Resilient facility location against the risk of disruptions

Yu, Guodong; Haskell, William B.; Liu, Yang

doi:10.1016/j.trb.2017.06.014

Cited by 51 publications

(16 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hsu et al [27] analyzed the logistics chain of prefab products and developed a stochastic optimization model for prefab production scheduling and inventory management. There are also studies on road transport [28][29][30][31] and maritime transport [32][33][34], but these studies do not examine the packing of cargoes onto the vehicles/vessels.…”

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Ship Loading Planning for Prefabricated Products in the Construction Industry

Phipps

Wang

2020

Sustainability

View full text Add to dashboard Cite

This paper focuses on sustainable transportation of prefab products from factories to construction sites by ship. Since the transportation cost for all the prefab products of a construction site is mainly dependent on the number of cargo holds used on ships, a loading plan for prefab products that minimizes the number of holds required is highly desirable. This paper is therefore devoted to the development of an optimal loading plan that decides which prefab products are loaded into each cargo hold and how to pack these prefab products into the holds so that as few holds as possible are used. We formulate the problem as a large-scale integer optimization model whose objective function is to minimize the total number of cargo holds used and whose constraints represent the cargo hold capacity limits. We develop a heuristic to solve the problem and obtain a high-quality solution. We have tested the model and algorithm on a case study that includes 20 prefab products. We find that different cargo holds carry prefab products that have quite different densities. Moreover, the orientations of many prefab products are different from their default orientations. The results demonstrate the applicability of the proposed model and algorithm.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Sustainable Ship Loading Planning for Prefabricated Products in the Construction Industry

Phipps

Wang

2020

Sustainability

View full text Add to dashboard Cite

show abstract

“…et al [8] developed a multistage inexact stochastic robust model for regional energy system management in Jining City, China. However, the robust approach is usually criticized by its overly conservative results, which may lead to excessive investments [23][24][25].…”

Section: Literature Reviewmentioning

confidence: 99%

Primal-Dual Learning Based Risk-Averse Optimal Integrated Allocation of Hybrid Energy Generation Plants under Uncertainty

Zhao

Xia

2019

Energies

Self Cite

View full text Add to dashboard Cite

A groundswell of opinion in utilizing environmentally friendly energy technologies has been put forth worldwide. In this paper, we consider an energy generation plant distribution and allocation problem under uncertainty to get the utmost out of available developments, as well as to control costs and greenhouse emissions. Different clean and traditional energy technologies are considered in this paper. In particular, we present a risk-averse stochastic mixed-integer linear programming (MILP) model to minimize the total expected costs and control the risk of CO2 emissions exceeding a certain budget. We employ the conditional value-at-risk (CVaR) model to represent risk preference and risk constraint of emissions. We prove that our risk-averse model can be equivalent to the traditional risk-neutral model under certain conditions. Moreover, we suggest that the risk-averse model can provide solutions generating less CO2 than traditional models. To handle the computational difficulty in uncertain scenarios, we propose a Lagrange primal-dual learning algorithm to solve the model. We show that the algorithm allows the probability distribution of uncertainty to be unknown, and that desirable approximation can be achieved by utilizing historical data. Finally, an experiment is presented to demonstrate the performance of our method. The risk-averse model encourages the expansion of clean energy plants over traditional models for the reduction CO2 emissions.

show abstract

“…In light of the results by Berman et al [16], Mak and Shen [17] and Yu et al [18], a risk-averse model is able to improve the reliability of the facility location solution. Moreover, it's also less conservative compared to that by robust optimization.…”

Section: Introductionmentioning

confidence: 97%

“…Moreover, it's also less conservative compared to that by robust optimization. Additionally, decision makers are more likely to be risk-averse than risk-neutral or risk-seeking under uncertainty, according to Mak and Shen [17] and Yu, Haskell and Liu [18]. Thus, increasing attention has now been paid to the risk-averse facility location problem.…”

Section: Introductionmentioning

confidence: 99%

“…We aim to control the total cost spent in opening facilities and operational day-to-day transportation, meanwhile ensuring emissions of CO 2 are under a certain level. We formulate the problem based on the basic model proposed in Yu et al (2017). By incorporating a coherent risk measure, i.e., conditional value-at-risk (CVaR), we develop a risk-averse model with a 0-1 mixed integer nonlinear programming, which is usually challenging to solve.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Risk-Averse Facility Location for Green Closed-Loop Supply Chain Networks Design under Uncertainty

et al. 2018

Self Cite

View full text Add to dashboard Cite

With the increasing attention given to environmentalism, designing a green closed-loop supply chain network has been recognized as an important issue. In this paper, we consider the facility location problem, in order to reduce the total costs and CO2 emissions under an uncertain demand and emission rate. Particularly, we are more interested in the risk-averse method for providing more reliable solutions. To do this, we employ a coherent risk measure, conditional value-at-risk, to represent the underlying risk of uncertain demand and CO2 emission rate. The resulting optimization problem is a 0-1 mixed integer bi-objective programming, which is challenging to solve. We develop an improved reformulation-linearization technique, based on decomposed piecewise McCormick envelopes, to generate lower bounds efficiently. We show that the proposed risk-averse model can generate a more reliable solution than the risk-neutral model, both in reducing penalty costs and CO2 emissions. Moreover, the proposed algorithm outperforms and classic reformulation-linearization technique in convergence rate and gaps. Numerical experiments based on random data and a ‘real’ case are performed to demonstrate the performance of the proposed model and algorithm.

show abstract

Resilient facility location against the risk of disruptions

Cited by 51 publications

References 48 publications

Sustainable Ship Loading Planning for Prefabricated Products in the Construction Industry

Sustainable Ship Loading Planning for Prefabricated Products in the Construction Industry

Primal-Dual Learning Based Risk-Averse Optimal Integrated Allocation of Hybrid Energy Generation Plants under Uncertainty

Risk-Averse Facility Location for Green Closed-Loop Supply Chain Networks Design under Uncertainty

Contact Info

Product

Resources

About