scholarly journals Application of a multi-objective multi traveling salesperson problem with time windows

2021 ◽  
Author(s):  
Justus Bonz
Keyword(s):  
Computational Study ◽  
Time Windows ◽  
Waiting Times ◽  
Efficient Solutions ◽  
Management Problem ◽  
Interactive Approach ◽  
Data Set ◽  
Multi Objective ◽  
Crowd Management ◽  
Hajj Season

AbstractThe pilgrimage to Mecca, which is called Hajj, is the largest annual pedestrian crowd management problem in the world. During the Hajj, the pilgrims are accommodated in camps. For safety reasons, exact times and directions are given to the pilgrims who are moving between holy sites. Despite the importance of complying with those schedules, violations can often be conjectured. Directing a small workforce between the camps to monitor the pilgrims’ compliance with the schedule is an important matter, which will be dealt with in this paper. A type of multi-objective multiple traveling salesperson optimization problem with time windows is introduced to generate the tours for the employees monitoring the flow of pilgrims at the campsite. Four objectives are being pursued: As many pilgrims as possible (1), should be visited with a preferably small workforce (2), the tours of the employees should be short (3) and employees should have short waiting times between visits (4). A goal programming, an enumeration, Augmecon2 and an interactive approach are developed. The topic of supported and non-supported efficient solutions is addressed by determining all efficient solutions with the enumeration approach. The suitability of the approaches is analyzed in a computational study, while using an actual data set of the Hajj season in 2015. For this application, the interactive approach has been identified as the most suitable approach to support the generation of an offer for the project.

scholarly journals New general mixed-integer linear programming model for mobile workforce management

2021 ◽  
Author(s):  
András Éles ◽  
István Heckl ◽  
Heriberto Cabezas
Keyword(s):  
Programming Model ◽  
Time Windows ◽  
Mutual Exclusion ◽  
Parallel Execution ◽  
Mixed Integer ◽  
Management Problem ◽  
Routing Problem ◽  
Workforce Management ◽  
Computational Performance ◽  
Wide Range

AbstractA mathematical model is introduced to solve a mobile workforce management problem. In such a problem there are a number of tasks to be executed at different locations by various teams. For example, when an electricity utility company has to deal with planned system upgrades and damages caused by storms. The aim is to determine the schedule of the teams in such a way that the overall cost is minimal. The mobile workforce management problem involves scheduling. The following questions should be answered: when to perform a task, how to route vehicles—the vehicle routing problem—and the order the sites should be visited and by which teams. These problems are already complex in themselves. This paper proposes an integrated mathematical programming model formulation, which, by the assignment of its binary variables, can be easily included in heuristic algorithmic frameworks. In the problem specification, a wide range of parameters can be set. This includes absolute and expected time windows for tasks, packing and unpacking in case of team movement, resource utilization, relations between tasks such as precedence, mutual exclusion or parallel execution, and team-dependent travelling and execution times and costs. To make the model able to solve larger problems, an algorithmic framework is also implemented which can be used to find heuristic solutions in acceptable time. This latter solution method can be used as an alternative. Computational performance is examined through a series of test cases in which the most important factors are scaled.

scholarly journals Multi-Objective Energy Management of a Micro-Grid Considering Stochastic Nature of Load and Renewable Energy Resources

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 403
Author(s):  
Deyaa Ahmed ◽  
Mohamed Ebeed ◽  
Abdelfatah Ali ◽  
Ali S. Alghamdi ◽  
Salah Kamel
Keyword(s):  
Renewable Energy ◽  
Objective Function ◽  
Energy Management ◽  
Energy Resources ◽  
Management Problem ◽  
Renewable Energy Resources ◽  
Multi Objective ◽  
Stochastic Nature ◽  
Whale Optimization ◽  
Micro Grid

