scholarly journals A Surrogate Model-Based Hybrid Approach for Stochastic Robust Double Row Layout Problem

Mathematics ◽  
2021 ◽  
Vol 9 (15) ◽  
pp. 1711
Author(s):  
Xing Wan ◽  
Xing-Quan Zuo ◽  
Xin-Chao Zhao
Keyword(s):  
Local Search ◽  
Surrogate Model ◽  
Large Scale ◽  
Hybrid Approach ◽  
Mixed Integer ◽  
Small Scale ◽  
Layout Problem ◽  
Double Row ◽  
Exact Approach ◽  
Mip Model

The double row layout problem is to arrange a number of machines on both sides of a straight aisle so as to minimize the total material handling cost. Aiming at the random distribution of product demands, we study a stochastic robust double row layout problem (SR-DRLP). A mixed integer programming (MIP) model is established for SR-DRLP. A surrogate model is used to linearize the nonlinear term in the MIP to achieve a mixed integer linear programming model, which can be readily solved by an exact method to yield high-quality solutions (layouts) for small-scale SR-DRLPs. Furthermore, we propose a hybrid approach combining a local search and an exact approach (LS-EA) to solve large-scale SR-DRLPs. Firstly, a local search is designed to optimize the machine sequences on two rows and the clearance from the most left machine on row 1 to the left boundary. Then, the exact location of each machine is further optimized by an exact approach. The LS-EA is applied to six problem instances ranging from 8 to 50 machines. Experimental results show that the surrogate model is effective and LS-EA outperforms the comparison approaches.

scholarly journals Multidepot Heterogeneous Vehicle Routing Problem for a Variety of Hazardous Materials with Risk Analysis

2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Bochen Wang ◽  
Qiyuan Qian ◽  
Zheyi Tan ◽  
Peng Zhang ◽  
Aizhi Wu ◽  
...  
Keyword(s):  
Risk Analysis ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Programming Model ◽  
Hazardous Materials ◽  
Mixed Integer ◽  
Small Scale ◽  
Routing Problem ◽  
Heterogeneous Vehicle Routing Problem

This study investigates a multidepot heterogeneous vehicle routing problem for a variety of hazardous materials with risk analysis, which is a practical problem in the actual industrial field. The objective of the problem is to design a series of routes that minimize the total cost composed of transportation cost, risk cost, and overtime work cost. Comprehensive consideration of factors such as transportation costs, multiple depots, heterogeneous vehicles, risks, and multiple accident scenarios is involved in our study. The problem is defined as a mixed integer programming model. A bidirectional tuning heuristic algorithm and particle swarm optimization algorithm are developed to solve the problem of different scales of instances. Computational results are competitive such that our algorithm can obtain effective results in small-scale instances and show great efficiency in large-scale instances with 70 customers, 30 vehicles, and 3 types of hazardous materials.

scholarly journals Solving balanced multi-weighted attribute set partitioning problem with variable neighborhood search

Filomat ◽  
2019 ◽  
Vol 33 (9) ◽  
pp. 2875-2891
Author(s):  
Dusan Dzamic ◽  
Bojana Cendic ◽  
Miroslav Maric ◽  
Aleksandar Djenic
Keyword(s):  
Local Search ◽  
Large Scale ◽  
Variable Neighborhood Search ◽  
Heuristic Method ◽  
Set Partitioning ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Computationally Efficient ◽  
Local Search Procedure ◽  
Problem Instances

This paper considers the Balanced Multi-Weighted Attribute Set Partitioning (BMWASP) problem which requires finding a partition of a given set of objects with multiple weighted attributes into a certain number of groups so that each attribute is evenly distributed amongst the groups. Our approach is to define an appropriate criterion allowing to compare the degree of deviation from the ?perfect balance? for different partitions and then produce the partition that minimizes this criterion. We have proposed a mathematical model for the BMWASP and its mixed-integer linear reformulation. We evaluated its efficiency through a set of computational experiments. To solve instances of larger problem dimensions, we have developed a heuristic method based on a Variable Neighborhood Search (VNS). A local search procedure with efficient fast swap-based local search is implemented in the proposed VNS-based approach. Presented computational results show that the proposed VNS is computationally efficient and quickly reaches all optimal solutions for smaller dimension instances obtained by exact solver and provide high-quality solutions on large-scale problem instances in short CPU times.

