scholarly journals A linear programming-based strategy to save pipette tips in automated DNA assembly

2021 ◽  
Author(s):  
Kirill Sechkar ◽  
Zoltan A Tuza ◽  
Guy-Bart V Stan
Keyword(s):  
Linear Programming ◽  
Large Scale ◽  
Real Life ◽  
Automatic Process ◽  
Dna Assembly ◽  
Routing Problem ◽  
Liquid Handling ◽  
Linear Programming Formulation ◽  
Pipette Tip ◽  
Mathematical Optimisation

Laboratory automation and mathematical optimisation are key to improving the efficiency of synthetic biology research. While there are algorithms optimising the construct designs and synthesis strategies for DNA assembly, the optimisation of how DNA assembly reaction mixes are prepared remains largely unexplored. Here, we focus on reducing the pipette tip consumption of a liquid-handling robot as it delivers DNA parts across a multi-well plate where several constructs are being assembled in parallel. We propose a linear programming formulation of this problem based on the capacitated vehicle routing problem, along with an algorithm which applies a linear programming solver to our formulation, hence providing a strategy to prepare a given set of DNA assembly mixes using fewer pipette tips. The algorithm performed well in randomly generated and real-life scenarios concerning several modular DNA assembly standards, proving capable of reducing the pipette tip consumption by up to 61% in large-scale cases. Combining automatic process optimisation and robotic liquid-handling, our strategy promises to greatly improve the efficiency of DNA assembly, either used alone or in combination with other algorithmic methods.

scholarly journals A variable neighborhood search algorithm with reinforcement learning for a real-life periodic vehicle routing problem with time windows and open routes

2020 ◽  
Vol 54 (5) ◽  
pp. 1467-1494
Author(s):  
Binhui Chen ◽  
Rong Qu ◽  
Ruibin Bai ◽  
Wasakorn Laesanklang
Keyword(s):  
Reinforcement Learning ◽  
Large Scale ◽  
Search Algorithm ◽  
Real Life ◽  
Planning Horizon ◽  
Neighborhood Search ◽  
Variable Neighbourhood Search ◽  
Container Transportation ◽  
Solution Quality ◽  
Routing Problem

This paper studies a real-life container transportation problem with a wide planning horizon divided into multiple shifts. The trucks in this problem do not return to depot after every single shift but at the end of every two shifts. The mathematical model of the problem is first established, but it is unrealistic to solve this large scale problem with exact search methods. Thus, a Variable Neighbourhood Search algorithm with Reinforcement Learning (VNS-RLS) is thus developed. An urgency level-based insertion heuristic is proposed to construct the initial solution. Reinforcement learning is then used to guide the search in the local search improvement phase. Our study shows that the Sampling scheme in single solution-based algorithms does not significantly improve the solution quality but can greatly reduce the rate of infeasible solutions explored during the search. Compared to the exact search and the state-of-the-art algorithms, the proposed VNS-RLS produces promising results.

APPLICATION OF GENETIC ALGORITHMS TO A LARGE-SCALE MULTIPLE-CONSTRAINT VEHICLE ROUTING PROBLEM

2003 ◽  
Vol 03 (01) ◽  
pp. 1-21 ◽  
Author(s):  
GEORGE MOURKOUSIS ◽  
MATHEW PROTONOTARIOS ◽  
THEODORA VARVARIGOU
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
High Performance ◽  
Large Scale ◽  
Hybrid Genetic Algorithm ◽  
Real Life ◽  
Search Space ◽  
Genetic Operators ◽  
Actual Problem ◽  
Routing Problem

This paper presents a study on the application of a hybrid genetic algorithm (HGA) to an extended instance of the Vehicle Routing Problem. The actual problem is a complex real-life vehicle routing problem regarding the distribution of products to customers. A non homogenous fleet of vehicles with limited capacity and allowed travel time is available to satisfy the stochastic demand of a set of different types of customers with earliest and latest time for servicing. The objective is to minimize distribution costs respecting the imposed constraints (vehicle capacity, customer time windows, driver working hours and so on). The approach for solving the problem was based on a "cluster and route" HGA. Several genetic operators, selection and replacement methods were tested until the HGA became efficient for optimization of a multi-extrema search space system (multi-modal optimization). Finally, High Performance Computing (HPC) has been applied in order to provide near-optimal solutions in a sensible amount of time.

scholarly journals Mixed Integer Linear Programming Models to Solve a Real-Life Vehicle Routing Problem with Pickup and Delivery

2021 ◽  
Vol 11 (20) ◽  
pp. 9551
Author(s):  
Ali Louati ◽  
Rahma Lahyani ◽  
Abdulaziz Aldaej ◽  
Racem Mellouli ◽  
Muneer Nusir
Keyword(s):  
Linear Programming ◽  
Vehicle Routing ◽  
Integer Linear Programming ◽  
Vehicle Routing Problem ◽  
Mixed Integer Linear Programming ◽  
Real Life ◽  
Mixed Integer ◽  
Pickup And Delivery ◽  
Routing Problem

