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An integration of algal biofuel production planning, scheduling, and order-based inventory distribution control systems

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BIOFUELS BIOPRODUCTS & BIOREFINING-BIOFPR 2019年第4期13卷 920-935页

作者： Nugroho, Yohanes K. Zhu, Liandong Wuhan Univ Sch Resource & Environm Sci 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China Wuhan Univ Hubei Biomass Resource Chem & Environm Biotechnol 129 Luoyu Rd Wuhan 430079 Hubei Peoples R China

This paper studies an integration model of production planning, scheduling, and order- based inventory distribution control systems. It models supply-chain and production dynamics and employs process control to supply-chain operations and the production process and scheduling. It embeds generalized disjunctive programming (GDP) and branch-and-reduce algorithms to obtain an optimal solution. Generalized disjunctive programming reformulates the original mixed integer nonlinear programming (MINLP) model and solves the new model with a lower number of integer variables. Afterwards, the branch and reduce algorithm, which is strengthened with outer approximation (OA) cutting planes, is able to find optimal solutions with permissible optimality gaps. The results show that supply network size does not affect forecast accuracy;there is a forecast error rate of 12.9%. Similar figures are also shown for the inventory level, which is expected to be 32% of demand level, production changeover time at 0.58 days, inventory ramp-up period to meet the expected raw materials and final products inventory at 0.02 and 0.04 days respectively, and extra delivery time to meet a certain level of demand change at 1.1 days. The results show that the model is capable of controlling demand forecast, inventory level, and delivery quantity, so that the supply chain can avoid stock out at a minimal level of inventory, regardless of the size of supply-chain networks. (c) 2019 Society of Chemical Industry and John Wiley & Sons, Ltd.

关键词： production planning scheduling process control branch and reduce generalized disjunctive programming mixed integer non-linear programming

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Zeus: A resource allocation algorithm for the cloud of sensors

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2019年 92卷 564-581页

作者： Santos, Igor L. Pirmez, Luci Delicato, Flavia C. Oliveira, Gabriel M. Farias, Claudio M. Khan, Samee U. Zomaya, Albert Y. Univ Fed Rio de Janeiro Programa Posgrad Informat PPGI BR-20001970 Rio De Janeiro RJ Brazil North Dakota State Univ Fac Elect & Comp Engn Fargo ND 58102 USA Univ Sydney Sch Informat Technol Ctr Distributed & High Performance Comp Sydney NSW 2006 Australia Ctr Fed Educ Tecnol Celso Suckow da Fonseca CEFET BR-20271110 Rio De Janeiro RJ Brazil

The cloud of sensors (CoS) paradigm brings together cloud computing, edge computing and wireless sensor and actuator networks (WSAN) in the form of a three-tier CoS architecture. By employing CoS virtualization, a set of virtual nodes (VNs) is made available to applications, decoupling them from the CoS infrastructure. Assigning VNs to application requests, in order to timely and efficiently meet application requirements, gives rise to the challenge of resource allocation in CoS. In this work, we formulate the problem of resource allocation in CoS and propose Zeus, a hybrid (partly-decentralized) algorithm for solving it. Zeus has two key features. First, it is able to identify requests that are common for multiple applications and perform only once their required tasks, sharing the results among the applications for saving resources. Second, its hybrid approach leveraging edge computing, makes Zeus scalable in terms of the number of VNs and applications supported, and suitable for meeting delay-sensitive applications. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Cloud of sensors Edge computing Resource allocation mixed integer non-linear programming Data provisioning Data freshness

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AC load flow based model for transmission expansion planning

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ELECTRIC POWER SYSTEMS RESEARCH 2019年 171卷 26-35页

作者： Farrag, Mahmoud Ali Ali, Kareem Mohamed Omran, Shaimaa Cairo Univ Elect Power & Machines Dept Fac Engn 1 Gamaa St Cairo 12613 Egypt Natl Res Ctr Syst & Informat Dept Engn Res Div El Buhouth St Cairo 12622 Egypt

This paper proposes an accurate model to solve the transmission network expansion planning (TNEP) problem. The TNEP problem is solved using a linear DC model, a mixed integer linear DC model, and the proposed accurate exact mixed integer non-linear AC model. A 19 bus system which is a part of the 220 kV Egyptian transmission network is used for the verification of the proposed load flow models. The proposed exact nonlinear AC load flow based model that considered control of active and reactive power flow in the network and considered the power losses in the system yielded promising results when compared to DC load flow based linear and mixed integer linear models. It provided a planned network with least number of added lines and consecutively with the least cost.

