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Location optimization of urban fire stations considering the variations in fire service demands: a bi-objective coverage model

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INTERNATIONAL JOURNAL OF DIGITAL EARTH 2025年第1期18卷

作者： Chen, Yulong Yao, Jing Wang, Shaohua Lai, Zhizhu Wu, Leying Dong, Guanpeng Henan Univ Key Res Inst Yellow River Civilizat & Sustainable Kaifeng 475001 Peoples R China Henan Univ Collaborat Innovat Ctr Yellow River Civilizat Hena Kaifeng Peoples R China Henan Univ Coll Environm & Planning Kaifeng 475004 Peoples R China Univ Glasgow Urban Big Data Ctr Sch Social & Polit Sci Glasgow Scotland Chinese Acad Sci Aerosp Informat Res Inst Key Lab Remote Sensing & Digital Earth Beijing Peoples R China Gannan Normal Univ Sch Geog & Environm Engn Ganzhou Peoples R China

Fire stations serve as operational hubs for firefighting and rescue teams, making their spatial optimization crucial to ensure effective urban firefighting and rescue operations. The traditional coverage model overlooks the difference in service satisfaction among various demand areas within the coverage radius, making it less effective at optimizing fire station locations. This study examines the differing demands for fire services among various areas based on their levels of fire risk. We propose a bi-objective coverage (BOC) model to optimize the locations of urban fire stations by improving upon the traditional maximum coverage model and integrating it with the set coverage model. The $\varepsilon$& varepsilon;-constraint method was applied to solve the constructed BOC model, obtaining the Pareto optimal frontier. Then, the BOC model was applied to address the spatial optimization of fire station locations in the central urban area of Zhengzhou City. Finally, a comparative analysis was performed on the optimization outcomes and performance of both the BOC and bi-objective spatial optimization (BSO) (integrating coverage and median goals) models. The computational results of the BOC model outperformed those of the BSO model in terms of both the effective service rate and the multiple coverage rate for demand areas.

关键词： Fire stations service satisfaction spatial optimization coverage model bi-objective model

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A bi-objective model to include workers' vibration exposure in assembly line design

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INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH 2021年第13期59卷 4017-4032页

作者： Finco, Serena Abdous, Mohammed-Amine Calzavara, Martina Battini, Daria Delorme, Xavier Univ Clermont Auvergne CNRS Mines St Etienne St Etienne France Univ Padua Dept Management & Engn Vicenza Italy

In several occupational sectors, workers are daily exposed to vibrations induced by automatic, pneumatic, or electric tools, with a consequent increase of musculoskeletal disorders. In this paper, a bi-objective manual assembly line design model is proposed, aiming to avoid excessive daily vibration exposures. The developed model allows to minimise both total equipment costs and vibration levels by respecting the threshold values defined in the ISO 5349-1. The epsilon-constraint approach is used to address both objectives and to find the Pareto frontier. The model is applied to several instances to evaluate the computational limit of the solving method, as well as to an industrial case to provide managerial guidelines. The results show that the method can solve small and medium size instances. Moreover, the case study points out that safe vibration exposure levels can be achieved also with a low additional investment and that solutions equal from an economic point of view can be different from a vibrations exposure one.

关键词： manual assembly line design bi-objective model ergonomics risk vibration exposure case study computational experimentation

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A bi-objective model with sequential search algorithm for optimizing network-wide train timetables

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COMPUTERS & INDUSTRIAL ENGINEERING 2019年 127卷 1259-1272页

作者： Tian, Xiaopeng Niu, Huimin Lanzhou Jiaotong Univ Sch Traff & Transportat Lanzhou 730070 Peoples R China

This study focuses on how to optimize train timetables with transfer synchronization for a high-speed rail network. Aiming at network-based and synchronization-oriented train operations, a bi-objective integer programming model integrated with irregular headways is proposed to maximize the number of connections and minimize the total transfer waiting time for one day. This study also reveals an interactive relationship for timetables, transfer schemes and number of connections. By optimizing the two objectives sequentially, we develop a novel heuristic search algorithm to solve the proposed model. Specifically, an initial solution is obtained by independently optimizing the first objective, and the improved timetables are iteratively searched to minimize the total waiting time under the maximum number of transfer connections. We also put forward a set of numerical experiments to demonstrate the effectiveness and availability of the proposed approach.

