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A Practical Urban Distribution Network Planning Method With Geographic Information System

IEEE TRANSACTIONS ON POWER SYSTEMS 2024年第6期39卷 7038-7049页

作者： Wang, Zhen Shen, Xinwei Sun, Hongbin Wu, Qiuwei Tsinghua Univ Tsinghua Shenzhen Int Grad Sch Shenzhen 518055 Peoples R China Taiyuan Univ Technol Taiyuan 030002 Peoples R China Tsinghua Univ Dept Elect Engn Beijing 100084 Peoples R China

Urban distribution network planning (UDNP) faces new requirements and restrictions due to rapid urbanization. This paper proposes a framework for UDNP, in which the geographic information system (GIS) and a hybrid optimization model are combined to co-optimize investment and operation decisions. Firstly, we extract the street layout graph from the GIS data and produce the shortest path between distribution transformers/power substations. Then the distance matrix could be formed and used in the following UDNP optimization. The proposed hybrid optimization model utilizes 1) Clark and Wright's Savings (CWS) algorithm to produce a close-loop network topology, and 2) mixed integer linear programming (MILP) to produce the corresponding open-loop operation strategy. Case studies on an urban area in China demonstrate the effectiveness of the proposed method. The CWS algorithm improved the computation efficiency by over 1000 times compared with MILP, while introducing the shortest path for UDNP leads to a 30%+ decrease in investment compared with that of using Euclidean or Manhattan distance.

关键词： geographic information system shortest path mixed integer linear programming urban distribution network planning urban distribution network planning shortest path urban distribution network planning

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Optimizing Cutting Log Operations in Softwood Sawmills: A Multi-Objective Approach Tailored for SMEs

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IEEE ACCESS 2024年 12卷 128141-128150页

作者： Ramos-Maldonado, Mario Munoz, Felipe T. Mora, Pablo Venegas-Vasconez, Diego Univ Bio Bio Fac Ingn Dept Ingn Maderas Concepcion 4050231 Chile Univ Bio Bio Fac Ingn Dept Ingn Ind Concepcion 4050231 Chile

The production planning problem in the Pinus radiata sawmill industry revolves around determining how to cut a set of logs of different diameters to obtain pieces with a rectangular base, typically of the same length as the original log. The primary objective is often to maximize the volumetric yield or the ratio between the total volume of the produced pieces and the available volume of the logs. Given the scarcity of timber forests, small and medium-sized (SME) sawmills must optimize their operations to maximize the volumetric yield of logs, usually procured from third parties and may vary in quality and dimensions. In this context, the absence of decision support tools directly contributes to inefficient raw material utilization, consequently impacting the business's profit. This study introduces a multi-objective mixed-integer linear programming model incorporating log availability and product demand as input parameters. The objective functions aim to minimize the total volumetric loss of utilized logs and the surplus quantity associated with products exceeding demand. The model integrates cutting patterns pre-determined for each log diameter as an additional input. The Cutlog (c) software was used to identify all the optimal cutting patterns. The $\varepsilon $ -constraint method, implemented in the CPLEX (c) solver, was employed to solve the model. The model was validated using representative instances designed to emulate the challenges faced by SME sawmills. Real industry data, surveys, and commercial records from SME sawmills in southern Chile were utilized. The results confirm the effectiveness of the proposed model in addressing the multi-objective challenges encountered by these businesses. The model successfully identifies multiple solutions on the Pareto frontier, offering valuable insights for decision-making.

关键词： Optimization methods linear programming Raw materials Industries Forests Pareto optimization mixed integer linear programming Production planning Wood industry Lumber industry Multi-objective optimization coniferous sawmills cutting stock problem small and medium-sized enterprises Pareto frontier mixed integer linear programming production planning

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Solution of a practical Vehicle Routing Problem for monitoring Water Distribution Networks

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JOURNAL OF THE OPERATIONAL RESEARCH SOCIETY 2024年第10期75卷 1989-2007页

作者： Atefi, Reza Iori, Manuel Salari, Majid Vezzali, Dario Ferdowsi Univ Mashhad Mashhad Iran Univ Modena & Reggio Emilia Reggio Emilia Italy Univ Modena & Reggio Emilia Modena Italy

In this work, we introduce a generalisation of the Vehicle Routing Problem for a specific application in the monitoring of a Water Distribution Network (WDN). In this problem, multiple technicians must visit a sequence of nodes in the WDN and perform a series of tests to check the quality of water. Some special nodes (i.e., wells) require technicians to first collect a key from a key centre. The key must then be returned to the same key centre after the test has been performed, thus introducing precedence constraints and multiple visits in the routes. To solve the problem, a mixed integer linear programming model and an Iterated Local Search have been implemented. The efficiency of the proposed methods is demonstrated by means of extensive computational tests on randomly created and real-world instances.