关键词： Transmission network expansion planning mixed integer linear programming mixed integer non-linear programming DC load flow model AC load flow model

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Multi-period multi-echelon inventory transportation problem considering stakeholders behavioural tendencies

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2020年 225卷 107566-107566页

作者： De, Arijit Mogale, D. G. Zhang, Mengdi Pratap, Saurabh Kumar, Sri Krishna Huang, George Q. Newcastle Univ Business Sch Newcastle Upon Tyne NE1 4SE NE England Indian Inst Technol Dept Ind & Syst Engn Kharagpur 721302 W Bengal India Nanjing Univ Posts & Telecommun Sch Internet Things Nanjing 210003 Peoples R China Indian Inst Informat Technol Design & Mfg Dept Mech Engn Jabalpur India Univ Hong Kong Dept Ind & Mfg Syst Engn HKU ZIRI Lab Phys Internet Hong Kong Peoples R China

An inventory transportation problem of manufacturing organization focusing on several stakeholders such as manufacturers, distributors, wholesalers, retailers and customers is addressed in this paper. The research study considered multi-echelon, multi-product, multi-modal and multi-period scenario. The mathematical model in the form of mixed integer non-linear programming is formulated to minimize the total cost associated with transportation, inventory holding and operational activities. A mathematical formulation based heuristic approach, which comprises of four algorithms, is proposed for solving purpose. The proposed heuristic approach considers the behavioural tendencies of stakeholders pertaining to the selection of shipment routes, transportation mode choice decisions and amount of products to be shipped. Fifteen practical problem instances are solved by using the developed heuristic approach while considering the behavioural aspects of stakeholders. Insights obtained from results will be beneficial for manufacturing organizations in making informed decisions related to transportation planning considering stakeholder's behavioural tendencies.

关键词： Inventory Transportation Supply chain network Manufacturing mixed integer non-linear programming Heuristic

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Unit Commitment with ACOPF Constraints: Practical Experience with Solution Techniques

Unit Commitment with ACOPF Constraints: Practical Experience...

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IEEE Milan PowerTech Conference

作者： Tejada-Arango, Diego A. Wogrin, Sonja Sanchez-Martin, Pedro Ramos, Andres Univ Pontificia Comillas Madrid Spain

ISBN: (纸本)9781538647226

This paper summarizes practical experiences of solving the UC problem with AC optimal power flow constraints using three main approaches: 1) solving a MINLP with commercial solvers, 2) an outer approximation approach with Successive linear programming (SLP), and 3) a Second Order Cone programming (SOCP) approximation. We show a comprehensive review of each approach, including main characteristics and drawbacks. Although non-linear solvers have improved their performance in recent years, our results suggest that the binary nature of the variables in the UC problem still increases the solution time up to 75 times in comparison to a relaxed version of the UC. SLP approaches have shown a good behavior finding integer feasible solutions, however, the solution times are almost twofold the ones obtained for the MINLP. SOCP is a promising approach because it improves almost 6 times the approximation made by the classic DC approach. Nevertheless, its solving times are comparable to those found using the MINLP. Finally, parallel computing techniques could improve the performance of each approach in order to make them more computationally efficient for large-scale UC problems.

关键词： Unit commitment AC optimal power flow mixed integer non-linear programming Second Order Cone programming Outer Approximations

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PRODUCTION OPTIMIZATION FOR DISTRICT HEATING: Short-term planning of district heating grid in Gavle, Sweden

PRODUCTION OPTIMIZATION FOR DISTRICT HEATING: Short-term pla...

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作者： KARL BROGREN Malardalen University

学位级别：硕士

Energy systems with a high portion of renewable energy from wind and solar power can suffer from fluctuations in production due to weak winds or cloudy weather, which may affect the electricity price. When producing heat and power in a combined heat and power plant, an additional heat storage tank can be used to store the heat surplus which is obtained when the power production is high, and the heat demand is low. To optimize heat and power production economically, short-term planning can be applied. Short-term planning covers the production in the near future of 1-3 days. The optimization in this degree project is based on the district heating production, which means that the heating demand always needs to be fulfilled. The district heating production is based on the weather. Therefore a suitable period for simulation is three days due to the accuracy of the weather forecasts are reasonable. The optimization is performed on the district heat system in Gavle, Sweden. The system comprises several different production units, such as combined heat and power plants, backup plants, and industrial waste heat recovery. Two different models are made, one using linear programming and one using mixed integer non-linear programming. The model stated as a linear programming problem is not as accurate as of the one stated as a mixed integer non-linear programming problem which uses binary variables. Historical input data from Bomhus Energi AB, a company owned together by the local heat and power supplier Gavle Energi AB and the pulp and paper manufacturer BillerudKorsnas AB, was given to simulate different scenarios. The different scenarios have various average temperatures and in some scenarios are there some issues with the pulp and paper industry affecting the waste heat recovery. In all scenarios is the heat storage tank charged when the demand is low and then discharged when the demand increases to avoid starting some of the more expensive backup plants if possible. The sim

关键词： Short-term planning Optimization District heating linear programming mixed integer non-linear programming Branch and bound Heat storage MATLAB TOMLAB

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Grain silo location-allocation problem with dwell time for optimization of food grain supply chain network

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TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW 2018年 111卷 40-69页

作者： Mogale, D. G. Kumar, Mukesh Kumar, Sri Krishna Tiwari, Manoj Kumar Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India Univ Cambridge Inst Mfg Dept Engn Cambridge England

In the last few decades, production and procurement of food grain in India have steadily increased, however, storage capacity has not increased proportionally. The government of India (GOI) is establishing the various capacitated silos across the country to bridge this storage capacity gap. This paper presents a novel integrated multi-objective, multi-modal and multi-period mathematical model for grain silo location-allocation problem with Dwell time to support the decision-making process of GOI. Two conflicting objectives-minimization of total supply chain network cost and total lead time (transit and dwell time) are simultaneously optimized using two Pareto based multi-objective algorithms with calibrated parameters.

