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

TIBALDO, ALDANA; MONTAGNA, JORGE MARCELO; FUMERO, YANINA

Buenos Aires

Conferencia; Latin-Iberoamerican Conference on Operations Research (CLAIO 2022); 2022

CLAIO

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 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. Furthermore, due to the combinatorial complexity of these problems, approximate approaches such as decomposition techniques or heuristics methods, have been used rather than exact methodologies. For this reason, a bi-objective mixed-integer linear formulation is presented to simultaneously manage production and distribution decisions in single-stage parallel-unit multiproduct batch plants, minimizing operating costs and the delay of the delivery times for all customer orders. The augmented ε-constraint method (AUGMECON) is used to solve the proposed 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 developed model determines the detailed production scheduling: number and size of batches processed for each product (batching), their allocation and sequencing in the units, and the start and end times for the processing of these batches, as well as the decisions regarding the distribution of the final products: number and type of vehicles to be used, allocation of batches to vehicles, departure and arrival times of each vehicle 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 proposed approach is evaluated through a case study and different trade-offs are analyzed.