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Multi-objective vehicle routing and loading with time window constraints: a real-life application

ANNALS OF OPERATIONS RESEARCH 2020年第1-2期291卷 799-825页

作者： Song, Xiang Jones, Dylan Asgari, Nasrin Pigden, Tim Univ Portsmouth Sch Math & Phys Ctr Operat Res & Logist Lion Gate BldgLion Terrace Portsmouth P01 3HF England Univ Roehampton Roehampton Business Sch London SW15 5SL England Optrak Distribut Software Ltd Suite 6Hoe Lane Ware SG12 9LR Herts England

Motivated by a real-life application, this research considers the multi-objective vehicle routing and loading problem with time window constraints which is a variant of the Capacitated Vehicle Routing Problem with Time Windows with one/two-dimensional loading constraints. The problem consists of routing a number of vehicles to serve a set of customers and determining the best way of loading the goods ordered by the customers onto the vehicles used for transportation. The three objectives pertaining to minimisation of total travel distance, number of routes to use and total number of mixed orders in the same pallet are, more often than not, conflicting. To achieve a solution with no preferential information known in advance from the decision maker, the problem is formulated as a mixed integer linear programming (MILP) model with one objective-minimising the total cost, where the three original objectives are incorporated as parts of the total cost function. A Generalised Variable Neighbourhood Search (GVNS) algorithm is designed as the search engine to relieve the computational burden inherent to the application of the MILP model. To evaluate the effectiveness of the GVNS algorithm, a real instance case study is generated and solved by both the GVNS algorithm and the software provided by our industrial partner. The results show that the suggested approach provides solutions with better overall values than those found by the software provided by our industrial partner.

关键词： Routing and loading mixed integer linear programming Generalised Variable Neighbourhood Search

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Optimal Process Capacity Allocation Under Abnormal Conditions

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PROCESS INTEGRATION AND OPTIMIZATION FOR SUSTAINABILITY 2020年第2期4卷 163-169页

作者： Gumilao, T. K. Aviso, K. B. Tan, R. R. De La Salle Univ Dept Chem Engn 2401 Taft Ave Manila 0922 Philippines

Plant operations may need to be optimized in response to abnormal conditions resulting from various disruptive events. In such cases, it is of interest to minimize interim economic losses by allocating the capacities of process units optimally while the plant operations deviate from the nominal design conditions. This note extends the mixed integer linear programming (MILP) model previously developed by Kasivisvanathan et al. (Applied Energy 102: 492-500, 2013) by considering the effect of financial penalties for failure to meet contractual obligations to customers;it is assumed that such penalties are paid in direct proportion to the magnitude of production deficit. The extended model is illustrated with a case study of a chlor-alkali industrial complex, and general implications of the results are discussed.

关键词： Inoperability Optimization mixed integer linear programming Abnormal operations

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Jaccard Median for Ego-network Segmentation

Jaccard Median for Ego-network Segmentation

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IEEE International Conference on Data Mining (ICDM)

作者： Haodi Zhong Grigorios Loukides Alessio Conte Solon P. Pissis Xidian University China King’s College London United Kingdom University of Pisa Italy CWI The Netherlands Vrije Universiteit The Netherlands

An ego-network is a graph representing the interactions of a node (ego) with its neighbors and the interactions among those neighbors. A sequence of ego-networks having the same ego can thus model the evolution of these interactions over time. We introduce the problem of segmenting a sequence of ego-networks into k segments, for any given integer k. Each segment is represented by a summary network, and the goal is to minimize the total loss of representing k segments by k summaries. The problem allows partitioning the sequence into homogeneous segments with respect to the activities or properties of the ego (e.g., to identify time periods when a user acquired different circles of friends in a social network) and to compactly represent each segment with a summary. The main challenge is to construct a summary that represents a collection of ego-networks with minimum loss. To address this challenge, we employ Jaccard Median (JM), a well-known NP-hard problem for summarizing sets, for which, however, no effective and efficient algorithms are known. We develop a series of algorithms for JM offering different effectiveness/efficiency trade-offs: (I) an exact exponential-time algorithm, based on mixed integer linear programming and (II) exact and approximation polynomial-time algorithms for minimizing an upper bound of the objective function of JM. By building upon these results, we design two algorithms for segmenting a sequence of ego-networks that are effective, as shown experimentally.

关键词： Upper bound Social networking (online) NP-hard problem Buildings Approximation algorithms linear programming mixed integer linear programming

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A Fast Solution Method for Large-Scale Unit Commitment Based on Virtual Power Boundary

A Fast Solution Method for Large-Scale Unit Commitment Based...

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Chinese Automation Congress (CAC)

作者： Can Liu Qiaozhu Zhai Yuzhou Zhou Systems Engineering Institute Xi’an Jiaotong University Xi’an China

This paper investigates the fast solution difficulty for large-scale unit commitment (UC) problems. The UC problem is a typical puzzle in the scheduling of power systems. Lagrangian relaxation (LR) and mixed-integer linear programming (MILP) are the two most widely adopted methods. However, in the LR framework, constructing feasible solutions and updating Lagrangian multipliers are two major challenges, and the efficiency of MILP methods is restricted by the scale of UC problems. To this end, this paper proposes a new fast solution method for large-scale UC based on virtual power boundary. Specifically, first, an initial trial solution is obtained based on the established state transition diagram. Second, a feasibility test model based on virtual power boundary is established to check the feasibility of trial UC solutions effectively. Third, the updating strategies for UC solutions are proposed based on the feasible gap solved by the feasibility test model. The procedure is implemented until a good feasible UC solution is obtained. Numerical results implemented on IEEE 24-bus, IEEE 31-bus, IEEE 118-bus, and Polish 2383-bus systems show the efficiency of the proposed method.

关键词： Automation Stochastic processes Numerical models Power systems mixed integer linear programming

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Bi-Objective Optimization for Uniform Parallel Batch Machine Scheduling under Time-of-Use Tariffs

Bi-Objective Optimization for Uniform Parallel Batch Machine...

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IEEE International Conference on Networking, Sensing and Control

作者： Junheng Cheng Jingya Cheng Feng Chu School of Economics Fujian Normal University Fuzhou China Laboratory IBISC Univ-Évry Université Paris-Saclay Évry France

ISBN: (纸本)9781665472449

Time-of-Use (ToU) electricity pricing scheme has been widely implemented to alleviate the grid's peak load, under which manufacturing companies obtain a good opportunity to save energy cost through more reasonable production scheduling. As a typical production system, batch processing machine manufacturing system has been widely used in modern manufacturing industry because of its advantages in improving production efficiency and reducing production costs. In this work, a new bi-objective uniform parallel batch machine scheduling problem with different job sizes under ToU tariffs is explored, with the goal of minimizing the total electricity cost and the number of enabled machines. We first establish a mixed integer linear programming model, and then propose an improved model. Both models are solved by CPLEX using the $\varepsilon$ -constraint method. The calculation results of randomly generated instances prove the effectiveness of the proposed model. At the same time, the calculation results show that the improved model is more effective than the original one.

关键词： Schedules Job shop scheduling Costs Tensors Tariffs mixed integer linear programming Sensors

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Unit Commitment Analysis in Hybrid Networks Containing Point-to-Point VSC-HVDC links

Unit Commitment Analysis in Hybrid Networks Containing Point...

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IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC)

作者： N. Gónzalez-Cabrera Luis M. Castro L. E. Hernández-Amador Department of Electrical Energy Engineering Faculty National Autonomous University of Mexico (UNAM) Mexico City Mexico Postgraduate of Engineering National Autonomous University of Mexico (UNAM) Mexico City Mexico

ISBN: (纸本)9781665458931

This paper presents an efficient modeling approach for solving the Unit Commitment (UC) problem in hybrid power grids. This is based on the linear modeling of the AC network and point-to-point High Voltage Direct Current (VSC-HVDC) links through a nodal power flow approach using AC voltage phase angles and DC voltages. This results in a mixed-integer linear programming (MILP) formulation. The obtained results permit to determine the optimal hourly scheduling for VSC-based transmission grids in the context of day-ahead electricity markets. The applicability of this formulation is showcased using two empirical power systems incorporating a point-to-point VSC-HVDC link.

关键词： Analytical models HVDC transmission High-voltage techniques Electricity supply industry Hybrid power systems Power grids mixed integer linear programming

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Supply-Demand Coordination of Wind-Solar-Hydrogen Integrated Energy System Considering Hydrogen Delivery

Supply-Demand Coordination of Wind-Solar-Hydrogen Integrated...

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Smart Power & Internet Energy Systems (SPIES), International Conference on

作者： Xiyan Jian Zhanbo Xu Xiangxiang Dong Jiang Wu Kun Liu Xiaohong Guan MOEKLINNS Xi’an Jiaotong University Xi’an China

Recently, more attention is attracted on renewable energy sources (RESs) that could alleviate energy and environmental issues. However, with the fast development of RESs, vast power curtailment arouses due to the mismatch of energy demand and renewable supply in time and spatial horizon, leading to low renewable energy sources utilization efficiency. In this paper, we propose a coordinated scheduling problem of a wind-solar-hydrogen integrated energy system (WSH-IES) to address the problem mentioned above. The curtailed RESs power can be converted into hydrogen in supply subsystem, and such hydrogen can be delivered to demand subsystem through tube trailers to meet multi-energy demand. The problem is formulated into a mixed integer linear programming problem. Numerical results show the developed method can achieve 100% RESs utilization efficiency and reduce 23.4% of the operation cost through the coordinated scheduling of all the energy devices and the hydrogen interaction between the demand and supply subsystems.

