Multiperiod Highway Improvement and Construction Scheduling: Model Development and Application

1998 ◽  
Vol 1617 (1) ◽  
pp. 96-104 ◽  
Author(s):  
Wael Eldessouki ◽  
Nagui Rouphail ◽  
Madalena Beja ◽  
S. Ranji Ranjithan
Keyword(s):  
Planning Process ◽  
Model Development ◽  
Mathematical Optimization ◽  
Planning Horizon ◽  
Optimization Techniques ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Mixed Integer Linear Program ◽  
Traffic Demand ◽  
Primary Model

A methodology is presented that emulates the transportation improvement planning process using mathematical optimization techniques. The scheduling problem is formulated as a mixed integer linear program (MILP) and can be considered as a multiperiod network design problem. The three primary model components are discussed: ( a) the input module in which the network, traffic demand, and pool of potential projects are identified over the planning horizon; ( b) the benefits estimation module using network travel time as the benefit criterion; and ( c) the schedule builder, an MILP that attempts to maximize the total benefits subject to annual resources and project precedence constraints. The proposed method is applied in a case-study context to the Lisbon metropolitan region’s network, a portion of Portugal’s highway network, and the results are discussed.

scholarly journals Investment Incentives in Competitive Electricity Markets

2018 ◽  
Vol 8 (10) ◽  
pp. 1978 ◽  
Author(s):  
Jaber Valinejad ◽  
Taghi Barforoshi ◽  
Mousa Marzband ◽  
Edris Pouresmaeil ◽  
Radu Godina ◽  
...  
Keyword(s):  
Electricity Markets ◽  
Electricity Market ◽  
Planning Horizon ◽  
Optimization Method ◽  
Design Criterion ◽  
Mixed Integer ◽  
Investment Incentives ◽  
Mixed Integer Linear Program ◽  
Interconnected Power System ◽  
The Impact

This paper presents the analysis of a novel framework of study and the impact of different market design criterion for the generation expansion planning (GEP) in competitive electricity market incentives, under variable uncertainties in a single year horizon. As investment incentives conventionally consist of firm contracts and capacity payments, in this study, the electricity generation investment problem is considered from a strategic generation company (GENCO) ′ s perspective, modelled as a bi-level optimization method. The first-level includes decision steps related to investment incentives to maximize the total profit in the planning horizon. The second-level includes optimization steps focusing on maximizing social welfare when the electricity market is regulated for the current horizon. In addition, variable uncertainties, on offering and investment, are modelled using set of different scenarios. The bi-level optimization problem is then converted to a single-level problem and then represented as a mixed integer linear program (MILP) after linearization. The efficiency of the proposed framework is assessed on the MAZANDARAN regional electric company (MREC) transmission network, integral to IRAN interconnected power system for both elastic and inelastic demands. Simulations show the significance of optimizing the firm contract and the capacity payment that encourages the generation investment for peak technology and improves long-term stability of electricity markets.

scholarly journals Multi-Period Network Design Problem in Regional Hazardous Waste Management Systems

2019 ◽  
Vol 16 (11) ◽  
pp. 2042 ◽  
Author(s):  
Jun Zhao ◽  
Lixiang Huang
Keyword(s):  
Network Design ◽  
Design Problem ◽  
Programming Model ◽  
Planning Horizon ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Hazardous Wastes ◽  
Total Risk ◽  
Total Cost ◽  
Treatment And Disposal

The management of hazardous wastes in regions is required to design a multi-echelon network with multiple facilities including recycling, treatment and disposal centers servicing the transportation, recycling, treatment and disposal procedures of hazardous wastes and waste residues. The multi-period network design problem within is to determine the location of waste facilities and allocation/transportation of wastes/residues in each period during the planning horizon, such that the total cost and total risk in the location and transportation procedures are minimized. With consideration of the life cycle capacity of disposal centers, we formulate the problem as a bi-objective mixed integer linear programming model in which a unified modeling strategy is designed to describe the closing of existing waste facilities and the opening of new waste facilities. By exploiting the characteristics of the proposed model, an augmented ε -constraint algorithm is developed to solve the model and find highly qualified representative non-dominated solutions. Finally, computational results of a realistic case demonstrate that our algorithm can identify obviously distinct and uniformly distributed representative non-dominated solutions within reasonable time, revealing the trade-off between the total cost and total risk objectives efficiently. Meanwhile, the multi-period network design optimization is superior to the single-period optimization in terms of the objective quality.

scholarly journals Mathematical Modeling of Ballistic Missile Defense

2020 ◽  
Vol 7 (9) ◽  
pp. 145
Author(s):  
Mariya Afshari Rad ◽  
Mohammad Salimi Khorshidi
Keyword(s):  
Mathematical Modeling ◽  
Mathematical Optimization ◽  
Ballistic Missile ◽  
Linear Program ◽  
Integer Linear Program ◽  
Mixed Integer ◽  
Missile Defense ◽  
Mixed Integer Linear Program ◽  
Ballistic Missile Defense ◽  
Potential Damage

