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  • 1.
    Cats, Oded
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Zhang, Chen
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Nissan, Albania
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport planning, economics and engineering.
    Empirical evaluation of an on-street parking pricing scheme in the city center2015Conference paper (Refereed)
    Abstract [en]

    Parking pricing policies can be used as a policy instrument to steer the parking market and reduce the externalities caused by traffic in general and parking in particular. A more efficient management of parking demand can improve the utilization of the limited parking capacity at high-demand areas. Even though parking policies are often a topic of public debate, there is lack of systematic empirical analysis of various parking measures. This paper proposes a methodology to empirically measure and evaluate the impacts of on-street parking policies. The utilization of on-street parking demand is computed based on transaction data from 70 ticket vending machines which is calibrated using floating car films. Measures of parking utilization such as occupancy and its temporal variation, throughput, parking duration and turnover are compared prior and following the introduction of a new parking scheme in the center of Stockholm, Sweden, in September 2013. The results indicate that the policy led to a reduction in parking occupancy although it did not yield the 85% occupancy level objective. Furthermore, the price increase has contradictory effects on throughput and turnover due to the interaction between parking occupancy and duration. The results also question the transferability of price elasticity. It is thus recommended to consider multiple measures of parking utilization when carrying out policy evaluation. 

  • 2.
    Cats, Oded
    et al.
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering. Delft University of Technology, Netherlands.
    Zhang, Chen
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Nissan, Albania
    KTH, School of Architecture and the Built Environment (ABE), Transport Science, Transport Planning, Economics and Engineering.
    Survey methodology for measuring parking occupancy: Impacts of an on-street parking pricing scheme in an urban center2016In: Transport Policy, ISSN 0967-070X, E-ISSN 1879-310X, Vol. 47, p. 55-63Article in journal (Refereed)
    Abstract [en]

    Parking pricing policies can be used as a policy instrument to steer the parking market and reduce the externalities caused by traffic in general and parking in particular. A more efficient management of parking demand can improve the utilization of the limited parking capacity in high-demand areas. Even though parking policies are often a topic of public debate, there is lack of systematic empirical analysis of various parking measures. This paper proposes a survey methodology to empirically measure the impacts of on-street parking policies based on automated parking transaction data. Parking performance is computed based on data available from ticket vending machines calibrated using floating car films. The survey method allows comparing parking occupancy including its temporal variations, allowing the analysis of the accumulated utilization pattern. Average and maximum parking occupancy levels, throughput, parking duration and total fare collection are compared prior and following the introduction of a new parking scheme for visitors to Stockholm inner-city, Sweden. The results indicate that the policy fulfilled its objective to increase the ease of finding a vacant parking place in the central areas and even resulted with underutilized parking spaces.

  • 3.
    Fragapane, Giuseppe
    et al.
    Norwegian University of Science and Technology.
    Zhang, Cevin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Sgarbossa, Fabio
    Norwegian University of Science and Technology.
    Strandhagen, Jan Ola
    Norwegian University of Science and Technology.
    An Agent-Based Simulation Approach To Model Hospital Logistics2019In: International Journal of Simulation Modelling, ISSN 1726-4529, E-ISSN 1996-8566, Vol. 18, no 4, p. 654-665Article in journal (Refereed)
    Abstract [en]

    The increasing rate of hospital admissions has resulted in a commensurate increase in the number of treatments and surgeries performed, as well as resource and material usage, and requires planners to improve hospitals' internal logistics. Logistics modelling of internal goods and corresponding material handling systems and simulating future scenarios can provide planners with necessary decision support. Introducing an agent-based simulation model using historical data generated by an automated guided vehicle (AGV) in a case hospital facilitates analysing the goods delivery system's current status and potential countermeasures to improve internal logistics. In comparison with other industries, such as manufacturing, AGVs utilised in hospitals have to interact with persons, patients and elevators, transport several different types of goods and cover a sizeable multi-floor area. By including these factors, the simulation model represents an appropriate method to test different scenarios and improve delivery performance and AGV utilisation. The study highlights the constraints related to operating AGVs in dynamic environments, such as those encountered in hospitals.

