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Aggregating Harvest Activities in Long Term Forest Planning by Minimizing Harvest Area Perimeters

SILVA FENNICA 2010年第1期44卷 77-89页

作者： Ohman, Karin Eriksson, Ljusk Ola Dept Forest Resource Management SE-90183 Umea Sweden

The study presents a new approach for aggregating stands for harvest in strategic forest planning. In fragmented landscapes this could benefit nature conservation as well as reduce costs. The approach is built on the idea of minimizing the outside perimeter of contiguous harvest areas. The formulation allows for the use of exact solution methods such as mixed integer programming. The method was tested in a landscape consisting of 2821 stands. The application showed that large and compact harvest areas were created with limited sacrifice of financial value. The mixed integer programs were in most cases solved within a couple of hours. The method needs to be tested on different landscapes with different degrees of fragmentation. It is also necessary to evaluate the long term consequences of the large clear cuts that appear to be a consequence of this problem formulation.

关键词： harvest scheduling mixed integer programming optimization spatial consideration trade off

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Yard space allocation of container port based on dual cycle strategy

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OCEAN & COASTAL MANAGEMENT 2024年 247卷

作者： Tan, Caimao Qin, Tiantian He, Junliang Wang, Yu Yu, Hang Shanghai Maritime Univ China Inst FTZ Supply Chain Shanghai Peoples R China Shanghai Maritime Univ Inst Logist Sci & Engn Shanghai Peoples R China Shanghai Maritime Univ Logist Engn Coll Shanghai Peoples R China

The dual-cycle operation in loading and unloading operations is widely adopted by container ports around the world. This study addresses the storage space allocation problem of the container yard based on the dual-cycle operation mode, and a mixed integer programming model for storage space allocation in the container yard is established. The container transportation distance is taken as the objective of the optimization model, and practical constraints such as ship operating time demand in ports and yard loading cluster limitations are added. The results of the numerical experiments reveal that the dual-cycle of internal trucks in the yard area can decrease transportation distance of containers, and the space allocation method based on it is more effective than traditional methods. The scenario analysis also provides several management insights for practitioners in container ports and puts forward effective reference suggestions to further solve the bottleneck problem often faced by container ports.

关键词： Container port mixed integer programming Yard space allocation Template generating Dual cycle

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Autonomous path planning with obstacle avoidance for smart assistive systems

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EXPERT SYSTEMS WITH APPLICATIONS 2023年第PartB期213卷

作者： Ntakolia, Charis Moustakidis, Serafeim Siouras, Athanasios Natl Tech Univ Athens Lab Maritime Transport Zografos 15780 Greece Hellen Air Force Acad Dept Aeronaut Studies Sect Mat Engn Machining Technol & Prod Management Dekeleia Base Acharnes 13672 Greece AiDEAS OU Narva mnt 5 EE-10117 Tallinn Harju maakond Estonia

Given the increased interest in smart assistive technologies and autonomous robot vehicles, path planning has emerged as one of the most researched and challenging topics in navigation. Moving to partially known or unknown environment, an assistive navigation system should be able to extract spatiotemporal information and dynamically identify objects and adjust the route. Current approaches typically rely on external services to perform high demanding computations and employ a plethora of overlapping sensors to accurately scan the surrounding environment. This increases their energy demands, size and weight, while incommodes their use in real time applications making their application to wearable assistive systems, such as smart glasses, a challenge. Aiming to provide a comfortable and computationally efficient wearable solution that can be used by human or robotic assistive systems, in this study we propose a novel two-level hierarchical architecture combining global and local path planning. The macroscale navigation involves the construction of the initial global path while the microscale navigation includes the local path planning with obstacle detection and avoidance. The methodology consists of: (i) a novel chaotic ant colony optimization algorithm with fuzzy logic (CACOF) for path construction;(ii) powerful and light weight deep convolutional neural networks for obstacle detection;and (iii) a Bug-like algorithm enhanced with fuzzy rules for obstacle avoidance in case of static objects. A vast experimental evaluation was conducted to test the proposed methodologies in a simulation environment based on the topology of real area. The results proved the computational efficiency and ability of the proposed path planning algorithms to address effectively multi-objective global and path planning problems which make them suitable for real time applications.

