scholarly journals Elements of Comprehensive Pipeline Optimization

1996 ◽  
Author(s):  
Michael Short ◽  
Steven H. Meller
Keyword(s):  
Real World ◽  
Dynamic Programming Algorithm ◽  
Computational Effort ◽  
Operating Conditions ◽  
Programming Algorithm ◽  
Cost Structures ◽  
Look Ahead ◽  
Full Analysis ◽  
Manageable Level ◽  
Utility Cost

It is well known that algorithms exist for reducing pipeline operating costs. These algorithms are exact for ideal pipelines and need to be modified to provide solutions for the real world. The issues include pipeline configurations, utility cost structures, and quantification of hydraulic safety. Successful modification requires understanding of the pipeline operating environment (on-line operations) and must be linked to pipeline operating conditions. Many of the optimization tools available to the pipeline industry today are based upon a dynamic programming algorithm attributed to Bellman. The costs of unit operations are balanced with the energy absorbed in heat due to frictional and other losses. This is carried out in such a way as to reduce the massive computational effort of an exhaustive solution search to a manageable level. For a pedagogical treatment of the problem, this is adequate. However, there are many significant factors which need to be added into and around this basic calculation. First, an algorithm with electrical cost factors only cannot evaluate penalties associated with poor hydraulics choices. Demand grouping, parallel pipelines, large amplitude pressure cycles, look ahead, and unit cycling also can and should be included in a full analysis. A modification to Bellman’s algorithm for non-linear pipeline configurations and electrical contracts will be developed and discussed in the context of a real-world petroleum pipeline operation.

Optimization for speed regulation strategy of compound coupled hydro-mechanical transmission

2020 ◽  
pp. 095440702098056
Author(s):  
Jin Yu ◽  
Pengfei Shen ◽  
Zhao Wang ◽  
Yurun Song ◽  
Xiaohan Dong
Keyword(s):  
Fuel Consumption ◽  
Dynamic Programming Algorithm ◽  
Operating Conditions ◽  
Programming Algorithm ◽  
Speed Ratio ◽  
Mechanical Transmission ◽  
Speed Regulation ◽  
Ratio Change ◽  
Low Efficiency ◽  
Wheel Loaders

Heavy duty vehicles, especially special vehicles, including wheel loaders and sprinklers, generally work with drastic changes in load. With the usage of a conventional hydraulic mechanical transmission, they face with these problems such as low efficiency, high fuel consumption and so forth. Some scholars focus on the research to solve these issues. However, few of them take into optimal strategies the fluctuation of speed ratio change, which can also cause a lot of problems. In this study, a novel speed regulation is proposed which cannot only solve problems above but also overcome impact caused by speed ratio change. Initially, based on the former research of the Compound Coupled Hydro-mechanical Transmission (CCHMT), the basic characteristics of CCHMT are analyzed. Besides, to solve these problems, dynamic programming algorithm is utilized to formulate basic speed regulation strategy under specific operating condition. In order to reduce the problem caused by speed ratio change, a new optimization is applied. The results indicate that the proposed DP optimal speed regulation strategy has better performance on reducing fuel consumption by up to 1.16% and 6.66% in driving cycle JN1015 and in ECE R15 working condition individually, as well as smoothing the fluctuation of speed ratio by up to 12.65% and 19.01% in those two driving cycles respectively. The processes determining the speed regulation strategy can provide a new method to formulate the control strategies of CCHMT under different operating conditions particularlly under real-world conditions.

scholarly journals Modeling Single-Picker Routing Problems in Classical and Modern Warehouses

2021 ◽  
Author(s):  
Dominik Goeke ◽  
Michael Schneider
Keyword(s):  
Dynamic Programming Algorithm ◽  
Practical Interest ◽  
Computational Effort ◽  
Programming Algorithm ◽  
Substantial Part ◽  
Routing Problem ◽  
Solution Approach ◽  
Compact Formulation ◽  
Single Block ◽  
The Cost

The standard single-picker routing problem (SPRP) seeks the cost-minimal tour to collect a set of given articles in a rectangular single-block warehouse with parallel picking aisles and a dedicated storage policy, that is, each stock-keeping unit is only available from one storage location in the warehouse. We present a compact formulation that forgoes classical subtour elimination constraints by directly exploiting two of the properties of an optimal picking tour used in the dynamic programming algorithm published in the seminal paper of Ratliff and Rosenthal. We extend the formulation to three important settings prevalent in modern e-commerce warehouses: scattered storage, decoupling of picker and cart, and multiple end depots. In numerical studies, our formulation outperforms existing standard SPRP formulations from the literature and proves able to solve large instances within short runtimes. Realistically sized instances of the three problem extensions can also be solved with low computational effort. For scattered storage, we note a rough tendency that runtimes increase with longer pick lists or a higher degree of duplication. In addition, we find that decoupling of picker and cart can lead to substantial cost savings depending on the speed and capacity of the picker when traveling alone, whereas additional end depots have rather limited benefits in a single-block warehouse. Summary of Contribution: Efficiently routing order pickers is of great practical interest because picking costs make up a substantial part of operational warehouse costs. For the prevalent case of a rectangular warehouse with parallel picking aisles, we present a highly effective modeling approach that covers—in addition to the standard setting—several important storage and order-picking strategies employed in modern e-commerce warehouses: scattered storage, decoupling of picker and cart, and multiple end depots. In this way, we provide practitioners as well as scientists with an easy and quick way of implementing a high-quality solution approach for routing pickers in the described settings. In addition, we shed some light on the cost benefits of the different storage and picking strategies in numerical experiments.

