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An automatic distillation sequence synthesis framework based on a preorder traversal algorithm

COMPUTERS & CHEMICAL ENGINEERING 2024年 181卷

作者： Wang, Anqing Guzman-Urbina, Alexander Ohno, Hajime Fukushima, Yasuhiro Tohoku Univ Dept Chem Engn 6-6-07 Aramaki Aza AobaAoba Ku Sendai Miyagi 9808579 Japan Tohoku Univ Dept Frontier Sci Adv Environm 6-6-07 Aramaki Aza AobaAoba Ku Sendai Miyagi 9808579 Japan

Distillation energy consumption dominates the process industry;hence, the selection of the distillation sequence will substantially affect the separation energy consumption. We propose a framework for the automatic synthesis and optimization of distillation sequences by integrating the Aspen Plus with the MATLAB programming platform. The framework combines the concept of binary trees in data structures and uses a preorder traversal algorithm to generate a network superstructure in the simulator. In the next step, it automatically formulates and solves a mixed-integer linear programming model based on calculation results;finally, the distillation model is optimized using an improved quadratic interpolation algorithm. Two case studies for C5 alkane and dimethyl carbonate separation showed that the optimal solution obtained by the framework reduced the total annual costs by 13.18 % and 2.88 %, respectively, compared with the processes without systematic optimization. The framework also allowed screening out promising alternatives by rapid evaluation of different process routes.

关键词： Process synthesis Distillation sequences Process optimization mixed-integer linear programming

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Enhancing power distribution performance through simultaneous optimization of voltage regulators placement and conductor selection: A multi-period MILP approach

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RESULTS IN ENGINEERING 2025年 25卷

作者： Pareja, Luis A. Gallego Lopez-Lezama, Jesus M. Carmona, Oscar Gomez State Univ Londrina UEL Dept Elect Engn Rodovia Celso Garcia Cid PR 445 Km 380 BR-86057970 Londrina Parana Brazil Univ Antioquia Dept Ingn Electr Res Grp Efficient Energy Management GIMEL Calle 67 53-108 Medellin 050010 Antioquia Colombia Univ Tecnol Pereira Fac Tecnol Cr 27 10-02 Risaralda 660003 Colombia

Maintaining optimal voltage profiles and minimizing technical losses are critical challenges in power distribution systems, particularly under significant load fluctuations. This paper presents a novel mathematical model that simultaneously optimizes voltage regulator (VR) placement and conductor selection (CS) using a mixed-integer linear programming (MILP) model. The model accounts for three distinct load periods, providing a realistic representation of real-world operating conditions. The principal advantage of using voltage regulators (VRs) and reconductoring is their ability to enhance voltage profiles and reduce technical losses. Two test systems, with 33 and 69 buses, were evaluated, and results demonstrated that the proposed multi-period MILP approach effectively performs optimal conductor selection (OCS) and optimal placement of voltage regulators (OPVRs), leading to substantial reductions in system losses and investment costs. Unlike the commonly employed metaheuristic techniques for addressing these problems, the proposed methodology ensures global optimality. Moreover, the integration of OCS and OPVRs has not been previously explored in the specialized literature. This approach represents a substantial advancement over traditional methods and plays a critical role in guaranteeing efficient power delivery in heavily loaded distribution networks.

关键词： Power distribution systems Voltage regulators placement Voltage regulators operation Conductor selection mixed-integer linear programming

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On the approximability and energy-flow modeling of the electric vehicle sharing problem

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 192卷

作者： Silva, Welverton R. Usberti, Fabio L. Schouery, Rafael C. S. Univ Campinas UNICAMP Inst Comp Ave Albert Einstein1251 Cidade Univ BR-13083852 Campinas SP Brazil

