| dc.description.abstract | Scheduling takes an important role in production planning in manufacturing systems. It
helps ensure the efficiency of production operations and facilitates timely delivery.
Although the scheduling problems receive much attention in recent years, there is a
minority of research about multi-objective while considering the uncertainty in data.
The project aims to address the flexible flow-shop scheduling problem (FFSP) and solve
multi-objective simultaneously. This project considered the manufacturing system
consisting of numerous production stages and multiple parallel machines in each stage,
while the machine downtime is assumed uncertain. The problem is first formulated as a
mixed-integer linear programming (MILP) model with parameters, decision variables,
objective functions and constraints, then goal programming (GP) is applied to solve the
multi-objective optimization problem. Next, the uncertainty in machine downtime is
incorporated into the proposed mathematical models. Finally, a meta-heuristic solution
approach called Discrete Artificial Bee Colony (DABC) algorithm is implemented to
solve the problem. The result showed that the MILP model had efficiency in small scale
data, while DABC algorithm could be more flexible in solving the problem on large
scale. However, CPLEX guaranteed the optimality of solutions in the optimization
problems through constraints and assumptions. Besides, the solutions of a case study
solved the problem statement by improving the utilization rate of machines, reducing
the idle time in a schedule as well as minimizing the considered objective functions. | en_US |
