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Models for two-dimensional bin packing problems with customer order spread

JOURNAL OF COMBINATORIAL OPTIMIZATION 2024年第1期48卷 1-27页

作者： Martin, Mateus Yanasse, Horacio Hideki Santos, Maristela O. Morabito, Reinaldo Univ Fed Sao Carlos Dept Engn Prod Rodovia Joao Leme St BR-18052780 Sorocaba SP Brazil Univ Fed Sao Paulo Inst Ciencia & Tecnol Ave Cesare Mansueto Giulio Lattes 1201 BR-12247014 Sao Jose Dos Campos SP Brazil Univ Sao Paulo Inst Ciencias Matemat & Comput Ave Trabalhador Sao carlense 400 BR-13566590 Sao Carlos SP Brazil Univ Fed Sao Carlos Dept Engn Prod Via Washington Luiz km 235 BR-13565905 Sao Carlos SP Brazil

In this paper, we address an extension of the classical two-dimensional bin packing (2BPP) that considers the spread of customer orders (2BPP-OS). The 2BPP-OS addresses a set of rectangular items, required from different customer orders, to be cut from a set of rectangular bins. All the items of a customer order are dispatched together to the next stage of production or distribution after its completion. The objective is to minimize the number of bins used and the spread of customer orders over the cutting process. The 2BPP-OS gains relevance in manufacturing environments that seek minimum waste solutions with satisfactory levels of customer service. We propose integer linear programming (ILP) models for variants of the 2BPP-OS that consider non-guillotine, 2-stage, restricted 3-stage, and unrestricted 3-stage patterns. We are not aware of integrated approaches for the 2BPP-OS in the literature despite its relevance in practical settings. Using a general-purpose ILP solver, the results show that the 2BPP-OS takes more computational effort to solve than the 2BPP, as it has to consider several symmetries that are often disregarded by the traditional 2BPP approaches. The solutions obtained by the proposed approaches have similar bin usage and significantly better metrics of customer satisfaction concerning the approaches that neglect the customer order spread.

关键词： Cutting & Packing mixed-integer linear programming Non-guillotine pattern 2-stage and 3-stage patterns Order spread minimization

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Particle swarm optimization for integrated scheduling problem with batch additive manufacturing and batch direct-shipping delivery

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COMPUTERS & OPERATIONS RESEARCH 2024年 161卷

作者： Sugianto, Willy Chandra Kim, Byung Soo Incheon Natl Univ Dept Ind & Management Engn 119 Acad Ro Incheon 22012 South Korea

This study addresses a novel proRblem for the integrated scheduling of additive manufacturing and delivery processes with the objective of minimizing the total weighted completion time of the ordered parts. In the manufacturing process, batch production on non-identical parallel additive manufacturing machines is considered. A single batch of production, which is referred to as a build, consists of parts requiring the same material. Sequence-dependent setup times occur between two adjacent builds. Once the production run is completed, the parts in the builds are regrouped into delivery batches within the same customer. In the delivery process, parts are delivered by homogeneous vehicles to the corresponding customers through direct-shipping method. To optimally solve the small problem, a mixed-integer linear programming model is formulated. For solving largescale problems, three rule-based heuristics that are embedded in particle swarm optimization (PSO) are proposed. A tight lower bound is developed to evaluate the performance of the proposed algorithms. The computational experiment shows that the proposed PSO with delivery waiting time minimization rule is effective to solve small-scale problems, while in large-scale problems, capacity usage maximization rule performs the best and both outperform another existing metaheuristic. Several sensitivity analyses are also performed to provide additional managerial insights.

