Meta-heuristic algorithms for integrating manufacturing and supply chain functions

Abstract

This study proposes an integrated framework that incorporates essential manufacturing and supply chain functions. These functions encompass process planning, scheduling, due-date assignment, and delivery optimization. The objective of this integrated approach is to achieve multiple benefits, including balanced workload distribution, enhanced company performance, generation of more realistic planning schedules, and ultimately, the achievement of shorter due dates. As a result, the overall efficiency of operations is substantially improved, with approximately a 50 % increase over isolated function management. Additionally, the isolated integration of the delivery function within systems comprising three integrated functions has been found to improve efficiency by 18%. The study employs various heuristic techniques, including genetic algorithms, simulated annealing, random search, hybrid search, and evolutionary strategy, to assess the optimal solution method and rules for these functions. The Taguchi technique is employed to ascertain the optimal values for critical parameters, such as population size, mutation rate, crossover points, and random search rate. Among the solution methods investigated, genetic algorithms consistently yielded superior results Additionally, the weighted slack rule consistently exhibited notable effectiveness compared to other due-date assignment rules. Similarly, the savings algorithm outperformed other delivery optimization rules. However, it is important to note that among the scheduling rules evaluated, none has emerged as dominant.