关键词： mixed integer programming Metaheuristic Algorithm Swarm Intelligence Computer-Assisted Path Planning Obstacle Avoidance

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An improved column-generation-based matheuristic for learning classification trees

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COMPUTERS & OPERATIONS RESEARCH 2024年 165卷

作者： Patel, Krunal Kishor Desaulniers, Guy Lodi, Andrea Canada Excellence Res Chair Data Sci Real Time Dec Montreal PQ Canada Polytech Montreal 2500 Chemin Polytech Montreal PQ H3T 1J4 Canada GERAD 2500 Chemin Polytech Montreal PQ H3T 1J4 Canada Cornell Tech Jacobs Technion Cornell Inst 2 West Loop RdBloomberg Bldg New York NY 10044 USA

Decision trees are highly interpretable models for solving classification problems in machine learning (ML). The standard ML algorithms for training decision trees are fast but generate suboptimal trees in terms of accuracy. Other discrete optimization models in the literature address the optimality problem but only work well on relatively small datasets. Firat et al. (2020) proposed a column -generation -based heuristic approach for learning decision trees. This approach improves scalability and can work with large datasets. In this paper, we describe improvements to this column generation approach. First, we modify the subproblem model to significantly reduce the number of subproblems in multiclass classification instances. Next, we show that the data -dependent constraints in the master problem are implied, and use them as cutting planes. Furthermore, we describe a separation model to generate data points for which the linear programming relaxation solution violates their corresponding constraints. We conclude by presenting computational results that show that these modifications result in better scalability.

关键词： Machine learning Decision trees Column generation Classification mixed integer programming

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Long-Term Railway Network Planning Using a Multiperiod Network Design Model

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JOURNAL OF TRANSPORTATION ENGINEERING PART A-SYSTEMS 2020年第1期146卷

作者： Seyedvakili, S. Alireza Zakeri, Jabbar-Ali Azadani, S. Masoud Nasr Shafahi, Yousef Iranian Univ Sci & Technol Sch Railway Engn Tehran *** Iran Iranian Univ Sci & Technol Sch Railway Engn Ctr Excellence Railway Transport Tehran *** Iran Iranian Univ Sci & Technol Sch Railway Engn Tehran *** Iran Sharif Univ Dept Civil Engn Tehran 113658639 Iran

Railway networks are one of the major parts of the transportation infrastructure. Development of this network by the construction of new lines or the capacity improvement of existing lines needs time and capital cost. Decision makers, who are responsible for the infrastructure network, always have a limited budget. They need to select the best package from the long list of new projects. Providing an optimal periodic plan for investment in the railway network is a major task for the government. In this paper, a solution for this task is developed by using a multiperiod network design model for the railway network. The formulation of the model and some of the concepts are new. The model considers development projects (new line construction and existing line improvement), available budget in each period, origin-destination demand matrix for each period, block capacity, and technical capacity. The suggested model is implemented for the Iranian railway network and is solved by an exact method that shows efficiency of the model. In addition, based on the nature of demand, different scenarios are considered in the proposed model. The selected projects by the proposed model and their usage percentage show the ability and efficiency of the model.

关键词： Multiperiod network design problem Railway network design problem mixed integer programming Long-term planning Iranian railway network Direct and indirect cost

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A web-based decision support system for examination timetabling

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 183卷 115363-115363页

作者： Guler, Mehmet Guray Gecici, Ebru Koroglu, Tugce Becit, Emre Yildiz Tech Univ Fac Mech Engn Dept Ind Engn Istanbul Turkey

We study exam timetabling problem (ETP) and supervisor assignment problem (SAP) of a vocational school offering associate level degrees in a university. The school has seven departments and plans almost 170 exams in each semester. We propose mixed integer programming (MIP) models for the ETP and the SAP. The optimal solution for ETP of the school is not attainable in two days with a commercial solver. We propose a decomposition method which is able to solve ETP using an open source solver in less than two minutes. The MIP models and the solution method are embedded into a web based decision support system (DSS). Using this DSS, a complete timetable can be prepared in less than two minutes by an average end-user.

关键词： Examination timetabling Supervisor assignment mixed integer programming Decision support system

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Finite inventory budgets in production capacity and safety stock placement under the guaranteed service approach

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COMPUTERS & OPERATIONS RESEARCH 2021年 131卷 105266-105266页

作者： Aouam, Tarik Ghadimi, Foad Vanhoucke, Mario Univ Ghent Fac Econ & Business Adm Tweekerkenstr 2 B-9000 Ghent Belgium Univ Int Rabat Rabat Business Sch BearLab Rabat Morocco Vlerick Business Sch Reep 1 B-9000 Ghent Belgium UCL UCL Sch Management 1 Canada Sq London E14 5AA England

Following the guaranteed service approach (GSA), several studies have investigated the interaction between production and inventory decisions in multistage supply systems and developed efficient algorithms to solve the large-scale optimization problems that emerge. These works have largely ignored limited inventory budgets and their effect on production and inventory decisions. In reality, however, firms tend to limit the amount of capital tied-up in inventories, and there is usually a limited amount of storage space at a warehouse or a retailer. This study investigates the problem of production capacity and safety stock placement with both limited capacity and inventory budgets in general acyclic supply chains. This problem allocates capacity to production stages and sets inventory targets at logistics stages, with the objective to minimize the expected total supply chain cost while meeting a target customer service level. The present paper contributes to the GSA literature in several ways. First, we compare guaranteed and chancec-onstraint approaches to model limited inventory budgets and analyze their effect on safety stock placement. Second, we present two new equivalent formulations of the production capacity and safety stock placement problem. Solving these new models using a successive piecewise linear approximation algorithm outperforms existing solution procedures in terms of solution quality and time, in both cases of infinite and finite inventory budgets. Third, taking advantage of the structure of these formulations, we develop a two-phase heuristic, which finds optimal or near-optimal solutions and greatly improves CPU times. Finally, our numerical experiments show that inventory budgets affect production capacity and safety stock placement decisions, increasing both work in process (WIP) and safety stock levels.

