shorter due dates. The study suggests that this integrated approach can improve overall operational efficiency by approximately 50% compared to managing these functions in isolation. The research employs various heuristic techniques, such as genetic algorithms, simulated annealing, random search, hybrid search, and evolutionary strategy, to identify the optimal solution method and rules for these functions. The Taguchi technique is used to determine the optimal values for critical parameters like population size, mutation rate, crossover points, and random search rate. The study finds that genetic algorithms consistently outperform other solution methods. The weighted slack rule is identified as the most effective due-date assignment rule, while the savings algorithm is the preferred delivery optimization rule. However, among the scheduling rules evaluated, no dominant rule has emerged. Overall, this research highlights the importance of integrating manufacturing and supply chain functions and provides insights into the optimal solution methods and rules for achieving this integration.

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Photo by Tamanna Rumee on Unsplash

Photo by Tamanna Rumee on Unsplash

Why is it important?

This research is important for several reasons: Operational Efficiency: Integrating manufacturing and supply chain functions can significantly improve operational efficiency by balancing workload distribution, enhancing company performance, and generating more realistic planning schedules. The study suggests that this integration can lead to a 50% increase in efficiency compared to managing these functions in isolation. Shorter Due Dates: The integrated approach aims to achieve shorter due dates, which can lead to faster delivery times and improved customer satisfaction. Optimal Solution Methods: The research evaluates various heuristic techniques and identifies genetic algorithms as consistently superior for optimizing integrated manufacturing and supply chain functions. This provides valuable guidance for industries seeking to improve their operations. Parameter Optimization: The use of the Taguchi technique to determine optimal values for critical parameters demonstrates a systematic approach to parameter optimization, which can lead to more efficient and effective decision-making. Delivery Optimization: The study highlights the importance of delivery optimization in integrated systems and identifies the savings algorithm as the most effective rule for this purpose. This can help industries streamline their delivery processes and reduce costs. Overall, this research provides valuable insights and methodologies for integrating manufacturing and supply chain functions, ultimately leading to improved operational efficiency, shorter delivery times, and enhanced company performance.

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