Time Scheduling of Transit Systems With Transfer Considerations Using Genetic Algorithms

1998 ◽  
Vol 6 (1) ◽  
pp. 1-24 ◽  
Author(s):  
Kalyanmoy Deb ◽  
Partha Chakroborty
Keyword(s):  
Genetic Algorithms ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Integer Optimization ◽  
Transit System ◽  
Decision Variables ◽  
Transit Scheduling ◽  
Algorithmic Approaches

Scheduling of a bus transit system must be formulated as an optimization problem, if the level of service to passengers is to be maximized within the available resources. In this paper, we present a formulation of a transit system scheduling problem with the objective of minimizing the overall waiting time of transferring and nontransferring passengers while satisfying a number of resource- and service-related constraints. It is observed that the number of variables and constraints for even a simple transit system (a single bus station with three routes) is too large to tackle using classical mixed-integer optimization techniques. The paper shows that genetic algorithms (GAs) are ideal for these problems, mainly because they (i) naturally handle binary variables, thereby taking care of transfer decision variables, which constitute the majority of the decision variables in the transit scheduling problem; and (ii) allow procedure-based declarations, thereby allowing complex algorithmic approaches (involving if then-else conditions) to be handled easily. The paper also shows how easily the same GA procedure with minimal modifications can handle a number of other more pragmatic extensions to the simple transit scheduling problem: buses with limited capacity, buses that do not arrive exactly as per scheduled times, and a multiple-station transit system having common routes among bus stations. Simulation results show the success of GAs in all these problems and suggest the application of GAs in more complex scheduling problems.

scholarly journals Optimal Reactive Power Scheduling Using Cuckoo Search Algorithm

2017 ◽  
Vol 7 (5) ◽  
pp. 2349 ◽  
Author(s):  
Surender Reddy Salkuti
Keyword(s):  
Power System ◽  
Reactive Power ◽  
Search Algorithm ◽  
Cuckoo Search ◽  
Cuckoo Search Algorithm ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Integer Programming Problem ◽  
Scheduling Problem ◽  
Power Scheduling

<p>This paper solves an optimal reactive power scheduling problem in the deregulated power system using the evolutionary based Cuckoo Search Algorithm (CSA). Reactive power scheduling is a very important problem in the power system operation, which is a nonlinear and mixed integer programming problem. It optimizes a specific objective function while satisfying all the equality and inequality constraints. In this paper, CSA is used to determine the optimal settings of control variables such as generator voltages, transformer tap positions and the amount of reactive compensation required to optimize the certain objective functions. The CSA algorithm has been developed from the inspiration that the obligate brood parasitism of some Cuckoo species lay their eggs in nests of other host birds which are of other species. The performance of CSA for solving the proposed optimal reactive power scheduling problem is examined on standard Ward Hale 6 bus, IEEE 30 bus, 57 bus, 118 bus and 300 bus test systems. The simulation results show that the proposed approach is more suitable, effective and efficient compared to other optimization techniques presented in the literature.</p>

Using genetic algorithms to evolve type-2 fuzzy logic systems for predicting bankruptcy

Kybernetes ◽  
2017 ◽  
Vol 46 (1) ◽  
pp. 142-156 ◽  
Author(s):  
Vasile Georgescu
Keyword(s):  
Genetic Algorithms ◽  
Fuzzy Logic ◽  
Degrees Of Freedom ◽  
Bankruptcy Prediction ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Generalization Capability ◽  
Content Type ◽  
Chromosome Representation

Purpose Type-2 fuzzy sets became attractive in practice because of their footprint of uncertainty that gives them more degrees of freedom. This paper aims to use genetic algorithms (GAs) to design an interval Type-2 fuzzy logic system (IT2FLS) for the purpose of predicting bankruptcy. Design/methodology/approach The shape of type-2 membership functions, the parameters giving their spread and location in the fuzzy partitions and the set of fuzzy rules are evolved at the same time by encoding all together into the chromosome representation. The enhanced Karnik–Mendel algorithms are used for the centroid type-reduction and defuzzification stage. The performance in predicting bankruptcy is evaluated by benchmarking IT2FLSs against type-1 FLSs. The experimental setup consists of evolving 100 configurations for both the T1FLS and IT2FLS and comparing their in-sample and out-of-sample average accuracy. Findings The experiments confirm that representing and capturing uncertainty with more degrees of freedom is an important advantage. It is this extra potential of IT2FLSs that allows them to outperform T1FLS, especially in terms of generalization capability. Originality/value The strategy followed in this paper is to train an IT2FLS from scratch rather than tuning the parameters of an existing T1FLS. Because this leads to solving a mixed integer optimization problem, the GA-based approach is specifically designed and uses genetic operators that are most suited for such a case: tournament selection, extended Laplace crossover and power mutation. Finally, the trained IT2FLS is applied to bankruptcy prediction, and its generalization capability is compared with related techniques.

