A Hybrid Simulated Annealing and Simplex Method for Fixed-Cost Capacitated Multicommodity Network Design

2012 ◽  
pp. 17-31 ◽  
Author(s):  
Masoud Yaghini ◽  
Mohammad Karimi ◽  
Mohadeseh Rahbar ◽  
Rahim Akhavan
Keyword(s):  
Simulated Annealing ◽  
Network Design ◽  
Simplex Method ◽  
Minimum Cost ◽  
Parameter Tuning ◽  
Fixed Cost ◽  
Hybrid Simulated Annealing ◽  
Np Hard Problem ◽  
Multicommodity Network Design ◽  
Opening And Closing

The fixed-cost Capacitated Multicommodity Network Design (CMND) problem is a well known NP-hard problem. This paper presents a matheuristic algorithm combining Simulated Annealing (SA) metaheuristic and Simplex method for CMND problem. In the proposed algorithm, a binary array is considered as solution representation and the SA algorithm manages open and closed arcs. Several strategies for opening and closing arcs are proposed and evaluated. In this matheuristic approach, for a given design vector, CMND becomes a Capacitated Multicommodity minimum Cost Flow (CMCF) problem. The exact evaluation of the CMCF problem is performed using the Simplex method. The parameter tuning for the proposed algorithm is done by means of design of experiments approach. The performance of the proposed algorithm is evaluated by solving different benchmark instances. The results of the proposed algorithm show that it is able to obtain better solutions in comparison with previous methods in the literature.

A Hybrid Simulated Annealing and Simplex Method for Fixed-Cost Capacitated Multicommodity Network Design

2011 ◽  
Vol 2 (4) ◽  
pp. 13-28 ◽  
Author(s):  
Masoud Yaghini ◽  
Mohammad Karimi ◽  
Mohadeseh Rahbar ◽  
Rahim Akhavan
Keyword(s):  
Simulated Annealing ◽  
Network Design ◽  
Simplex Method ◽  
Minimum Cost ◽  
Parameter Tuning ◽  
Fixed Cost ◽  
Hybrid Simulated Annealing ◽  
Np Hard Problem ◽  
Multicommodity Network Design ◽  
Opening And Closing

The fixed-cost Capacitated Multicommodity Network Design (CMND) problem is a well known NP-hard problem. This paper presents a matheuristic algorithm combining Simulated Annealing (SA) metaheuristic and Simplex method for CMND problem. In the proposed algorithm, a binary array is considered as solution representation and the SA algorithm manages open and closed arcs. Several strategies for opening and closing arcs are proposed and evaluated. In this matheuristic approach, for a given design vector, CMND becomes a Capacitated Multicommodity minimum Cost Flow (CMCF) problem. The exact evaluation of the CMCF problem is performed using the Simplex method. The parameter tuning for the proposed algorithm is done by means of design of experiments approach. The performance of the proposed algorithm is evaluated by solving different benchmark instances. The results of the proposed algorithm show that it is able to obtain better solutions in comparison with previous methods in the literature.

Supply Chain Design Including Quality Considerations

2014 ◽  
pp. 102-140 ◽  
Author(s):  
Krystel K. Castillo-Villar ◽  
Neale R. Smith
Keyword(s):  
Genetic Algorithm ◽  
Supply Chain ◽  
Simulated Annealing ◽  
Network Design ◽  
Parameter Tuning ◽  
Supply Chain Design ◽  
Background Information ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Solution Methods

This chapter introduces the reader to Supply Chain Network Design (SCND) models that include the Cost Of Quality (COQ) among the relevant costs. In contrast to earlier models, the COQ is computed internally as a function of decisions taken as part of the design of the supply chain. Earlier models assume exogenously given COQ functions. Background information is provided on previous COQ modeling and on supply chain network design models. The authors’ COQ modeling is described in detail as is the SCND model that incorporates COQ. The COQ modeling includes prevention, appraisal, and both internal and external failure costs. Solution methods based on metaheuristics such as simulated annealing and the genetic algorithm are provided, including details on parameter tuning and computational testing. A genetic algorithm was found to yield the best results, followed by the simulated annealing approach. Topics for further research are provided as well as an extensive list of references for further reading.

