@book{
 title = {Incorporating game theory in the life cycle optimization of decentralized supply chains},
 type = {book},
 year = {2017},
 source = {Chemical Engineering Transactions},
 identifiers = {[object Object]},
 volume = {61},
 id = {5e68aa6c-4c62-353f-bea1-c905e77aa605},
 created = {2017-12-15T21:38:15.395Z},
 file_attached = {false},
 profile_id = {f27171f8-b200-3870-9028-dcb6a084733d},
 last_modified = {2017-12-15T21:38:15.395Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {false},
 hidden = {false},
 private_publication = {true},
 abstract = {Copyright © 2017, AIDIC Servizi S.r.l. In this paper, we propose a novel modeling framework to account for multiple strategic decisions in the shale gas supply chain, including the well drilling schedule, water management, installation of gathering pipelines, allocation, capacity and design of processing plants, and selection of processing contract. We incorporate a leader-follower game theory and the life cycle optimization method into a holistic modeling framework, which enables us to simultaneously address the trade-offs between conflicting objectives as well as the interactions between different players. In this supply chain, the shale gas producer is identified as the leader. Due to the key role of leader in a game, the producer not only enjoys the priority to make decisions first, but senses the responsibility to mitigate the life cycle greenhouse gas (GHG) emission embedded in the final product. The midstream player shale gas processor is identified as the follower, who will take actions rationally according to the leader's decisions to pursue its own profit. The resulting problem is a multiobjective mixed-integer bilevel linear programming problem and solved by a novel projection-based reformulation and decomposition algorithm. Based on a case study of Marcellus shale play, the leader's net present value ranges from $ 29.2 M to $ 50.7 M, and the corresponding total GHG emissions range from 329 kt CO 2 -eq to 367 kt CO 2 -eq.},
 bibtype = {book},
 author = {Gao, J. and You, F.}
}

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