scholarly journals A Linear Relaxation-Based Heuristic for Iron Ore Stockyard Energy Planning

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5232
Author(s):  
Marcos Wagner Jesus Servare Junior ◽  
Helder Roberto de Oliveira Rocha ◽  
José Leandro Félix Salles ◽  
Sylvain Perron
Keyword(s):  
Energy Cost ◽  
Average Distance ◽  
Optimal Solution ◽  
Computation Time ◽  
Electrical Energy ◽  
Linear Relaxation ◽  
Mixed Integer ◽  
Management Decision ◽  
Management Decision Making ◽  
Feasible Solutions

Planning the use of electrical energy in a bulk stockyard is a strategic issue due to its impact on efficiency and responsiveness of these systems. Empirical planning becomes more complex when the energy cost changes over time. The mathematical models currently studied in the literature consider many actors involved, such as equipment, sources, blends, and flows. Each paper presents different combinations of actors, creating their own transportation flows, thus increasing the complexity of this problem. In this work, we propose a new mixed integer linear programming (MILP) model for stockyard planning solved by a linear relaxation-based heuristic (LRBH) to minimize the plan’s energy cost. The proposed algorithm will allow the planner to find a solution that saves energy costs with an efficient process. The numerical results show a comparison between the exact and heuristic solutions for some different instances sizes. The linear relaxation approach can provide feasible solutions with a 3.99% average distance of the objective function in relation to the optimal solution (GAP) in the tested instances and with an affordable computation time in instances where the MILP was not able to provide a solution. The model is feasible for small and medium-sized instances, and the heuristic proposes a solution to larger problems to aid in management decision making.

An Accurate Optimal Sizing Method of a Hybrid PV/Wind Energy Conversion System with Battery Storage

2020 ◽  
Vol 48 ◽  
pp. 179-192 ◽  
Author(s):  
Ahmed Souissi
Keyword(s):  
Power Supply ◽  
Meteorological Data ◽  
Optimal Solution ◽  
Computation Time ◽  
Battery Storage ◽  
Optimal Sizing ◽  
Good Convergence ◽  
Wind Energy Conversion ◽  
Load Demand ◽  
Feasible Solutions

The present paper gives an accurate iterative methodology for optimal sizing of a hybrid photovoltaic/wind system with battery storage, using the concept of the loss of power supply probability and the total net present cost. In the first part, the optimum combinations that can meet the load demand while respecting the desired loss of power supply probability level are determinate, following two steps: First, all feasible solutions are determinate. Second, each feasible solution is optimized iteratively.In the second part, the optimal solution which has the lowest net present cost is selected based on an economical study. Lastly, the proposed and adopted methodology is verified and discussed with the meteorological data of the area of Hawaria (Tunisia) to prove its fast computation time and good convergence accuracy.

scholarly journals Modeling to Generate Alternatives in a Multiperiod Context: Apple Growers and Alar

1988 ◽  
Vol 17 (2) ◽  
pp. 139-146
Author(s):  
Martha A. Kimball
Keyword(s):  
Decision Making ◽  
Optimal Solution ◽  
Farm Management ◽  
Management Decision ◽  
Optimal Solutions ◽  
Black And White ◽  
Management Decision Making ◽  
Farm Manager

Farm management decision making would be enhanced if solutions to farm problems were offered as a set of feasible alternatives, rather than as a single “best” solution for achieving favorable results. A farm manager could evaluate an array of alternatives against the farm's unique characteristics, which frequently are difficult to quantify and model, and select the most efficient action for the farm. Often, when agricultural economists use optimization, a single optimal solution is presented, with the corresponding best method for implementation. If the optimal solution is not appealing, the farmer does not move toward more efficient practices because alternatives are not offered. It is possible, however, to eliminate black-and-white solutions and increase the choices offered to operators. Two techniques for this are the examination of nearly optimal solutions (NOS) and modeling to generate alternatives (MGA).

scholarly journals An Agent-Based Model for Dispatching Real-Time Demand-Responsive Feeder Bus