Optimal inclusion of a photovoltaic system and wind energy resources in electrical grids is a strenuous task due to the continuous variation of their output powers and stochastic nature. Thus, it is mandatory to consider the variations of the Renewable energy resources (RERs) for efficient energy management in the electric system. The aim of the paper is to solve the energy management of a micro-grid (MG) connected to the main power system considering the variations of load demand, photovoltaic (PV), and wind turbine (WT) under deterministic and probabilistic conditions. The energy management problem is solved using an efficient algorithm, namely equilibrium optimizer (EO), for a multi-objective function which includes cost minimization, voltage profile improvement, and voltage stability improvement. The simulation results reveal that the optimal installation of a grid-connected PV unit and WT can considerably reduce the total cost and enhance system performance. In addition to that, EO is superior to both whale optimization algorithm (WOA) and sine cosine algorithm (SCA) in terms of the reported objective function.

Auto-sharing parameters for transfer learning based on multi-objective optimization

2021 ◽  
pp. 1-13
Author(s):  
Hailin Liu ◽  
Fangqing Gu ◽  
Zixian Lin
Keyword(s):  
Transfer Learning ◽  
Optimization Problem ◽  
Data Sets ◽  
Multi Objective Optimization ◽  
Particle Swarm Optimizer ◽  
Real World Data ◽  
Data Set ◽  
Target Task ◽  
Main Research ◽  
Multi Objective

Transfer learning methods exploit similarities between different datasets to improve the performance of the target task by transferring knowledge from source tasks to the target task. “What to transfer” is a main research issue in transfer learning. The existing transfer learning method generally needs to acquire the shared parameters by integrating human knowledge. However, in many real applications, an understanding of which parameters can be shared is unknown beforehand. Transfer learning model is essentially a special multi-objective optimization problem. Consequently, this paper proposes a novel auto-sharing parameter technique for transfer learning based on multi-objective optimization and solves the optimization problem by using a multi-swarm particle swarm optimizer. Each task objective is simultaneously optimized by a sub-swarm. The current best particle from the sub-swarm of the target task is used to guide the search of particles of the source tasks and vice versa. The target task and source task are jointly solved by sharing the information of the best particle, which works as an inductive bias. Experiments are carried out to evaluate the proposed algorithm on several synthetic data sets and two real-world data sets of a school data set and a landmine data set, which show that the proposed algorithm is effective.

scholarly journals Complex networks of earthquakes and aftershocks

2005 ◽  
Vol 12 (1) ◽  
pp. 1-11 ◽  
Author(s):  
M. Baiesi ◽  
M. Paczuski
Keyword(s):  
Complex Networks ◽  
Time Windows ◽  
Scaling Law ◽  
Relative Strength ◽  
Modern Theory ◽  
Significant Information ◽  
Data Set ◽  
Scale Free ◽  
Main Shocks ◽  
Aftershock Sequences

Abstract. We invoke a metric to quantify the correlation between any two earthquakes. This provides a simple and straightforward alternative to using space-time windows to detect aftershock sequences and obviates the need to distinguish main shocks from aftershocks. Directed networks of earthquakes are constructed by placing a link, directed from the past to the future, between pairs of events that are strongly correlated. Each link has a weight giving the relative strength of correlation such that the sum over the incoming links to any node equals unity for aftershocks, or zero if the event had no correlated predecessors. A correlation threshold is set to drastically reduce the size of the data set without losing significant information. Events can be aftershocks of many previous events, and also generate many aftershocks. The probability distribution for the number of incoming and outgoing links are both scale free, and the networks are highly clustered. The Omori law holds for aftershock rates up to a decorrelation time that scales with the magnitude, m, of the initiating shock as tcutoff~10β m with β~-3/4. Another scaling law relates distances between earthquakes and their aftershocks to the magnitude of the initiating shock. Our results are inconsistent with the hypothesis of finite aftershock zones. We also find evidence that seismicity is dominantly triggered by small earthquakes. Our approach, using concepts from the modern theory of complex networks, together with a metric to estimate correlations, opens up new avenues of research, as well as new tools to understand seismicity.

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