scholarly journals Stochastic Operation of a Solar-Powered Smart Home: Capturing Thermal Load Uncertainties

2020 ◽  
Vol 12 (12) ◽  
pp. 5089 ◽  
Author(s):  
Esmaeil Ahmadi ◽  
Younes Noorollahi ◽  
Behnam Mohammadi-Ivatloo ◽  
Amjad Anvari-Moghaddam
Keyword(s):  
Energy Cost ◽  
Smart Grids ◽  
Thermal Load ◽  
Electricity Demand ◽  
Hot Water ◽  
Mixed Integer ◽  
Smart Buildings ◽  
Monte Carlo Simulation Technique ◽  
Proposed Model ◽  
Milp Model

This study develops a mixed-integer linear programming (MILP) model for the optimal and stochastic operation scheduling of smart buildings. The aim of this study is to match the electricity demand with the intermittent solar-based renewable resources profile and to minimize the energy cost. The main contribution of the proposed model addresses uncertainties of the thermal load in smart buildings by considering detailed types of loads such as hot water, heating, and ventilation loads. In smart grids, buildings are no longer passive consumers. They are controllable loads, which can be used for demand-side energy management. Smart homes, as a domain of Internet of Things (IoT), enable energy systems of the buildings to operate as an active load in smart grids. The proposed formulation is cast as a stochastic MILP model for a 24-h horizon in order to minimize the total energy cost. In this study, Monte Carlo simulation technique is used to generate 1000 random scenarios for two environmental factors: the outdoor temperature, and solar radiation. Therefore in the proposed model, the thermal load, the output power of the photovoltaic panel, solar collector power generation, and electricity load become stochastic parameters. The proposed model results in an energy cost-saving of 20%, and a decrease of the peak electricity demand from 7.6 KWh to 4.2 KWh.

scholarly journals A Fast Approach for Reoptimization of Railway Train Platforming in Case of Train Delays

2020 ◽  
Vol 2020 ◽  
pp. 1-20
Author(s):  
Yongxiang Zhang ◽  
Qingwei Zhong ◽  
Yong Yin ◽  
Xu Yan ◽  
Qiyuan Peng
Keyword(s):  
Heuristic Algorithm ◽  
Mixed Integer ◽  
Train Station ◽  
Fast Approach ◽  
Time Space ◽  
Proposed Model ◽  
Efficiency And Effectiveness ◽  
Speed Up ◽  
Milp Model ◽  
Train Platforming

Train platforming is critical for ensuring the safety and efficiency of train operations within the stations, especially when unexpected train delays occur. This paper studies the problem of reoptimization of train platforming in case of train delays, where the train station is modeled using the discretization of the platform track time-space resources. To solve the reoptimization problem, we propose a mixed integer linear programming (MILP) model, which minimizes the weighted sum of total train delays and the platform track assignment costs, subject to constraints defined by operational requirements. Moreover, we design an efficient heuristic algorithm to solve the MILP model such that it can speed up the reoptimization process with good solution precision. Furthermore, a real-world case is taken as an example to show the efficiency and effectiveness of the proposed model and algorithm. The computational results show that the MILP model established in this paper can describe the reoptimization of train platforming accurately, and it can be solved quickly by the proposed heuristic algorithm. In addition, the model and algorithm developed in this paper can provide an effective computer-aided decision-making tool for the train dispatchers in case of train delays.

scholarly journals A Multi-objective MILP Model Proposed to Optimize a Supply Chain Network for Microalgae-based Biofuels and Co-products: A Case Study in Iran

2021 ◽  
Author(s):  
Mohammad Ehsan Zerafati ◽  
Ali Bozorgi-Amiri ◽  
Amir-Mohammad Golmohammadi ◽  
Fariborz Jolai
Keyword(s):  
Supply Chain ◽  
Objective Function ◽  
Environmental Impacts ◽  
Deterministic Model ◽  
Mixed Integer ◽  
Supply Chain Network ◽  
Multi Objective ◽  
Proposed Model ◽  
Milp Model

Abstract Recently, due to the efficiency of cultivating microalgae, researchers and investors have paid considerable attention to the production of different biofuel products that are environmentally friendly. In this study, a two-stage deterministic model is proposed to design a microalgae-based biofuels and co-products supply chain network (MBCSCN). In the first stage, the appropriate locations for the cultivation of microalgae are identified through the analytical hierarchy process (AHP). In the second stage, a deterministic mathematical mixed integer linear programing (MILP) model is developed for a period of five years based on the criteria of economic and environmental impacts. The economic objective function maximizes the overall profit, while the environmental impacts objective function seeks to minimize the consumed fossil fuel throughout the supply chain. Then, a multi-objective MILP optimization problem is solved using the ε-constraint method. The proposed model is evaluated through a case study in Iran. It has helped to identify appropriate locations for the cultivation of microalgae and to specify the required quantity of feedstock, the species of microalgae, the required technology, and the transportation modes in each step of the supply chain.

