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Robust Design of a Closed-Loop Supply Chain Considering Multiple Recovery Options and Carbon Policies Under Uncertainty

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IEEE ACCESS 2021年 9卷 1167-1189页

作者： Mohammed, Fareeduddin Hassan, Adnan Selim, Shokri Z. Univ Teknol Malaysia Dept Mat Mfg & Ind Engn Sch Mech Engn Fac Engn Johor Baharu 81310 Malaysia King Fahd Univ Petr & Minerals Dept Syst Engn Coll Comp Sci & Engn Dhahran 31261 Saudi Arabia

Increasing global warming, climate change and stringent governmental legislations are driving industry practitioners and decision makers to implement various strategies to reduce carbon emissions. One of the effective approaches to mitigate carbon emissions is the implementation of closed-loop supply chain (CLSC). The key motivation for considering multiple recovery options in the CLSC is to capture the remaining economic value and to reduce carbon emissions in the collection and recovery operations. Customer's willingness to return used product depends on the acquisition price and nearness to the collection center. This research proposes a deterministic mixed-integer linear programming (MILP) model for a multi-period and multi-product CLSC network under carbon pricing and carbon trading policies consideration. The model includes different acquisition price for returned products and multiple recovery options. Further, the study takes into consideration uncertainty in procurement cost, demand, and quantity of returned products. A robust optimization approach is adopted to address uncertainty in network parameters. Numerical results show that the proposed model captures trade-offs between total cost and carbon emission. Overall, the study reveals that the carbon trading policy incurs relatively lower total cost compared to the carbon pricing policy. Repair and recycling activities in the reverse supply chain contribute significantly to the total cost and carbon emission. This study provide evidence that it is possible to achieve an optimal CLSC network with reduced carbon emission at a moderate total supply chain cost. The proposed model could be used to guide firms to choose an appropriate budget of uncertainty toward achieving a robust supply chain network.

关键词： Sustainable manufacturing closed-loop supply chain mixed-integer linear programming carbon policies multi recovery options robust optimization uncertainty reverse logistics

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Exact Methods for the Traveling Salesman Problem with Drone

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TRANSPORTATION SCIENCE 2021年第2期55卷 315-335页

作者： Roberti, Roberto Ruthmair, Mario Vrije Univ Amsterdam Dept Supply Chain Analyt NL-1081 HV Amsterdam Netherlands Univ Vienna Dept Stat & Operat Res A-1090 Vienna Austria

Efficiently handling last-mile deliveries becomes more and more important nowadays. Using drones to support classical vehicles allows improving delivery schedules as long as efficient solution methods to plan last-mile deliveries with drones are available. We study exact solution approaches for some variants of the traveling salesman problem with drone (TSP-D) in which a truck and a drone are teamed up to serve a set of customers. This combination of truck and drone can exploit the benefits of both vehicle types: the truck has a large capacity but usually low travel speed in urban areas;the drone is faster and not restricted to street networks, but its range and carrying capacity are limited. We propose a compact mixed-integer linear program (MILP) for several TSP-D variants that is based on timely synchronizing truck and drone flows;such an MILP is easy to implement but nevertheless leads to competitive results compared with the state-of-the-art MILPs. Furthermore, we introduce dynamic programming recursions to model several TSP-D variants. We show how these dynamic programming recursions can be exploited in an exact branch-and-price approach based on a set partitioning formulation using ng-route relaxation and a three-level hierarchical branching. The proposed branch-and-price can solve instances with up to 39 customers to optimality outperforming the state-of-the-art by more than doubling the manageable instance size. Finally, we analyze different scenarios and show that even a single drone can significantly reduce a route's completion time when the drone is sufficiently fast.

