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Bi-criteria formulation for green scheduling with unrelated parallel machines with sequence-dependent setup times

INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2021年第2期28卷 996-1017页

作者： Cota, Luciano P. Coelho, Vitor N. Guimaraes, Frederico G. Souza, Marcone J. F. Univ Fed Minas Gerais Grad Program Elect Engn BR-31270901 Belo Horizonte MG Brazil Vale Inst Technol ITV Min BR-35400000 Ouro Preto MG Brazil Univ Fed Fluminense Inst Comp Sci BR-24210346 Niteroi RJ Brazil Univ Fed Minas Gerais Dept Elect Engn BR-31270010 Belo Horizonte MG Brazil Univ Fed Ouro Preto Dept Comp Sci BR-35400000 Ouro Preto MG Brazil

Given the important role of machine scheduling in manufacturing industry, we discuss power consumption in sequencing jobs in a scheduling problem, assuming variable speed operation in machines. The problem involves defining the allocation of jobs to machines, the order of processing jobs and the speed of processing each job in each machine. This problem can be viewed as a type of green scheduling problem, dealing with sustainable use of energy consumption and environmental effects. We propose a mixed integer linear programming (MILP) model for the unrelated parallel machine-scheduling problem with sequence-dependent setup times, with independent and non-preemptible jobs, minimizing the makespan and the total consumption of electricity. Furthermore, we employ a novel math-heuristic algorithm, named multi-objective smart pool search matheuristic (or simply smart pool), for finding solutions near the Pareto front, in a restricted computational budget. As a case study, a new set of instances is created for the problem. Those instances are solved using the classical epsilon-constrained method and the smart pool method. The obtained sets of non-dominated solutions indicate the conflict between both objectives, highlighting the relevance of the suggested approach to industry. From the obtained results, it was verified that the smart pool achieved good convergence towards the true Pareto front, as indicated by the hyper-volume metric, presenting lower average time for finding solutions on the Pareto front. In small to medium size instances, the smart pool search method can achieve very good approximations of the Pareto front with less computational effort than traditional methods.

关键词： green scheduling multi-objective optimization parallel machines mixed integer linear programming

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Improving Hosting Capacity of Unbalanced Distribution Networks via Robust Allocation of Battery Energy Storage Systems

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IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第3期36卷 2174-2185页

作者： Wang, Bo Zhang, Cuo Dong, Zhao Yang Li, Xuejun Univ New South Wales Sch Elect Engn & Telecommun Sydney NSW 2052 Australia Jemena Melbourne Vic 3000 Australia

Distribution system operators aim to improve hosting capacity (HC) of distribution networks (DNs) to accommodate more distributed rooftop photovoltaics (PVs). Although PV power generation delivers numerous benefits, power unbalance and voltage rise are two major obstacles that limit the network HC. To mitigate these issues, battery energy storage systems (BESSs) can be applied. Thus, this paper proposes a robustly optimal allocation method for BESSs, which aims to reduce the power unbalance and alleviate the voltage rise, and thus improve the HC of the unbalanced three-phase DNs. Considering that locations and capacities of distributed rooftop PVs are determined by customers, future PV installations are regarded as uncertainties. In addition, to deal with the uncertainties, the proposed BESS allocation problem is formulated as an adaptive robust optimization (ARO) model with integer recourse variables. Accordingly, a solution algorithm which integrates an alternating optimization procedure into a column-and-constraint generation algorithm is developed to efficiently solve the ARO model. With the proposed BESS allocation method, a new perspective on HC improvement is provided, which not only considers the worst power unbalance situation but also satisfies the allowed maximum PV capacity. The simulation results verify high efficiency and solution robustness of the proposed allocation method.

关键词： Resource management Uncertainty Robustness Computational modeling Energy storage Optimization Load flow Adaptive robust optimization battery energy storage system hosting capacity mixed integer linear programming unbalanced distribution network

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An Elementary Approach to the Vehicle Routing Problem via Python and Google API

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American Journal of Operations Research 2024年第6期14卷 169-190页

作者： Kai Q. Lian Gareth A. Tribello Centre for Quantum Materials and Technologies School of Mathematics and Physics Queens University Belfast Northern Ireland UK

Commercial organisations commonly use operational research tools to solve vehicle routing problems. This practice is less commonplace in charity and voluntary organisations. In this paper, we provide an elementary approach for solving the Vehicle Routing Problem (VRP) that we believe can be easily implemented in these types of organisations. The proposed model leverages mixed integer linear programming to optimize the pickup sequence of all customers, each with distinct time windows and locations, transporting them to a final destination using a fleet of vehicles. To ensure ease of implementation, the model utilises Python, a user-friendly programming language, and integrates with the Google Maps API, which simplifies data input by eliminating the need for manual entry of travel times between locations. Troubleshooting methods are incorporated into the model design to ensure easy debugging of the model’s infeasibilities. Additionally, a computation time analysis is conducted to evaluate the efficiency of the code. A node partitioning approach is also discussed, which aims to reduce computational times, especially when handling larger datasets, ensuring this model is realistic and practical for real-world application. By implementing this optimized routing strategy, logistics companies or organisations can expect significant improvements in their day-to-day operations, with minimal computational cost or need for specialised expertise. This includes reduced travel times, minimized fuel consumption, and thus lower operational costs, while ensuring punctuality and meeting the demands of all passengers.

关键词： Vehicle Routing Problem (VRP) Google Maps Distance Matrix API Python Pulp mixed integer linear programming Transportation Optimisation Problem Time Window Mathematical Modelling

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Optimization of the electric vehicle charging strategy problem for sustainable intercity travels with multiple refueling stops

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SUSTAINABLE ENERGY GRIDS & NETWORKS 2024年 40卷

作者： Yilmaz, Hilal Yagmahan, Betul Bursa Tech Univ Fac Engn & Nat Sci Dept Ind Engn TR-16310 Bursa Turkiye Bursa Uludag Univ Fac Engn Dept Ind Engn Gorukle Campus TR-16059 Bursa Turkiye

Electric vehicle (EV) drivers considering long-distance trips still face range anxiety due to the limited range of EVs and the scarcity of charging stations. Thus, it becomes important to ensure the feasibility of the selected route and determine an optimal charging strategy. As a crucial aspect of decision support for EV drivers, this study proposes a mixed integer linear programming (MILP) approach for the EV charging strategy problem (EVCSP), incorporating a piecewise linear approximation technique to address the challenges posed by nonlinear charging times. The proposed optimization model, namely CSPM determines where, when, and how much to charge an EV for a specified route to minimize travel time and cost. The solution time of large-scale test problems and a case study on T & uuml;rkiye reveal the robustness and reliability of the CSPM. Furthermore, two multi-objective optimization methods (the weighted sum and the lexicographic method) are applied to the case study, and the results are analyzed. The results indicate that the travel cost is more sensitive to the selected charging strategy, with a range of 46.09% across the applied charging strategies, whereas travel time remains more resilient, with a maximum fluctuation of 19.77%. A comparative analysis with a full charging strategy reveals that the CSPM reduces the travel time by 60.1 % and improves the cost efficiency by 105.72%.

关键词： Electric vehicles Charging strategy mixed integer linear programming Multi-objective optimization Lexicographic method Multiple refueling stops

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Modeling and solving assembly line worker assignment and balancing problem with sequence-dependent setup times

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SOFT COMPUTING 2021年第20期25卷 12899-12914页

作者： Yilmaz, Hamid Bayburt Univ Fac Engn Ind Engn Dept Bayburt Turkey

Assembly lines appear with various differentiations in order to better include the disabled in the labor market and to increase production efficiency. In this way, the optimal workforce assignment problem that emerges heterogeneously is called assembly line worker assignment and balancing problem (ALWABP). This paper addresses the ALWABP where the simple version is enriched by considering sequence-dependent setup times between tasks. A mixed integer linear programming model is presented, and a simulated annealing algorithm is developed such as an NP-hard problem. In order to test the proposed solutions, 640 benchmark problems in the literature were combined and used. The solutions obtained through using the proposed algorithm are compared with the mixed integer programming model on the small-size test problems. Experimental results show that the proposed algorithm is more effective and robust for a large set of benchmark problems.

