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Partitioning and solving large-scale tactical harvest scheduling problems for industrial plantation forests

CANADIAN JOURNAL OF FOREST RESEARCH 2020年第8期50卷 811-818页

作者： Bellavenutte, Pedro Chung, Woodam Diaz-Balteiro, Luis Univ Politecn Madrid Coll Forestry Jose Antonio Novais 10 Madrid 28040 Spain Oregon State Univ Dept Forest Engn Resources & Management 238 Peavy Hall Corvallis OR 97331 USA

Spatially explicit, tactical forest planning is a necessary but challenging task in the management of plantation forests. It involves harvest scheduling and planning for road access and log transportation over time and space. This combinatorial problem can be formulated into the fixed-charge transportation problem (FCTP), in which the sum of fixed and variable costs is minimized while meeting harvest volume requirements and allowing necessary road maintenance and log hauling activities. The problem can be solved using general optimization methods such as mixed-integer linear programming (MILP), but the computational efficiency of the MILP-based approach quickly drops as the size and complexity of the problem increases. We developed a new optimization procedure that partitions the large planning problem into smaller subproblems. We applied a hybrid optimization approach using both MILP and heuristic rules to efficiently solve the large FCTP that otherwise may not be solvable using traditional methods. We applied our approach to an industrial plantation forest in Brazil. Our applications demonstrate the performance of the new optimization procedure and the benefit of solving large forest planning problems that integrate harvest scheduling with road access and transportation.

关键词： forest management planning mixed-integer linear programming heuristics optimization

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Optimal operation of a wind-electrolytic hydrogen storage system in the electricity/hydrogen markets

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2020年第46期45卷 24412-24423页

作者： Xiao, Pengfei Hu, Weihao Xu, Xiao Liu, Wen Huang, Qi Chen, Zhe Univ Elect Sci & Technol China Sch Mech & Elect Engn Chengdu Peoples R China Univ Utrecht Copernicus Inst Sustainable Dev Princetonlaan 8a NL-3584 CB Utrecht Netherlands Aalborg Univ Dept Energy Technol Pontoppidanstr 111 Aalborg Denmark

Wind power, the most promising renewable energy source in the world, plays an important role in the electricity markets. Wind power curtailment cannot be avoided in some countries due to its output has a special feature of randomness and volatility. Since the excess wind power being converted into hydrogen and sold to the hydrogen market will be the future trend. This study proposes a wind-electrolytic hydrogen storage system to participate in the electricity/hydrogen markets for selling electricity and hydrogen, which can help to improve the benefits of wind power in the electricity markets and addree the wind power curtailment effectively. With considering the uncertainties of wind power outputs and electricity prices, the optimal operation strategy is proposed with the objective of maximizing profits. The scenario-based stochastic method is adopted to describe the uncertainties, and the financial risk is evaluated using conditional value-at-risk. The operational problem of the proposed system is formulated into a mixed-integer linear programming model. Finally, the feasibility of the proposed operational strategy is validated by a case study. The results show that the expected revenue increases with the increase of the hydrogen selling price, indicating that investors can obtain profits by converting electricity into hydrogen. The optimal expected revenue increases by 33.42% when hydrogen price increases from 1.2 DKK/kWh to 1.8 DKK/kWh and the risk factor is equal to 0. Based on the analysis of the results, the importance of hydrogen can be proven. (C) 2020 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Electricity market Hydrogen market Wind power Conditional value-at-risk mixed-integer linear programming

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Do unit commitment constraints affect generation expansion planning? A scalable stochastic model

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ENERGY SYSTEMS-OPTIMIZATION MODELING SIMULATION AND ECONOMIC ASPECTS 2020年第2期11卷 247-282页

作者： Schwele, Anna Kazempour, Jalal Pinson, Pierre Tech Univ Denmark Dept Elect Engn DK-2800 Lyngby Denmark

