scholarly journals Optimization flow model for handling car flows at the marshalling yard

2018 ◽  
Vol 216 ◽  
pp. 02024
Author(s):  
Alexander Alexandrov ◽  
Alexander Galkin ◽  
Elena Timukhina ◽  
Nikolai Tushin
Keyword(s):  
Process Model ◽  
Heuristic Algorithms ◽  
Complex Structure ◽  
Optimal Solution ◽  
Operational Planning ◽  
Mixed Integer ◽  
Railway Transport ◽  
Mixed Integer Linear Program ◽  
Marshalling Yard

Mathematical modeling is widely used in studies of operational planning in railway transport. Mainly heuristic algorithms and simulation modeling is used in short-term planning at marshalling yards. There is an opportunity to improve the quality of planning using strict optimization models. The objective of the study was to find a way to optimize the multi-terminal transport flows of the complex structure by the example of operational planning for handling car flows at the marshalling yard. The process model was built on the basis of Mixed Integer Linear Program (MILP) method. The article provides the mathematical setting and describes results of calculations of the reference example. The LpSolve application package was used. The technological effect is estimated through checking the optimal solution on the simulation model.

An Intelligent Traffic Engineering Method over Software Defined Networks for Video Surveillance Systems Based on Artificial Bee Colony

2016 ◽  
Vol 12 (4) ◽  
pp. 45-62 ◽  
Author(s):  
Reza Mohammadi ◽  
Reza Javidan
Keyword(s):  
Video Surveillance ◽  
Traffic Engineering ◽  
Surveillance System ◽  
Artificial Bee Colony ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Video Data ◽  
Surveillance Systems ◽  
Bee Colony

In applications such as video surveillance systems, cameras transmit video data streams through network in which quality of received video should be assured. Traditional IP based networks cannot guarantee the required Quality of Service (QoS) for such applications. Nowadays, Software Defined Network (SDN) is a popular technology, which assists network management using computer programs. In this paper, a new SDN-based video surveillance system infrastructure is proposed to apply desire traffic engineering for practical video surveillance applications. To keep the quality of received videos adaptively, usually Constraint Shortest Path (CSP) problem is used which is a NP-complete problem. Hence, heuristic algorithms are suitable candidate for solving such problem. This paper models streaming video data on a surveillance system as a CSP problem, and proposes an artificial bee colony (ABC) algorithm to find optimal solution to manage the network adaptively and guarantee the required QoS. The simulation results show the effectiveness of the proposed method in terms of QoS metrics.

Application of Mixed-Integer Programming and Dispatching Rules on Parallel Machine Scheduling with Inserted Idle Time

2013 ◽  
Vol 437 ◽  
pp. 748-751
Author(s):  
Chi Yang Tsai ◽  
Yi Chen Wang
Keyword(s):  
Integer Programming ◽  
Mixed Integer Programming ◽  
Parallel Machines ◽  
Optimal Solution ◽  
Parallel Machine Scheduling ◽  
Idle Time ◽  
Dispatching Rules ◽  
Mixed Integer ◽  
Scheduling Problems

This research considers the problem of scheduling jobs on unrelated parallel machines with inserted idle times to minimize the earliness and tardiness. The aims at investigating how particular objective value can be improved by allowing machine idle time and how quality solutions can be more effectively obtained. Two mixed-integer programming formulations combining with three dispatching rules are developed to solve such scheduling problems. They can easy provide the optimal solution to problem involving about nine jobs and four machines. From the results of experiments, it is found that: (1) the inserted idle times decreases objective values more effectively; (2) three dispatching rules are very competitive in terms of efficiency and quality of solutions.

scholarly journals A Heuristic Algorithm for Resource Allocation/Reallocation Problem

2011 ◽  
Vol 2011 ◽  
pp. 1-11
Author(s):  
S. Raja Balachandar ◽  
K. Kannan
Keyword(s):  
Resource Allocation ◽  
Heuristic Algorithms ◽  
Optimal Solution ◽  
Heuristic Approach ◽  
Benchmark Problems ◽  
Test Problems ◽  
Large Size ◽  
Multidimensional Knapsack ◽  
Hard Problems

This paper presents a1-optheuristic approach to solve resource allocation/reallocation problem which is known as 0/1 multichoice multidimensional knapsack problem (MMKP). The intercept matrix of the constraints is employed to find optimal or near-optimal solution of the MMKP. This heuristic approach is tested for 33 benchmark problems taken from OR library of sizes upto 7000, and the results have been compared with optimum solutions. Computational complexity is proved to be of solving heuristically MMKP using this approach. The performance of our heuristic is compared with the best state-of-art heuristic algorithms with respect to the quality of the solutions found. The encouraging results especially for relatively large-size test problems indicate that this heuristic approach can successfully be used for finding good solutions for highly constrained NP-hard problems.

