University of Groningen
Supply chain design and planning for LNG as a transportation fuel
Lopez Alvarez, Jose
DOI:
10.33612/diss.131459842
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Publication date: 2020
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Lopez Alvarez, J. (2020). Supply chain design and planning for LNG as a transportation fuel. University of Groningen, SOM research school. https://doi.org/10.33612/diss.131459842
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Chapter 7
Conclusions
In this thesis, we studied four different problems related to the design and man-agement of a distribution network for LNG as a fuel. In Chapter 2, we studied the network design of the LNG distribution network, and in Chapters 3–5, we studied problems related to the inventory management of LNG inventories while taking the quality of the fuel into consideration. In this section, we summarize the content of each of the chapters and mention some opportunities for future research related to the specific chapters.
In Chapter 2, we studied the network design problem related to the expansion of the European supply chain for LNG as a fuel. We introduced the two-echelon loc-ation routing problem with split deliveries, which is closely related to the practical problem of designing the LNG distribution network. One of the characteristics of this problem was that it allowed direct shipments from terminals at different levels of the LNG supply chain to the end-users. We have improved the performance of a hybrid exact algorithm, which outperforms both its previous version and a commer-cial solver. A case study considering the LNG network along the EU TEN-T network corridors shed light on the development of opening satellite facilities and investing in bunker barges when expanding the supply chain for LNG as a fuel into Europe.
In Chapter 3, we studied a dual-sourcing inventory control problem for deterior-ating liquids (e.g. LNG) whose quality can be influenced via mixing. An analytical analysis of this problem yielded a partial characterization of the optimal policies.
116 Chapter 7 This characterization is not only useful to design heuristics or exact approaches, but also provides intuition on the effect of the properties of LNG on inventory control decision-making. For example, we proved that in this particular setting, it is optimal to postpone replenishment orders that induce an increase in the quality of invent-ories for as long as possible. To show the relevance of the structural properties en-countered in this study, we presented a numerical example, which showed that the computational time required to obtain optimal policies is significantly lower when these properties are considered. One possible research opportunity is to extend our problem by incorporating pricing mechanisms to influence demand, which can be useful to improve revenue and mitigate quality issues. It would also be interesting to study the case where multiple storage tanks are available in the system, each of which may hold liquid with different quality.
In Chapter 4, we studied a dynamic lot-sizing problem with multiple suppliers for deteriorating and mixable substances, such as LNG. Initially, we formulated the problem as an MINLP, which we approximately reformulated using a linearization technique in which some of the variables were discretized. In addition to this for-mulation, we designed two heuristics, which are inspired by the Wagner and Within algorithm for the standard lot-sizing problem (Wagner and Whitin, 1958). The first heuristic is an approach where replenishment orders are exclusively placed in peri-ods when the inventory level is zero, while the second approach is a decomposition heuristic where we exclusively consider policies in which the number of times where the LNG is mixed between zero-inventory periods is limited. By means of a numer-ical analysis, we showed that the second approach outperforms the other approaches in terms of solution quality and computation times. One possible direction for future research is to consider a setting where different customers have different quality re-quirements; this case may be relevant for LNG, since end-users of the fuel can have vehicles with different engines. It would also be interesting to study the case where there is more than one storage tank; consideration of more storage tanks can allow for further optimization of the system, since each tank may have inventories with different quality.
In Chapter 5, we studied the LNG inventory control problem that arises in LNG storage and refueling facilities. In this problem, we assumed that there is a single supplier of LNG and that the demand for the fuel is stochastic. Owing to the stochastic nature of the problem, we included the removal of LNG from the inventory system as
Conclusions 117 a decision variable of our problem. The problem was modeled as a Markov decision process. By means of illustrative examples, we gained insights into the behavior of optimal policies. These insights were later used to design a new policy, namely the (S, s, v, k)policy. In a numerical study, we showed that the difference in performance of the (S, s, v, k) policy with respect to the optimal was 1.65% on average. One of the main conclusions of Chapter 5 was that it is important to take quality considerations into account when designing inventory policies for LNG. When inventory manage-ment responses are purely driven by quantity triggers, such as in (S, s) policies, the inventory system might fall into a temporal deadlock situation, i.e. one where the on-hand LNG can neither be used to serve demand (owing to the low quality of the LNG) nor upgraded to meet the minimum quality requirement (owing to the lack of response of the policy to the quality of the LNG). Another interesting conclusion is that inventory policies that use both removal and mixing of LNG as mechanisms to cope with quality issues perform better than those policies that exclusively use removal or mixing. However, when only one of the mechanisms is considered, a policy that does not allow removal on average performs worse than a policy where all LNG is removed when quality falls below a given threshold.
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