This paper presents multiple readings to solve a vehicle routing problem with pickup and delivery (VRPPD) based on a real-life case study. Compared to theoretical problems, real-life ones are more difficult to address due to their richness and complexity. To handle multiple points of view in modeling our problem, we developed three different Mixed Integer Linear Programming (MILP) models, where each model covers particular constraints. The suggested models are designed for a mega poultry company in Tunisia, called CHAHIA. Our mission was to develop a prototype for CHAHIA that helps decision-makers find the best path for simultaneously delivering the company’s products and collecting the empty boxes. Based on data provided by CHAHIA, we conducted computational experiments, which have shown interesting and promising results.

scholarly journals Computer Oriented Interior Point Algorithm for Solving Linear Programming Problem with Application

2017 ◽  
Vol 65 (1) ◽  
pp. 41-47
Author(s):  
Farhana Ahmed Simi ◽  
Md Ainul Islam
Keyword(s):  
Linear Programming ◽  
Interior Point ◽  
Linear Programming Problem ◽  
Large Scale ◽  
Real Life ◽  
Computer Code ◽  
Interior Point Algorithm ◽  
Hand Calculation ◽  
Diet Problem ◽  
Do So

In this paper, we study the interior point algorithm for solving linear programming (LP) problem developed by Narendra Karmarkar. As interior point algorithm for LP problem involves tremendous calculations, it is quite impossible to do so by hand calculation. To fulfill the requirement we develop computer code in MATLAB for LP which is based on this algorithm procedure. To illustrate the purpose, we formulate a real life sizeable large-scale linear program for diet problem and solve it using our computer code for interior point algorithm in MATLAB. Dhaka Univ. J. Sci. 65(1): 41-47, 2017 (January)

scholarly journals Selected Genetic Algorithms for Vehicle Routing Problem Solving

Electronics ◽  
2021 ◽  
Vol 10 (24) ◽  
pp. 3147
Author(s):  
Joanna Ochelska-Mierzejewska ◽  
Aneta Poniszewska-Marańda ◽  
Witold Marańda
Keyword(s):  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Large Scale ◽  
Real Life ◽  
Economic Benefits ◽  
Added Value ◽  
Genetic Operators ◽  
Routing Problem ◽  
Starting Point

The traveling salesman problem (TSP) consists of finding the shortest way between cities, which passes through all cities and returns to the starting point, given the distance between cities. The Vehicle Routing Problem (VRP) is the issue of defining the assumptions and limitations in mapping routes for vehicles performing certain operational activities. It is a major problem in logistics transportation. In specific areas of business, where transportation can be perceived as added value to the product, it is estimated that its optimization can lower costs up to 25% in total. The economic benefits for more open markets are a key point for VRP. This paper discusses the metaheuristics usage for solving the vehicle routing problem with special attention toward Genetic Algorithms (GAs). Metaheuristic algorithms are selected to solve the vehicle routing problem, where GA is implemented as our primary metaheuristic algorithm. GA belongs to the evolutionary algorithm (EA) family, which works on a “survival of the fittest” mechanism. This paper presents the idea of implementing different genetic operators, modified for usage with the VRP, and performs experiments to determine the best combination of genetic operators for solving the VRP and to find optimal solutions for large-scale real-life examples of the VRP.

scholarly journals Optimal Target Assignment with Seamless Handovers for Networked Radars

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4555
Author(s):  
Juhyung Kim ◽  
Doo-Hyun Cho ◽  
Woo-Cheol Lee ◽  
Soon-Seo Park ◽  
Han-Lim Choi
Keyword(s):  
Linear Programming ◽  
Large Scale ◽  
Time Windows ◽  
Mixed Integer ◽  
Network Systems ◽  
Detection Range ◽  
Radar Network ◽  
Target Assignment ◽  
Linear Programming Formulation ◽  
Optimal Target

This paper proposes a binary linear programming formulation for multiple target assignment of a radar network and demonstrates its applicability to obtain optimal solutions using an off-the-shelf mixed-integer linear programming solver. The goal of radar resource scheduling in this paper is to assign the maximum number of targets by handing over targets between networked radar systems to overcome physical limitations such as the detection range and simultaneous tracking capability of each radar. To achieve this, time windows are generated considering the relation between each radar and target considering incoming target information. Numerical experiments using a local-scale simulation were performed to verify the functionality of the formulation and a sensitivity analysis was conducted to identify the trend of the results with respect to several parameters. Additional experiments performed for a large-scale (battlefield) scenario confirmed that the proposed formulation is valid and applicable for hundreds of targets and corresponding radar network systems composed of five distributed radars. The performance of the scheduling solutions using the proposed formulation was better than that of the general greedy algorithm as a heuristic approach in terms of objective value as well as the number of handovers.

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