  • 4.
    Sun, Yan
    et al.
    Shandong Univ Finance & Econ, Sch Management Sci & Engn, 7366 Second Ring East Rd, Jinan 250014, Shandong, Peoples R China..
    Hrusovsky, Martin
    WU Vienna Univ Econ & Business, Inst Prod Management, Welthandelspl 1, A-1020 Vienna, Austria..
    Zhang, Chen
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Lang, Maoxiang
    Beijing Jiaotong Univ, Sch Traff & Transportat, Beijing 100044, Peoples R China..
    A Time-Dependent Fuzzy Programming Approach for the Green Multimodal Routing Problem with Rail Service Capacity Uncertainty and Road Traffic Congestion2018In: Complexity, ISSN 1076-2787, E-ISSN 1099-0526, article id 8645793Article in journal (Refereed)
    Abstract [en]

    This study explores an operational-level container routing problem in the road-rail multimodal service network. In response to the demand for an environmentally friendly transportation, we extend the problem into a green version by using both emission charging method and bi-objective optimization to optimize the CO2 emissions in the routing. Two uncertain factors, including capacity uncertainty of rail services and travel time uncertainty of road services, are formulated in order to improve the reliability of the routes. By using the triangular fuzzy numbers and time-dependent travel time to separately model the capacity uncertainty and travel time uncertainty, we establish a fuzzy chance-constrained mixed integer nonlinear programming model. A linearization-based exact solution strategy is designed, so that the problem can be effectively solved by any exact solution algorithm on any mathematical programming software. An empirical case is presented to demonstrate the feasibility of the proposed methods. In the case discussion, sensitivity analysis and bi-objective optimization analysis are used to find that the bi-objective optimization method is more effective than the emission charging method in lowering the CO2 emissions for the given case. Then, we combine sensitivity analysis and fuzzy simulation to identify the best confidence value in the fuzzy chance constraint. All the discussion will help decision makers to better organize the green multimodal transportation.

  • 5.
    Sun, Yan
    et al.
    School of Management Science and Engineering, Shandong University of Finance and Economics.
    Li, Xinya
    Institute of Technology, Shandong TV University.
    Liang, Xia
    School of Management Science and Engineering, Shandong University of Finance and Economics.
    Zhang, Cevin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    A Bi-Objective Fuzzy Credibilistic Chance-Constrained Programming Approach for the Hazardous Materials Road-Rail Multimodal Routing Problem under Uncertainty and Sustainability2019In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 11, no 9, article id 2577Article in journal (Refereed)
    Abstract [en]

    Hazardous materials transportation involves extensive risk and cannot be avoided in practice. An advanced routing, however, can help to reduce the risk by planning the best transportation routes for hazardous materials that can make effective tradeoffs between the risk objective and the economic objective. In this study, we explore the hazardous materials routing problem in the road-rail multimodal transportation network with a hub-and-spoke structure, in which the risk is measured by the multiplication of population exposure and the associated volume of hazardous materials, and minimizing the total risk of all the transportation orders of hazardous materials is set as the risk objective. It is difficult to estimate the population exposure exactly during the routing decision-making process, which results in its uncertainty. In this study, we formulate the uncertain population exposure from a fuzzy programming perspective by using triangular fuzzy numbers. Moreover, the carbon dioxide emission constraint is formulated to realize the sustainable transportation of hazardous materials. To optimize the problem under the above framework, we first establish a bi-objective fuzzy mixed integer nonlinear programming model, and then develop a three-stage exact solution strategy that the combines fuzzy credibilistic chance constraint, linearization technique, and the normalized weighting method. Finally, a computational experiment is carried out to verify the feasibility of the proposed method in dealing with the problem. The experimental results indicate that tradeoffs between the two conflicting objectives can be effectively made by using the Pareto frontier to the hazardous materials routing problem. Furthermore, the credibility level and carbon dioxide emission cap significantly influence the hazardous materials routing optimization. Their effects on the optimization result are quantified by using sensitivity analysis, which can draw some useful insights to help decision makers to better organize the hazardous materials road-rail multimodal transportation under uncertainty and sustainability.

  • 6.
    Sun, Yan
    et al.
    School of Management Science and Engineering, Shandong University of Finance and Economics.
    Liang, Xia
    School of Management Science and Engineering, Shandong University of Finance and Economics.
    Li, Xinya
    Institute of Technology, Shandong TV University.
    Zhang, Chen
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    A Fuzzy Programming Method for Modeling Demand Uncertainty in the Capacitated Road–Rail Multimodal Routing Problem with Time Windows2019In: Symmetry, ISSN 2073-8994, E-ISSN 2073-8994, Vol. 11, no 1, article id 91Article in journal (Refereed)
    Abstract [en]