关键词： mixed integer linear programming Assembly line balancing Simulated annealing Sequence-dependent setup times

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Optimally solving the generalized serial-lock scheduling problem from a graph-theory-based multi-commodity network perspective

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期288卷 47-62页

作者： Ji, Bin Zhang, Dezhi Yu, Samson S. Zhang, Binqiao Cent South Univ Sch Traff & Transportat Engn Changsha 410075 Peoples R China Deakin Univ Sch Engn Ctr Adv Design & Engn Waurn Ponds Vic 3216 Australia China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Peoples R China

In this study, we propose a general model for the generalized serial-lock scheduling problem (GSLSP), innovatively from a multi-commodity network (MCN) perspective. The MCN-based approach allows the formulation of a mixed integer linear programming (MILP) model, which is capable of finding the optimal solution to the GSLSP. In order to verify the effectiveness of the proposed method, a large number of instances with various lock configurations are tested, which corroborates that the proposed MCN-based GSLSP approach attests optimality for single-lock problems with less computational burden than the conventional exact methods. It can also solve multiple series-connected lock problems to optimality within reasonable computational time. Thereafter, we also investigate the impact of a serial-lock system's symmetry on the performance of the proposed MCN-based method when used for transferring ships. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Combinatorial optimization Inland waterway Serial-lock scheduling Multi-commodity network mixed integer linear programming

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Optimal Energy Scheduling for Data Center With Energy Nets Including CCHP and Demand Response

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IEEE ACCESS 2021年 9卷 6137-6151页

作者： Wang, Dongxiao Xie, Changhong Wu, Runji Lai, Chun Sing Li, Xuecong Zhao, Zhuoli Wu, Xueqing Xu, Yi Lai, Loi Lei Wei, Jinxiao Guangdong Univ Technol Sch Automat Dept Elect Engn Guangzhou 510006 Peoples R China Brunel Univ London Brunel Interdisciplinary Power Syst Res Ctr London UB8 3PH England Guangdong Foshan Power Construct Corp Grp Co Ltd Foshan 528010 Peoples R China

Internet data centers are growing rapidly in recent years and they operate with intensive energy activity. Combined cooling, heating and power (CCHP) brings new opportunities for reducing the electricity cost in internet data centers. The main objective of this study is to optimize the energy resources scheduling in the data center coupled energy nets considering the involvement of CCHP and different demand response techniques. In this paper, internet data center coupled energy nets are proposed, where power grid, solar photovoltaic, CCHP, and battery energy storage systems are the primary energy sources. The adjunct residential buildings and commercial buildings near the internet data centers are also included in the proposed energy nets, where different types of load and demand response characteristics are utilized. A two-stage optimized energy management model considering the coordinated operation of CCHP and demand response technologies is established for internet data center coupled energy nets. In the day-ahead stage, the control objective is to minimize system cost while satisfying various constraints. Consider the electricity tariff chance between day-ahead market and real-time market, real-time control is implemented to minimize the imbalance cost between two electricity markets. Case studies are conducted on a practical internet data center coupled energy nets in Foshan City, China. It is observed that the proposed control framework can optimally schedule the energy resources in the energy network to meet system demand and improve the energy efficiency. The economic evaluation demonstrates that the proposed control scheme reduces system daily cost by 22.01%.

