A three-objective stochastic location-inventory-routing model for agricultural waste-based biofuel supply chain. (December 2021) - Morales Chavez, Marcela María; Costa, Yasel; Sarache, William
Highlights: Our mathematical model integrates plant location, inventory and routing decisions. A three-objective stochastic mixed-integer linear model is studied in this paper. We propose a better biofuel supply chain design using dynamic facility setting. A two-phase heuristic method is used to solve a set of created problem instances. Our proposed method is efficient and very competitive in terms of solution quality. Abstract: The utilization of agricultural wastes has visibly emerged as a promising policy towards enhancing the fragile global energy system characterized by a limited fossil fuel reserve. To this aim, this paper proposes a multi-objective model for the design of agricultural waste-based biofuel production with integrated formulation of location, inventory and routing decisions. Our model allows the decision makers to determine the number and location of residue gathering centers and biorefineries, the routes by which a heterogeneous fleet of vehicles collects the different agricultural waste, and the adequate material flow to meet the biofuel demand. Instead of assuming a fixed facility setting along the supply chain planning horizon, we construct a comprehensive mathematical model that includes: (1) the opening of a certain facility at any time period; (2) plant capacity expansion within the planning horizon; and (3) the facility closing not necessarily at last time period. The original model formulation is a three-objective stochastic Mixed-Integer Non-linear Programming. However, we propose a linearization strategy to efficiently convert our model into a MILP formulation. Since the proposed optimization model belongs to the class of NP-hard problems, a two-phase heuristic method is utilized to solve the formulated model. The constructive phase of our heuristic provides an initial solution that is further enhanced by a Simulated Annealing algorithm. We tested the proposed heuristic method for small, medium and large-scale problem instances. The computational results demonstrate that our heuristic is running-time efficient and highly competitive in terms of solution quality, compared to the exact method outcomes.