Integrated Planning of Industrial Gas Supply Chains

In this work, we propose a Mixed Integer Linear Programming (MILP) model for optimal planning of industrial gas supply chain, which integrates supply contracts, production scheduling, truck and rail-car scheduling, as well as inventory management under the Vendor Managed Inventory (VMI) paradigm. The objective used here is minimisation of the total operating cost consisting of purchasing of raw material, production, and transportation costs by trucks/rail-cars so as to satisfy customer demands over a given time horizon. The key decisions for production sites include production schedule and purchase schedule of raw material, while the distribution decisions involve customer to plant/depot allocation, quantity transported through rail network, truck delivery amounts, and times. In addition, a relaxation approach is proposed to solve the problem efficiently. An industrial case study is evaluated to illustrate the applicability of the integrated optimisation framework

Integrated production and inventory routing planning of oxygen supply chains

In this work, we address a production and inventory routing problem for a liquid oxygen supply chain comprising production facilities, distribution network, and distribution resources. The key decisions of the problem involve production levels of production plants, delivery schedule and routing through heterogeneous vehicles, and inventory strategies for national stock-out prevention. Due to the problem complexity, we propose a two-level hybrid solution approach that solves the problem using both exact and metaheuristic methods. At the upper level, we develop a mixed-integer linear programming (MILP) model that determines production and inventory decisions and customer allocation. In the lower level, the original problem is reduced to several multi-trip heterogeneous vehicle routing problems by fixing the optimal production, inventory, and allocation decisions and clustering customers. A well-recognised metaheuristic, guided local search method, is adapted to solve the low-level routing problems. A real-world case study in the UK illustrates the applicability and effectiveness of the proposed optimisation framework