A Practical Algorithmic Approach Towards Multi-Modal Resource Constrained Multi-Project Scheduling Problems (MMRCMPSP)

2015 ◽  
Author(s):  
Michael Völker ◽  
Taiba Zahid ◽  
Thorsten Schmidt
Keyword(s):  
Project Scheduling ◽  
Decision Support Tool ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Decision Strategy ◽  
Resource Constrained ◽  
Support Tool ◽  
Decision Variables ◽  
Np Hard Problem ◽  
Project Scheduling Problem

The literature concerning resource constrained project scheduling problems (RCPSP) are mainly based on series or parallel schedule generation schemes with priority sequencing rules to resolve conflicts. Recently, these models have been extended for scheduling multi-modal RCPSP (MMRCPSP) where each activity has multiple possibilities to be performed thus providing decision managers a useful tool for manipulating resources and activities. Nonetheless, this further complicates the scheduling problem inflicted by increase of decision variables. Multiple heuristics have been proposed for this NP-hard problem. The main solution strategy adopted by such heuristics is a two loops decision strategy. Basically the problem is split between two parts where first part is conversion of MMRCPSP to RCPSP (mode fix) while second is finding feasible solution for a resource constrained project and is restricted to single project environments. This research aims on the development of scheduling heuristics, exploring the possibilities of scheduling MMRCPSP with parallel assignment of modes while sequencing the activities. The work addresses Multi-Mode Resource Constrained Multi-Project Scheduling Problem, (MMRCMPSP) by formulating a mathematical model that regards practical requirements of working systems. The algorithm is made intelligent and flexible in order to adopt and shift among various defined heuristic rules under different objectives to function as a decision support tool for managers.

scholarly journals Momo – Multi-Objective Metabolic mixed integer Optimization: application to yeast strain engineering

2018 ◽  
Author(s):  
Ricardo Andrade ◽  
Mahdi Doostmohammadi ◽  
João L. Santos ◽  
Marie-France Sagot ◽  
Nuno P. Mira ◽  
...  
Keyword(s):  
Metabolic Engineering ◽  
Strain Engineering ◽  
Mixed Integer ◽  
Cell Factory ◽  
Integer Optimization ◽  
Multi Objective ◽  
Genetic Manipulations ◽  
Multi Objective Programming ◽  
Decision Variables

AbstractIn this paper, we explore the concept of multi-objective optimization in the field of metabolic engineering when both continuous and integer decision variables are involved in the model. In particular, we propose a multi-objective model that may be used to suggest reaction deletions that maximize and/or minimize several functions simultaneously. The applications may include, among others, the concurrent maximization of a bioproduct and of biomass, or maximization of a bioproduct while minimizing the formation of a given by-product, two common requirements in microbial metabolic engineering.Production of ethanol by the widely used cell factorySaccharomyces cerevisiaewas adopted as a case study to demonstrate the usefulness of the proposed approach in identifying genetic manipulations that improve productivity and yield of this economically highly relevant bioproduct. We did anin vivovalidation and we could show that some of the predicted deletions exhibit increased ethanol levels in comparison with the wild-type strain. The multi-objective programming framework we developed, called Momo, is open-source and uses PolySCIP‡as underlying multi-objective solver. Momo is available athttp://momo-sysbio.gforge.inria.fr

scholarly journals Multi-objective optimization model for planning metro-based underground logistics system network: Nanjing case study

2021 ◽  
Vol 0 (0) ◽  
pp. 0
Author(s):  
Xiliang Sun ◽  
Wanjie Hu ◽  
Xiaolong Xue ◽  
Jianjun Dong
Keyword(s):  
Programming Model ◽  
Freight Transport ◽  
Optimization Techniques ◽  
Economic Feasibility ◽  
Environmental Cost ◽  
Mixed Integer ◽  
Logistics System ◽  
Transit System ◽  
Multi Objective ◽  
Good Ability

<p style='text-indent:20px;'>Utilizing rail transit system for collaborative passenger-and-freight transport is a sustainable option to conquer urban congestion. This study proposes effective modeling and optimization techniques for planning a city-wide metro-based underground logistics system (M-ULS) network. Firstly, a novel metro prototype integrating retrofitted underground stations and newly-built capsule pipelines is designed to support automated inbound delivery from urban logistics gateways to in-city destinations. Based on four indicators (i.e. unity of freight flows, regional accessibility, environmental cost-saving, and order priority), an entropy-based fuzzy TOPSIS evaluation model is proposed to select appropriate origin-destination flows for underground freight transport. Then, a mixed integer programming model, with a well-matched solution framework combining multi-objective PSO algorithm and A* algorithm, are developed to optimize the location-allocation-routing (LAR) decisions of M-ULS network. Finally, real-world simulation based on Nanjing metro case is conducted for validation. The best facility configurations and flow assignments of the three-tier M-ULS network are reported in details. Results confirm that the proposed algorithm has good ability in providing high-quality Pareto-optimal LAR decisions. Moreover, the Nanjing M-ULS project shows strong economic feasibility while bringing millions of Yuan of annual external benefit to the society and environment.</p>

