An Efficient MILP-Based Decomposition Strategy for Solving Large-Scale Scheduling Problems

This paper presents a MILP-based decomposition algorithm for solving large-scale scheduling problems with assembly operations in flexible flow shop environments. First, a rigorous mixed-integer linear (MILP) formulation based on the general precedence notion is developed for the problem under study. Then, the MILP model is embedded within a decomposition algorithm in order to accelerate the resolution of large-size industrial problems. The proposed solution approach is tested on several examples derived from a real-world case study arising in a shipbuilding company.Sociedad Argentina de Informática e Investigación Operativ

An Efficient MILP-Based Decomposition Strategy for Solving Large-Scale Scheduling Problems

This paper presents a MILP-based decomposition algorithm for solving large-scale scheduling problems with assembly operations in flexible flow shop environments. First, a rigorous mixed-integer linear (MILP) formulation based on the general precedence notion is developed for the problem under study. Then, the MILP model is embedded within a decomposition algorithm in order to accelerate the resolution of large-size industrial problems. The proposed solution approach is tested on several examples derived from a real-world case study arising in a shipbuilding company.Sociedad Argentina de Informática e Investigación Operativ

An Efficient MILP-Based Decomposition Strategy for Solving Large-Scale Scheduling Problems

This paper presents a MILP-based decomposition algorithm for solving large-scale scheduling problems with assembly operations in flexible flow shop environments. First, a rigorous mixed-integer linear (MILP) formulation based on the general precedence notion is developed for the problem under study. Then, the MILP model is embedded within a decomposition algorithm in order to accelerate the resolution of large-size industrial problems. The proposed solution approach is tested on several examples derived from a real-world case study arising in a shipbuilding company.Sociedad Argentina de Informática e Investigación Operativ

An efficient MILP-based decomposition strategy for solving large-scale scheduling problems in the shipbuilding industry

This work presents a novel hybrid and systematic MILP-based solution approach for the resolution of multi-stage scheduling problems arising in the shipbuilding industry. The manufacturing problem involves the processing of a large number of sub-blocks and blocks, which should be rigorously produced and assembled with the aim of finalizing a project on time. Firstly, this paper presents three alternative rigorous MILP mathematical formulations relied on a continuous-time representation for solving the problem under study. Although the objective values reported by these exact optimization approaches outperform the results found through other solution techniques proposed in the literature to solve the same problem instances, the main drawback of the MILP models is the high computation time. Therefore, this work proposes an algorithm for solving the mathematical models in a decomposable way with the goal of accelerating the resolution times. The applicability of our proposal is demonstrated by effectively coping with several instances of a real-world case study dealing with the construction of a ship for the development of marine resources. Computational results show that the proposed decomposition method is able to obtain high-quality solutions in few seconds of CPU time for all examples considered.Fil: Basán, Natalia Paola. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Cóccola, Mariana E.. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: García del Valle, Alejandro. Universidad da Coruña; EspañaFil: Mendez, Carlos Alberto. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentin