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Flow Formulation of Demand Propagation in Guaranteed Service Models

IFAC-PapersOnLine 2022年第20期55卷 505-510页

作者： Dominik Kamp Jörg Rambau Chair of Economathematics University of Bayreuth 95440 Bayreuth Germany

In this paper we present two alternative models for the Stochastic Guaranteed Service Model with Demand Propagation (SGSM-DP) in multi-echelon inventory theory. While the original SGSM-DP could so far not be solved for standard networks with more than 15 nodes in 1000 seconds, the new equivalent model F-SGSM-DP based on dynamic flows with arcs going backwards in time can be solved even for heavy instances with up to around 50 nodes.

关键词： Operation Research Logistics Planning Comparison of Methods for Modeling Optimization Multi-Echelon Inventory Management Stochastic programming Network Flow Guaranteed Service Model mixed-integer linear programming Demand Propagation

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Optimal placement of phase-reconfiguration devices in low-voltage distribution network with residential PV generation

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IET RENEWABLE POWER GENERATION 2020年第18期14卷 3752-3761页

作者： Liu, Bin Meng, Ke Dong, Zhao Yang Wong, Peter K. C. Wei, Wei Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Jemena Elect Networks Vic Ltd Melbourne Vic 3000 Australia Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

As residential PV generation penetrates in the low-voltage distribution network (LVDN), the unbalance issue may be intensified due to the asymmetry of generations/loads in different phases. Phase-reconfiguration device (PRD), which can reconfigure connected phases of residential customers, provides an effective method to address this issue. Noting that although the benefit brought by PRDs can vary if they are placed at different locations in the network, little literature has been reported on this topic. To bridge the research gap, this paper presents a novel method to optimally place PRDs in an LVDN aiming at minimizing the power unbalance running through the distribution transformer. The problem considers both installation, operational constraints, and boils down to a challenging mixed-integer non-convex programming problem, which is then reformulated as an efficient solvable mixed-integer linear programming problem. Moreover, operational constraints are relaxed with slack variables penalized in the objective function, which makes sure a feasible solution is always available without or with minimal operational violations. Case studies based on a modified IEEE system and a practical system in Australia demonstrate that an efficient strategy can be provided to address the unbalance issue while improving the network's power supply qualities.

关键词： distribution networks power supply quality linear programming integer programming convex programming photovoltaic power systems optimal placement phase-reconfiguration device low-voltage distribution network residential PV generation residential photovoltaic generation LVDN unbalance issue residential customers PRDs power unbalance distribution transformer operational constraints mixed-integer nonconvex programming problem mixed-integer linear programming voltage magnitude limits load profiles modified IEEE system

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mixed-integer programming for unrelated parallel machines scheduling problem considering electricity cost and makespan penalty cost

Mixed-integer programming for unrelated parallel machines sc...

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International Conference on Industrial Engineering and Systems Management (IESM)

作者： Cheng, Junheng Wu, Peng Chu, Feng Fujian Normal Univ Sch Econ Fuzhou Peoples R China Fujian Normal Univ Sch Econ & Management Fuzhou Peoples R China Univ Paris Saclay Univ Evry Val dEssonne Paris France

ISBN: (纸本)9781728115665

A recent study proposed an interesting unrelated parallel machines scheduling problem under time-varying electricity cost, which assumes significant importance for industrial enterprises competitiveness improvement and sustainable economic development. The existing solution method is a hybrid genetic algorithm, which cannot ensure the optimality of the solutions. In this work, we provide a mixed-integer linear programming model to exactly solve the problem. The computational results show that the proposed model can obtain optimal solutions efficiently.

