An Experimental Study of the GAMS/AlphaECP MINLP Solver

Lastusilta, Toni; Bussieck, Michael R.; Westerlund, Tapio

doi:10.1021/ie801378n

Cited by 18 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Many solvers have been developed using these methods. Some of the popular global solvers for MINLP problems are AlphaECP (Lastusilta, Bussieck, & Westerlund, 2009), ANTIGONE (Misener & Floudas, 2014), BARON (Sahinidis, 1996), Bonmin (Bonami & Lee, 2007), Couenne (Belotti, 2009), DICOPT (Grossman et al, 2002), Knitro (Byrd, Nocedal, & Waltz, 2006), LINDOGlobal (Lin & Schrage, 2009), SBB (Bussieck & Drud, 2001), Scip (Achterberg, 2009), Cbc (Forrest & Lougee-Heimer, 2005), and Gurobi (Gu, Rothberg, & Bixby, 2012). To choose the best solver for this framework, a comparative performance evaluation is performed using a case study.…”

Section: Optimization Solver Selectionmentioning

confidence: 99%

Wireless charging utility maximization and intersection control delay minimization framework for electric vehicles

Khan

Chowdhury

et al. 2019

Computer aided Civil Eng

View full text Add to dashboard Cite

This study presents the Wireless Charging Utility Maximization (WCUM) framework, which aims to maximize the utility of Wireless Charging Units (WCUs) for electric vehicle (EV) charging through the optimal WCU deployment at signalized intersections. Furthermore, the framework aims to minimize the control delay at all signalized intersections of the network. The framework consists of a two‐step optimization formulation, a dynamic traffic assignment model to calculate the user equilibrium, a traffic microsimulator to formulate the objective functions, and a global Mixed Integer Non‐Linear Programming (MINLP) optimization solver. An optimization problem is formulated for each intersection, and another for the entire network. The performance of the WCUM framework is tested using the Sioux Falls network. We perform a comparative study of 12 global MINLP solvers with a case study. Based on solution quality and computation time, we choose the Couenne solver for this framework.

show abstract

Section: Optimization Solver Selectionmentioning

confidence: 99%

Wireless charging utility maximization and intersection control delay minimization framework for electric vehicles

Khan

Chowdhury

et al. 2019

Computer aided Civil Eng

View full text Add to dashboard Cite

show abstract

“…Two recent papers [57,58] demonstrate that commercial MINLP solvers can successfully solve nonlinear, discrete transportation problems. We obtained benchmark solutions by using two commercial solvers -BONMIN [59] and FilMINT [60], to justify our decomposition method by using the small-scale network.…”

Section: Nine-node Networkmentioning

confidence: 99%

A mean-risk mixed integer nonlinear program for transportation network protection

Gupte

Huang

2018

European Journal of Operational Research

View full text Add to dashboard Cite

This paper focuses on transportation network protection to hedge against extreme events such as earthquakes. Traditional two-stage stochastic programming has been widely adopted to obtain solutions under a risk-neutral preference through the use of expectations in the recourse function. In reality, decision makers hold different risk preferences. We develop a mean-risk two-stage stochastic programming model that allows for greater flexibility in handling risk preferences when allocating limited resources. In particular, the first stage minimizes the retrofitting cost by making strategic retrofit decisions whereas the second stage minimizes the travel cost. The conditional value-at-risk (CVaR) is included as the risk measure for the total system cost. The two-stage model is equivalent to a nonconvex mixed integer nonlinear program (MINLP). To solve this model using the Generalized Benders Decomposition (GBD) method, we derive a convex reformulation of the second-stage problem to overcome algorithmic challenges embedded in the non-convexity, nonlinearity, and non-separability of first-and second-stage variables. The model is used for developing retrofit strategies for networked highway bridges, which is one of the research areas that can significantly benefit from mean-risk models. We first justify the model using a hypothetical nine-node network. Then we evaluate our decomposition algorithm by applying the model to the Sioux Falls network, which is a large-scale benchmark network in the transportation research community. The effects of the chosen risk measure and critical parameters on optimal solutions are empirically explored.

show abstract

“…There are only a few systematic comparisons on the performance of different MINLP algorithms , and very few on distillation system optimization , . The reason is mainly that MINLP algorithms are not implemented in commercial process simulators, which makes an easy and objective comparison on a common basis, i.e., same model equations and physical property data, difficult.…”

Section: Evaluation Of Different Mixed‐integer Nonlinear Programming mentioning

confidence: 99%

Design of Dividing‐Wall Columns by Mixed‐Integer Nonlinear Programming Optimization

Franke

2017

Chemie Ingenieur Technik

View full text Add to dashboard Cite

Dividing‐wall columns (DWCs) offer an interesting alternative to conventional distillation towers. Savings of up to 30 % in both energy and capital cost are reported. While these high savings are realistic in some applications, DWCs offer less or no advantage at all in other applications. In any case, it is important to make a fair comparison between the conventional system and the DWC based on an optimized process design. Mixed‐integer nonlinear programming (MINLP), especially the presented modified Generalized Benders Decomposition approach, could be the tool to make this comparison, as shown here.

show abstract

An Experimental Study of the GAMS/AlphaECP MINLP Solver

Cited by 18 publications

References 13 publications

Wireless charging utility maximization and intersection control delay minimization framework for electric vehicles

Wireless charging utility maximization and intersection control delay minimization framework for electric vehicles

A mean-risk mixed integer nonlinear program for transportation network protection

Design of Dividing‐Wall Columns by Mixed‐Integer Nonlinear Programming Optimization

Contact Info

Product

Resources

About