关键词： traveling salesman problem drones mixed-integer linear programming compact formulation dynamic programming set partitioning branch-and-price

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A two-echelon location-routing problem with synchronisation

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2021年第1期72卷 145-160页

作者： Mirhedayatian, Seyed Mostafa Crainic, Teodor Gabriel Guajardo, Mario Wallace, Stein W. NHH Norwegian Sch Econ Dept Business & Management Sci N-5045 Bergen Norway Univ Montreal CIRRELT Ctr Ville Montreal PQ Canada

Motivated by an actual problem of a national postal service company, we introduce and define a new two-echelon location-routing problem (2E-LRP). The 2E-LRP is defined in a two-echelon distribution system where products are transported from origins to destinations through intermediate facilities. A major question that arises in a two-echelon distribution system is how to synchronise the flows of the two echelons at intermediate facilities. The synchronisation is important due to limited storage space or waiting times for transshipments at the intermediate facilities. In our new 2E-LRP, the activities in the two echelons are organised into two waves;a delivery wave, where products are sent from the primary facility to the customers through the intermediate facilities, and a following pickup wave, where the flow of products is reversed. The model only considers temporal constraints, assuming that capacities are never binding;the vehicles are always large enough given the constraints on time. As a solution approach, we propose a decomposition-based heuristic. Besides the solution approach, we propose data-driven schemes for use in combination with the model and we provide the computational results for different sets of instances.

关键词： Two-echelon location-routing synchronisation pickup and delivery waves mixed-integer linear programming

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Optimized Integration of a Set of Small Renewable Sources Into a Bulk Power System

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IEEE TRANSACTIONS ON POWER SYSTEMS 2021年第1期36卷 248-260页

作者： Santos, Caio dos Rider, Marcos J. Lyra, Christiano Univ Campinas UNICAMP BR-13083852 Campinas Brazil

Electric power systems are increasingly relying on small renewable plants that must be connected to a bulk grid. This paper proposes a strategy to solve the transmission line planning problem for such an integration. The model minimizes the costs of investment and annual energy losses, considering as planning alternatives the building of transmission lines, the collector substation allocation and the voltage magnitude levels of the transmission line while ensuring the operational constraints of the network and the voltage magnitude at connection points with the bulk power system (BPS). linearization and approximation techniques are used to obtain an approximate mixed-integer linear programming (MILP) model from the original mixed-integer nonlinear programming model. A search-space reduction procedure is proposed to obtain the most qualified sites to accommodate collector substations. The combined strategy leads to a reduction in the computational effort, which allows full-scale studies of the optimal integration of a set of small renewable sources. For intermittent renewable sources, the proposed approach is used to obtain the Pareto front considering the total planning cost and the annual distributed energy. The results of two scenarios, using a real-world Brazilian case, certify the benefits of the proposals.

关键词： Substations Conductors Power transmission lines Planning Resource management Investment Collector substation allocation integration of small renewable sources mixed-integer linear programming transmission expansion planning

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Alternative Mathematical Models for the Optimal Transmission Switching Problem

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IEEE SYSTEMS JOURNAL 2021年第1期15卷 1245-1255页

作者： Flores, Moises Macedo, Leonardo H. Romero, Ruben Natl Univ Engn Dept Elect Engn Lima 15333 Peru Sao Paulo State Univ Dept Elect Engn BR-15385000 Ilha Solteira SP Brazil

In this article, the optimal transmission switching (OTS) problem is solved in order to optimize the operation cost of an electrical power system. This is accomplished by disconnecting some transmission lines, which enables a change to the profile of the power flow distribution in the system, allowing for increased generation at the buses with lower costs;thus, the hourly operation cost of the generation is minimized to meet the demand of the system. This article presents contributions to topics related to the issue of the high number of transmission lines that are disconnected when the OTS problem is solved, the problem of system islanding, and the causes of Braess's paradox, in the context of the OTS problem. Finally, tests are conducted using the 41-bus southern Brazilian system and the 92-bus Colombian system. The results demonstrate the effectiveness of the proposals for reducing the number of lines disconnected from the system and for avoiding islanding.

