scholarly journals Optimal Management of Combined-Cycle Gas Units with Gas Storage under Uncertainty

Energies ◽  
2019 ◽  
Vol 13 (1) ◽  
pp. 113
Author(s):  
Hernán Gómez-Villarreal ◽  
Miguel Carrión ◽  
Ruth Domínguez
Keyword(s):  
Programming Model ◽  
Planning Horizon ◽  
Gas Storage ◽  
Combined Cycle ◽  
Expected Profit ◽  
Storage Unit ◽  
Over The Counter ◽  
Proposed Model ◽  
Stochastic Programming Model

We formulated a problem faced by a power producer who owns a combined-cycle gas turbine (CCGT) and desires to maximize its expected profit in a medium-term planning horizon. We assumed that this producer can participate in the spot and over-the-counter markets to buy and sell natural gas and electricity. We also considered that the power producer has gas storage available that can be used for handling the availability of gas and the uncertainty of gas prices. A stochastic programming model was used to formulate this problem, where the electricity and gas prices were characterized as stochastic processes using a set of scenarios. The proposed model includes the technical constraints resulting from the operation of the combined cycle power plant and the gas storage and a detailed description of the different markets in which the power producer can participate. Finally, the performance of the proposed model is tested in a realistic case study. The numerical results show that the usage of the gas storage unit allows the power producer to increase its expected profit. Additionally, it is observed that bilateral contracting decisions are not influenced by the presence of the gas storage unit.

scholarly journals A study of production and harvesting planning for the chicken industry

2018 ◽  
Vol 64 (No. 7) ◽  
pp. 316-327 ◽  
Author(s):  
You Peng-Sheng ◽  
Hsieh Yi-Chih
Keyword(s):  
Sensitivity Analysis ◽  
Production Planning ◽  
Programming Model ◽  
Computational Results ◽  
Mathematical Programming Model ◽  
Planning Period ◽  
Hybrid Heuristic ◽  
Proposed Model ◽  
Parameter Variations

To order to raise chickens for meat, chicken farmers must select an appropriate breed and determine how many broilers to raise in each henhouse. This study proposes a mathematical programming model to develop a production planning and harvesting schedule for chicken farmers. The production planning comprises the number of batches of chickens to be raised in each henhouse, the number of chicks to be raised for each batch, what breed of chicken to raise, when to start raising and the duration of the raising period. The harvesting schedule focuses on when to harvest and how many broilers to harvest each time. Our aim was to develop proper production and harvesting schedules that enable chicken farmers to maximise profits over a planning period. The problem is a highly complicated one. We developed a hybrid heuristic approach to address the issue. The computational results have shown that the proposed model can help chicken farmers to deal with the problems of chicken-henhouse assignment, chicken raising and harvesting, and may thus contribute to increasing profits. A case study of a chicken farmer in Yunlin County (Taiwan) was carried out to illustrate the application of the proposed model. Sensitivity analysis was also conducted to explore the influence of parameter variations.

scholarly journals A Bi-Level Programming Model for Optimal Bus Stop Spacing of a Bus Rapid Transit System

Mathematics ◽  
2019 ◽  
Vol 7 (7) ◽  
pp. 625
Author(s):  
Cheng ◽  
Zhao ◽  
Zhang
Keyword(s):  
Programming Model ◽  
Bus Rapid Transit ◽  
Rapid Transit ◽  
Transit System ◽  
Bus Stop ◽  
Proposed Model ◽  
Level Model ◽  
Bus Stop Spacing ◽  
Stop Spacing

The purpose of this study is to create a bi-level programming model for the optimal bus stop spacing of a bus rapid transit (BRT) system, to ensure simultaneous coordination and consider the interests of bus companies and passengers. The top-level model attempts to optimize and determine optimal bus stop spacing to minimize the equivalent costs, including wait, in-vehicle, walk, and operator costs, while the bottom-level model reveals the relation between the locations of stops and spatial service coverage to attract an increasing number of passengers. A case study of Chengdu, by making use of a genetic algorithm, is presented to highlight the validity and practicability of the proposed model and analyze the sensitivity of the coverage coefficient, headway, and speed with different spacing between bus stops.

scholarly journals The unit train make-up scheme for loaded direction in the heavy haul railway

2019 ◽  
Vol 1 (1) ◽  
pp. 30-44 ◽  
Author(s):  
Yuqiang Wang ◽  
Yuguang Wei ◽  
Hua Shi ◽  
Xinyu Liu ◽  
Liyuan Feng ◽  
...  
Keyword(s):  
Integer Programming ◽  
Design Methodology ◽  
Arrival Time ◽  
Programming Model ◽  
Nonlinear Integer Programming ◽  
Content Type ◽  
Proposed Model ◽  
Heavy Haul ◽  
Heavy Haul Railway