Analysis of Optimal Unit Numbers and Sizes for Microturbine Cogeneration Systems

2004 ◽  
Author(s):  
Satoshi Gamou ◽  
Koichi Ito ◽  
Ryohei Yokoyama
Keyword(s):  
Energy Demand ◽  
Large Scale ◽  
Maximum Energy ◽  
Mixed Integer ◽  
Small Scale ◽  
Optimization Approach ◽  
Operational Strategies ◽  
Energy Demands ◽  
Exhaust Heat ◽  
Annual Total

The relationships between unit numbers and capacities to be installed for microturbine cogeneration systems are analyzed from an economic viewpoint. In analyzing, an optimization approach is adopted. Namely, unit numbers and capacities are determined together with maximum contract demands of utilities such as electricity and natural gas so as to minimize the annual total cost in consideration of annual operational strategies corresponding to seasonal and hourly energy demand requirements. This optimization problem is formulated as a large-scale mixed-integer linear programming one. The suboptimal solution of this problem is obtained efficiently by solving several small-scale subproblems. Through numerical studies carried out on systems installed in hotels by changing the electrical generating/exhaust heat recovery efficiencies, the initial capital cost of the microturbine cogeneration unit and maximum energy demands as parameters, the influence of the parameters on the optimal numbers and capacities of the microturbine cogeneration units is clarified.

Computationally Efficient Hybrid Method for Transmission Network Expansion Planning

2017 ◽  
Vol 18 (2) ◽  
Author(s):  
Ashu Verma ◽  
Soumya Das ◽  
P. R. Bijwe
Keyword(s):  
Power System ◽  
Large Scale ◽  
Optimization Problems ◽  
Hybrid Approach ◽  
Solution Process ◽  
System Optimization ◽  
Mixed Integer ◽  
Transmission Network ◽  
Network Expansion ◽  
Expansion Planning

Abstract Transmission network expansion planning (TNEP) is an important and computationally very demanding problem in power system. Many computational approaches have been proposed to handle TNEP in the past. The problem is mixed integer, large scale and its complexity increases exponentially with the size of the system. Metaheuristic techniques have gained a lot of importance in last few years to solve the power system optimization problems, due to their ability to handle complex optimization functions and constraints. Many of them have been successfully applied for TNEP. The biggest challenge in these techniques is the requirement of large computational efforts. This paper uses a two-stage solution process to solve the TNEP problems. The first stage uses compensation based method to generate a quick, suboptimal solution. The valuable information contained in this solution is used to generate a set of heuristics aimed at drastically reducing the number of population for fitness evaluations required in the 2nd stage with application of metaheuristic method. The resulting hybrid approach produces very good quality solutions very efficiently. Results for 24 bus and 93 bus test systems have been obtained with the proposed method to ascertain the potential of the method in comparison to earlier approaches.

scholarly journals A Multiobjective Optimization for Train Routing at the High-Speed Railway Station Based on Tabu Search Algorithm

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Ziyan Feng ◽  
Chengxuan Cao ◽  
Yutong Liu ◽  
Yaling Zhou
Keyword(s):  
Tabu Search ◽  
High Speed ◽  
Large Scale ◽  
Computing Time ◽  
Mixed Integer ◽  
Small Scale ◽  
Railway Station ◽  
High Speed Railway ◽  
Routing Problem ◽  
Train Routing