Today, many countries are looking for missile strikes to achieve their goals. To optimize ballistic missile defense, missile defense centers identify potential launch points for enemy missiles to anticipate enemy attacks to reduce potential damage. One of these measures is mathematical modeling for the scenario of a possible enemy attack and defensive cover against this attack. In this research, mathematical optimization and mixed integer linear program have been used to reduce the damage against the enemy attack. The purpose of this study is to minimize the maximum damage caused by enemy missile attacks.

scholarly journals A new planning model to support logistics service providers in selecting mode, route, and terminal location

2013 ◽  
Vol 20 (Special-Issue) ◽  
pp. 67-73 ◽  
Author(s):  
Nathan Huynh ◽  
Fateme Fotuhi
Keyword(s):  
Design Problem ◽  
Service Providers ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Planning Model ◽  
Mixed Integer Linear Program ◽  
Logistics Service ◽  
Logistics Service Providers ◽  
Proposed Model ◽  
Different Types

Abstract In this paper, we address thefreight network design problem. A mixed integer linear program is formulated to help logistics service providers jointlyselect the best terminal locations among a set of candidate locations, shipping modes, and route for shipping different types of commodities. The developed model isapplied to two different networksto show its applicability. Results obtained from CPLEX for the case studiesare presented, and the benefit of the proposed model is discussed

scholarly journals Matheuristics for the Design of a Multi-Step, Multi-Product Supply Chain with Multimodal Transport

2021 ◽  
Vol 11 (21) ◽  
pp. 10251
Author(s):  
David A. Ruvalcaba-Sandoval ◽  
Elias Olivares-Benitez  ◽  
Omar Rojas ◽  
Guillermo Sosa-Gómez
Keyword(s):  
Supply Chain ◽  
Network Design ◽  
Network Design Problem ◽  
Mixed Integer ◽  
Supply Chain Network Design ◽  
Supply Chain Network ◽  
Exact Methods ◽  
Mixed Integer Linear Program ◽  
Global Networks ◽  
Four Levels

Supply-chain network design is a complex task because there are many decisions involved, and presently, global networks involve many actors and variables, for example, in the automotive, pharmaceutical, and electronics industries. This research addresses a supply-chain network design problem with four levels: suppliers, factories, warehouses, and customers. The problem considered decides on the number, locations, and capacities of factories and warehouses and the transportation between levels in the supply chain. The problem is modeled as a mixed-integer linear program. The main contribution of this work is the proposal of two matheuristic algorithms to solve the problem. Matheuristics are algorithms that combine exact methods and heuristics, attracting interest in the literature because of their fast execution and high-quality solutions. The matheuristics proposed to select the warehouses and their capacities following heuristic rules. Once the warehouses and their capacities are fixed, the algorithms solve reduced models using commercial optimization software. Medium and large instances were generated based on a procedure described in the literature. A comparison is made between the algorithms and the results obtained, solving the model with a time limit. The algorithms proposed are successful in obtaining better results for the largest instances in shorter execution times.

scholarly journals REopt: A Platform for Energy System Integration and Optimization

2014 ◽  
Author(s):  
T. Simpkins ◽  
D. Cutler ◽  
K. Anderson ◽  
D. Olis ◽  
E. Elgqvist ◽  
...  
Keyword(s):  
System Integration ◽  
Net Present Value ◽  
Planning Process ◽  
Cost Savings ◽  
Integrated Approach ◽  
Energy System ◽  
Energy Performance ◽  
Mixed Integer ◽  
Energy Planning ◽  
Mixed Integer Linear Program

REopt is an energy planning platform offering concurrent, multiple technology integration and optimization capabilities to help clients meet their cost savings and energy performance goals. The REopt platform provides techno-economic decision support analysis throughout the energy planning process, from agency-level screening and macro planning to project development to energy asset operation. REopt employs an integrated approach to optimizing the energy costs of a site by considering electricity and thermal consumption, resource availability, complex tariff structures including time-of-use, demand and export rates, incentives, net metering, and interconnection limits. Formulated as a mixed integer linear program, REopt recommends an optimally sized mix of conventional and renewable energy, and energy storage technologies; estimates the net present value associated with implementing those technologies; and provides the cost-optimal dispatch strategy for operating them at maximum economic efficiency. The REopt platform can be customized to address a variety of energy optimization scenarios including policy, microgrid, and operational energy applications. This paper presents the REopt techno-economic model along with two examples of recently completed analysis projects.

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.