关键词： Supply chain management Production capacity Inventory budget Safety stocks Guaranteed service mixed integer programming

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Cost-Efficient Coverage of Wastewater Networks by IoT Monitoring Devices

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SENSORS 2022年第18期22卷 6854-6854页

作者： Sikorski, Arkadiusz Donado, Fernando Solano Kozdrowski, Stanislaw Warsaw Univ Technol Inst Comp Sci Nowowiejska 15-19 PL-00665 Warsaw Poland Warsaw Univ Technol Inst Telecommun Nowowiejska 15-19 PL-00665 Warsaw Poland

Wireless sensor networks are fundamental for technologies related to the Internet of Things. This technology has been constantly evolving in recent times. In this paper, we consider the problem of minimising the cost function of covering a sewer network. The cost function includes the acquisition and installation of electronic components such as sensors, batteries, and the devices on which these components are installed. The problem of sensor coverage in the sewer network or a part of it is presented in the form of a mixed-integer programming model. This method guarantees that we obtain an optimal solution to this problem. A model was proposed that can take into account either only partial or complete coverage of the considered sewer network. The CPLEX solver was used to solve this problem. The study was carried out for a practically relevant network under selected scenarios determined by artificial and realistic datasets.

关键词： sewer network wireless sensor network internet of things combinatorial optimization mixed integer programming

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Reshore or not Reshore: A Stochastic programming Approach to Supply Chain Optimization

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2023年 118卷

作者： Sawik, Tadeusz Reykjavik Univ Dept Engn IS-101 Reykjavik Iceland

This paper presents a scenario-based stochastic mixed integer programming model for risk-neutral or risk-averse optimization of supply chain reshoring to domestic region, under the ripple effect propagated from a foreign disruption source region. The reshoring decisions with respect to tier-one suppliers of parts and tier-zero OEM (Original Equipment Manufacturer) assembly plants are considered under disruptions in supply, manufacturing, logistics and demand rippling across the entire supply chain. The proposed in-novative approach integrates strategic supply chain reshoring and operational supply chain scheduling, which allows the decision maker to evaluate the operational impact of the strategic decision. Results of computational experiments, partially modeled after a supply chain reshoring problem in the smartphone industry, are provided. The findings indicate that reshoring decisions are strongly dependent on the level of government subsidy for capital expenditure and for risk-neutral reshoring, a portfolio of supply chain nodes with positive expected net savings can be considered only. In general, the reshored supply chain can better meet domestic market demand. Moreover, full reshoring of a supply chain improves its busi-ness as usual performance and even partial reshoring mitigates the impact of the ripple effect. However, for risk-averse decision-making, if reshoring is incapable of reducing worst-case cost, in particular, worst-case lost sales, no reshoring is selected.(c) 2023 Elsevier Ltd. All rights reserved.

关键词： supply chain risk management ripple effect reshoring stochastic programming mixed integer programming

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Margin optimal classification trees

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COMPUTERS & OPERATIONS RESEARCH 2024年 161卷

作者： D'Onofrio, Federico Grani, Giorgio Monaci, Marta Palagi, Laura Sapienza Univ Rome Dept Comp Control & Management Engn Antonio Rubert Rome Italy Sapienza Univ Rome Dept Stat Sci Rome Italy

In recent years, there has been growing attention to interpretable machine learning models which can give explanatory insights on their behaviour. Thanks to their interpretability, decision trees have been intensively studied for classification tasks and, due to the remarkable advances in mixed integer programming (MIP), various approaches have been proposed to formulate the problem of training an Optimal Classification Tree (OCT) as a MIP model. We present a novel mixed integer quadratic formulation for the OCT problem, which exploits the generalization capabilities of Support Vector Machines for binary classification. Our model, denoted as Margin Optimal Classification Tree (MARGOT), encompasses maximum margin multivariate hyperplanes nested in a binary tree structure. To enhance the interpretability of our approach, we analyse two alternative versions of MARGOT, which include feature selection constraints inducing sparsity of the hyperplanes' coefficients. First, MARGOT has been tested on non-linearly separable synthetic datasets in a 2 -dimensional feature space to provide a graphical representation of the maximum margin approach. Finally, the proposed models have been tested on benchmark datasets from the UCI repository. The MARGOT formulation turns out to be easier to solve than other OCT approaches, and the generated tree better generalizes on new observations. The two interpretable versions effectively select the most relevant features, maintaining good prediction quality.

关键词： Machine learning Optimal classification trees Support vector machines mixed integer programming

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