2018 ◽  
Vol 2018 ◽  
pp. 1-11 ◽  
Author(s):  
Xin Li ◽  
Ming Wei ◽  
Jia Hu ◽  
Yun Yuan ◽  
Huifu Jiang
Keyword(s):  
Real Time ◽  
Optimal Solution ◽  
Computation Time ◽  
Walking Distance ◽  
Mixed Integer ◽  
Access Time ◽  
Metro Station ◽  
Mip Model ◽  
Feeder Bus ◽  
Operation Cost

This research proposed a feeder bus dispatching tool that reduces rides’ effort to reach a feeder bus. The dispatching tool takes in real-time user specific request information and optimizes total cost accordingly (passenger access time cost and transit operation cost) by choosing the best pick-up locations and feeder buses’ routes. The pick-up locations are then transmitted back to passengers along with GPS guidance. The tool fits well with the Advanced Traveler Information Services (ATIS) which is one of the six high-priority dynamic mobility application bundles currently being promoted by the United State Department of Transportation. The problem is formulated into a Mixed Integer Programming (MIP) model. For small networks, out-of-the-shelf commercial solvers could be used for finding the optimal solution. For large networks, this research developed a GA-based metaheuristic solver which generates reasonably good solutions in a much shorter time. The proposed tool is evaluated on a real-world network in the vicinity of Jiandingpo metro station in Chongqing, China. The results demonstrated that the proposed ATIS tool reduces both buses operation cost and passenger walking distance. It is also able to significantly bring down computation time from more than 1 hour to about 1 min without sacrificing too much on solution optimality.

scholarly journals A High-speed Train Timetable Rescheduling Approach in case of Disturbances

2020 ◽  
Vol 308 ◽  
pp. 02001
Author(s):  
Xinyu Gao
Keyword(s):  
High Speed ◽  
Programming Model ◽  
Optimal Solution ◽  
Computation Time ◽  
Mixed Integer ◽  
Railway Line ◽  
Train Timetable ◽  
Train Operation ◽  
Time Required ◽  
Short Time

This paper from a macroscopic viewpoint develops a train timetable rescheduling approach on a single high-speed railway line under disturbances, i.e. inevitable train delays in the duration of the train operation. A mixed-integer linear programming model is formulated to minimize the arrival delay and the departure delay altogether. The commercial optimization software CPLEX is adopted in an effort to seek the optimal solution in an acceptably short time required in the real-time rescheduling process. The proposed approach is further tested on a real-world case study and the numerical results show that compared with the results obtain by the traditional genetic algorithm, using CPLEX to solve the model can yield better solutions and consume the desired computation time, thereby demonstrating its effectiveness and efficiency.

scholarly journals Biased Random-key Genetic Algorithm for theHybrid Vehicle-drone Routing Problem for Pick-upand Delivery

2021 ◽  
Author(s):  
Anderson Zudio ◽  
Igor Machado Coelho ◽  
Luiz Satoru Ochi
Keyword(s):  
Carrying Capacity ◽  
Vehicle Routing Problem ◽  
Computation Time ◽  
Mixed Integer ◽  
Solution Quality ◽  
Test Set ◽  
Routing Problem ◽  
Mip Model ◽  
Flight Range ◽  
Feasible Solutions

The Hybrid Vehicle drone Routing Problem (HVDRP) was recently introduced as an extension of the classic Vehicle Routing Problem (VRP). In this version, one vehicle is equipped with multiple drones to serve customers with demands for pick-up and delivery. The vehicle travels between stations that serve as parking locations to dispatch drones to attend clients. The drones have limitations in their maximum flight range and carrying capacity. We propose a BRKGA algorithm to solve HVDRP with a decoder component specially tailored to find feasible solutions. The proposed method is empirically analyzed in solution quality through a test set that a mixed-integer programming (MIP) model implementation can optimally solve in reasonable computation time. The computational result shows that the best solution found by BRKGA for each instance of the test set matches the solution quality devised by the MIP implementation. The data also show that the proposed algorithm achieves the best solution consistently through many independent executions. The instance set used and its respective best solutions attained for this work are publicly available.

scholarly journals Metaheuristics for a Flow Shop Scheduling Problem with Urgent Jobs and Limited Waiting Times