scholarly journals Profit Maximizing Control of a Microgrid with Renewable Generation and BESS Based on a Battery Cycle Life Model and Energy Price Forecasting

Energies ◽  
2019 ◽  
Vol 12 (15) ◽  
pp. 2904 ◽  
Author(s):  
Wenhao Zhuo ◽  
Andrey V. Savkin
Keyword(s):  
Dynamic Programming ◽  
Wind Farm ◽  
Optimization Problem ◽  
Cost Model ◽  
Dynamic Programming Algorithm ◽  
Operating Conditions ◽  
Programming Algorithm ◽  
Renewable Generation ◽  
The Cost ◽  
Charge Discharge

In this paper, an optimal control strategy is presented for grid-connected microgrids with renewable generation and battery energy storage systems (BESSs). In order to optimize the energy cost, the proposed approach utilizes predicted data on renewable power, electricity price, and load demand within a future period, and determines the appropriate actions of BESSs to control the actual power dispatched to the utility grid. We formulate the optimization problem as a Markov decision process and solve it with a dynamic programming algorithm under the receding horizon approach. The main contribution in this paper is a novel cost model of batteries derived from their life cycle model, which correlates the charge/discharge actions of batteries with the cost of battery life loss. Most cost models of batteries are constructed based on identifying charge–discharge cycles of batteries on different operating conditions, and the cycle counting methods used are analytical, so cannot be expressed mathematically and used in an optimization problem. As a result, the cost model proposed in this paper is a recursive and additive function over control steps that will be compatible with dynamic programming and can be included in the objective function. We test the proposed approach with actual data from a wind farm and an energy market operator.

Cooperative Multi-Vehicle Search and Coverage Problem in an Uncertain Environment

2015 ◽  
Vol 03 (01) ◽  
pp. 35-47 ◽  
Author(s):  
Farid Sharifi ◽  
Mostafa Mirzaei ◽  
Youmin Zhang ◽  
Brandon W. Gordon
Keyword(s):  
Forest Fires ◽  
A Priori ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Coverage Problem ◽  
Uncertain Environment ◽  
Distributed Approach ◽  
Probability Maps ◽  
Look Ahead ◽  
Service Vehicles

A distributed approach is proposed in this paper to address a cooperative multi-vehicle search and coverage problem in an uncertain environment such as forest fires monitoring and detection. Two different types of vehicles are used for search and coverage tasks: search and service vehicles. The search vehicles have a priori probability maps of targets in the environment. These vehicles update the probability maps based on their sensors measurements during the search mission. The search vehicles use a limited look-ahead dynamic programming algorithm to find their own path individually while their objective is to maximize the amount of information gathered by the whole team. The task of the service vehicles is to optimally spread out over the environment to cover the interested area for a mission. A Voronoi-based coverage control strategy is proposed to modify the configuration of service vehicles in such a way that a prescribed coverage cost function is minimized using the updated probability maps which are provided by the search vehicles. The improved performance of the proposed approach compared to conventional coverage methods is demonstrated by numerical simulation and experimental results.

scholarly journals An ALNS algorithm for the static dial-a-ride problem with ride and waiting time minimization

OR Spectrum ◽  
2021 ◽  
Author(s):  
Christian Pfeiffer ◽  
Arne Schulz
Keyword(s):  
Waiting Time ◽  
Dynamic Programming Algorithm ◽  
Computational Effort ◽  
Programming Algorithm ◽  
Ride Sharing ◽  
Time Minimization ◽  
Neighbourhood Search ◽  
Large Neighbourhood ◽  
Adaptive Large Neighbourhood Search ◽  
Problem Setting

AbstractThe paper investigates the static dial-a-ride problem with ride and waiting time minimization. This is a new problem setting of significant practical relevance because several ride-sharing providers launched in recent years in large European cities. In contrast to the standard dial-a-ride problem, these providers focus on the general public. Therefore, they are amongst others in competition with taxis and private cars, which makes a more customer-oriented objective necessary. We present an adaptive large neighbourhood search (ALNS) as well as a dynamic programming algorithm (DP), which are tested in comprehensive computational studies. Although the DP can only be used for a single tour and, due to the computational effort, as a restricted version or for small instances, the ALNS also works efficiently for larger instances. The results indicate that ride-sharing proposals may help to solve the trade-off between individual transport, profitability of the provider, and reduction of traffic and pollution.

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