关键词： Renewable energy sources Costs Supply and demand Hydrogen Optimal scheduling Electron tubes mixed integer linear programming

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Backup Resource Allocation Model with Two-Stage Probabilistic Protection

Backup Resource Allocation Model with Two-Stage Probabilisti...

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IEEE Conference on Network Softwarization (NetSoft)

作者： Kento Yokouchi Fujun He Eiji Oki Kyoto University Kyoto Japan

ISBN: (数字)9781665406949

ISBN: (纸本)9781665406956

This paper proposes a backup resource allocation model with two-stage probabilistic protection to minimize the total required backup capacity for multiple simultaneous failures of physical machines (PMs). Probabilistic protection ensures that the probability that the PM used for backup fails to backup due to lack of computing capacity does not exceed a given survivability parameter. The proposed model protects the primary virtual machines by allocating computing capacity to backup PMs with probabilistic protection. Since it is uncertain which primary PMs fail, we apply robust optimization to the probabilistic protection. By using a table that takes into account the survivability parameter and the failure probability of PMs, this proposed model is formulated as a mixed integer linear programming problem. The proposed model extends the probabilistic protection to two stages; the VMs that fail to be protected in the first stage are protected in the second stage to achieve the probabilistic protection with the final survivability parameter. This model can reduce the total required backup capacity compared to the conventional model with one-stage probabilistic protection.

关键词： Computational modeling Conferences Probabilistic logic Virtual machining Numerical models mixed integer linear programming Resource management

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Multi-agent Optimization Model of Power Spot Market under Renewable Portfolio Standard

Multi-agent Optimization Model of Power Spot Market under Re...

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IEEE Conference on Energy Internet and Energy System Integration (EI2)

作者： Xiaofan Lin Jinchun Chen Simin Chen Wanqing Chen Han Chen Yuanfei Li Power Economic Research Institute of State Grid Fujian Electric Power Company Fuzhou China

China officially began to implement the Renewable Portfolio Standard (RPS) in 2021, which requires power users to take the actual consumption of renewable energy electricity as the main way to achieve the target. At the same time power users can also purchase Renewable energy certificate (REC) and surplus renewable energy consumption as a supplementary means. Based on the above framework, a bilevel multi-agent decision optimization model including renewable energy day-ahead market, conventional energy day-ahead market and real-time market is established under the RPS, and transformed by the Karush-Kuhn-Tucker condition, strong duality theorem and binary expansion method. It is transformed into mixed integer linear programming to find the market equilibrium. The effects of assessment index of RPS, REC price and renewable power penetration on market equilibrium characteristics are discussed. The results show that in the market equilibrium state, the day-ahead market clearing price of renewable energy is equal to the conventional energy clearing price plus the REC price, and with the increase of the penetration rate of renewable energy, the power spot market clearing price of electricity shows a downward trend.

关键词： Renewable energy sources Supply and demand System integration Market research linear programming Real-time systems mixed integer linear programming

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Interpreting Deep-Learned Error-Correcting Codes

Interpreting Deep-Learned Error-Correcting Codes

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IEEE International Symposium on Information Theory

作者： N. Devroye N. Mohammadi A. Mulgund H. Naik R. Shekhar Gy. Turá n Y. Wei M. Ž efran University of Illinois at Chicago Chicago IL USA MTA-SZTE Research Group on Artificial Intelligence ELRN Szeged Hungary

ISBN: (数字)9781665421591

ISBN: (纸本)9781665421607

Deep learning has been used recently to learn error-correcting encoders and decoders which may improve upon previously known codes in certain regimes. The encoders and decoders are learned "black-boxes", and interpreting their behavior is of interest both for further applications and for incorporating this work into coding theory. Understanding these codes provides a compelling case study for Explainable Artificial Intelligence (XAI): since coding theory is a well-developed and quantitative field, the interpretability problems that arise differ from those traditionally considered. We develop post-hoc interpretability techniques to analyze the deep-learned, autoencoder-based encoders of TurboAE-binary codes, using influence heatmaps, mixed integer linear programming (MILP), Fourier analysis, and property testing. We compare the learned, interpretable encoders combined with BCJR decoders to the original black-box code.

关键词： Training Deep learning Neural networks Turbo codes Robustness Decoding mixed integer linear programming

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