A bi-objective optimization model for simultaneous short-term production and distribution scheduling in process industries

TIBALDO, ALDANA; MONTAGNA, JORGE M.; FUMERO, YANINA

Buenos Aires

Conferencia; XXI Latin-Iberoamerican Conference on Operations Research; 2022

Facultad de Ciencias Exactas y Naturales, Universidad de Buenos Aires

The emergence of advanced manufacturing and Industry 4.0 technologies are moving companies towards new ways of operating in order to follow market trends and maintain their positioning. In environments that operate using the make-to-order manufacturing approach, where products must be supplied directly to customers after their completion, as well as at industries that due to the characteristics of their products (perishable or time-sensitive) adopt a just-in-time policy, a joint consideration of production and distribution scheduling is crucial. However, the integrated resolution of these activities requires a great effort considering that the objectives, capabilities and performance criteria of the different stakeholders are often conflicting (e.g., cost versus customer satisfaction). In the area literature, some works have studied the integrated problem with multiple objectives but considering certain limitations and simplifications for the operational decisions involved. For this reason, a bi-objective mixed-integer linear formulation is presented to simultaneously manage production and distribution decisions in multiproduct batch plants. The augmented ε-constraint method (AUGMECON) is used to solve the bi-objective model, minimizing production and distribution costs for different admissible values in the delay of delivery times to customers, and generating a set of optimal Pareto solutions [1]. The model determines: number and size of batches processed for each product (batching), their allocation and sequencing in the units, and timing of these batches, as well as number and type of vehicles to be used, allocation of batches to vehicles, departure and arrival times of vehicles to customers. Finally, once the efficient solutions to the problem are obtained, the decision maker can select among them the preferred one. The capability of the approach is evaluated through a case study and different trade-offs are analyzed.[1] https://doi.org/10.1016/j.amc.2009.03.037