关键词： Power transmission lines Proposals Switches Switching circuits Mathematical model Optimization Braess&apos s paradox mixed-integer linear programming optimal transmission switching (OTS) system islanding transmission systems operation planning

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Day-Ahead Scheduling of Power System With Short-Circuit Current Constraints Considering Transmission Switching and Wind Generation

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IEEE ACCESS 2021年 9卷 110735-110745页

作者： Hua, Haocheng Liu, Tianqi He, Chuan Nan, Lu Zeng, Hong Hu, Xiaotong Che, Bin Sichuan Univ Coll Elect Engn Chengdu 610065 Sichuan Peoples R China State Grid Sichuan Elect Power Co Tianfu Power Supply Co Chengdu 610041 Sichuan Peoples R China State Grid Sichuan Elect Power Co Chengdu 610041 Sichuan Peoples R China State Grid Ningxia Elect Power Co Ltd Econ & Technol Res Inst Yinchuan 750004 Ningxia Peoples R China

The expansion of the power network and integration of wind farms pose challenges in the short-circuit current (SCC) problem. Transmission switching performs better in both flexibility and effectiveness compared with other SCC restriction measures, while the power grid security would be threatened as the number of switched-off-lines increases. This paper proposes a day-ahead scheduling model considering commitment of units and N-1 criterion, to avoid the excessive SCC problem caused by wind farms integration. Specially, the SCC calculation model of the grid-connected wind farms is put forward to aggregate wind farms and calculate SCC. A novel SCC formulation considering transmission switching, commitment of units as well as wind farms integration is deduced and converted to SCC constraints. The SCC constrained mixed-integer linear programming (MILP) based day-ahead scheduling model is proposed, which minimizes system operation cost and transmission switching cost. In addition, the N-1 security requirement is considered in the proposed day-ahead scheduling model to ensure system security by avoiding switching off too many lines. Numerical results of a modified IEEE 30-bus system with two wind farms illustrate effectiveness of the proposed model.

关键词： Wind farms Switches Security Analytical models Systems operation Switching circuits Short-circuit currents Short-circuit current wind farm integration transmission switching day-ahead scheduling mixed-integer linear programming

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Pickup and delivery problem with recharging for material handling systems utilising autonomous mobile robots

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第3期289卷 1153-1168页

作者： Jun, Sungbum Lee, Seokcheon Yih, Yuehwern Dongguk Univ Dept Ind & Syst Engn 3-26 Pil Dong 3ga Seoul 100715 South Korea Purdue Univ Sch Ind Engn 315 Grant St W Lafayette IN 47907 USA

Whereas automated guided vehicles (AGVs) have traditionally been used for material handling, the utilisation of autonomous mobile robots (AMRs) is growing quickly owing to their scalability, versatility, and lower costs. In this paper, we address the pickup and delivery problem with consideration of the characteristics of AMRs in manufacturing environments. To solve the problem, we first propose a new mathematical formulation with consideration of both partial and full recharging strategies for minimisation of the total tardiness of transportation requests. We then propose two constructive heuristic algorithms with high computation speed, which are called the Transportation-Request-Initiated Grouping Algorithm (TRIGA) and the Vehicle-Initiated Grouping Algorithm (VIGA). Additionally, we develop a memetic algorithm (MA) that incorporates a genetic algorithm into local-search techniques for finding near-optimal solutions within a reasonable time. We evaluate the performance of the proposed algorithms in comparison with two dispatching rules, genetic algorithm, and neighbourhood search through simulation experiments with three sets of problem instances under different battery levels. The simulation results indicate that the proposed algorithms outperform the others with regard to the average total tardiness and the relative deviation index. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Pickup and delivery problem Autonomous mobile robots Material handling Memetic algorithm mixed-integer linear programming

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Timetable coordination in a rail transit network with time-dependent passenger demand

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第1期295卷 183-202页

作者： Yin, Jiateng D'Ariano, Andrea Wang, Yihui Yang, Lixing Tang, Tao Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing Peoples R China Univ Roma Tre Dept Engn Rome Italy

With the expansion of urban rail networks and the increase of passengers demand, the coordination of strongly connected lines becomes more and more important, because passengers transfer several times during their trips and major transfer stations in the rail network often suffer from over-crowdedness, especially during peak-hours. In this paper, we study the optimization of coordinated train timetables for an urban rail network, which is a tactical timetabling problem and includes several operational constraints and time-dependent passenger-related data. We propose a mathematical formulation with the objective of minimizing the crowdedness of stations during peak hours to synchronously generate the optimal coordinated train timetables. By introducing several sets of passenger flow variables, the timetable coordination problem is formulated as a mixed-integer linear programming problem, that is possible to solve to optimum. To capture the train carrying capacity constraints, we explicitly incorporate the number of in vehicle passengers in the modelling framework by considering the number of boarding and alighting passengers as passenger flow variables. To improve the computational efficiency of large-scale instances, we develop an Adaptive Large Neighborhood Search (ALNS) algorithm with a set of destroying and repairing operators and a decomposition-based ALNS algorithm. Real-world case studies based on the operational data of Beijing urban rail network are conducted to verify the effectiveness of timetable coordination. The computational results illustrate that the proposed approaches reduce the level of crowdedness of metro stations by around 8% in comparison with the current practical timetable of the investigated Beijing urban rail network. (c) 2021 Elsevier B.V. All rights reserved.