The electric vehicle sharing problem (EVSP) arises from the planning and operation of one-way electric carsharing systems. It aims to maximize the total rental time of a fleet of electric vehicles while ensuring that all the demands of the customer are fulfilled. In this paper, we first show that the EVSP is NP -hard to approximate to within a factor of n(1-epsilon )in polynomial time, for any epsilon > 0 , where n denotes the number of customers. In addition, we also show that the problem does not have a monotone structure, which can be detrimental to the development of heuristics employing constructive strategies. Moreover, we propose a novel approach for modeling the EVSP based on energy flows. Based on this new model, we propose a relax-and-fix strategy and an exact algorithm. Our computational results show that our formulation outperforms the previous best-performing formulation in the literature in the number of optimal solutions obtained, optimality gaps, and computational times. Previously, 32.7% of the instances remained unsolved (within a time limit of one hour), while our formulation obtained optimal solutions for all instances. To stress our approaches, two more challenging new sets of instances are generated, for which we can solve 49.5% of the instances, with an average optimality gap of 2.91% for those not solved optimally.

关键词： One-way car-sharing Electric vehicle mixed-integer linear programming

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Heuristic and Exact Approaches to Optimize the Production Scheduling of Mines Transitioning from the Open-Pit to Block Caving

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MINING METALLURGY & EXPLORATION 2024年第6期41卷 3769-3787页

作者： Anani, Angelina Flores, Ignacio Ortiz Li, Haitao Jalilzadeh, Afrooz Univ Arizona Dept Min & Geol Engn Tucson AZ 85721 USA Pontificia Univ Catolica Chile Dept Min Engn Santiago Chile Univ Missouri St Louis Coll Business Adm Supply Chain & Analyt Dept One Univ Blvd St Louis MO USA Univ Arizona Dept Syst & Ind Engn Tucson AZ 85721 USA

Mine planning engineers perform production schedule optimization to determine the time sequence in which ore blocks should be extracted to maximize value. For mines transitioning from a surface mining method to an underground extraction method (transition mines), the production schedule optimization is complex with no applicable solutions. We present two optimization approaches for the transition mine production scheduling problem (TMPSP)-a disintegrated heuristic approach and an integrated exact approach-and investigate the conditions in which an integrated approach to the TMPSP is superior to a disintegrated approach. mixed-integer linear programming (MILP) models are developed to optimize the production schedule per period and crown pillar placement for the TMPSP. The MILP is implemented in Python and solved with the Gurobi (R) Optimizer. A case study is performed to validate the models, with a comparative analysis to obtain operational insights. The computational results show that the integrated model achieves 5% higher NPV than the disintegrated approach with less computational effort.

关键词： Transition mine Disintegrated approach Integrated approach Production scheduling mixed-integer linear programming

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Enhancing cut selection through reinforcement learning

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Science China Mathematics 2024年第6期67卷 1377-1394页

作者： Shengchao Wang Liang Chen Lingfeng Niu Yu-Hong Dai Academy of Mathematics and Systems Science Chinese Academy of SciencesBeijing100190China Research Center on Fictitious Economy and Data Science Chinese Academy of SciencesBeijing100190China

With the rapid development of artificial intelligence in recent years,applying various learning techniques to solve mixed-integer linear programming(MILP)problems has emerged as a burgeoning research *** from constructing end-to-end models directly,integrating learning approaches with some modules in the traditional methods for solving MILPs is also a promising *** cutting plane method is one of the fundamental algorithms used in modern MILP solvers,and the selection of appropriate cuts from the candidate cuts subset is crucial for enhancing *** to the reliance on expert knowledge and problem-specific heuristics,classical cut selection methods are not always transferable and often limit the scalability and generalizability of the cutting plane *** provide a more efficient and generalizable strategy,we propose a reinforcement learning(RL)framework to enhance cut selection in the solving process of ***,we design feature vectors to incorporate the inherent properties of MILP and computational information from the solver and represent MILP instances as bipartite ***,we choose the weighted metrics to approximate the proximity of feasible solutions to the convex hull and utilize the learning method to determine the weights assigned to each ***,a graph convolutional neural network is adopted with a self-attention mechanism to predict the value of weighting ***,we transform the cut selection process into a Markov decision process and utilize RL method to train the *** experiments are conducted based on a leading open-source MILP solver *** on both general and specific datasets validate the effectiveness and efficiency of our proposed approach.