关键词： mixed-integer linear programming parallel machines scheduling electricity cost makespan time-of-use tariffs

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Quantifying smart parcel station network usage as a logistical solution for the last-mile problem

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IFAC-PapersOnLine 2022年第10期55卷 127-132页

作者： Abdullah Sweidan Adel Elomri Laoucine Kerbache College of Science and Engineering Hamad Bin Khalifa University Qatar

Numerous research studies was centered around supply chain design, development, and operation. One of the trending researches is the ones concerned with The “last mile delivery” stage, which involves the clients’ willingness to be part of the supply chain. Henceforth, this work contemplates the Parcel Locker Stations as a solution to the “last-mile delivery” problem. The aim is to provide the logistics courier with a planning and optimization tool that strikes an optimum balance between two critical factors: the logistics company profits and the customers’ satisfaction levels. The problem employs (MILP) mixed-integer linear programming with the aid of OPL CPLEX software to select the optimal allocation of lockers' sites in a particular network based in the state of Qatar.

关键词： Last-Mile Delivery Parcel Station mixed-integer linear programming E-Commerce

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Reverse logistics optimization of an industrial air conditioner manufacturing company for designing sustainable supply chain: a fuzzy hybrid multi-criteria decision-making approach

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WIRELESS NETWORKS 2020年第8期26卷 5759-5782页

作者： Ali, Sadia Samar Paksoy, Turan Torgul, Belkiz Kaur, Rajbir Coll Engn Dept Ind Engn GC Jeddah Saudi Arabia Fac Engn & Nat Sci Dept Ind Engn Konya Turkey

Magnified resource consumption and depletion of natural resources calls for non-flexible or strict regulations and penalties on industrial operations, increased rate of processing and reuse of waste material as a substitute for raw material and political and legal interventions at global scale. Product recovery involves reuse, repair, refurbishing, remanufacturing and materials recycling, requires an efficient network design known as reverse logistic network and offers economical benefits in terms of fewer procurement of raw material, inventory management and less disposal. In current study, a mixed integer linear programming model designed on a multi-stage reverse logistics network for product recovery is proposed which considers different recovery options-product remanufacturing, component reprocessing and material recycling for sustainable outcomes. The model is designed to find optimal solutions for fulfilling demand and revenue needs by focusing on strategic locations for collection centers, reprocessing centers, remanufacturing plants and transportation options and simultaneously achieving sustainability goals. The model is applied on an Indian based manufacturing unit of a Saudi Arabian Industrial Air conditioner manufacturing organization and the case is presented here. The model is converted into a multi-objective programming model in accordance with the importance of each objective suiting the business needs. All relevant objective functions are evaluated using BWM, AHP and FAHP methods to obtain weights for integration into a fuzzy linear programming model which eventually provides three separate results. The model applied has originality and uniqueness for applications to solve multi-objective problems under uncertain environment and tends to strike a balance between economic and environmental objectives. The study provides for a base for further scope covering uncertainty about the amount and quality of returned products and even can be implemented by p

关键词： BWM Fuzzy multi-objective linear programming mixed-integer linear programming Network design Product recovery Reverse logistics

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A MILP-based modular energy management system for urban multi-energy systems: Performance and sensitivity analysis

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APPLIED ENERGY 2020年第0期261卷 114342-000页

作者： Moser, A. Muschick, D. Goelles, M. Nageler, P. Schranzhofer, H. Mach, T. Tugores, C. Ribas Leusbrock, I. Stark, S. Lackner, F. Hofer, A. BEST Bioenergy & Sustainable Technol GmbH Inffeldgasse 21b A-8010 Graz Austria Graz Univ Technol Inst Automat & Control Inffeldgasse 21b A-8010 Graz Austria Graz Univ Technol Inst Thermal Engn Pluddemanngasse 104-IV A-8010 Graz Austria AEE INTEC Feldgasse 19 A-8200 Gleisdorf Austria TB STARCHEL Ingenieurburo GmbH Sudbahnstr 6 A-8430 Leibnitz Austria PMC Gebaudetech Planungs GmbH Neuseiersberger Str 155 A-8055 Graz Austria