Due to increasing penetration of stochastic renewable energy sources in electric power systems, the need for flexible resources especially from fast-start conventional generation units (e.g., combined cycle gas turbine plants) is growing. The fast-start conventional units are being operated more frequently in order to respond to the variability and uncertainty of stochastic generation. This raises two important technical questions: as it is common in the literature, is it still an appropriate simplification to ignore the operational unit commitment (UC) constraints of conventional units within the generation expansion planning optimization? And if not, which UC constraint impacts most the expansion planning outcomes? To answer these questions, this paper aims at measuring the planning inefficiency (i.e., the underestimation of need for new generation capacity) caused by ignoring each UC constraint. To this purpose, we develop a centralized network-constrained generation expansion planning model incorporating UC constraints. In particular, we model start-up and shut-down costs, minimum production level and hourly ramping limits of conventional units. Wind power production is considered as the only source of uncertainty, and is modeled through a set of scenarios. A two-stage stochastic programming tool is used, whose first stage determines the long-term expansion and short-term UC decisions over different hours of representative days, while the second stage models the real-time operation for accommodating imbalances arising from wind deviation under different scenarios. Since this problem is potentially hard to solve especially with a large number of representative days and scenarios, a multi-cut Benders' decomposition algorithm is implemented. The well-functioning of the proposed model and the impact of each UC constraint on planning outcomes are evaluated using an extensive numerical study. In our case studies, the exclusion of ramping constraints from planning opti

关键词： Generation expansion planning (GEP) Unit commitment (UC) constraints Wind power uncertainty Two-stage stochastic programming mixed-integer linear programming Multi-cut Benders' decomposition

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MEMO: A Method for Computing Metabolic Modules for Cell-Free Production Systems

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ACS SYNTHETIC BIOLOGY 2020年第3期9卷 556-566页

作者： von Kamp, Axel Klamt, Steffen Max Planck Inst Dynam Complex Tech Syst D-39106 Magdeburg Germany

Cell-free bioproduction systems represent a promising alternative to classical microbial fermentation processes to synthesize value-added products from biological feedstocks. An essential step for establishing cell-free production systems is the identification of suitable metabolic modules with defined properties. Here we present MEMO, a novel computational approach to find smallest metabolic modules with specified stoichiometric and thermodynamic constraints supporting the design of cell-free systems in various regards. In particular, one key challenge for a sustained operation of cell-free systems is the regeneration of utilized cofactors (such as ATP and NAD(P)H). Given a production pathway with certain cofactor requirements, MEMO can be used to compute smallest regeneration modules that recover these cofactors with required stoichiometries. MEMO incorporates the stoichiometric and thermodynamic constraints in a single mixed-integer linear program, which can then be solved to find smallest suitable modules from a given reaction database. We illustrate the applicability of MEMO by calculating regeneration modules for the recently published synthetic CETCH cycle for in vitro carbon dioxide fixation. We demonstrate that MEMO is very flexible in taking into account the diverse constraints of the CETCH cycle (e.g., regeneration of 1 ATP, 4 NADPH and of 1 acetyl-group without net production of CO2 and with permitted side production of malate) and is able to determine multiple solutions in reasonable time in two large reaction databases (MetaCyc and BiGG). The most promising regeneration modules found utilize glycerol as substrate and require only 8 enzymatic steps. It is also shown that some of these modules are robust against spontaneous loss of cofactors (e.g., oxidation of NAD(P)H or hydrolysis of ATP). Furthermore, we demonstrate that MEMO can also find cellfree production systems with integrated product synthesis and cofactor regeneration. Overall, MEMO provides a

关键词： synthetic biology design of cell-free systems constraint-based modeling metabolic networks cofactor regeneration mixed-integer linear programming

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Integrating deep learning models and multiparametric programming

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COMPUTERS & CHEMICAL ENGINEERING 2020年 136卷 106801-000页

作者： Katz, Justin Pappas, Iosif Avraamidou, Styliani Pistikopoulos, Efstratios N. Texas A&M Univ Artie McFerrin Dept Chem Engn College Stn TX 77843 USA Texas A&M Univ Texas A&M Energy Inst College Stn TX 77843 USA

Deep learning models are a class of approximate models that are proven to have strong predictive capabilities for representing complex phenomena. The introduction of deep learning models into an optimization formulation provides a means to reduce the problem complexity and maintain model accuracy. Recently it has been shown that deep learning models in the form of neural networks with rectified linear units can be exactly recast as a mixed-integer linear programming formulation. However, developing the optimal solution of problems involving mixed-integer decisions in online applications remains challenging. Multiparametric programming alleviates the online computational burden of solving an optimization problem involving bounded uncertain parameters. In this work, a strategy is presented to integrate deep learning and multiparametric programming. This integration yields a unified methodology for developing accurate surrogate models based on deep learning and their offline, explicit optimal solution. The proposed strategy is demonstrated on the optimal operation of a chemostat. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Deep learning Neural networks Rectified linear units mixed-integer linear programming Multiparametric programming

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Cellular manufacturing design and replenishment strategy in a capacitated supply chain system: A simulation-based analysis

