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A new DEA common-weight multi-criteria decision-making approach for technology selection

INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2020年第12期58卷 3686-3700页

作者： Chu, Junfei Wu, Jie Chu, Chengbin Liu, Ming Cent South Univ Sch Business Changsha Hunan Peoples R China Univ Sci & Technol China Sch Management Hefei Peoples R China Fuzhou Univ Sch Econ & Management Fuzhou Fujian Peoples R China Univ Paris Est ESIEE Paris Noisy Le Grand France Tongji Univ Sch Econ & Management Shanghai Peoples R China

This paper addresses an advanced manufacturing technology selection problem by proposing a new common-weight multi-criteria decision-making (MCDM) approach in the evaluation framework of data envelopment analysis (DEA). We improve existing technology selection models by giving a new mathematical formulation to simplify the calculation process and to ensure its use in more general situations with multiple inputs and multiple outputs. Further, an algorithm is provided to solve the proposed model based on mixed-integer linear programming and dichotomy. Compared with previous approaches for technology selection, our approach brings new contributions. First, it guarantees that only one decision-making unit (DMU) (referring to a technology) can be evaluated as efficient and selected as the best performer while maximising the minimum efficiency among all the DMUs. Second, the number of mixed-integer linear programs to solve is independent of the number of candidates. In addition, it guarantees the uniqueness of the final optimal set of common weights. Two benchmark instances are used to compare the proposed approach with existing ones. A computational experiment with randomly generated instances is further proceeded to show that the proposed approach is more suitable for situations with large datasets.

关键词： Technology selection data envelopment analysis common weight multi-criteria decision-making mixed-integer linear programming

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An efficient method for Contingency-Constrained Transmission Expansion Planning

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ELECTRIC POWER SYSTEMS RESEARCH 2020年第0期182卷 106208-000页

作者： Gutierrez-Alcaraz, Guillermo Gonzalez-Cabrera, Nestor Gil, Esteban Tecnol Nacl Mexico IT Morelia Dept Elect Engn Morelia Michoacan Mexico Natl Autonomous Univ Mexico UNAM Dept Elect Energy Cdmx Mexico Univ Tecn Federico Santa Maria Dept Elect Engn Valparaiso Chile

This paper proposes an efficient method based on a mixed-integer linear programming (MILP) model to solve the multistage Contingency-Constrained Transmission Expansion Planning (CCTEP) problem. To account for security, an iterative algorithm based on Line Outage Distribution Factors (LODF) screens the worst-case contingency (either in existing or candidate lines) and dynamically adds constraints to the Transmission Expansion Planning (TEP) formulation to enforce the security criterion, reducing both the number of decision variables and simulation times with respect to alternative approaches. Transmission losses are included through the use of piecewise linear expressions. Furthermore, operational flexibility of generation resources is considered through the use of representative daily load curves (RDLC) to allow modeling of generator ramping constraints. The method is initially illustrated and validated through numerical simulations of the classic Garver's system. Then, the IEEE 118-bus and 300-bus systems are used to test its performance. The proposed approach can avoid under-investment in network capacity caused by neglecting transmission losses, security, and flexibility constraints.

关键词： Optimization Transmission Expansion Planning mixed-integer linear programming Power system security Risk management

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AC/DC Hybrid Distribution System Expansion Planning Under Long-Term Uncertainty Considering Flexible Investment

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IEEE ACCESS 2020年 8卷 94956-94967页

作者： Wu, Zhi Sun, Qirun Gu, Wei Chen, Yiwei Xu, Hua Zhang, Jing Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Taizhou Elect Power Supply Co State Grid Zhejiang Elect Power Co Ltd Taizhou 318000 Peoples R China