Purpose The purpose of this paper is to study the unit train make-up scheme for loaded direction in the heavy haul railway. Design/methodology/approach A 0-1 nonlinear integer programming model with the aim of minimizing the idling period between actual train arrival time and expected train arrival time for all loaded unit trains are proposed. Findings The proposed model is applied into a case study based on Daqin heavy haul railway. Results show that the proposed model can offer operators an optimal unit train make-up scheme for loaded direction in heavy haul railway. Originality/value The proposed model can offer operators an optimal unit train make-up scheme for loaded direction in heavy haul railway.

scholarly journals OPTIMIZING THE CONTAINER TRUCK PATHS WITH UNCERTAIN TRAVEL TIME IN CONTAINER PORTS

Transport ◽  
2021 ◽  
Vol 36 (6) ◽  
pp. 444-462
Author(s):  
Jiaming Liu ◽  
Bin Yu ◽  
Wenxuan Shan ◽  
Baozhen Yao ◽  
Yao Sun
Keyword(s):  
Travel Time ◽  
Shortest Path ◽  
Programming Model ◽  
Mixed Integer ◽  
A Algorithm ◽  
Shuffled Complex Evolution ◽  
Proposed Model ◽  
Container Ports ◽  
Yard Template

The yard template problem in container ports determines the assignment of space to store containers for the vessels, which could impact container truck paths. Actually, the travel time of container truck paths is uncertain. This paper considers the uncertainty from two perspectives: (1) the yard congestion in the context of yard truck interruptions, (2) the correlation among adjacent road sections (links). A mixed-integer programming model is proposed to minimize the travel time of container trucks. The reliable shortest path, which takes the correlation among links into account is firstly discussed. To settle the problem, a Shuffled Complex Evolution Approach (SCE-UA) algorithm is designed to work out the assignment of yard template, and the A* algorithm is presented to find the reliable shortest path according to the port operator’s attitude. In our case study, one yard in Dalian (China) container port is chosen to test the applicability of the model. The result shows the proposed model can save 9% of the travel time of container trucks, compared with the model without considering the correlation among adjacent links.

scholarly journals Impact of government policies on Sustainable Petroleum Supply Chain (SPSC): A case study – Part I (Models)

2020 ◽  
Vol 14 (1) ◽  
Author(s):  
Davoud Ghahremanlou ◽  
Wieslaw Kubiak
Keyword(s):  
Supply Chain ◽  
General Model ◽  
Programming Model ◽  
The United States ◽  
Policy Impact ◽  
Renewable Fuel Standard ◽  
Stochastic Programming Model ◽  
The Government ◽  
The Impact

Environmental concerns and energy security have led governments to establish legislations to convertConventional Petroleum Supply Chain (CPSC) to Sustainable Petroleum Supply Chain (SPSC). The United States(US), one of the biggest oil consumers in the world, has created regulations to manage ethanol production and con-sumption for the last half century. Though these regulations have created new opportunities, they have also added newburdens to the obligated parties. It is thus key for the government, the obligated parties, and related businesses to studythe impact of the policies on the SPSC. We develop a two-stage stochastic programming model, General Model (GM),which incorporates Renewable Fuel Standard 2 (RFS2), Tax Credits, Tariffs, and Blend Wall (BW) to study the policyimpact on the SPSC using cellulosic ethanol. The model, as any other general model available in the literature, makesit highly impractical to study the policy impact due to the model’s computational complexity. We use the GM to derivea Lean Model (LM) to study the impact by running computational experiments more efficiently and consequently byarriving at robust managerial insights much faster. We present a case study of the policy impact on the SPSC in theState of Nebraska using the LM in the accompanying part II (Ghahremanlou and Kubiak 2020).

scholarly journals Crew Scheduling Considering both Crew Duty Time Difference and Cost on Urban Rail System

2016 ◽  
Vol 28 (5) ◽  
pp. 449-460 ◽  
Author(s):  
Wenliang Zhou ◽  
Xia Yang ◽  
Lianbo Deng ◽  
Jin Qin
Keyword(s):  
Ant Colony Algorithm ◽  
Programming Model ◽  
Crew Scheduling ◽  
Computational Time ◽  
Mathematical Programming Model ◽  
Urban Rail ◽  
Proposed Model ◽  
Train Services ◽  
Duty Time