High Speed Information Network Planning using Stochastic Optimization Method

2013 ◽  
Author(s):  
Ki-Sang Song ◽  
Arun K. Somani
Keyword(s):  
Stochastic Optimization ◽  
Network Design ◽  
Multimedia Information ◽  
High Speed ◽  
Minimum Cost ◽  
Optimization Method ◽  
Multimedia Communication ◽  
Network Design Problem ◽  
Information Network ◽  
Traffic Characteristics

From the 1994 CAIS Conference: The Information Industry in Transition McGill University, Montreal, Quebec. May 25 - 27, 1994.Broadband integrated services digital network (B-ISDN) based on the asynchronous transmission mode (ATM) is becoming reality to provide high speed, multi bit rate multimedia communications. Multimedia communication network has to support voice, video and data traffics that have different traffic characteristics, delay sensitive or loss sensitive features have to be accounted for designing high speed multimedia information networks. In this paper, we formulate the network design problem by considering the multimedia communication requirements. A high speed multimedia information network design alogrithm is developed using a stochastic optimization method to find good solutions which meet the Quality of Service (QoS) requirement of each traffic class with minimum cost.

scholarly journals A Peptides Prediction Methodology for Tertiary Structure Based on Simulated Annealing

2021 ◽  
Vol 26 (2) ◽  
pp. 39
Author(s):  
Juan P. Sánchez-Hernández ◽  
Juan Frausto-Solís ◽  
Juan J. González-Barbosa ◽  
Diego A. Soto-Monterrubio ◽  
Fanny G. Maldonado-Nava ◽  
...  
Keyword(s):  
Simulated Annealing ◽  
Ab Initio ◽  
Primary Structure ◽  
Structure Prediction ◽  
Tertiary Structure ◽  
Secondary Structure Prediction ◽  
Golden Ratio ◽  
Small Peptides ◽  
Hybrid Simulated Annealing ◽  
Ab Initio Models

The Protein Folding Problem (PFP) is a big challenge that has remained unsolved for more than fifty years. This problem consists of obtaining the tertiary structure or Native Structure (NS) of a protein knowing its amino acid sequence. The computational methodologies applied to this problem are classified into two groups, known as Template-Based Modeling (TBM) and ab initio models. In the latter methodology, only information from the primary structure of the target protein is used. In the literature, Hybrid Simulated Annealing (HSA) algorithms are among the best ab initio algorithms for PFP; Golden Ratio Simulated Annealing (GRSA) is a PFP family of these algorithms designed for peptides. Moreover, for the algorithms designed with TBM, they use information from a target protein’s primary structure and information from similar or analog proteins. This paper presents GRSA-SSP methodology that implements a secondary structure prediction to build an initial model and refine it with HSA algorithms. Additionally, we compare the performance of the GRSAX-SSP algorithms versus its corresponding GRSAX. Finally, our best algorithm GRSAX-SSP is compared with PEP-FOLD3, I-TASSER, QUARK, and Rosetta, showing that it competes in small peptides except when predicting the largest peptides.

scholarly journals A Generalized Minimum Cost Flow Model for Multiple Emergency Flow Routing

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Jianxun Cui ◽  
Shi An ◽  
Meng Zhao
Keyword(s):  
Flow Model ◽  
Time Window ◽  
Programming Model ◽  
Real Life ◽  
Minimum Cost ◽  
Emergency Evacuation ◽  
Mixed Integer ◽  
Fixed Cost ◽  
Minimum Cost Flow ◽  
Cost Flow

During real-life disasters, that is, earthquakes, floods, terrorist attacks, and other unexpected events, emergency evacuation and rescue are two primary operations that can save the lives and property of the affected population. It is unavoidable that evacuation flow and rescue flow will conflict with each other on the same spatial road network and within the same time window. Therefore, we propose a novel generalized minimum cost flow model to optimize the distribution pattern of these two types of flow on the same network by introducing the conflict cost. The travel time on each link is assumed to be subject to a bureau of public road (BPR) function rather than a fixed cost. Additionally, we integrate contraflow operations into this model to redesign the network shared by those two types of flow. A nonconvex mixed-integer nonlinear programming model with bilinear, fractional, and power components is constructed, and GAMS/BARON is used to solve this programming model. A case study is conducted in the downtown area of Harbin city in China to verify the efficiency of proposed model, and several helpful findings and managerial insights are also presented.

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