A mixed integer programming approach to improve oil spill response resource allocation in the Canadian arctic

• Published in: Multimodal Transportation
• Main Authors: Tanmoy Das, Floris Goerlandt, Ronald Pelot
• Format: Article in Journal/Newspaper
• Language: English
• Published: Elsevier 2024
• Subjects: Location allocation model; Operations research; Sensitivity analysis; Arctic shipping; Data visualization; Transportation engineering; TA1001-1280; Arctic
• Online Access: https://doi.org/10.1016/j.multra.2023.100110; https://doaj.org/article/178ea650ab374cfbbacf615918bfdee3

Determining proper locations to establish emergency response facilities is a critical strategic element of pollution preparedness and response planning for oil spills in remote areas. Many location-allocation models are available in the literature, but Arctic contexts such as remoteness and environmental sensitivities are still inadequately investigated while building optimization models. A Mixed Integer Programming (MIP) based optimization model is developed to devise a location-allocation problem: maximizing weighted spill coverage considering spill size, environmental sensitivity, and response time. Strategic decisions - e.g. allocation of stockpiling resources to resource stations and which response stations to open - are incorporated into the model as decision variables. Input parameters of the model are estimated using numerical and geospatial data of potential oil spills and response stations. The model is illustrated for hypothetical oil spill scenarios in the Canadian Arctic. The model provides optimal allocation of resources and recommends best-suited locations to build response facilities. Data visualization tools including Network Diagrams and sensitivity analysis on different model configurations, show the adequacy of the proposed mathematical modelling approach to solve the given problem. Multiple facility locations have been compared to cover all possible oil spills along Arctic shipping routes, further revealing a few better locations considering realistic constraints. Decision makers can use such optimization modelling information – e.g., how many stations to build in the Arctic to adequately cover potential oil spills – to aid strategic decision-making of maritime shipping.