关键词： reinforcement learning mixed-integer linear programming cutting plane method cut selection

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A multi-visit flexible-docking vehicle routing problem with drones for simultaneous pickup and delivery services

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第1期312卷 125-137页

作者： Jiang, Jie Dai, Ying Yang, Fei Ma, Zujun Southwest Jiaotong Univ Sch Econ & Management Chengdu 610031 Peoples R China Zhejiang Univ Finance & Econ Sch Business Adm Hangzhou 310018 Peoples R China

This study investigates a multi-visit flexible-docking vehicle routing problem that uses a truck and drone fleet to fulfill pickup and delivery requests in rural areas. In this collaborative truck-drone system, each drone may serve multiple customers per trip (multi-visit services), dock to the same or different truck from where it launched (flexible docking), and perform simultaneous pickup and delivery. These characteristics complicate the temporal, spatial, and loading synchronization for trucks and drones, making the decisions of order allocation and vehicle routing highly interdependent and intractable. This problem is formulated as a mixed-integer linear programming model and solved by a tailored adaptive large neighborhood search metaheuristic. Numerical experiments are conducted on sparse rural networks to demonstrate the efficiency of the proposed method. We observe that the proposed truck-drone system shows an average cost saving of 34% compared to the truck-only case. Moreover, deep insights into the impacts of multi-visit services, flexible docking, and simultaneous pickup and delivery on the performance of the truck-drone system are discussed.

关键词： Logistics Vehicle routing problem with drones Pickup and delivery services mixed-integer linear programming Adaptive large neighborhood search

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Dispatching AGVs with battery constraints using deep reinforcement learning

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COMPUTERS & INDUSTRIAL ENGINEERING 2024年 187卷

作者： Singh, Nitish Akcay, Alp Dang, Quang-Vinh Martagan, Tugce Adan, Ivo Eindhoven Univ Technol Dept Ind Engn & Innovat Sci POB 513 NL-5600 MB Eindhoven Netherlands

This paper considers the problem of real-time dispatching of a fleet of automated guided vehicles (AGVs) with battery constraints. AGVs must be immediately assigned to transport requests, which arrive randomly. In addition, the AGVs must be repositioned and recharged, awaiting future transport requests. Each transport request has a soft time window with late delivery incurring a tardiness cost. This research aims to minimize the total costs, consisting of tardiness costs of transport requests and travel costs of AGVs. We extend the existing literature by making a distinction between parking and charging nodes, where AGVs wait idle for incoming transporting requests and satisfy their charging needs, respectively. Also, we formulate this online decision -making problem as a Markov decision process and propose a solution approach based on deep reinforcement learning. To assess the quality of the proposed approach, we compare it with the optimal solution of a mixed -integer linear programming model that assumes full knowledge of transport requests in hindsight and hence serves as a lower-bound on the costs. We also compare our solution with a heuristic policy used in practice. We assess the performance of the proposed solutions in an industry case study using real-world data.

关键词： Dispatching Automated guided vehicles Deep reinforcement learning mixed-integer linear programming

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A mathematical model and a memetic algorithm for berth allocation at inland waterway terminals in developing countries

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2025年 152卷

作者： Pasha, Adnan Liu, Jiyin Rastogi, Rajat Indian Inst Technol Roorkee Dept Civil Engn Roorkee 247667 India Loughborough Univ Loughborough Business Sch Loughborough LE11 3TU England