This paper presents a novel flexible multistage AC/DC distribution system expansion planning model, where the flexible investment strategy is taken into account to address the long-term development uncertainty of load demand and DG. Uncertainty is modeled through a multistage scenario tree to capture the possible system states across the planning horizon. As uncertain information revealed gradually over the planning horizon, the planning decisions at each stage are made sequentially within limited disposable reservation funds (RFs). The overall problem is formulated as a mixed-integer linear programming model that guarantees the convergence to optimality by using commercially available software. We compare the total costs of the optimal planning solutions within different proportions of RFs and analyze the flexibility value of RFs for long-term planning problems. In addition, the impact of RFs on the adjustment ability of the plans and the investment risks under uncertainty are evaluated. Case studies are carried out on an AC/DC hybrid system to validate the effectiveness of the proposed model to constitute flexible options when facing long-term uncertainty. The obtained results show the importance of reserving a suitable proportion of RFs in the planning stage, which has a significant influence on the adaptability of the planning solution to uncertain factors.

关键词： AC DC distribution system expansion planning mixed-integer linear programming flexible investment long-term uncertainty

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Assessing the costs of contributing to climate change targets in sub-Saharan Africa: The case of the Ghanaian electricity system

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ENERGY FOR SUSTAINABLE DEVELOPMENT 2020年 57卷 32-47页

作者： Diawuo, Felix Amankwah Scott, Ian J. Baptista, Patricia C. Silva, Carlos A. Univ Tecn Lisboa Ctr Innovat Technol & Policy Res IN Inst Super Tecn Av Rovisco Pais 1 P-1049001 Lisbon Portugal Univ Energy & Nat Resources UENR Sch Engn POBx 214 Sunyani Ghana MIT Lab Informat & Decis Syst 77 Massachusetts Ave Cambridge MA 02139 USA

Ghana is one of the few countries within the sub-Saharan region which has been successful in reducing energy poverty. However, ensuring energy security, affordability, and environmental sustainability remains a significant challenge for the future development of the sub-region. Here, we examine how the electricity supply can evolve into the future tomeet potential emission obligations for the period of 2020-2040. A generation expansion planning model which is able to incorporate the reality of fuel shortages and fuel switching typical of a developing country's power system is used. In doing so, we generate a range of emission reduction costs that provide important benchmarks for the relatively under-studied sub-Saharan region and identify drivers of these costs specific to developing countries. Results indicate that the total discounted cost in expanding generation to meet the demand for all scenarios range from 13-17 billion US$, while the expected emission ranges from 99-189 mtCO(2). Subsequently, the cost ofmeeting different emission targets up to 2040 was quantified for each scenario ranging from 11-39 US$/tonne, which could be used as a benchmark for comparison in developed countries. We find that discount rates, representing Ghana's access to capital, are a particularly important variable for developing countries. We find that lower discount rates can lead to more investment in capital intensive renewable energy in the long run but can also lock in an additional conventional generation investment in the short term. Sensitivity analysis of demand growth reduction shows that with a 1% growth rate, the requirement of generation capacity could be reduced by 84%, providing initial evidence for the benefits of investing in demand-side measures. The study provides data and policy recommendations needed to inform decision-makers in developing countries as well as a comparison point for identifying decarbonization costs internationally. (c) 2020 International Energy In

关键词： Generation expansion plan Unit commitment Renewable energy integration mixed-integer linear programming CO2 emissions

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Shared backup resource assignment for middleboxes

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OPTICAL SWITCHING AND NETWORKING 2020年 37卷 100569-100569页

作者： Fujita, Risa He, Fujun Sato, Takehiro Oki, Eiji Kyoto Univ Grad Sch Informat Sakyo Ku Yoshida Honmachi Kyoto 6068501 Japan

This paper presents five approaches to solve the problem of finding the optimal backup resource assignment which maximizes the survival probability of network functions of middleboxes. In the previous work, no mathematical model to solve this problem is provided, so we formulate the problem as a mixed-integer linear programming (MILP) problem as the first approach. Formulating this MILP problem includes some special steps, which are not considered in the previous work. The MILP problem is not always solved in a practical time when the problem size becomes large. Then, we develop two heuristic approaches by replacing the objective of the original MILP problem relying on the idea of balancing the failure probabilities of functions of connected components. We develop two heuristic approaches by extending the way of assigning functions and servers from the conventional heuristic algorithm. Numerical results show that our four developed heuristic approaches improve the survival probability from the conventional heuristic algorithm in some cases and reduce computation time compared to obtaining the optimal solution. Furthermore, one of our developed heuristic approaches provides exactly the optimal solution with shorter computation time compared to the time solving the original MILP problem in a special case.