关键词： Scheduling Additive manufacturing mixed-integer linear programming Particle swarm optimization Meta-heuristic Local search

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Site and capacity selection for on-site production facilities in a nationwide hydrogen supply chain deployment plan

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 50卷 968-987页

作者： Lee, Seungyeop Kim, Hyunjoon Kim, Byung-In Song, Minseok Lee, Deoksang Ryu, Hyunyoung Pohang Univ Sci & Technol POSTECH Dept Ind & Management Engn Gyeongbuk 37673 South Korea Hanyang Univ Dept Ind & Management Engn Ansan 15588 Gyeonggi Do South Korea

The Republic of Korea intends to establish a nationwide hydrogen supply chain (HSC) infrastructure to promote the adoption of hydrogen fuel cell vehicles. As the hydrogen demand for mobility may be higher than the estimated production capacity of existing facilities, additional on-site hydrogen refueling stations (HRSs) are required. To address infrastructure deployment problems, a two-stage (demand and supply stages) optimization approach is proposed based on mixed-integer linear programming models. The maximal covering location and p-median models at the demand stage determine the location and chargers of the HRSs and the demand allocation to the stations. At the supply stage, the supply model determines the installation of additional on-site production facilities for the deployed HRSs and the supply allocation to the stations by minimizing the installation and transportation costs. We estimated highresolution demand data using public sources and utilized investigated supply data from reputable institutions representing the hydrogen economy in the Republic of Korea. The proposed approach provides realistic HSC infrastructure deployment plans, satisfying over 90% of the hydrogen demand in the years 2025 and 2030. Additionally, a sensitivity analysis indicated the effects of various parameters on the deployment plan, including the demand coverage and the need for additional on-site HRSs to address potential hydrogen supply shortages. (c) 2023 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Hydrogen supply chain Hydrogen refueling station Hydrogen supply shortage On-site production mixed-integer linear programming

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Proximity results in convex mixed-integer programming

arXiv

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arXiv 2024年

作者： Kocuk, Burak Morán, R. Diego A. Industrial Engineering Program Sabanci University Istanbul34956 Turkey Industrial and Systems Engineering Department Rensselaer Polytechnic Institute TroyNY12180-3590 United States

We study proximity (resp. integrality gap), that is, the distance (resp. difference) between the optimal solutions (resp. optimal values) of convex integer programs (IP) and the optimal solutions (resp. optimal values) of their continuous relaxations. We show that these values can be upper bounded in terms of the recession cone of the feasible region of the continuous relaxation when the recession cone is fulldimensional. If the recession cone is not full-dimensional we give sufficient conditions to obtain a finite integrality gap. We then specialize our analysis to second-order conic IPs. In the case the feasible region is defined by a single Lorentz cone constraint, we give upper bounds on proximity and integrality gap in terms of the data of the problem (the objective function vector, the matrix defining the conic constraint, the right-hand side, and the covering radius of a related lattice). We also give conditions for these bounds to be independent of the right-hand side, akin to the linear IP case. Finally, in the case the feasible region is defined by multiple Lorentz cone constraints, we show that, in general, we cannot give bounds that are independent of the corresponding right-hand side. Although our results are presented for the integer lattice Zn, the bounds can be easily adapted to work for any general lattice, including the usual mixed-integer lattice Zn1 × Rn2 , by considering the appropriate covering radius when needed. Copyright © 2024, The Authors. All rights reserved.

关键词： mixed-integer linear programming

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Space Optimization in Single-Space Racking Planning of Efficient Shelves for Containers with Different Heights

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ZWF Zeitschrift fuer Wirtschaftlichen Fabrikbetrieb 2025年第3期120卷 133-138页

作者： Rief, Jona Fottner, Johannes Lehrstuhl für Fördertechnik Materialfluss Logistik Technische Universität München Boltzmannstr. 15 Garching85748 Germany Lehrstuhl für Fördertechnik Materialfluss Logistik TU München Garching Germany

Automatic miniload warehouses with storage and retrieval machines are used in intralogistics to decouple demand and supply. The design of the racks is a critical factor for the space-efficient configuration of these systems. This publication presents two approaches for optimizing rack design: a heuristic inspired by practical methods and a mixedinteger linear optimization problem. Both aproaches are compared in a parameter study. © 2025 Walter de Gruyter GmbH, Berlin/Boston.