The continuous increase of (volatile) renewable energy production and the coupling of different energy sectors such as heating, cooling and electricity have significantly increased the complexity of urban energy systems. Such multi-energy systems (MES) can be operated more efficiently with the aid of optimization-based energy management systems (EMS). However, most existing EMS are tailor-made for one specific system or class of systems, i.e. are not generally applicable. Furthermore, only limited information on the actual savings potential of the usage of an EMS under realistic conditions is available. Therefore, this paper presents a novel modular modeling approach for an EMS for urban MES, which also enables the modeling of complex system configurations. To assess the actual savings potential of the proposed EMS, a comprehensive case study was carried out. In the course of this the influence of different user behavior, changing climatic conditions and forecast errors on the savings potential was analyzed by comparing it with a conventional control strategy. The results showed that using the proposed EMS in conjunction with supplementary system components (thermal energy storage and battery) an annual cost savings potential of between 3 and 6% could be achieved.

关键词： Energy management system Modular Multi-energy system Model predictive control mixed logical dynamical mixed-integer linear programming

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Optimal Design of Electric Bus Transport Systems With Minimal Total Ownership Cost

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IEEE ACCESS 2020年 8卷 119184-119199页

作者： Lotfi, Mohamed Pereira, Pedro Paterakis, Nikolaos G. Gabbar, Hossam A. Catalao, Joao P. S. Univ Porto Fac Engn P-4200465 Porto Portugal Inst Syst & Comp Engn Technol & Sci INESC TEC P-4200465 Porto Portugal Ontario Tech Univ Fac Energy Syst & Nucl Sci Oshawa ON L1G 8C4 Canada Eindhoven Univ Technol TUe Dept Elect Engn NL-5600 MB Eindhoven Netherlands Ontario Tech Univ Fac Engn & Appl Sci Oshawa ON L1G 8C4 Canada

In this work, a generalized mathematical formulation is proposed to model a generic public transport system, and a mixed-integer linear programming (MILP) optimization is used to determine the optimal design of the system in terms of charging infrastructure deployment (with on-route and off-route charging), battery sizing, and charging schedules for each route in the network. Three case studies are used to validate the proposed model while demonstrating its universal applicability. First, the design of three individual routes with different characteristics is demonstrated. Then, a large-scale generic transport system with 180 routes, consisting of urban and suburban routes with varying characteristics is considered and the optimal design is obtained. Afterwards, the use of the proposed model for a long-term transport system planning problem is demonstrated by adapting the system to a 2030 scenario based on forecasted technological advancements. The proposed formulation is shown to be highly versatile in modeling a wide variety of components in an electric bus (EB) transport system and in achieving an optimal design with minimal TOC.

关键词： Electric buses mixed-integer linear programming charging infrastructure

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Analysis on the demand response potential in hotels with varying probabilistic influencing time-series for the Canary Islands

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RENEWABLE ENERGY 2020年 160卷 1480-1491页

作者： Meschede, Henning Univ Kassel Dept Sustainable Prod & Proc Kassel Germany

Tourism represents a significant portion of the total economic activity of many islands throughout the world. This high level of tourist activity has a substantial impact on the energy and water demand. Previous research into smart energy systems on islands has demonstrated that there is significant potential for hotels to partake in energy demand shifting. However, the total potential of such shifting is influenced by variables such as air temperature and the level of occupancy of the hotels. The objective of this work is to quantify the sensitivity of these parameters and their impact on the overall effectiveness of demand shifting within hotels. The assessment contained within this paper utilises mixed integer linear programming to determine the dispatch of supply and demand on a case study hotel in the Canary Islands. The results show that highest reduction of fossil fuels is reached in a fully electrified hotel energy system. The potential is assumed as independent from weather and guests' behaviour. If only PV is used, demand shifting in only one hotel leads to a relative change of the degree of self-sufficiency of 1.6-1.8% but the results visualise a saturation effect for an already high share of renewable energies. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Demand side management Hotel facilities Sector coupling mixed-integer linear programming Island energy system Smart renewable energy systems