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COMPUTERS & INDUSTRIAL ENGINEERING 2020年 141卷 106282-106282页

作者： Sadeghi, Azadeh Suer, Gursel Sinaki, Roohollah Younes Wilson, Darin Ohio Univ Dept Ind & Syst Engn Athens OH 45701 USA

This paper studies the integration of design and control phases in a three-echelon supply chain system of a blood sugar strip manufacturer. The first step is manufacturing system design based on layered Cellular Manufacturing System (CMS) for which a mixed-integer linear programming approach is proposed in order to minimize the required number of cells. The novelty of the proposed model is in incorporating shifts into layered CMS design. In the second step of the design phase inventory parameters in different levels of the supply chain are estimated based on the continuous review, fixed order quantity (Q,r) model. Control phase is triggered by releasing customer order for multiple types of products and includes replenishments in the three echelons of the supply chain and scheduling in the layered CMS. In order to evaluate the system cost, the capacitated supply chain system is simulated in Simio software package. In the first phase of the simulation, cost components are obtained based on the inventory parameters estimated in the design phase. Next, simulation based on OptQuest feature is performed to improve total cost and estimate the inventory parameters and expected cell utilization. Comparing system cost and the parameters from the design phase to the results of the OptQuest approach evaluates the manufacturing system design and the replenishment policy proposed in the design phase. Sensitivity analysis is performed to analyze the effect of shortage penalty and inventory carrying rate on the total cost of the system. Finally, statistical analysis result indicate Re-Order Point (ROP) values obtained by the OptQuest have significantly changed compared with ROP values estimated in the design phase;however, the same conclusion is not applicable for order quantity values.

关键词： Capacitated supply chain Layered cellular manufacturing system mixed-integer linear programming Inventory management Discrete event simulation OptQuest

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Influence Evaluation of Integrated Energy System on the Unit Commitment in Power System

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IEEE ACCESS 2020年 8卷 163344-163356页

作者： Liu, Hechuan Zhou, Xiaoxin Yang, Xiaoyu Li, Yalou Li, Xiong China Elect Power Res Inst State Grid Simulat Ctr Beijing 100192 Peoples R China North China Elect Power Univ Sch Elect & Elect Engn Beijing 102206 Peoples R China

This article presents a research on unit commitment(UC), which consider the interaction between conventional power system and district-level integrated energy system(IES). The increasing number and scale of IES projects at distribution network bring incremental effect to the whole energy system. Their influence on power system transmission level unit commitment becomes one of the notable topics. Within these IES projects, gas-power and cooling-heating-power are two main types of 'integrated' forms. In this article, a model system is built for UC analysis in which several district-level IESs are connected to a conventional power system. Heating, cooling and electrical forms of energy are integrated in these IESs. The UC problem is modeled and solved by mixed integer linear programming(MILP), the partial load characteristics of these power plants are handled by piecewise linearization. The common piecewise linearization method used in power and gas system is modified to reduce binary variables and constraints. The purpose of this modification is to increase the MILP solving speed. Case studies are carried out to evaluate the influence of IESs on two different time scales. The simulation result shows that the application of IESs can reduce the whole energy system service cost. As the number of applied district-level IESs increases, the cost reduction becomes more significant. The reduction relies on preventing high-cost small capacity plants go into operation, reducing the number of generator startup, and choosing an economical power output level. These benefits are owing to the reinforcement from gas network via CCHPs (Combine cooling, heating and power) and the buffer from multiple forms of energy storage devices (thermal and cooling).

关键词： Cooling Power systems Generators Resistance heating Heat pumps Cogeneration Integrated energy system combined cooling heating and power (CCHP) thermal and cooling storage system unit commitment mixed-integer linear programming piecewise linearization

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An integrated machine learning and quantitative optimization method for designing sustainable bioethanol supply chain networks

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Decision Analytics Journal 2023年 7卷

作者： Momenitabar, Mohsen Dehdari Ebrahimi, Zhila Abdollahi, Atefeh Helmi, Wesam Bengtson, Kelly Ghasemi, Peiman Department of Transportation Logistics and Finance College of Business North Dakota State University Fargo 58105 ND United States Department of Geographical Sciences Center for Geospatial Information Science University of Maryland College Park 20742 MD United States University of Guilan Rasht Iran Upper Great Palin Transportation Institute North Dakota State University Fargo 58105 ND United States University of Vienna Department of Business Decisions and Analytics Kolingasse 14-16 Vienna 1090 Austria