This paper focuses on the train routing problem at a high-speed railway station to improve the railway station capacity and operational efficiency. We first describe a node-based railway network by defining the turnout node and the arrival-departure line node for the mathematical formulation. Both considering potential collisions of trains and convenience for passengers’ transfer in the station, the train routing problem at a high-speed railway station is formulated as a multiobjective mixed integer nonlinear programming model, which aims to minimize trains’ departure time deviations and total occupation time of all tracks and keep the most balanced utilization of arrival-departure lines. Since massive decision variables for the large-scale real-life train routing problem exist, a fast heuristic algorithm is proposed based on the tabu search to solve it. Two sets of numerical experiments are implemented to demonstrate the rationality and effectiveness of proposed method: the small-scale case confirms the accuracy of the algorithm; the resulting heuristic proved able to obtain excellent solution quality within 254 seconds of computing time on a standard personal computer for the large-scale station involving up to 17 arrival-departure lines and 46 trains.

scholarly journals A Hybrid Approach Using an Artificial Bee Algorithm with Mixed Integer Programming Applied to a Large-Scale Capacitated Facility Location Problem

2012 ◽  
Vol 2012 ◽  
pp. 1-14 ◽  
Author(s):  
Guillermo Cabrera G. ◽  
Enrique Cabrera ◽  
Ricardo Soto ◽  
L. Jose Miguel Rubio ◽  
Broderick Crawford ◽  
...  
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Large Scale ◽  
Location Problem ◽  
Hybrid Approach ◽  
Optimal Solution ◽  
Facility Location Problem ◽  
Mixed Integer ◽  
Capacitated Facility Location Problem ◽  
Capacitated Facility Location

We present a hybridization of two different approaches applied to the well-known Capacitated Facility Location Problem (CFLP). The Artificial Bee algorithm (BA) is used to select a promising subset of locations (warehouses) which are solely included in the Mixed Integer Programming (MIP) model. Next, the algorithm solves the subproblem by considering the entire set of customers. The hybrid implementation allows us to bypass certain inherited weaknesses of each algorithm, which means that we are able to find an optimal solution in an acceptable computational time. In this paper we demonstrate that BA can be significantly improved by use of the MIP algorithm. At the same time, our hybrid implementation allows the MIP algorithm to reach the optimal solution in a considerably shorter time than is needed to solve the model using the entire dataset directly within the model. Our hybrid approach outperforms the results obtained by each technique separately. It is able to find the optimal solution in a shorter time than each technique on its own, and the results are highly competitive with the state-of-the-art in large-scale optimization. Furthermore, according to our results, combining the BA with a mathematical programming approach appears to be an interesting research area in combinatorial optimization.

scholarly journals The Use of Unmanned Aerial Vehicles for Dynamic Site Layout Planning in Large-Scale Construction Projects

Buildings ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 602
Author(s):  
Ahmed W. A. Hammad ◽  
Bruno B. F. da Costa ◽  
Carlos A. P. Soares ◽  
Assed N. Haddad
Keyword(s):  
Unmanned Aerial Vehicles ◽  
Construction Projects ◽  
Large Scale ◽  
Mixed Integer ◽  
Transport Costs ◽  
Construction Operations ◽  
Layout Planning ◽  
Mip Model ◽  
Aerial Vehicles ◽  
Site Layout

Construction sites are increasingly complex, and their layout have an impact on productivity, safety, and efficiency of construction operations. Dynamic site layout planning (DSLP) considers the adjustment of construction facilities on-site, on an evolving basis, allowing the relocation of temporary facilities according to the stages of the project. The main objective of this study is to develop a framework for integrating unmanned aerial vehicles (UAVs) and their capacity for effective photogrammetry with site layout planning optimisation and Building Information Modelling (BIM) for automating site layout planning in large construction projects. The mathematical model proposed is based on a mixed integer programming (MIP) model, which was employed to validate the framework on a realistic case study provided by an industry partner. Allocation constraints were formulated to ensure the placement of the facilities in feasible regions. Using information from the UAV, several parameters could be considered, including proximity to access ways, distances between the facilities, and suitability of locations. Based on the proposed framework, a layout was developed for each stage of the project, adapting the location of temporary facilities according to current progress on-site. As a result, the use of space was optimised, and internal transport costs were progressively reduced.

Sign in / Sign up

Export Citation Format

Share Document