Research on assembly scheduling problem with nested operations

Abstract

A two-stage assembly scheduling problem with nested operations (nASP) is proposed to minimise the bi-criteria of makespan and the specified average passage time (Pave) in the rocket tank welding workshop. The nASP's tanks are assembled utilising a variety of parts and machining techniques. Depending on the type of tank, there are different numbers of usable parts. Additionally, at the first stage, parallel available machines, operation-dependent setup times, unpredictable rework, and dynamic machining times are considered. Eight heuristics and two lower bounds are first developed, then a logistic-based improved genetic algorithm (LBIGA) is presented while considering the characteristics of the solutions. To further optimize the Pave while maintaining the makespan, a heuristic built on backward computations is also suggested. Then, utilising the offered algorithms as well as genetic algorithm (GA), Tabu search (TS), and variable neighbourhood search (VNS) from the literature, randomly generated instances of nASP are resolved and compared. Ultimately, the numerical trials show the viability and efficacy of the presented algorithms.