Urban rail crew scheduling problem is to allocate train services to crews based on a given train timetable while satisfying all the operational and contractual requirements. In this paper, we present a new mathematical programming model with the aim of minimizing both the related costs of crew duty and the variance of duty time spreads. In addition to iincorporating the commonly encountered crew scheduling constraints, it also takes into consideration the constraint of arranging crews having a meal in the specific meal period of one day rather than after a minimum continual service time. The proposed model is solved by an ant colony algorithm which is built based on the construction of ant travel network and the design of ant travel path choosing strategy. The performances of the model and the algorithm are evaluated by conducting case study on Changsha urban rail. The results indicate that the proposed method can obtain a satisfactory crew schedule for urban rails with a relatively small computational time.

scholarly journals A Lexicographic Approach to Postdisaster Relief Logistics Planning Considering Fill Rates and Costs under Uncertainty

2014 ◽  
Vol 2014 ◽  
pp. 1-17 ◽  
Author(s):  
Yajie Liu ◽  
Bo Guo
Keyword(s):  
Stochastic Programming ◽  
Heuristic Algorithms ◽  
Programming Model ◽  
Mixed Integer ◽  
Integer Optimization ◽  
Solution Algorithms ◽  
Evacuation Plans ◽  
Proposed Model ◽  
Transportation Assets ◽  
Stochastic Programming Model

Predicting the occurrences of earthquakes is difficult, but because they often bring huge catastrophes, it is necessary to launch relief logistics campaigns soon after they occur. This paper proposes a stochastic optimization model for post-disaster relief logistics to guide the strategic planning with respect to the locations of temporary facilities, the mobilization levels of relief supplies, and the deployment of transportation assets with uncertainty on demands. In addition, delivery plans for relief supplies and evacuation plans for critical population have been developed for each scenario. Two objectives are featured in the proposed model: maximizing the expected minimal fill rate of affected areas, where the mismatching distribution among correlated relief demands is penalized, and minimizing the expected total cost. An approximate lexicographic approach is here used to transform the bi-objective stochastic programming model into a sequence of single objective stochastic programming models, and scenario-decomposition-based heuristic algorithms are furthermore developed to solve these transformed models. The feasibility of the proposed bi-objective stochastic model has been demonstrated empirically, and the effectiveness of the developed solution algorithms has also been evaluated and compared to that of commercial mixed-integer optimization software.

Stochastic Programming Based on Traveling Salesman Problem with Stochastic Cost

2013 ◽  
Vol 321-324 ◽  
pp. 2076-2079
Author(s):  
Xiao Zheng Yang ◽  
Qing Chun Li ◽  
Lei Zhou ◽  
Hong Qi Hui
Keyword(s):  
Stochastic Programming ◽  
Traveling Salesman Problem ◽  
Programming Model ◽  
Real Life ◽  
Traveling Salesman ◽  
Factors Affecting ◽  
Logistics Distribution ◽  
Proposed Model ◽  
Stochastic Programming Model ◽  
Cost Factors

The research of traveling salesman problem (TSP) is important in logistics distribution. Because many stochastic cost factors affecting logistics distribution in real life, a class of stochastic programming model is considered in TSP in this paper. Through given a class of synthesizing effect functions which synthesis expectation and variance of cost factors, stochastic programming model of TSP with stochastic cost can be converted into a class of crisp programming models. A genetic algorithm based on real coding is used in an illustrative example. It is provided to show the effectiveness of the proposed model and method.

scholarly journals Transfer Coordination for Metro Networks during the Start- or End-of-Service Period

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Liqiao Ning ◽  
Peng Zhao ◽  
Wenkai Xu ◽  
Ke Qiao
Keyword(s):  
Integer Programming ◽  
Adverse Effects ◽  
Waiting Time ◽  
Programming Model ◽  
Mixed Integer ◽  
Service Period ◽  
Proposed Model ◽  
Transfer Success ◽  
Metro Networks

When travelling via metro networks during the start- or end-of-service period, transferring passengers may suffer a transfer failure. Accordingly, the synchronization timetabling problem necessitates consideration of transfer waiting time and transfer availability with respect to the first or last train. Hence, transfer train index (TTI) is formulated to identify the transfer train and calculate the transfer waiting time. Furthermore, two types of connection indexes, the last connection train index (LCTI) and the first connection train index (FCTI), are devised to distinguish transfer failure from transfer success, and the penalty constraints are implemented together to reflect the adverse effects of transfer failure. Then, a mixed integer programming model is developed to concurrently reduce transfer waiting time and improve transfer availability, which can be solved by CPLEX. Finally, a case study on Beijing metro network is made to verify the method. Experimental results show that our proposed model can yield synchronization solutions with significant reductions in both the average transfer waiting time and the proportion of transfer failure passengers.

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