关键词： High-speed rail Network-wide timetable bi-objective model Connection Sequential search algorithm

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Blood supply planning during natural disasters under uncertainty: a novel bi-objective model and an application for red crescent

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ANNALS OF OPERATIONS RESEARCH 2022年第1期319卷 73-113页

作者： Farrokhizadeh, Elmira Seyfi-Shishavan, Seyed Amin Satoglu, Sule Itir Istanbul Tech Univ Dept Ind Engn Fac Management TR-34369 Istanbul Turkiye

In natural disasters, having a capable network of collecting and distributing crucial items such as blood is one of the major concerns. However, due to damage to the infrastructure after disasters, mobile blood collecting facilities (blood mobiles) are usually required. This paper aims to decide the locations of mobile facilities in each period for collecting donated blood, plan the blood distribution from the fixed and mobile facilities to the main blood centers, as well as from blood centers to the hospitals and field-hospitals, under uncertain conditions. To do so, a multi-period, bi-objective mixed-integer mathematical model is developed under a multiple-scenario, aiming to minimize the unsatisfied blood demand as well as the total cost of the network. In the proposed model, the blood group compatibility matrix, failure rate of the facilities, and patients' urgency levels are considered. An augmented epsilon-constraint method is applied to solve this bi-objective model. Due to the complex nature of the proposed blood supply chain model, the Lagrangian relaxation approach is used to solve the proposed model. An expected Istanbul earthquake is considered, and the blood supply planning through the Red Crescent's European branch is performed utilizing the proposed model to examine its validity. According to the numerical results, the mobile facilities' locations in each period under each scenario are determined, the unsatisfied demand in each hospital and field-hospital for each blood type are reported, and the tradeoff between the supply chain costs and unsatisfied demand are discussed in detail. Finally, to illustrate the robustness of the proposed model, a detailed sensitivity analysis is performed. According to the study results, opening new blood centers near the high-demand sub-districts for faster testing and supply, increasing the hospitals' capacities, and usage of drones and helicopters for blood distribution are suggested can be considered as managerial i

关键词： Blood supply chain Disaster bi-objective model Lagrangian relaxation Augmented-epsilon constraint method

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A bi-objective model for a Stage Shop Scheduling Problem with HumanRobot Collaboration 10th

A Bi-Objective Model for a Stage Shop Scheduling Problem wit...

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10th IFAC Triennial Conference on Manufacturing modelling, Management and Control (MIM)

作者： Ghasemkhani, Ahmad Tavakkoli-Moghaddam, Reza Hamid, Mahdi Nasiri, Mohammad Mahdi Univ Tehran Coll Engn Sch Ind Engn Tehran Iran

Stage shop scheduling is a type of manufacturing problem, in which operations need to be done on a group of jobs and are separated into different phases depending on their types. The stages are carried out in a sequential order;however, the operations within each stage are carried out in any order. In other words, the operations of a stage cannot be initiated until all operations of the previous stage are completed. Human-robot collaboration has become more popular as technologies and business empowerment has brought humans and robots closer together. In this study, jobs are allocated to humans and robots by presenting a bi-objective linear model to minimize the makespan and the cost of utilizing agents in a stage shop problem. Moreover, an interactive method is used to convert the model into a single objective one. The sensitivity results show that the makespan is minimized noticeably by collaborating the human and robots. Copyright (C) 2022 The Authors.

关键词： Stage shop scheduling Human-Robot collaboration bi-objective model TH method

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A bi-objective model for shelf space allocation using a hybrid genetic algorithm

A bi-objective model for shelf space allocation using a hybr...

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International Joint Conference on Neural Networks

作者： Liang, Cunli Cheung, Yiu-ming Wang, Yuping Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China

ISBN: (纸本)9781424413799

The existing shelf space allocation methods only optimize the profits without considering the sales time. Actually, the time to sale the products is important as well to retailers because the time has a significant impact on the liquidity and long-term profits of a retail shop. Hence, this paper proposes a bi-objective model, in which one objective is to maximize the total profits of all store's products whereas the other objective is to minimize the total time to sale the store's products. By transforming this model into a single objective problem, we optimize the shelf space allocation problem in terms of the retail profits and sales time using a hybrid genetic algorithm (GA), in which three improved local search methods are designed to improve the GA performance on local search. Further, the new multi-criteria selection scheme is also designed to maintain diversity of population. The experimental results show that the proposed model outperforms the existing method in terms of profit per unit time.

关键词： hybrid genetic algorithm bi-objective model shelf space allocation

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Managing Mass Customization through Delayed Product Differentiation: a bi-objective model for product platforms design 57

Managing Mass Customization through Delayed Product Differen...

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18th IFAC Workshop on Time Delay Systems, TDS 2024

作者： Bortolini, Marco Galizia, Francesco Gabriele Naldi, Ludovica Diletta Regattieri, Alberto Department of Industrial Engineering Alma Mater Studiorum University of Bologna Viale del Risorgimento 2 Bologna40136 Italy

In recent years, the heterogeneity of customer needs caused a wide proliferation of product variants, asking industrial companies to adopt new strategies to remain competitive in the transition from mass production to mass customization. Traditional production strategies such as Make-to-Order (MTO) and Make-to-Stock (MTS) are no longer adequate for the efficient manufacturing of multiple product variants. In such a scenario, the Delayed Product Differentiation (DPD) rose as a hybrid strategy overcoming the main limitations of traditional production strategies, best balancing high product variety and quick response time with low storage cost through the so-called product platforms. These platforms are subsystems of components, forming a common base structure from which a stream of product variants can be efficiently derived. Platforms are produced and stocked in advance, following an MTS strategy, and customized into different variants after the order arrival, according to an MTO strategy. Platforms dimension affects in an opposite manner their storage cost and their customization time, as platforms made of few components require long time for customization activities, reducing the storage cost. The best platform design and association to product variants are open topics in current literature, having major impact on the trade-off between platform storage cost and customization time. This paper contributes to applied research in mass customization, proposing a bi-objective optimization model for platform design and association to product variants, determining at the same time the best production strategy among MTO, MTS and DPD for each product variant to best balance platforms storage cost and customization time. The model is applied to a reference case study providing a multi-scenario analysis about the main effects of the limitation in the number of possible platform types on platform configurations, customization tasks and their correspondent customization time and