关键词： mixed-integer linear programming

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Optimisation models for project selection in asset management: an application to the water sector

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INTERNATIONAL TRANSACTIONS IN OPERATIONAL RESEARCH 2024年第5期31卷 2956-2987页

作者： Vilarinho, Hermilio Barbosa, Flavia Novoa, Henriqueta Silva, Jaime Gabriel Yamada, Luciana Camanho, Ana S. Univ Porto Fac Engn R Dr Roberto Frias P-4200465 Porto Portugal Inst Engn Sistemas & Comp Tecnol & Ciencia INESC T R Dr Roberto Frias Porto Portugal Aguas Douro & Paiva Rua Vilar 235 5 Andar P-4050626 Porto Portugal Inst Politecn Porto IPP Inst Super Engn Porto ISEP R Dr Antonio Bernardino Almeida 431 Porto Portugal

A significant challenge in asset management is the selection of investment projects for infrastructures, which often relies on subjective judgement and lacks structured decision support methods. This challenge is particularly complex in water systems due to the diverse and heterogeneous nature of the components requiring investment. While the infrastructure value index (IVI) is widely used to characterise assets and support investment decisions in the water sector, its application in optimisation models for generating efficient project portfolios remains unexplored. To address this research gap, this study introduces optimisation models for generating investment portfolio plans in water systems' asset management. The proposed approach includes two mixed-integer linear programming (MILP) models that determine optimal solutions and an evolutionary algorithm that offers sub-optimal alternative investment selection plans to provide decision-makers with additional choices for balancing optimal outcomes. The primary contribution of this research is the combined utilisation of MILP and evolutionary algorithms, integrating the IVI into the decision-making process. These tools provide decision-makers with structured methods for defining investment plans and minimising the subjective elements typically associated with such processes. To illustrate the effectiveness of the models, a case study is presented involving a pumping station of a Portuguese water company. The results demonstrate the practical application and benefits of the proposed approach in optimising investment decisions. This research contributes to advancing asset management practices by integrating quantitative optimisation techniques and leveraging the IVI, thereby enhancing the objectivity and efficiency of investment planning in water systems' asset management.

关键词： asset management capital investment planning project selection performance indicator mixed-integer linear programming multi-objective programming

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Quantitative insights into the integrated push and pull production problem for lean supply chain planning 4.0

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2024年第17期62卷 6251-6275页

作者： Reyes, John Mula, Josefa Diaz-Madronero, Manuel Univ Politecn Valencia Res Ctr Prod Management & Engn CIGIP Alcoy Alicante Spain Univ Tecn Ambato Fac Syst Engn Ambato Ecuador

Validated quantitative models for lean supply chain planning (LSCP) are still scarce in the literature, particularly because conventional push systems have not been widely integrated and tested with pull systems in sustainable and resilient environments in the Industry 4.0 context. Hence the main contribution of this paper is to develop an optimisation model that is able to contribute to the LSCP with the combination of push and pull strategies. Here we present an integrated just-in-time (JIT) production system with material requirement planning (MRP) for a SC that takes a traditional five-level structure based on a mixed-integer linear programming model (MILP) dubbed as LSCP 4.0. The model is able to simultaneously plan the production and inventory of materials and finished goods to satisfy demand from forecasts and firm orders. The selection of alternative suppliers as a proactive measure to face disruptive events is also considered. Furthermore, sustainable practices are included in the objective function for profit maximisation by considering CO2 emissions. This proposal is tested in the footwear sector. The results demonstrate that the combined use of JIT and MRP through a quantitative approach improve performance in leanness, sustainability and resilience by decreasing the bullwhip effect at different SC levels.