scholarly journals ORNInA: A decentralized, auction-based multi-agent coordination in ODT systems

2021 ◽  
pp. 1-17
Author(s):  
Alaa Daoud ◽  
Flavien Balbo ◽  
Paolo Gianessi ◽  
Gauthier Picard
Keyword(s):  
Synthetic Data ◽  
Continuous Optimization ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Vehicle Communication ◽  
Vehicle To Vehicle ◽  
Multi Agent ◽  
Run Time ◽  
Agent Coordination

On-Demand Transport (ODT) systems have attracted increasing attention in recent years. Traditional centralized dispatching can achieve optimal solutions, but NP-Hard complexity makes it unsuitable for online and dynamic problems. Centralized and decentralized heuristics can achieve fast, feasible solution at run-time with no guarantee on the quality. Starting from a feasible not optimal solution, we present in this paper a new solution model (ORNInA) consisting of two parallel coordination processes. The first one is a decentralized insertion-heuristic based algorithm to build vehicle schedules in order to solve a particular case of the dynamic Dial-A-Ride-Problem (DARP) as an ODT system, in which vehicles communicate via Vehicle-to-vehicle communication (V2V) and make decentralized decisions. The second coordination scheme is a continuous optimization process namely Pull-demand protocol, based on combinatorial auctions, in order to improve the quality of the global solution achieved by decentralized decision at run-time by exchanging resources between vehicles (k-opt). In its simplest implementation, k is set to 1 so that vehicles can exchange only one resource at a time. We evaluate and analyze the promising results of our contributed techniques on synthetic data for taxis operating in Saint-Étienne city, against a classical decentralized greedy approach and a centralized one that uses a classical mixed-integer linear program (MILP) solver.

scholarly journals Offline Joint Network and Computational Resource Allocation for Energy-Efficient 5G and beyond Networks

2021 ◽  
Vol 11 (22) ◽  
pp. 10547
Author(s):  
Marios Gatzianas ◽  
Agapi Mesodiakaki ◽  
George Kalfas ◽  
Nikos Pleros ◽  
Francesca Moscatelli ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Energy Efficient ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Network Function Virtualization ◽  
User Association ◽  
Computational Resource ◽  
Mixed Integer Linear Program ◽  
Resource Allocation Algorithm ◽  
Network Function

In order to cope with the ever-increasing traffic demands and stringent latency constraints, next generation, i.e., sixth generation (6G) networks, are expected to leverage Network Function Virtualization (NFV) as an enabler for enhanced network flexibility. In such a setup, in addition to the traditional problems of user association and traffic routing, Virtual Network Function (VNF) placement needs to be jointly considered. To that end, in this paper, we focus on the joint network and computational resource allocation, targeting low network power consumption while satisfying the Service Function Chain (SFC), throughput, and delay requirements. Unlike the State-of-the-Art (SoA), we also take into account the Access Network (AN), while formulating the problem as a general Mixed Integer Linear Program (MILP). Due to the high complexity of the proposed optimal solution, we also propose a low-complexity energy-efficient resource allocation algorithm, which was shown to significantly outperform the SoA, by achieving up to 78% of the optimal energy efficiency with up to 742 times lower complexity. Finally, we describe an Orchestration Framework for the automated orchestration of vertical-driven services in Network Slices and describe how it encompasses the proposed algorithm towards optimized provisioning of heterogeneous computation and network resources across multiple network segments.

scholarly journals A Model-Based Optimisation Approach for Process Synthesis of Olefins from Petroleum with Application to the Malaysian Petrochemical Industry

2019 ◽  
Vol 12 ◽  
pp. 1-15
Author(s):  
Khor Cheng Seong
Keyword(s):  
Practical Knowledge ◽  
Optimal Solution ◽  
Raw Material ◽  
Mixed Integer ◽  
Hydrocarbon Mixture ◽  
Continuous Variables ◽  
Mixed Integer Linear Program ◽  
Actual Case ◽  
Model Based ◽  
Recent Developments

The shale gas revolution has rekindled interest in olefins production due to the abundance of ethane as a raw material resource. However, the main technology still revolves around the cost-intensive distillation operation. Hence this work aims to investigate the economic optimisation of olefins synthesis from petroleum in the light of recent developments. A model-based approach is applied to determine the optimal sequencing of separation and reaction processes for a multi-component hydrocarbon mixture feed to produce mainly ethylene and propylene. a mixed-integer linear program (MILP) is formulated based on a superstructure that captures numerous plausible synthesis alternatives. The model comprises linear mass balance reactor representation and simple sharp distillation based on split fractions for product recovery. Integer binary variablesis used for selecting the task for equipment and continuous variables for representing the flowrate of each task. To expedite converging to an optimal solution of a least total annualised cost configuration, the formulation is appended with logical constraints on the design and structural specifications derived from heuristics based on practical knowledge and experience. The modelling approach on actual case studies based on two such petrochemical facilities operating in Malaysia is implemented. Additionally, the solution analysis is enriched with the investigation on a second- and third-best (suboptimal) configurations obtained through appropriate integer cuts as constraints to the model. The results show good agreement with existing plant configurations, thus substantiating the value and verification of the proposed model-based optimisation approach.