关键词： bi-objective model Mass customization multi-scenario analysis product platforms

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Managing Mass Customization through Delayed Product Differentiation: a bi-objective model for product platforms design

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Procedia CIRP 2024年 130卷 1250-1255页

作者： Marco Bortolini Francesco Gabriele Galizia Ludovica Diletta Naldi Alberto Regattieri Department of Industrial Engineering Alma Mater Studiorum University of Bologna Viale del Risorgimento 2 40136 Bologna Italy

In recent years, the heterogeneity of customer needs caused a wide proliferation of product variants, asking industrial companies to adopt new strategies to remain competitive in the transition from mass production to mass customization. Traditional production strategies such as Make-to-Order (MTO) and Make-to-Stock (MTS) are no longer adequate for the efficient manufacturing of multiple product variants. In such a scenario, the Delayed Product Differentiation (DPD) rose as a hybrid strategy overcoming the main limitations of traditional production strategies, best balancing high product variety and quick response time with low storage cost through the so-called product platforms. These platforms are sub-systems of components, forming a common base structure from which a stream of product variants can be efficiently derived. Platforms are produced and stocked in advance, following an MTS strategy, and customized into different variants after the order arrival, according to an MTO strategy. Platforms dimension affects in an opposite manner their storage cost and their customization time, as platforms made of few components require long time for customization activities, reducing the storage cost. The best platform design and association to product variants are open topics in current literature, having major impact on the trade-off between platform storage cost and customization time. This paper contributes to applied research in mass customization, proposing a bi-objective optimization model for platform design and association to product variants, determining at the same time the best production strategy among MTO, MTS and DPD for each product variant to best balance platforms storage cost and customization time. The model is applied to a reference case study providing a multi-scenario analysis about the main effects of the limitation in the number of possible platform types on platform configurations, customization tasks and their correspondent customization time an

关键词： Mass customization product platforms bi-objective model multi-scenario analysis

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A bi-objective model for a Stage Shop Scheduling Problem with Human-Robot Collaboration

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IFAC-PapersOnLine 2022年第10期55卷 1290-1295页

作者： Ahmad Ghasemkhani Reza Tavakkoli-Moghaddam Mahdi Hamid Mohammad Mahdi Nasiri

Stage shop scheduling is a type of manufacturing problem, in which operations need to be done on a group of jobs and are separated into different phases depending on their types. The stages are carried out in a sequential order; however, the operations within each stage are carried out in any order. In other words, the operations of a stage cannot be initiated until all operations of the previous stage are completed. Human-robot collaboration has become more popular as technologies and business empowerment has brought humans and robots closer together. In this study, jobs are allocated to humans and robots by presenting a bi-objective linear model to minimize the makespan and the cost of utilizing agents in a stage shop problem. Moreover, an interactive method is used to convert the model into a single objective one. The sensitivity results show that the makespan is minimized noticeably by collaborating the human and robots.

关键词： Stage shop scheduling Human-Robot collaboration bi-objective model TH method

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A bi-objective model for scheduling green investments in two-stage supply chains

Supply Chain Analytics

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Supply Chain Analytics 2023年 3卷

作者： Caramia, Massimiliano Stecca, Giuseppe Dipartimento di Ingegneria dell'Impresa University of Rome “Tor Vergata” Via del Politecnico Rome 1-00133 Italy Istituto di Analisi dei Sistemi ed Informatica del Consiglio Nazionale delle Ricerche Via dei Taurini 19 Rome 00185 Italy

Investing in green technologies to increase sustainability in supply chains has become a common practice for two reasons: the first is directly related to the defense of the environment and people's health to smooth the emissions of pollutants;the second is the increasing consumer awareness of green products. Despite the higher costs of producing with green technologies and processes, there is also a higher markup on the price of products which rewards the former costs. This study proposes a mathematical model for scheduling green investments over time in a two-stage supply chain to minimize the impact of production on the environment and the economic costs deriving from the investment. The resulting bi-objective model has nonlinear constraints and is solved using a commercial solver. Given its complexity, we propose an upper-bound heuristic and a lower-bound model to reduce the optimality gap attained at a given time limit. Tests on synthetic instances have been conducted, and an example demonstrates the applicability and efficacy of the proposed model. © 2023 The Author(s)

关键词： bi-objective model Green Supply Chain Heuristic Investment Scheduling Sustainability Two-stage model

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