Using Improved Particle Swarm Optimization to Solve Open Shop Scheduling Problem with Two Criteria

2012 ◽  
Vol 433-440 ◽  
pp. 4936-4941
Author(s):  
Mohammad Reza Kabarazad Ghadim ◽  
Behnam Bahrami ◽  
Arash Bayat
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Problems ◽  
Particle Swarm ◽  
Optimization Techniques ◽  
Open Shop ◽  
Scheduling Problem ◽  
Scheduling Problems ◽  
Open Shop Scheduling ◽  
Swarm Optimization ◽  
Shop Scheduling

The shop-scheduling problem can be simply introduced as a problem of redistribution of resources or a problem of rearrangement of operation orders. Open shop scheduling problems (OSSP) are one of the most time-consuming works in scheduling problems. In other hand Optimization techniques have obtained much attention during the past decades. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) are the most important methods used to solve optimization problems such as Open Shop Scheduling. In this paper, a hybrid optimization algorithm (IPSO) is proposed to solve Open Shop Scheduling more efficiently and accurately. Most literature referring to OSSP focuses on the optimization of one single objective.This paper offered a mathematic model for OSSP with the goal of minimizing average completion time and the number of late jobs simultaneously. The proposed method was then compared whit the results of the LINGO software and GA. The results of this comparison show that IPSO can achieve better results for the solution in a faster time. Introduction

scholarly journals Optimizing Day-Ahead Electricity Market Prices: Increasing the Total Surplus for Energy Exchange Istanbul

2020 ◽  
Vol 22 (4) ◽  
pp. 700-716 ◽  
Author(s):  
Kursad Derinkuyu ◽  
Fehmi Tanrisever ◽  
Nermin Kurt ◽  
Gokhan Ceyhan
Keyword(s):  
Genetic Algorithms ◽  
Tabu Search ◽  
Social Welfare ◽  
Electricity Market ◽  
Energy Exchange ◽  
Heuristic Algorithms ◽  
Optimization Techniques ◽  
Problem Definition ◽  
Combinatorial Auction ◽  
Mixed Integer

Problem definition: We design a combinatorial auction to clear the Turkish day-ahead electricity market, and we develop effective tabu search and genetic algorithms to solve the problem of matching bidders and maximizing social welfare within a reasonable amount of time for practical purposes. Academic/practical relevance: A double-sided blind combinatorial auction is used to determine electricity prices for day-ahead markets in Europe. Considering the integer requirements associated with market participants’ bids and the nonlinear social welfare objective, a complicated problem arises. In Turkey, the total number of bids reaches 15,000, and this large problem needs to be solved within minutes every day. Given the practical time limit, solving this problem with standard optimization packages is not guaranteed, and therefore, heuristic algorithms are needed to quickly obtain a high-quality solution. Methodology: We use nonlinear mixed-integer programming and tabu search and genetic algorithms. We analyze the performance of our algorithms by comparing them with solutions commercially available to the market operator. Results: We provide structural results to reduce the problem size and then develop customized heuristics by exploiting the problem structure in the day-ahead market. Our algorithms are guaranteed to generate a feasible solution, and Energy Exchange Istanbul has been using them since June 2016, increasing its surplus by 448,418 Turkish liras (US$128,119) per day and 163,672,570 Turkish liras (US$46,763,591) per year, on average. We also establish that genetic algorithms work better than tabu search for the Turkish day-ahead market. Managerial implications: We deliver a practical tool using innovative optimization techniques to clear the Turkish day-ahead electricity market. We also modify our model to handle similar European day-ahead markets and show that performances of our heuristics are robust under different auction designs.

Optimization on Supply Chain Network Based on High-Tech Products Life Cycle

2013 ◽  
Vol 416-417 ◽  
pp. 1920-1925
Author(s):  
Lei Zhang ◽  
Yan Yan Kong
Keyword(s):  
Life Cycle ◽  
Profit Maximization ◽  
Transportation Cost ◽  
Pso Algorithm ◽  
Mixed Integer ◽  
High Tech ◽  
Integer Optimization ◽  
Time Model ◽  
Decision Variables ◽  
Mixed Integer Optimization Model

In view of the characteristics of market demands of high-tech products life cycle at all stages, the response time model of the products life cycle at all stages was established according to the differences of products stage response points. Based on this, the production cost/time, transportation cost/time, shortage cost, sales price, and other factors of the products life cycle at all stages were taken into consideration. The 0-1 mixed integer optimization model of multi-factory and multi-distribution center with profit maximization as the optimization objective was established. Particle Swarm Optimization (PSO) algorithm was used to solve the decision variables. Finally, a numerical example was used to illustrate the practical application value of this model.

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