Modeling and Optimization for Multi-Objective Nonidentical Parallel Machining Line Scheduling with a Jumping Process Operation Constraint

This paper investigates the nonidentical parallel production line scheduling problem derived from an axle housing machining workshop of manufacturer. The characteristics lines are analyzed, and a model with jumping process operation (NPPLS-JP), which considers mixed production, machine eligibility constraints, fuzzy due dates, is established so as to minimize makespan earliness/tardiness penalty cost. While physical structures in NPPLS-JP symmetric, capacities capabilities asymmetric for different models. Different general problem, allows job transfer another complete subsequent operations (i.e., operations), unidirectional. significance that it meets demands multivariety makes full use lines. Aiming solve we propose hybrid algorithm named multi-objective grey wolf optimizer based on decomposition (MOGWO/D). new combines GWO evolutionary (MOEA/D) balance exploration exploitation abilities original MOEA/D. Furthermore, coding decoding rules developed according features problem. To evaluate effectiveness proposed MOGWO/D algorithm, set instances scales, types, scenarios designed, results compared those three other famous optimization algorithms. experimental show exhibits superiority most instances.