关键词： OR in Service Industries Vehicle Routing Problem Water Distribution Networks mixed integer linear programming Iterated Local Search

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Towards Improving Unit Commitment Economics: An Add-On Tailor for Renewable Energy and Reserve Predictions

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IEEE TRANSACTIONS ON SUSTAINABLE ENERGY 2024年第4期15卷 2547-2566页

作者： Chen, Xianbang Liu, Yikui Wu, Lei Stevens Inst Technol Dept Elect Commun Engn Hoboken NJ 07030 USA Sichuan Univ Elect Engn Coll Chengdu 610017 Peoples R China

Generally, day-ahead unit commitment (UC) is conducted in a predict-then-optimize process: it starts by predicting the renewable energy source (RES) availability and system reserve requirements;given the predictions, the UC model is then optimized to determine the economic operation plans. In fact, predictions within the process are raw. In other words, if the predictions are further tailored to assist UC in making the economic operation plans against realizations of the RES and reserve requirements, UC economics will benefit significantly. To this end, this paper presents a cost-oriented tailor of RES-and-reserve predictions for UC, deployed as an add-on to the predict-then-optimize process. The RES-and-reserve tailor is trained by solving a bi-level mixed-integer programming model: the upper level trains the tailor based on its induced operating cost;the lower level, given tailored predictions, mimics the system operation process and feeds the induced operating cost back to the upper level;finally, the upper level evaluates the training quality according to the fed-back cost. Through this training, the tailor learns to customize the raw predictions into cost-oriented predictions. Moreover, the tailor can be embedded into the existing predict-then-optimize process as an add-on, improving the UC economics. Lastly, the presented method is compared to traditional, binary-relaxing, neural network-based, stochastic, and robust methods.

关键词： Training Optimization Costs Predictive models Mathematical models Renewable energy sources Power system economics mixed integer linear programming Bi-level mixed-integer programming prescriptive analytics renewable energy unit commitment

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Capacity planning and scheduling for jobs with uncertainty in resource usage and duration

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JOURNAL OF SUPERCOMPUTING 2024年第15期80卷 22428-22461页

作者： Patra, Sunandita Pathan, Mehtab Mahfouz, Mahmoud Zehtabi, Parisa Ouaja, Wided Magazzeni, Daniele Veloso, Manuela JP Morgan AI Res New York NY 10017 USA JP Morgan CIB Athena Appl Intelligence London England JP Morgan AI Res London England

Organizations around the world schedule jobs (programs) regularly to perform various tasks dictated by their end users. With the major movement toward using a cloud computing infrastructure, our organization follows a hybrid approach with both cloud and on-prem servers. The objective of this work is to perform capacity planning, i.e., estimate resource requirements, and job scheduling for on-prem grid computing environments. A key contribution of our approach is handling uncertainty in both resource usage and duration of the jobs, a critical aspect in the finance industry where stochastic market conditions significantly influence job characteristics. For capacity planning and scheduling, we simultaneously balance two conflicting objectives: (a) minimize resource usage and (b) provide high quality of service to the end users by completing jobs by their requested deadlines. We propose approximate approaches using deterministic estimators and pair sampling-based constraint programming. Our best approach (pair sampling-based) achieves up to 41.6% estimated peak reduction in resource usage compared to manual scheduling without compromising on the quality of service.