This paper addresses the Berth Allocation Problem (BAP) in multipurpose Inland Water Transport (IWT) terminals operated in developing countries like India. Conceptually, these are different from the specialized IWT ports operated in developed countries. The IWT terminals in developing countries face issues due to complex combination of cargo types, their mix on barges, variability in handling equipment, and direct transloading in trucks. This paper adds to the research on BAP which otherwise has primarily focused on specialized seaports or IWT ports in developed countries. We developed a mathematical model to deal with BAP in multipurpose IWT terminals which minimizes the waiting and total completion times of the barges. We further propose a novel memetic algorithm (MA), an artificial intelligence (AI) based optimization technique that integrates a genetic algorithm with a local search to obtain near-optimal solutions efficiently. Using real-world data from an Indian IWT terminal, extensive computational experiments are conducted to validate the models' effectiveness and the algorithm's efficiency. A sensitivity analysis is carried out to deliver managerial insights. The developed AI-driven method can replace the first-come-first-served (FCFS) approach that is presently in use at IWT terminals in developing nations like India. It would improve their operational efficiency and competitiveness.

关键词： Berth allocation problem Inland water transport Multipurpose terminal Memetic algorithm mixed-integer linear programming

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Algorithms for minimization of total flow time to achieve resilience in a flow shop environment

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2025年 1-49页

作者： Thiagarajan, Vaidyanathan Jessin, Thamarassery Abduljaleel Rajendran, Chandrasekharan Indian Inst Technol Madras Dept Management Studies Chennai 600036 Tamil Nadu India TKM Coll Engn Kollam 691005 Kerala India

Resilient inventory systems in every stage of a supply chain are becoming increasingly crucial to ensure its smooth functioning. In the context of production and manufacturing systems, flow time minimization plays a pivotal role in enhancing the resilience of inventories. By minimizing flow times, firms decrease the amount of Work-In-Progress (WIP) inventory at any given moment, thereby reducing vulnerability to disruptions caused by the bullwhip effect, supply chain delays or market fluctuations. Optimizing flow time to control WIP is extremely important in flow shop environments, where the high production volume places a heavy premium on avoidable delays. In this work, the aim is to develop models and approaches for flow time minimization in Permutation Flow Shops that cleverly exploit problem characteristics to quickly provide optimal solutions or strong lower bounds. To this end, we first propose two polynomial-time algorithms-position-based lower bound algorithm (PBLBA) and Improved Position-Based Lower Bound Algorithm (IPBLBA)-to rapidly determine lower bounds on Total Flow Time. Then, we develop a novel, parameter-free Self-Tuning Lower Bound Algorithm (STLBA) that iteratively solves a mixed-integer linear programming model to strengthen the lower bounds identified by the PBLBA and the IPBLBA and provides upper bounds. This STLBA is then integrated with a Local Search procedure to further improve the upper bounds and get an implementable schedule. Finally, we test the developed approaches on 600 well-known benchmark instances to ascertain their strengths and gather valuable theoretical and practical insights.

关键词： Inventory Supply chain resilience mixed-integer linear programming Scheduling Permutation flow shops Lower bounds

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Last fifty years of integer linear programming: A focus on recent practical advances

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2025年第3期324卷 707-731页

作者： Clautiaux, Francois Ljubic, Ivana Univ Bordeaux Inst Math Bordeaux CNRS Inria Bordeaux France ESSEC Business Sch Cergy Pontoise France

mixed-integer linear programming (MILP) has become a cornerstone of operations research. This is driven by the enhanced efficiency of modern solvers, which can today find globally optimal solutions within seconds for problems that were out of reach a decade ago. The versatility of these solvers allowed successful applications in many areas, such as transportation, logistics, supply chain management, revenue management, finance, telecommunications, and manufacturing. Despite the impressive success already obtained, many challenges remain, and MILP is still a very active field. This article provides an overview of the most significant results achieved in advancing the MILP solution methods. Given the immense literature on this topic, we made deliberate choices to focus on computational aspects and recent practical performance improvements, emphasizing research that reports computational experiments. We organize our survey into three main parts, dedicated to branch-and-cut methods, Dantzig-Wolfe decomposition, and Benders decomposition. The paper concludes by highlighting ongoing challenges and future opportunities in MILP research.

关键词： Combinatorial optimization mixed-integer linear programming Branch-and-cut Dantzig-Wolfe decomposition Benders decomposition

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