关键词： Data centers Real-time systems Load management Batteries Turbines Resistance heating Servers Combined cooling heating and power data centers mixed integer linear programming renewable energy sources two-stage optimal scheduling

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Assessing Program-Level Objectives of Space Exploration Architectures Using Portfolio-Optimization Methods

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JOURNAL OF SPACECRAFT AND ROCKETS 2021年第5期58卷 1248-1262页

作者： O'Neill, William J. Delaurentis, Daniel A. Purdue Univ Sch Aeronaut & Astronaut 701 West Stadium Ave W Lafayette IN 47906 USA

The large number and significant variety of systems available for space exploration missions produce countless potential architecture combinations. Compounding this are the scheduling intricacies of system life cycle phases and time-dependent operational dependencies, as well as the uncertainty associated with each system and technology in terms of cost, schedule, and performance. Traditional architecting emphasizes the individual design of component systems over the wide-ranging and robust assessment of architecture options early in mission design. A top-down method that can assess the capabilities, requirements, and risks associated with the diversity of available space systems and form optimal portfolios of interdependent systems is necessary. Our work describes and demonstrates a portfolio-optimization technique that can design and assess lunar space exploration architectures by optimizing on programmatic objectives such as cost, performance, schedule, and robustness while simultaneously accounting for system operational interdependencies and schedule dependencies of the selected systems.

关键词： Space Exploration Space Systems Super Heavy Lift Launch Vehicle Chemical Space Propulsion NTR mixed integer linear programming Deep Space Habitat Propulsion System Chemical Rocket Propulsion Earth

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Unit commitment optimization of a micro-grid with a MILP algorithm: Role of the emissions, bio-fuels and power generation technology

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ENERGY REPORTS 2021年 7卷 8639-8651页

作者： Nicolosi, Francesco F. Alberizzi, Jacopo C. Caligiuri, Carlo Renzi, Massimiliano Free Univ Bozen Bolzano Fac Sci & Technol Piazza Univ 5 I-39100 Bozen Bolzano South Tyrol Italy

Management strategies of complex energy systems composed by different technologies is mandatory to exploit optimally the characteristics of each power generator, to reduce the cost of energy, the impact of greenhouse gases emissions and to increase the penetration of mini- and micro-grids into energy systems. To this purpose, optimization methods and algorithms have to be developed to assess the unit commitment of generators and to suggest decision variables in the definition of the emission costs. In this paper, a novel mixed integer linear programming (MILP) optimization algorithm has been developed to compute the optimal management of a micro-energy grid composed either by four Internal Combustion Generators (ICGs), or three ICGs and a Micro Gas Turbine (MGT). The algorithm optimizes a multi objective function that takes in consideration the total cost, the NOx and the CO2 emissions of the system, while setting some technological constraints, like start-ups and transients that are typically neglected. Moreover, different fuelling of the devices is evaluated. The model proved the importance of including an accurate model of the greenhouse gases emissions as they can significantly affect the optimization results. Furthermore, it proved to be very flexible and to be a proper basis to be adopted in more complex systems embedding energy storage devices and renewable energy systems. (C) 2021 The Author(s). Published by Elsevier Ltd.

关键词： Energy systems unit commitment mixed integer linear programming Internal Combustion Engines Micro Gas Turbine Greenhouse gases emissions

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Revisiting the Optimization of Cauchy Reed-Solomon Coding Matrix for Fault-Tolerant Data Storage

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IEEE TRANSACTIONS ON COMPUTERS 2021年第8期71卷 1839-1846页

作者： Makovenko, Mykyta Cheng, Min Tian, Chao Texas A&M Univ Dept Ind & Syst Engn College Stn TX 77843 USA Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA

Cauchy Reed-Solomon (CRS) codes are a class of erasure-resilient codes which are widely applicable in modern data storage systems. Several existing works have considered making the coding process of the extended CRS codes more efficient. It has been found that this efficiency is highly dependent on the underlying Cauchy coding matrices, particularly, the density of the associated bitmatrices. In this work, we revisit the problem of optimizing the coding bitmatrices, and propose three approaches aiming to find the bitmatrices with the lowest density in this context, namely a mixed integer linear programming approach, a local optimal algorithm with heuristic perturbation, and a branch-and-bound algorithm. Experimental results show that the proposed approaches are able to find bitmatrices with significantly lower density than those found using existing techniques. Moreover, the local optimal algorithm with heuristic perturbation is surprisingly efficient in finding good solutions under constrained computation time.

关键词： Codes Encoding Systematics Reed-Solomon codes Perturbation methods Generators Heuristic algorithms Reed-Solomon codes erasure codes integer programming mixed integer linear programming

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