关键词： Capacity planning Stochastic resource usage Stochastic job durations Scheduling under uncertainty Sample average approximation Constraint programming mixed integer linear programming

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A constraint programming approach to a real-world workforce scheduling problem for multi-manned assembly lines with sequence-dependent setup times

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第9期62卷 3212-3229页

作者： Guner, Funda Gorur, Abdul K. Satir, Benhur Kandiller, Levent Drake, John H. Cankaya Univ Dept Ind Engn Ankara Turkiye Cankaya Univ Software Engn Ankara Turkiye Yasar Univ Dept Ind Engn Izmir Turkiye Univ Leicester Sch Comp & Math Sci Leicester England

For over five decades, researchers have presented various assembly line problems. Recently, assembly lines with multiple workers at each workstation have become very common in the literature. These lines are often found in the manufacturing of large vehicles, where workers at a workstation may perform their assigned tasks at the same time. Most research on multi-manned assembly lines focuses on balancing tasks and workers among workstations and scheduling tasks for workers. This study, however, concentrates on assigning tasks to workers already assigned to a specific workstation, rather than balancing the entire line. The problem was identified through an industrial case study at a large vehicle manufacturing company. The study presents two methods, one using mixed integer linear programming and the other using constraint programming, to minimise the number of workers required on a multi-manned assembly line with sequence-dependent setup times. The results of the computational experiments indicate that the constraint programming method performs better than the mixed integer linear programming method on several modified benchmark instances from the literature. The constraint programming model is also tested on the real-world scenario of our industrial case study and leads to significant improvements in the productivity of the workstations.

关键词： Workforce scheduling multi-manned assembly line constraint programming sequence-dependent setup times mixed integer linear programming

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minChemBio: Expanding Chemical Synthesis with Chemo-Enzymatic Pathways Using Minimal Transitions

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ACS SYNTHETIC BIOLOGY 2025年第3期14卷 756-770页

作者： Anand, Mohit Upadhyay, Vikas Maranas, Costas D. Penn State Univ State Coll PA 16802 USA

Chemo-enzymatic pathway design aims to combine the strengths of enzymatic with chemical synthesis to traverse biomolecular design space more efficiently. While chemical reactions often struggle with regioselectivity and stereoselectivity, enzymatic conversions often encounter limitations of low enzyme activity or availability. Optimally integrating both approaches provides an opportunity to identify efficient pathways beyond the capabilities of either modality. Recently, studies have shown the advantage of leveraging enzymatic steps into industrial-scale chemical processes, such as for the blood sugar regulator Sitagliptin (Merck) and the HIV protease inhibitor Darunavir (Prozomix). Designing optimal chemo-enzymatic pathways is a complex task. It requires navigating a high-dimensional search space of potential reactions that combine individual chemical and biochemical steps while at the same time minimizing transitions between chemical catalysis and bioreactions. Here, we introduce an algorithmic approach, minChemBio, that relies on solving a mixed-integer linear programming (MILP) problem by optimally searching through known chemical and enzymatic steps extracted from the United States Patent Office (USPTO) and MetaNetX databases, respectively. minChemBio allows for the minimization of transitions between chemical and biological reactions in the pathway, thus reducing the need for costly separation and purification steps required. minChemBio was benchmarked on three case studies involving the synthesis of 2-5-furandicarboxylic acid, terephthalate, and 3-hydroxybutyrate. Identified designs included both established literature pathways as well as unexplored ones which were compared against pathways identified by existing retrosynthetic tools. minChemBio fills a current gap in the space of pathway retrosynthesis tools by controlling and minimizing the transitions between chemical catalysis and biocatalytic steps. It is accessible to users through open-source code (htt

关键词： Computational Synthesis Planning Chemo-enzymatic Pathways Minimal Transitions mixed integer linear programming

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Optimization models for the installation planning of offshore wind farms

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EUROPEAN JOURNAL OF OPERATIONAL RESEARCH 2024年第3期315卷 1182-1196页

作者： Amorosi, Lavinia Fischetti, Martina Paradiso, Rosario Roberti, Roberto Sapienza Univ Rome Dept Stat Sci Rome Italy Univ Seville Inst Matemat Seville Spain Vrije Univ Amsterdam Dept Operat Analyt Amsterdam Netherlands Univ Padua Dept Informat Engn Padua Italy