scholarly journals Allocation and Scheduling of Handling Resources in the Railway Container Terminal Based on Crossing Crane Area

2021 ◽  
Vol 13 (3) ◽  
pp. 1190
Author(s):  
Gang Ren ◽  
Xiaohan Wang ◽  
Jiaxin Cai ◽  
Shujuan Guo
Keyword(s):  
Programming Model ◽  
Container Terminal ◽  
Simultaneous Optimization ◽  
Mixed Integer ◽  
Gantry Crane ◽  
Optimization Scheme ◽  
Long Distance ◽  
Proposed Model ◽  
Hybrid Heuristic Algorithm ◽  
Searching Ability

The integrated allocation and scheduling of handling resources are crucial problems in the railway container terminal (RCT). We investigate the integrated optimization problem for handling resources of the crane area, dual-gantry crane (GC), and internal trucks (ITs). A creative handling scheme is proposed to reduce the long-distance, full-loaded movement of GCs by making use of the advantages of ITs. Based on this scheme, we propose a flexible crossing crane area to balance the workload of dual-GC. Decomposing the integrated problem into four sub-problems, a multi-objective mixed-integer programming model (MIP) is developed. By analyzing the characteristic of the integrated problem, a three-layer hybrid heuristic algorithm (TLHHA) incorporating heuristic rule (HR), elite co-evolution genetic algorithm (ECEGA), greedy rule (GR), and simulated annealing (SA) is designed for solving the problem. Numerical experiments were conducted to verify the effectiveness of the proposed model and algorithm. The results show that the proposed algorithm has excellent searching ability, and the simultaneous optimization scheme could ensure the requirements for efficiency, effectiveness, and energy-saving, as well as the balance rate of dual-GC.

scholarly journals Managing Smart Grids Using Price Responsive Smart Buildings

2017 ◽  
Vol 134 ◽  
pp. 21-28 ◽  
Author(s):  
Joseph Carr ◽  
Alexander Brissette ◽  
Enrico Ragaini ◽  
Luca Omati
Keyword(s):  
Smart Grids ◽  
Smart Buildings

scholarly journals Optimising the selection of food items for FFQs using Mixed Integer Linear Programming

2014 ◽  
Vol 18 (1) ◽  
pp. 68-74 ◽  
Author(s):  
Johanna C Gerdessen ◽  
Olga W Souverein ◽  
Pieter van ‘t Veer ◽  
Jeanne HM de Vries
Keyword(s):  
Linear Programming ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Selection Process ◽  
Saturated Fat ◽  
Mixed Integer ◽  
Monounsaturated Fat ◽  
Food Items ◽  
Milp Model ◽  
Selection Of

AbstractObjectiveTo support the selection of food items for FFQs in such a way that the amount of information on all relevant nutrients is maximised while the food list is as short as possible.DesignSelection of the most informative food items to be included in FFQs was modelled as a Mixed Integer Linear Programming (MILP) model. The methodology was demonstrated for an FFQ with interest in energy, total protein, total fat, saturated fat, monounsaturated fat, polyunsaturated fat, total carbohydrates, mono- and disaccharides, dietary fibre and potassium.ResultsThe food lists generated by the MILP model have good performance in terms of length, coverage and R2 (explained variance) of all nutrients. MILP-generated food lists were 32–40 % shorter than a benchmark food list, whereas their quality in terms of R2 was similar to that of the benchmark.ConclusionsThe results suggest that the MILP model makes the selection process faster, more standardised and transparent, and is especially helpful in coping with multiple nutrients. The complexity of the method does not increase with increasing number of nutrients. The generated food lists appear either shorter or provide more information than a food list generated without the MILP model.

scholarly journals An Optimized Mathematical Model for Items Supplies Planning of a Logistic System

2016 ◽  
Vol 10 (10) ◽  
pp. 133
Author(s):  
Mohammad Ali Nasiri Khalili ◽  
Mostafa Kafaei Razavi ◽  
Morteza Kafaee Razavi
Keyword(s):  
Mathematical Model ◽  
Mixed Integer ◽  
Logistics System ◽  
Proposed Model ◽  
Multiple Objective Decision Making ◽  
System Requirements ◽  
Purchasing Logistics ◽  
The Cost ◽  
The Mathematical Model ◽  
First Time

Items supplies planning of a logistic system is one of the major issue in operations research. In this article the aim is to determine how much of each item per month from each supplier logistics system requirements must be provided. To do this, a novel multi objective mixed integer programming mathematical model is offered for the first time. Since in logistics system, delivery on time is very important, the first objective is minimization of time in delivery on time costs (including lack and maintenance costs) and the cost of purchasing logistics system. The second objective function is minimization of the transportation supplier costs. Solving the mathematical model shows how to use the Multiple Objective Decision Making (MODM) can provide the ensuring policy and transportation logistics needed items. This model is solved with CPLEX and computational results show the effectiveness of the proposed model.

