A three-objective stochastic location-inventory-routing model for agricultural waste-based biofuel supply chain

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 multiobjective 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.

Automated summary

This paper proposes a multiobjective model for designing agricultural waste-based biofuel production, with a focus on location, inventory, and routing decisions. The model utilizes a two-phase heuristic approach and optimization using simulated annealing. Results from testing the heuristic method on problem instances of different scales demonstrate its competitiveness in terms of running-time efficiency and solution quality compared to exact methods.