关键词： Middlebox Backup Survivability Optimization mixed-integer linear programming

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Program File Placement Strategies for Machine-to-Machine Service Network Platform in Dynamic Scenario

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IEICE TRANSACTIONS ON COMMUNICATIONS 2020年第11期E103B卷 1353-1366页

作者： Sato, Takehiro Oki, Eiji Kyoto Univ Grad Sch Informat Kyoto 6068501 Japan

The machine-to-machine (M2M) service network platform that accommodates and controls various types of Internet of Things devices has been presented. This paper investigates program file placement strategies for the M2M service network platform that achieve low blocking ratios of new task requests and accommodate as many tasks as possible in the dynamic scenario. We present four strategies for determining program file placement, which differ in the computation method and whether the relocation of program files being used by existing tasks is allowed or not. Simulation results show that a strategy based on solving a mixed-integer linear programming model achieves the lowest blocking ratio, but a heuristic algorithm-based strategy can be an attractive option by allowing recomputation of the placement when the placement cannot be obtained at the timing of new task request arrival.

关键词： machine to machine placement problem mixed-integer linear programming heuristic algorithm online algorithm

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Unmanned Aerial Vehicle Location Routing Problem With Charging Stations for Belt Conveyor Inspection System in the Mining Industry

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IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 2020年第10期21卷 4186-4195页

作者： Ribeiro, Roberto G. Junior, Jose R. C. Cota, Luciano P. Euzebio, Thiago A. M. Guimaraes, Frederico G. Inst Tecnol Vale BR-35400000 Ouro Preto Brazil Vale SA BR-65085580 Sao Luis Maranhao Brazil Univ Fed Minas Gerais Dept Elect Engn BR-31270010 Belo Horizonte MG Brazil

Technological advances have opened up the possibility of using unmanned aerial vehicles (UAVs) in diverse environments. The mining industry has been looking for solutions to handle periodic inspections of the belt conveyors that transport iron ore. The state of the art indicates the use of UAVs for this task as an attractive, low-cost and safe alternative, allowing for a significant increase in security. A new concise mixed-integer linear programming (MILP) model is developed to address UAV routing and charging station planning for belt conveyor inspection. We conduct computational tests covering a real conveyor belt system in Brazil to validate the model in practical applications. The loading terminal possesses approximately 120 km of belt conveyors, leading to 230 inspection points. Instances of different sizes were generated by randomly sampling a subset of these points and using two different drone specifications. The results show that the new optimization modeling satisfies the problem requirements and is a significant contribution to the automation of inspection in the mining industry.

关键词： Belts Inspection Routing Charging stations Planning Optimization Unmanned aerial vehicles Combinatorial optimization UAV routing charging station planning belt conveyor inspection mixed-integer linear programming

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Hybrid Ship Unit Commitment with Demand Prediction and Model Predictive Control

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ENERGIES 2020年第18期13卷 4748页

作者： Huotari, Janne Ritari, Antti Vepsalainen, Jari Tammi, Kari Aalto Univ Dept Mech Engn Otakaari 4 Espoo 02150 Finland

We present a novel methodology for the control of power unit commitment in complex ship energy systems. The usage of this method is demonstrated with a case study, where measured data was used from a cruise ship operating in the Caribbean and the Mediterranean. The ship's energy system is conceptualized to feature a fuel cell and a battery along standard diesel generating sets for the purpose of reducing local emissions near coasts. The developed method is formulated as a model predictive control (MPC) problem, where a novel 2-stage predictive model is used to predict power demand, and a mixed-integer linear programming (MILP) model is used to solve unit commitment according to the prediction. The performance of the methodology is compared to fully optimal control, which was simulated by optimizing unit commitment for entire measured power demand profiles of trips. As a result, it can be stated that the developed methodology achieves close to optimal unit commitment control for the conceptualized energy system. Furthermore, the predictive model is formulated so that it returns probability estimates of future power demand rather than point estimates. This opens up the possibility for using stochastic or robust optimization methods for unit commitment optimization in future studies.