Increasing demand for energy is pushing decision-makers in the Bioethanol Supply Chain Network (BSCN) to adopt second-generation biomass feedstocks to meet sustainability criteria. This study proposes an integrated Machine Learning (ML) and quantitative optimization model to design a Sustainable Bioethanol Supply Chain Network (SBSCN). We use ML methods, such as Random Forest (RF), Extreme Gradient Boosting Method (XGBoost), and Ensemble learning algorithm (Bagging), to project the bioethanol demand. We select the RF method as a superior method to forecast the bioethanol demand as inputs to the model by comparing the performance criteria for these three methods. We then propose a mixed-integer linear programming (MILP) model to meet the sustainability criteria defined by three objective functions. We present a case study to demonstrate the applicability of the proposed approach. The sensitivity analysis confirms that the costs of establishing preprocessing and biorefinery centers constitute 37% of the total costs of the network. More importantly, we find that the square bale harvest method is among the methods that utilized the most switchgrass land area. More interestingly, our model determined that the square bale harvest method led to 18,450 tons of switchgrass loss in the case study. Finally, our results can be utilized by policymakers and investors to develop efficient SBSCNs. © 2023 The Author(s)

关键词： Bioethanol supply chain network Demand projection Machine learning mixed-integer linear programming Sustainability Switchgrass cultivation

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Identifying sufficient sufficient deception in military logistics

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EXPERT SYSTEMS WITH APPLICATIONS 2020年 141卷 112974-000页

作者： Ausseil, Rosemonde Gedik, Ridvan Bednar, Amy Cowan, Mark Rensselaer Polytech Inst Dept Ind & Syst Engn Troy NY 12180 USA Quinnipiac Univ Dept Management Hamden CT 06518 USA Univ New Haven Dept Mech & Ind Engn West Haven CT 06518 USA US Army Engineer Res & Dev Ctr CEERD IE C Vicksburg MS 39180 USA

In the context of modern warfare, military forces are faced with challenges that threaten their power projection operations. Adversarial strategies could hinder the actions of the forces within operational areas by inferring and anticipating a military's potential mission objectives including military logistics and transportation activities. Thus, this research investigates the potential ways of incorporating two military deception strategies into the military logistics decision making process: (i) falsifying signifies including empty convoys on routes on which commodities are being hauled, and (ii) hiding refers to concealing routes in the logistics network. Assuming that the adversaries can monitor the shipments in the transportation network, we develop a two-stage integer linear programming model that quantifies the sufficient amounts of each deception needed to deceive the adversaries. In addition, the mathematical model accurately captures several other military logistics challenges, such as satisfying the demands for military commodities at destinations subject to limited capacities at supply points, deciding the best route option, and respecting the time limit constraint. However, the two-stage model is non-convex and therefore, reformulation and linearization steps are applied to convert it to a convex single-stage model. Due to the computational complexity in solving the single-stage model for realistic-size instances, two greedy heuristics are developed that can efficiently solve those instances. Moreover, the computational experiments reveal that the greedy heuristics obtain near optimal solutions when compared to the solutions of the smaller instances solved to optimality via a commercial solver. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Military logistics mixed-integer linear programming Deception Greedy heuristics Anti-access/area-denial

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Multi-type electric vehicle relocation problem considering required battery-charging time

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EUROPEAN JOURNAL OF INDUSTRIAL ENGINEERING 2020年第3期14卷 335-368页

作者： Singgih, Ivan Kristianto Kim, Byung-in Pohang Univ Sci & Technol POSTECH Dept Ind & Management Engn Pohang 37673 Gyeongbuk South Korea

This research discusses an electric vehicle (EV) relocation problem, wherein multiple types of EVs are transported using heterogeneous trucks. The initial position, battery level of the EVs, and the required number of EVs and empty parking slots at each station are provided as inputs. Relocations are performed during the night, while no EVs are used. Before the end of the relocation planning horizon, each EV must be charged to a certain battery level. The charging process can only be performed when the EV is not being transported. The objectives are to minimise the total transportation costs, the total truck fixed costs, and the total unsatisfied empty parking slot requirements while ensuring that all EV demands are satisfied. A mixed-integer linear programming (MILP) model and construction and improvement heuristic approaches are proposed. The results of the computational experiments indicate that the proposed approaches perform well. [Received: 25 February 2019;Accepted: 26 August 2019]

关键词： electric vehicle relocation battery-charging heterogeneous truck heuristic adaptive large-neighbourhood search mixed-integer linear programming MILP

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