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Stochastic Hierarchical Multimodal Hub Location Problem for Cargo Delivery Systems: Formulation and Algorithm

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IEEE ACCESS 2020年 8卷 55076-55090页

作者： Shang, Xiaoting Yang, Kai Wang, Weiqiao Wang, Weiping Zhang, Haifeng Celic, Selena Beijing Jiaotong Univ Key Lab Transport Ind Big Data Applicat Technol C Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Univ Windsor ED Lumley Ctr Engn Innovat Windsor ON N9B 3P4 Canada

This study aims at developing a stochastic hierarchical multimodal hub location modeling framework for cargo delivery systems to capture uncertainty in hub construction cost and travel time at the strategic level. From a ring-star-star type network design perspective, a stochastic model is established to formulate this problem formally via the expected value and chance-constrained programming techniques. In particular, three types of chance constraints are proposed to ensure that the on-time delivery with pre-specified confidence levels in their respective layer networks. For normal distributions, the original stochastic model can be reformulated as a crisp equivalent mixed-integer linear programming (MILP) model by invoking the central limit theorem. Since the number of constraints and variables increases drastically with the size of cargo delivery distribution network, a memetic algorithm (MA) is designed. This algorithm incorporates genetic search and local intensification to obtain optimal/near-optimal solutions for realistic instance size within a reasonable time limit. For general distributions, it is difficult to convert the stochastic model into its deterministic counterpart. Hence, a hybrid methodology is further designed by combining the MA and Monte Carlo (MC) simulation to solve the proposed stochastic model. To demonstrate the properties of the proposed model and the performance of the designed algorithm, a series of numerical experiments are set up based on the Civil Aeronautics Board (CAB) and Turkish network data sets. Computational results indicate as the confidence level increases, the airport hubs are located further apart in the cargo delivery distribution network for gaining a greater time advantage. In addition, comparative results demonstrate that the MA algorithm proposed herein performs better than the genetic algorithm (GA) in terms of computing speed and quality of the solution.

关键词： Hierarchical multimodal hub location cargo delivery systems stochastic programming mixed-integer linear programming memetic algorithm Monte Carlo simulation

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Determination of optimal reserve contribution of thermal units to afford the wind power uncertainty

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2020年第4期11卷 1565-1576页

作者： Esmaeeli, Mostafa Goishannavaz, Sajjad Siano, Pierluigi Birjand Univ Technol Fac Comp & Ind Engn Birjand Iran Urmia Univ Elect Engn Dept Orumiyeh Iran Univ Salerno Dept Management & Innovat Syst Fisciano Italy

Due to unforeseen variations in wind speed profiles, wind farm integrations are recognized as intermittent and uncertain energy contributors. More specifically, integration of such renewable energy resources aligned with the conventional thermal units although reduces the emissions and brings about a clean environment, it introduces serious problems in assigning optimal and reliable level of these units in load supplying and spinning reserve provision. This situation is more intensified considering the uncertainties arisen by the power system loading demand. To facilitate such operational hurdles, the ongoing study puts forward an efficient model for assigning the optimal spinning reserve which accommodates the uncertainties in both the wind speed and load profiles. Stochastic behavior of these parameters is simulated by generating a proper number of scenarios through the Monte Carlo simulation (MCS) approach. Then, each of these scenarios is evaluated based on the established linear mixed integer approach in a deterministic fashion. Accordingly, a computationally efficient approach is obtained paving the way for real-world implementations and assuring the global optimum results. The proposed approach is applied to a 12-unit test system including 10 thermal units and 2 wind farms. Results are reflected in terms of the commitment status, energy dispatches, and reserve contributions of each committed unit. A comprehensive discussion is conducted to disclose the possible improvements.

关键词： Wind farm integrations Wind speed and load uncertainties Stochastic analysis mixed-integer linear programming Increased wind energy deployment Emission reduction

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