关键词： Lean manufacturing sustainability supply chain resilience mixed-integer linear programming industry 4.0

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Combination Chemotherapy Optimization with Discrete Dosing

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INFORMS JOURNAL ON COMPUTING 2024年第2期36卷 305-704, C2页

作者： Ajayi, Temitayo Hosseinian, Seyedmohammadhossein Schaefer, Andrew J. Fuller, Clifton D. Nat Source Improved Plants Ithaca NY 14850 USA Univ Cincinnati Dept Mech & Mat Engn Cincinnati OH 45221 USA Rice Univ Dept Computat Appl Math & Operat Res Houston TX 77005 USA Univ Texas MD Anderson Canc Ctr Dept Radiat Oncol Houston TX 77030 USA

Chemotherapy drug administration is a complex problem that often requires expensive clinical trials to evaluate potential regimens;one way to alleviate this burden and better inform future trials is to build reliable models for drug administration. This paper presents a mixed-integer program for combination chemotherapy (utilization of multiple drugs) optimization that incorporates various important operational constraints and, besides dose and concentration limits, controls treatment toxicity based on its effect on the count of white blood cells. To address the uncertainty of tumor heterogeneity, we also propose chance constraints that guarantee reaching an operable tumor size with a high probability in a neoadjuvant setting. We present analytical results pertinent to the accuracy of the model in representing biological processes of chemotherapy and establish its potential for clinical applications through a numerical study of breast cancer.

关键词： combination chemotherapy differential equations mixed-integer linear programming

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Change-Point Detection in Time Series Using mixed integer programming

arXiv

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arXiv 2024年

作者： Prokhorov, Artem Radchenko, Peter Semenov, Alexander Skrobotov, Anton University of Sydney Business School Australia University of Florida United States Russian Presidential Academy of National Economy and Public Administration Russia St. Petersburg State University Russia University of Montreal Canada

We use cutting-edge mixed integer optimization (MIO) methods to develop a framework for detection and estimation of structural breaks in time series regression models. The framework is constructed based on the least squares problem subject to a penalty on the number of breakpoints. We restate the l0-penalized regression problem as a quadratic programming problem with integer- and real-valued arguments and show that MIO is capable of finding provably optimal solutions using a well-known optimization solver. Compared to the popular l1-penalized regression (LASSO) and other classical methods, the MIO framework permits simultaneous estimation of the number and location of structural breaks as well as regression coefficients, while accommodating the option of specifying a given or minimal number of breaks. We derive the asymptotic properties of the estimator and demonstrate its effectiveness through extensive numerical experiments, confirming a more accurate estimation of multiple breaks as compared to popular non-MIO alternatives. Two empirical examples demonstrate usefulness of the framework in applications from business and economic statistics. © 2024, CC BY-NC-ND.

关键词： mixed-integer linear programming

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Collaborative optimization of passenger flow control and bus-bridging services in commuting metro lines

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APPLIED MATHEMATICAL MODELLING 2024年 130卷 806-826页

作者： Li, Xiangjiang Lu, Yahan Yang, Lixing Beijing Jiaotong Univ Sch Syst Sci Beijing 100044 Peoples R China Beijing Jiaotong Univ Sch Traff & Transportat Beijing 100044 Peoples R China

With the advantages of high speed and punctuality, urban rail transit has become the preferred choice for many commuters. However, overcrowding of urban rail transit lines during peak hours is a common problem in megacities owing to the excessive passenger demand. To address this problem, this study proposes an integrated optimization method that combines passenger flow control and bus -bridging services. An integer nonlinear optimization model is developed to minimize the weighted passenger waiting time and operating cost of bus -bridging services by considering the coupling relationships between passenger movements on both the metro and buses. Then, the proposed model is equivalently transformed into a linear form with better mathematical properties, and variants of this model are further formulated to extend the usability. Finally, real -world case studies based on the operational data of the Beijing Metro Batong line are conducted to verify the effectiveness of the proposed methods. The computational results indicate that the proposed approach can effectively alleviate overcrowding and reduce passengers' waiting time, thereby improving the operational efficiency and safety of the urban rail transit system.

关键词： Public transport Integrated optimization Time-dependent passenger demand mixed-integer linear programming Metro-bus intermodal coordination Congestion reduction strategies

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