Algorithms ◽  
2021 ◽  
Vol 14 (11) ◽  
pp. 323
Author(s):  
BongJoo Jeong ◽  
Jun-Hee Han ◽  
Ju-Yong Lee
Keyword(s):  
Manufacturing Systems ◽  
Simulated Annealing Algorithm ◽  
Flow Shop ◽  
Waiting Times ◽  
Optimal Solution ◽  
Computation Time ◽  
Metaheuristic Algorithms ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Scheduling Problem

This study considers a scheduling problem for a flow shop with urgent jobs and limited waiting times. The urgent jobs and limited waiting times are major considerations for scheduling in semiconductor manufacturing systems. The objective function is to minimize a weighted sum of total tardiness of urgent jobs and the makespan of normal jobs. This problem is formulated in mixed integer programming (MIP). By using a commercial optimization solver, the MIP can be used to find an optimal solution. However, because this problem is proved to be NP-hard, solving to optimality requires a significantly long computation time for a practical size problem. Therefore, this study adopts metaheuristic algorithms to obtain a good solution quickly. To complete this, two metaheuristic algorithms (an iterated greedy algorithm and a simulated annealing algorithm) are proposed, and a series of computational experiments were performed to examine the effectiveness and efficiency of the proposed algorithms.

scholarly journals Feasible Islanding Operation of Electric Networks with Large Penetration of Renewable Energy Sources considering Security Constraints

Energies ◽  
2019 ◽  
Vol 12 (3) ◽  
pp. 537 ◽  
Author(s):  
Seyed Arash Alavi ◽  
Valentin Ilea ◽  
Alireza Saffarian ◽  
Cristian Bovo ◽  
Alberto Berizzi ◽  
...  
Keyword(s):  
Renewable Energy ◽  
Renewable Energy Sources ◽  
Optimal Solution ◽  
Computation Time ◽  
Mixed Integer ◽  
Energy Sources ◽  
Distribution Grid ◽  
Electric Networks ◽  
Emergency Situations ◽  
The One

The high penetration of Renewable Energy Sources into electric networks shows new perspectives for the network’s management: among others, exploiting them as resources for network’s security in emergency situations. The paper focuses on the frequency stability of a portion of the grid when it remains islanded following a major fault. It proposes an optimization algorithm that considers the frequency reaction of the relevant components and minimizes the total costs of their shedding. The algorithm predicts the final frequency of the island and the active power profiles of the remaining generators and demands. It is formulated as a Mixed-Integer Non-Linear Programming problem and the high computation time due to a large-size problem is mitigated through a simplified linear version of the model that filters the integer variables. The algorithm is designed to operate on-line and preventively compute the optimal shedding actions to be engaged when islanding occurs. The algorithm is validated for a typical distribution grid: the minimum amount of shedding actions is obtained while the most frequency reactive resources are maintained in operation to assure a feasible frequency. Finally, time-domain simulations show that the optimal solution corresponds to the one at the end of the network’s transients following the islanding.

Centralization and Effectiveness of Reward Management in Multinational Enterprises

2018 ◽  
Vol 17 (2) ◽  
pp. 55-65 ◽  
Author(s):  
Michael Tekieli ◽  
Marion Festing ◽  
Xavier Baeten
Keyword(s):  
Decision Making ◽  
Social Identity Theory ◽  
Multinational Enterprises ◽  
Role Identity ◽  
Management Decision ◽  
Role Identities ◽  
Management Decision Making ◽  
New Perspective ◽  
Subsidiary Managers ◽  
The Relationship

Abstract. Based on responses from 158 reward managers located at the headquarters or subsidiaries of multinational enterprises, the present study examines the relationship between the centralization of reward management decision making and its perceived effectiveness in multinational enterprises. Our results show that headquarters managers perceive a centralized approach as being more effective, while for subsidiary managers this relationship is moderated by the manager’s role identity. Referring to social identity theory, the present study enriches the standardization versus localization debate through a new perspective focusing on psychological processes, thereby indicating the importance of in-group favoritism in headquarters and the influence of subsidiary managers’ role identities on reward management decision making.

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