A MILP model for planning the trips of dynamic positioned tankers with variable travel time

The transportation of the crude oil produced in offshore oilfields to onshore terminals is performed by vessels, known as shuttle tankers. Scheduling shuttle-tanker operations entails solving complex problems to ensure a timely offloading of the platforms, taking into account several logistics and inventory constraints. This work proposes a new MILP formulation that advances previous works by considering variable travel time between platforms and terminals. The combination of the MILP formulation with an optimization solver constitutes a decision-support tool to aid engineers reach optimal decisions for a planning horizon. To handle large-scale instances, rolling-horizon and relax-and-fix strategies are proposed.     

Modeling downstream petroleum supply chain: The importance of multi-mode transportation to strategic planning

This paper proposes a deterministic mixed integer linear programming (MILP) model for downstream petroleum supply chain (PSC) network to determine the optimal distribution center (DC) locations, capacities, transportation modes, and transfer volumes. The model minimizes multi-echelon multi-product cost along the refineries, distribution centers, transportation modes and demand nodes. The relationship between strategic planning and multimodal transportation is further elucidated. A case study was considered with real data from the U.S. petroleum industry and transportation networks within Geographic Information System (GIS). A scenario analysis is also conducted to demonstrate the impact of key parameters on PSC decisions and total cost.     

Supplier evaluation and demand allocation among suppliers in a supply chain

This paper presents a hybrid algorithm that prioritizes the suppliers and then allocates the demand among the suppliers. The objective here is to maximize the total purchase value of the items taking into consideration budget constraint, demand condition, delivery lead-time and supplier capacity. Since the problem is multi-criteria decision making, we solve this problem by integrating the supplier rating with mixed linear integer programming method. The customer demand is allocated by using a hybrid algorithm based on the technique for order preference by similarity to ideal solution (TOPSIS) and the mixed linear integer programming (MILP) approaches. The effectiveness of the proposed algorithm is validated with computational results. Drawing to a case, a supplier S₃ is identified as the best supplier by using the TOPSIS method for demand allocation under no restrictions. On the contrary, under constrained scenario, supplier S₂ is selected as the best supplier by using the hybrid algorithm for demand allocation and maximum units are allocated to S₂.     

Comparison of solution approaches for the train load planning problem in seaport terminals

In this paper the train load planning problem arising in a seaport container terminal is considered. This problem consists in determining the optimal assignment of containers to wagon slots in order to maximize the train utilization and, at the same time, to minimize unproductive operations in the terminal. Different solution approaches based on a mathematical programming model are compared. The best solution procedure, satisfactory both in terms of quality of the obtained solutions and for the computational times, is identified through extensive experimental tests. This procedure could be included in a planning tool to be used in real seaport terminals.     

Developing an emission risk control model in coal-fired power plants for investigating CO2 reduction strategies for sustainable business development

The climatic risk consists of financial and environmental risks, predominantly evolutes from carbon dioxide emissions from coal-fired power plants. We develop a climatic risk control model to investigate the coexistence of renewable energy and the post-combustion carbon capture technology as CO₂ reduction strategies. Additionally, our proposed framework explicitly considers the acceptance of such strategies based on a balance of electricity supply according to demand. This paper adopts an additive fuzzy mixed-integer optimization approach to model uncertain parameters and determines the optimal solution focusing on business and the environment. Furthermore, we investigate the feasibility of such emission control strategies with scenario analysis that help to execute the country's emission reduction policies. The usefulness of our methodology is demonstrated using data from the coal-based power sector in the Eastern part of India. Overall, with the proposed model, we can achieve 30% reduction in emission release, which provides strategies to the decision-maker for investment towards sustainable development.