关键词： Facility location-allocation problem Multi-objective optimization mixed integer non-linear programming Food grain supply chain non-dominated sorting chemical reaction optimization (NCRO)

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An MINLP model to support the movement and storage decisions of the Indian food grain supply chain

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CONTROL ENGINEERING PRACTICE 2018年 70卷 98-113页

作者： Mogale, D. G. Kumar, Sri Krishna Tiwari, Manoj Kumar Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India

This paper addresses the novel three stage food grain distribution problem of Public Distribution System (PDS) in India which comprises of farmers, procurement centers, base silos and field silos. The Indian food grain supply chain consists of various activities such as procurement, storage, transportation and distribution of food grain. In order to curb transportation and storage losses of food grain, the Food Corporation of India (FCI) is moving towards the modernized bulk food grain supply chain system. This paper develops a mixed integer non-linear programming (MINLP) model for planning the movement and storage of food grain from surplus states to deficit states considering the seasonal procurement, silo capacity, demand satisfaction and vehicle capacity constraints. The objective function of the model seeks to minimize the bulk food grain transportation, inventory holding, and operational cost. Therein, shipment cost contains the fixed and variable cost, inventory holding and operational cost considered at the procurement centers and base silos. The developed mathematical model is computationally complex in nature due to nonlinearity, the presence of numerous binary and integer variables along with a huge number of constraints, thus, it is very difficult to solve it using exact methods. Therefore, recently developed, Hybrid Particle-Chemical Reaction Optimization (HP-CRO) algorithm has been employed to solve the MINLP model. Different problem instances with growing complexities are solved using HP-CRO and the results are compared with basic Chemical Reaction Optimization (CRO) and Particle Swarm Optimization (PSO) algorithms. The results of computational experiments illustrate that the HP-CRO algorithm is competent enough to obtain the better quality solutions within reasonable computational time. (C) 2017 Elsevier Ltd. All rights reserved.

关键词： Food grain distribution problem Transportation Inventory mixed integer non-linear programming Chemical reaction optimization

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Optimal operation of water distribution networks with intermediate storage facilities

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COMPUTERS & CHEMICAL ENGINEERING 2018年 119卷 215-227页

作者： Kurian, Varghese Chinnusamy, Saravanan Natarajan, Ashok Narasimhan, Sridharakumar Narasimhan, Shankar Indian Inst Technol Madras Dept Chem Engn Madras Tamil Nadu India Tamil Nadu Water Investment Co Madras Tamil Nadu India

The nexus between water and energy reveals that transporting water for end use is a highly energy intensive operation. In this work we consider the optimal operation of a water distribution network consisting of pumps delivering water to different reservoirs, with each reservoir catering to a time varying demand. Pumps and ON/OFF valves are used as manipulated variables to minimize energy consumption while meeting the demand. Due to the nonlinear nature of the pump operating curve and the hydraulics, this results in a mixed integer nonlinear Program (MINLP). We propose a three step decomposition approach to solve this problem efficiently. The applicability of this technique is demonstrated on a water network proposed for a municipality in India and the potential advantages are reported. We also compare the solution times required for the proposed technique and a standard solver and demonstrate the efficiency of the proposed approach. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Water distribution networks Energy minimization Scheduling mixed integer non-linear programming

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A multistage stochastic programming formulation to evaluate feedstock/process development for the chemical process industry

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CHEMICAL ENGINEERING SCIENCE 2018年 187卷 223-244页

作者： Christian, Brianna Cremaschi, Selen Auburn Univ Dept Chem Engn Samuel Ginn Coll Engn 345 W Magnolia Ave Auburn AL 36849 USA

Industry trends suggest that the feedstock and product portfolios along with utilized technologies of chemical process industry (CPI) may grow and be quite different compared to today's in the near future. Incorporation of new feedstocks and technologies into the existing CPI infrastructure may require significant amounts of investments. Determining the investment decisions, i.e., how much to invest, which technologies to invest in, and when to invest in each technology for research and development and for capacity expansion, is a challenging task, because there are often many emerging technologies and the future performances of these technologies are uncertain. Here, we present a multistage stochastic programming (MSSP) formulation accounting for both endogenous and exogenous uncertain parameters associated with the new technology investment planning (NTIP) problem. The MSSP formulation is used to determine investment decisions for four case studies, including a biomass to ethylene production network. The solution for the biomass to ethylene case study suggests that, given the current market conditions, the investment in biomass technologies is not financially advantageous. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Multistage stochastic programming Endogenous uncertainty Exogenous uncertainty Investment planning mixed integer non-linear programming

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