关键词： Transportation Timetable coordination Rail transit network mixed-integer linear programming Adaptive large neighborhood search

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Optimal insurance contract specification in the upstream sector of the oil and gas industry

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2021年第2期295卷 718-732页

作者： Torraca, Ana Patricia Fanzeres, Bruno Pontifical Catholic Univ Rio de Janeiro PUC Rio Ind Engn Dept Rio De Janeiro RJ Brazil

The upstream sector of the Oil and Gas (O&G) industry is recognized by its capital-intensive projects and complex and hazardous associated recovery and production processes, thus susceptible for large and financially damaging accidents. In this context, to avoid the risk and impact of high expenses, O&G companies usually acquire insurance contracts. In practice, although the contract format is typically pre-specified, its parameter magnitudes can be adjusted aiming at maximizing the company total wealth. Therefore, this work proposes a holistic methodology to assess the optimal parameter specification of an insurance contract in the upstream sector of the O&G industry. A non-convex stochastic optimization problem is constructed aiming at maximizing a risk-adjusted measure of the policyholder total wealth. The modeling takes into account the uncertainty on the financial loss of an accident by making use of the safety barriers and precursor information framework. The non-convex optimization problem is cast as an equivalent mixed-integer linear programming problem by combining the scenario-based representation approach with a set of binary reformulation procedures. We illustrate the applicability of the proposed methodology with a set of numerical experiments. In a nutshell, we found that the proposed parameter specification methodology resulted in greater predictability when compared to two quantile-based speci-fication policies and an uninsured company. In fact, the second best policy presented a standard deviation 103% higher than the proposed methodology. Furthermore, the model also provided greater protection against extreme events, since the second best policy presented a Conditional Value-at-Risk 41% higher than the proposed methodology. (c) 2021 Elsevier B.V. All rights reserved.

关键词： (R) OR in energy Insurance contract specification mixed-integer linear programming Risk measures Safety barriers

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mixed-integer Optimal Control With Disjunctive Characteristic Time Constraints for Energy-Optimal Speed Planning

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IEEE Transactions on Transportation Electrification 2025年

作者： Dong, Shiying Chen, Hong Luo, Xi Meng, Xiangyu Huang, Yanjun Liu, Qifang Gao, Bingzhao Jilin University Department of Control Science and Engineering Changchun China Tongji University College of Automotive Studies Shanghai China Louisiana State University Division of Electrical and Computer Engineering Baton RougeLA70803 United States

This paper considers the energy optimization problem with multiple time window constraints, which is motivated by the eco-driving of connected and automated vehicles (CAVs) in urban traffic. Timely crossing of signalized intersections defines complex spatio-temporal constraints, and multiple available open time windows introduce the discontinuous feasible domains of crossing times. The CAVs should select an open time-window for each intersection, determine the crossing times, and optimize the speed profiles accordingly, which turns out to be a hard-to-solve optimization problem. To address this challenge, this paper proposes a unifying approach that transforms the energy optimization problem into a novel mixed-integer optimal control problem with characteristic time constraints (MIOCP-CT). This approach facilitates the simultaneous optimization of open time windows, crossing times, and speed profiles within a single optimization problem, which can be solved by our proposed novel numerical solution procedure. The application of the proposed method in the eco-driving of CAVs approaching multiple signalized intersections demonstrates its performance within real-world urban driving cycles. The experimental results of the hardware-in-the-loop (HiL) demonstrate a superior energy-saving performance of 5.83% compared to existing methods, while also achieving real-time feasibility within 100 milliseconds. © 2015 IEEE.

关键词： mixed-integer linear programming

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