A Least Cost Transmission Planning Model Considering Operation Cost

2014 ◽  
Vol 15 (2) ◽  
pp. 121-128
Author(s):  
Jorge Hans Alayo
Keyword(s):  
Test System ◽  
Mixed Integer ◽  
Transmission Planning ◽  
Integer Linear Programming Problem ◽  
Transmission Expansion ◽  
Planning Models ◽  
Proposed Model ◽  
Expansion Model ◽  
Operation Cost ◽  
Linearization Techniques

Abstract Existing transmission planning models consider basic aspects of the problem. In practice, a transmission utility needs to model other important details such as operation cost of the power system. In this article, a least cost transmission expansion model is proposed considering the operation cost in order to model the trade-off between building new transmission capacity and increasing the power system’s operation cost. The proposed model is transformed into a mixed integer linear programming problem using linearization techniques and solved with CPLEX. Finally, results of the model for the Garver test system and IEEE 24-bus test system are shown.

A Multi-Period Evacuation Vehicle Routing Problem Model

2014 ◽  
Vol 931-932 ◽  
pp. 578-582
Author(s):  
Sunarin Chanta ◽  
Ornurai Sangsawang
Keyword(s):  
Vehicle Routing ◽  
Public Transportation ◽  
Vehicle Routing Problem ◽  
Programming Model ◽  
High Water ◽  
Mixed Integer ◽  
Routing Problem ◽  
Problem Model ◽  
Proposed Model ◽  
Evacuation Routing

In this paper, we proposed an optimization model that addresses the evacuation routing problem for flood disaster when evacuees trying to move from affected areas to safe places using public transportation. A focus is on the situation of evacuating during high water level when special high vehicles are needed. The objective is to minimize the total traveled distance through evacuation periods where a limited number of vehicles is given. We formulated the problem as a mixed integer programming model based on the capacitated vehicle routing problem with multiple evcuation periods where demand changing by the time. The proposed model has been tested on a real-world case study affected by the severe flooding in Thailand, 2011.

How can mixed integer linear programming assist dairy manufacturers by integrating supply decisions and production planning?

2021 ◽  
Vol 11 (2) ◽  
pp. 178-193
Author(s):  
Juliana Emidio ◽  
Rafael Lima ◽  
Camila Leal ◽  
Grasiele Madrona
Keyword(s):  
Linear Programming ◽  
Supply Chain ◽  
Integer Linear Programming ◽  
Mixed Integer Linear Programming ◽  
Dairy Products ◽  
Raw Milk ◽  
Mixed Integer ◽  
Content Type ◽  
Proposed Model ◽  
By Products

PurposeThe dairy industry needs to make important decisions regarding its supply chain. In a context with many available suppliers, deciding which of them will be part of the supply chain and deciding when to buy raw milk is key to the supply chain performance. This study aims to propose a mathematical model to support milk supply decisions. In addition to determining which producers should be chosen as suppliers, the model decides on a milk pickup schedule over a planning horizon. The model addresses production decisions, inventory, setup and the use of by-products generated in the raw milk processing.Design/methodology/approachThe model was formulated using mixed integer linear programming, tested with randomly generated instances of various sizes and solved using the Gurobi Solver. Instances were generated using parameters obtained from a company that manufactures dairy products to test the model in a more realistic scenario.FindingsThe results show that the proposed model can be solved with real-world sized instances in short computational times and yielding high quality results. Hence, companies can adopt this model to reduce transportation, production and inventory costs by supporting decision making throughout their supply chains.Originality/valueThe novelty of the proposed model stems from the ability to integrate milk pickup and production planning of dairy products, thus being more comprehensive than the models currently available in the literature. Additionally, the model also considers by-products, which can be used as inputs for other products.

scholarly journals A Synchronous Optimization Model for Multiship Shuttle Tanker Fleet Design and Scheduling Considering Hard Time Window Constraint

2018 ◽  
Vol 2018 ◽  
pp. 1-8
Author(s):  
Zhenfeng Jiang ◽  
Dongxu Chen ◽  
Zhongzhen Yang
Keyword(s):  
Time Window ◽  
Programming Model ◽  
Transportation Cost ◽  
Exact Algorithm ◽  
Mixed Integer ◽  
Routing Problem ◽  
Hard Time ◽  
Proposed Model ◽  
Total Transportation Cost ◽  
Time Window Constraints

A Synchronous Optimization for Multiship Shuttle Tanker Fleet Design and Scheduling is solved in the context of development of floating production storage and offloading device (FPSO). In this paper, the shuttle tanker fleet scheduling problem is considered as a vehicle routing problem with hard time window constraints. A mixed integer programming model aiming at minimizing total transportation cost is proposed to model this problem. To solve this model, we propose an exact algorithm based on the column generation and perform numerical experiments. The experiment results show that the proposed model and algorithm can effectively solve the problem.

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