scholarly journals Exact and heuristic algorithms for post prognostic decision in a single multifunctional machine

2020 ◽  
Vol 8 (2) ◽  
Author(s):  
Asma Ladj ◽  
Christophe Varnier ◽  
Fatima Benbouzid Si Tayeb ◽  
Noureddine Zerhouni
Keyword(s):  
Decision Making ◽  
Heuristic Algorithms ◽  
Health Management ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Maintenance Cost ◽  
System Safety ◽  
Average Deviation ◽  
Integrated Scheduling ◽  
New Interpretation

Prognostic and Health Management (PHM) benefits are strongly tied to the decision-making that follows the assimilation and interpretation of prognostics information. Hence, we deal inthis study with the post prognostic decision making in order to improve system safety and avoid downtime and inopportune maintenance spending. We investigate the problem of scheduling production jobs in a single multifunctional machine subjected to predictive maintenance based on PHM results. For this reason, we propose a new interpretation of PHM outputs to define the machine degradation corresponding to each job. We develop a Mixed Integer Linear Programming (MILP) model to find the best integrated scheduling that optimizes the total maintenance cost. Unfortunately, the MILP is not able to compute the optimal solution for large instances. Therefore, we design a Prognostic based Genetic Algorithm (Pro-GA). Computational results of different benchmarks setup show the efficiency and robustness of our scheme with an average deviation of about 0.2% over a newly proposed lower bound.

scholarly journals An Optimal Transportation Schedule of Mobile Equipment

2012 ◽  
Vol 10 (5) ◽  
Author(s):  
S. Guillén-Burguete ◽  
H. Sánchez-Larios ◽  
J.G Vázquez-Vázquez
Keyword(s):  
Optimal Solution ◽  
Road Construction ◽  
Transportation Cost ◽  
Convex Polyhedra ◽  
Mixed Integer ◽  
Time Intervals ◽  
Mixed Integer Linear Program ◽  
Arrival Times ◽  
Indefinite Number ◽  
Total Transportation Cost

Motivated by a problem faced by road construction companies, we develop a new model to obtain an optimaltransportation schedule of mobile machines which have to travel to execute tasks. In this problem, each task ischaracterized by the location where it is to be executed, a work-content in terms of machine-time units, and one ormore time intervals within which it can be performed. The machines can be transported from one location to anotherat any time, thus the problem has an indefinite number of variables. However, this indefinite number of variables canbe reduced to a definite one because, as we prove, the problem has an optimal solution in which the arrivals ofmachines occur only at certain time instants. The objective is to minimize the total transportation cost such that all thetasks are executed within their time intervals. The constraints ensuring that the tasks are processed within theirprescribed time intervals are nonlinear; nevertheless, due to the sets of the possible arrival times of the machinesforming bounded convex polyhedra, our problem can be transformed into a mixed integer linear program by the samedevice used in the decomposition principle of Dantzig-Wolfe.

Using a genetic algorithm to solve the troops-to-tasks problem in military operations planning

2017 ◽  
Vol 14 (4) ◽  
pp. 439-446 ◽  
Author(s):  
Maria Fleischer Fauske
Keyword(s):  
Genetic Algorithm ◽  
Time Frame ◽  
Optimization Techniques ◽  
Operational Planning ◽  
Mixed Integer ◽  
Data Sets ◽  
Military Operations ◽  
Mixed Integer Linear Program ◽  
Problem Instances ◽  
Feasible Solutions

The troops-to-tasks analysis in military operational planning is the process where the military staff investigates who should do what, where, and when in the operation. In this paper, we describe a genetic algorithm for solving troops-to-tasks problems, which are typically solved manually. The study was motivated by a request from Norwegian military staff, who acknowledged the potential for solving the troops-to-tasks analysis more effectively by using optimization techniques. Also, NATO’s operational planning tool, TOPFAS, lacks an optimization module for the troops-to-tasks analysis. The troops-to-tasks problem generalizes the well-known resource-constrained project scheduling problem, and thus it is very difficult to solve. As the troops-to-tasks problem is particularly complex, the main purpose of our study was to develop an algorithm capable of solving real-sized problem instances. We developed a genetic algorithm with new features, which were crucial to finding good solutions. We tested the algorithm on two different data sets representing high-intensity military operations. We compared the performance of the algorithm to that of a mixed integer linear program solved by CPLEX. In contrast to CPLEX, the algorithm found feasible solutions within an acceptable time frame for all instances.

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