Challenges posed by global warming motivate the increasing interest in green energy sources, such as wind energy. To make wind energy attractive from an economic viewpoint, the decision -making problems faced in designing, installing, and maintaining wind farms have to be optimized. In this paper, we focus on the problem of optimally planning the installation process of offshore wind farms, as faced by Vattenfall, a leading European energy company. We first formulate the deterministic version of this Installation Planning of Offshore Wind Farms (IPOWF) problem as a mixed integer linear programming (MILP) model. From this model, we then derive other MILPs that can provide lower and upper bounds to the optimal installation cost. To cope with weather uncertainty, we also provide a light -robust MILP. These models are tested on real -life instances corresponding to two wind farms that Vattenfall has recently built in Denmark and Germany. Computational results show that tight primal and dual solutions can be computed for the deterministic case with the proposed models and that the light -robust model yields solutions that are robust to weather uncertainty.

关键词： Wind farms Wind energy Installation mixed integer linear programming

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Mobile healthcare service planning in rural areas: A hybrid record to record travel algorithm

JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GA...

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JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY 2024年第1期39卷 593-606页

作者： Akkus, Ilhami Yildiz, Ece Arzu Karaoglan, Ismail Altiparmak, Fulya Gazi Univ Fac Engn Ind Engn Dept TR-06570 Maltepe Ankara Turkiye Konya Tech Univ Fac Engn & Nat Sci Ind Engn Dept TR-42250 Selcuklu Konya Turkiye

Due to the economic infeasibility of establishing comprehensive healthcare facilities in sparsely populated areas and the responsibility of governments to ensure equal healthcare access for citizens, different approaches have been sought in healthcare service delivery. The Republic of Turkiye's Ministry of Health provides mobile healthcare services (MHS) to 9 million people monthly through 3400 healthcare facilities and 7500 doctors. This study focuses on the Mobile Healthcare Service Routing and Scheduling Problem (MHS-RSP). The objective of MHS-RSP is to determine the daily routes of doctors on a monthly basis to minimize total distance traveled, taking into account constraints such as working hours, route time, minimum service time per visit, and dedicated doctor assignments to villages. A mixed-integer mathematical model is developed to address the problem, which is NP-hard and equivalent to the multi-depot time-constrained periodic vehicle routing problem. To solve medium-and large-sized problems efficiently, a hybrid metaheuristic algorithm named "GCH-RRT" is introduced, based on greedy constructive heuristic(GCH) and record-to-record travel(RRT) metaheuristic. The performance of the mathematical model and the GCH-RRT algorithm is compared with existing service plans in Ankara, resulting in a 15.6% improvement with the mathematical model and a 22.3% improvement with the GCH-RRT algorithm in total distance traveled.

关键词： Healthcare logistics mixed integer linear programming Multi-depot VRP Periodic VRP Metaheuristic algorithm

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A dual population collaborative genetic algorithm for solving flexible job shop scheduling problem with AGV

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SWARM AND EVOLUTIONARY COMPUTATION 2024年 86卷

作者： Han, Xiaoqing Cheng, Weiyao Meng, Leilei Zhang, Biao Gao, Kaizhou Zhang, Chaoyong Duan, Peng Liaocheng Univ Sch Comp Sci Liaocheng 252000 Peoples R China Macau Univ Sci & Technol Inst Syst Engn Taipa 999078 Macao Peoples R China Huazhong Univ Sci & Technol State Key Lab Digital Mfg Equipment & Technol Wuhan 430074 Peoples R China

With the increase in labor costs and the development of manufacturing automation technology, automatic guided vehicle (AGV) is widely used in various flexible workshop scenarios. The integrated scheduling of processing machines and AGV is of great significance in real -world workshop production. This article studies the integration problem of flexible job shop scheduling problem (FJSP) and AGV with minimizing the makespan, and proposes a novel mixed integer linear programming (MILP) model and a dual population collaborative genetic algorithm (DCGA). In DCGA, a two-layer encoding strategy based on machine selection and operation sequencing is used. Two decoding methods are designed to determine AGV selection, and each population uses a decoding method. Moreover, a population collaboration operation is designed. The feasibility and effectiveness of the MILP model and DCGA are verified through experimental simulation. Specifically, the DCGA improves 18 current best solutions for benchmarks in the existing studies.

关键词： Flexible job shop scheduling problem Automatic guided vehicle Genetic algorithm Dual population collaboration mixed integer linear programming

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