Modeling and Solution Techniques Used for Hydro Generation Scheduling

Water ◽  
2019 ◽  
Vol 11 (7) ◽  
pp. 1392 ◽  
Author(s):  
Iram Parvez ◽  
JianJian Shen ◽  
Mehran Khan ◽  
Chuntian Cheng
Keyword(s):  
Genetic Algorithm ◽  
Dynamic Programming ◽  
Stochastic Programming ◽  
Short Interval ◽  
Optimization Techniques ◽  
Mixed Integer ◽  
Fuzzy Logics ◽  
Spinning Reserve ◽  
Lagrange Relaxation ◽  
Generation Scheduling

The hydro generation scheduling problem has a unit commitment sub-problem which deals with start-up/shut-down costs related hydropower units. Hydro power is the only renewable energy source for many countries, so there is a need to find better methods which give optimal hydro scheduling. In this paper, the different optimization techniques like lagrange relaxation, augmented lagrange relaxation, mixed integer programming methods, heuristic methods like genetic algorithm, fuzzy logics, nonlinear approach, stochastic programming and dynamic programming techniques are discussed. The lagrange relaxation approach deals with constraints of pumped storage hydro plants and gives efficient results. Dynamic programming handles simple constraints and it is easily adaptable but its major drawback is curse of dimensionality. However, the mixed integer nonlinear programming, mixed integer linear programming, sequential lagrange and non-linear approach deals with network constraints and head sensitive cascaded hydropower plants. The stochastic programming, fuzzy logics and simulated annealing is helpful in satisfying the ramping rate, spinning reserve and power balance constraints. Genetic algorithm has the ability to obtain the results in a short interval. Fuzzy logic never needs a mathematical formulation but it is very complex. Future work is also suggested.

Hydrogen Integration in Refinery Using MINLP Method

2012 ◽  
Vol 622-623 ◽  
pp. 720-725
Author(s):  
Mir Esmaeil Masoumi ◽  
Zahra Firooz Jahantighy
Keyword(s):  
Mathematical Optimization ◽  
Critical Issue ◽  
Mixed Integer ◽  
Total Annual Cost ◽  
Optimum Placement ◽  
Production Of Hydrogen ◽  
Hydrogen Network ◽  
Tehran Refinery ◽  
A Site ◽  
Low Sulfur

Hydrogen is an important utility in the production of clean fuels as low-sulfur gasoline and diesel. The combination of low-sulfur fuel specifications and reduced production of hydrogen in catalytic reformers make hydrogen management a critical issue. In this paper a systematic approach for the retrofit design of hydrogen networks in refineries was proposed. The methodology is based upon mathematical optimization of a superstructure and maximizing the amount of hydrogen recovered across a site. The techniques account fully for pressure constraints as well as the existing equipment. The optimum placement of new equipment such as compressors and purification units is also considered. Total annual cost and fresh hydrogen required by the refinery are employed as the optimizing objects. Equations obtained from superstructure method are solved with mixed-integer nonlinear programming of the general algebraic modeling system. In this work the Tehran refinery was considered as a case study. The results of optimization show that the 28% reduction was achieved in hydrogen production of north section and this is 35.7% for south section of refinery. Also adding the new hydrogen recovery unit in hydrogen network will cause 20% reduction in total costs of north and 31.2% in south sections.

Optimization of the Receipt and Delivery Schedules for a Transfer Tank Farm of Products Pipeline Network

2017 ◽  
Author(s):  
Li Wang ◽  
Changchun Wu ◽  
Lili Zuo ◽  
Yanfei Huang ◽  
Haihong Chen
Keyword(s):  
Material Balance ◽  
Optimal Solution ◽  
Planning Horizon ◽  
Oil Products ◽  
Mixed Integer ◽  
Pipeline Network ◽  
Schedule Optimization ◽  
Tank Farm ◽  
Dynamic Time ◽  
Products Pipeline

Transfer tank farms play an important role in an oil products pipeline network, which receive oil products from upstream pipelines and deliver them to downstream pipelines. The scheduling problem for oil products supply chain is very complicated because of numerous constraints to be considered. The published literatures on schedule optimization of oil products pipeline network usually focus on the batch plans of each pipeline, without consideration on the receipt and delivery schedule of transfer tank farm. In this paper, a mixed-integer linear programming (MILP) model is developed for the schedule optimization of transfer tank farm. The objective of the model is to minimize switching times of the tank operations of a tank farm during a planning horizon, while fulfilling the products transmission requirements of the upstream and downstream pipelines of the tank farm. The constraints of the model include material balance, the operational rules of tanks, the topological structure constraints of the tank farm, the settling period of the oil products stored in dedicated tank and so on. To satisfy the constraint of fulfilling the specific transmission requirements of pipelines, concepts of static and dynamic time slot are proposed. A continuous time representation is used to obtain accurate optimal schedules and decrease scale of the model by reducing the number of variables. The model is solved by CPLEX solver for a transfer tank farm of an oil products pipeline network in China. Some examples are tested under different scenarios and the results show that global optimal solution can be obtain at acceptable computational costs.

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