关键词： maritime optimization model predictive control predictive model Gaussian Process mixed-integer linear programming

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Designing a green meat supply chain network: A multi-objective approach

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INTERNATIONAL JOURNAL OF PRODUCTION ECONOMICS 2020年 219卷 312-327页

作者： Mohebalizadehgashti, Fatemeh Zolfagharinia, Hossein Amin, Saman Hassanzadeh Ryerson Univ Dept Mech & Ind Engn Toronto ON Canada Ryerson Univ Ted Rogers Sch Management Toronto ON Canada

Traditional logistics management has not focused on environmental concerns when designing and optimizing food supply chain networks. However, the protection of the environment is one of the main factors that should be considered based on environmental protection regulations of countries. In this paper, environmental concerns are considered in formulating a mathematical model to design and configure a multi-period, multi-product, multi-echelon green meat supply chain network. We develop a multi-objective mixed-integer linear programming formulation to optimize three objectives simultaneously: minimization of the total cost, minimization of the total CO2 emissions released from transportation, and maximization of the total capacity utilization of facilities. To demonstrate the efficiency of the proposed optimization model, we design a green meat supply chain network for Southern Ontario, Canada. A solution approach based on augmented epsilon-constraint method is employed to solve the proposed model. As a result, a set of Pareto-optimal solutions is obtained. The set of Pareto-optimal solutions gives decision-makers the opportunity to make a trade-off between economic, environmental, and capacity utilization objectives. Our example shows that it is possible to keep emissions reasonably low without incurring high total costs. Finally, the impacts of uncertainty on the proposed model are investigated using several decision trees. Optimization of a food supply chain, particularly a meat supply chain, based on multiple objectives under uncertainty using decision trees is a new approach in the literature.

关键词： Meat supply chain Decision tree Multi-objective programming mixed-integer linear programming Augmented epsilon-constraint

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Receding Horizon Scheduling of On-Demand Urban Air Mobility With Heterogeneous Fleet

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 2020年第4期56卷 2751-2761页

作者： Kim, Sang Hyun Korea Aerosp Univ Sch Air Transport Transportat & Logist Goyang Si 10540 Gyeonggi Do South Korea

Mobility on demand (MoD) is a new paradigm of personal mobility that responds to passengers' demands in real time, and urban air mobility (UAM) is an area of MoD enabled by advances in electric vertical take-off and landing aircraft. This demand-responsive nature of MoD poses a challenge for optimally scheduling vehicles, and it has attracted much attention in recent years. However, there is a lack of research in the MoD scheduling literature: a homogeneous fleet is assumed, but it is not necessarily true all the time. Hence, this article proposes a novel formulation of the scheduling problem for UAM with a heterogeneous fleet and presents particle swarm optimization and a genetic algorithm that utilize a greedy algorithm to keep solutions feasible. The proposed algorithms are implemented with a model-predictive control scheme to effectively manage the demand-responsive nature. As a result, the proposed algorithms can find a near-optimal solution in a short time. Using the algorithms, a numerical experiment with six different fleet mixes is conducted, and impacts of fleet heterogeneity are analyzed. As a result, it is shown that the fleet heterogeneity affects both the quality of service and operational efficiency, and there is a tradeoff: the more vehicles and seats, the better the service, but the less efficient it is.

关键词： Optimal scheduling Scheduling Genetic algorithms Quality of service Schedules Heuristic algorithms Routing Genetic algorithm (GA) mixed-integer linear programming mobility on demand (MoD) model-predictive control (MPC) optimal scheduling particle swarm optimization urban air mobility (UAM)

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