    Demand uncertainty is an important issue that influences the strategic, tactical, and operational-level decision making in the transportation/logistics/supply chain planning. In this study, we explore the effect of demand uncertainty on the operational-level freight routing problem in the capacitated multimodal transportation network that consists of schedule-based rail transportation and time-flexible road transportation. Considering the imprecise characteristic of the demand, we adopt fuzzy set theory to model its uncertainty and use trapezoidal fuzzy numbers to represent the fuzzy demands. We set multiple transportation orders as the optimization object and employ soft time windows to reflect the customer requirement on on-time transportation. Under the above situation, we establish a fuzzy mixed integer nonlinear programming (FMINLP) model to formulate the capacitated road–rail multimodal routing problem with demand uncertainty and time windows. We first use the fuzzy expected value model and credibility measure based fuzzy chance-constrained programming to realize the defuzziness of the model and then adopt linearization technique to reformulate the crisp model to finally generate an equivalent mixed integer linear programming (MILP) model that can be solved by standard mathematical programming software. Finally, a numerical case is presented to demonstrate the feasibility of the proposed method. Sensitivity analysis and fuzzy simulation are combined to quantify the effect of demand uncertainty on the routing problem and also reveal some helpful insights and managerial implications.

  • 7.
    Sun, Yan
    et al.
    School of Management Science and Engineering, Shandong University of Finance and Economics.
    Lu, Yue
    School of Traffic and Transportation, Beijing Jiaotong University.
    Zhang, Cevin
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Fuzzy Linear Programming Models for a Green Logistics Center Location and Allocation Problem under Mixed Uncertainties Based on Different Carbon Dioxide Emission Reduction Methods2019In: Sustainability, ISSN 2071-1050, E-ISSN 2071-1050, Vol. 11, no 22, article id 6448Article in journal (Refereed)
    Abstract [en]

    This study explores a foundational logistics center location and allocation problem in a three-stage logistics network that consists of suppliers, logistics centers, and customers. In this study, the environmental sustainability of the logistics network is improved by optimizing the carbon dioxide emissions of the logistics network based on multi-objective optimization and carbon tax regulation. Mixed uncertainties in the planning stage, including the supply capacities of suppliers, operation capacities of logistics centers, and demands of customers, are modeled using triangular fuzzy numbers based on the fuzzy set theory to order to enhance the reliability of the logistics center location and allocation planning. To solve the green logistics center location and allocation problem under mixed uncertainties, we establish two fuzzy mixed integer linear programming models. The fuzzy credibilistic chance-constrained programming is then adopted to obtain the crisp and linear reformulations of the fuzzy programming models. A numerical case is given to verify the feasibility of the proposed methods, in which the performance of carbon tax regulation in reducing carbon dioxide emissions is then tested based on the benchmark provided by the multi-objective optimization. Lastly, sensitivity analysis and fuzzy simulation are utilized to reveal the effect of the mixed uncertainties on the logistics location and allocation planning and further determine the best confidence level in the fuzzy chance constraints to provide decision makers with a crisp plan.

  • 8.
    Sun, Yan
    et al.
    School of Management Science and Engineering, Shandong University of Finance and Economics.
    Zhang, Chen
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Dong, Kunxiang
    School of Management Science and Engineering, Shandong University of Finance and Economics.
    Lang, Maoxiang
    School of Traffic and Transportation Beijing Jiaotong University.
    Multiagent Modelling and Simulation of a Physical Internet Enabled Rail-Road Intermodal Transport System2018In: Urban Rail Transit, ISSN 2199-6687, Vol. 4, p. 1-14Article in journal (Refereed)
    Abstract [en]

    Simulation-based analysis has been used for planning, control, and decision-making support of physical internet enabled logistics networks. However, multiagent modelling and simulation based on micro-level interactions have been rarely developed for the pre-studies of digital transformation of urban rail transit systems. This hinders a wider industrial deployment of agent technology in the physical internet enabled transport infrastructure. To fill in this knowledge gap, this work presents an agent-based simulation that explicitly models the micro-level protocols of mobile recourse units and their interaction with the physical infrastructure in a rail-road intermodal transport network. Parameterisation of the simulation model is changeable to examine the influences of different efficiency factors. This allows understanding of which structural functions and resource configuration would make an impact system-wide. Through a practical application, a multiagent system is developed for modelling and analysis of sustainable logistics with individually operated mobile resource units. An agent-based simulation assessment is performed to quantify the improvement options. The results reveal that the physical internet can prevent trucks from empty driving, which has a positive effect on the sustainable logistics operations. The proposed model can be used to support the deployment and planning of digital transformation that could be implemented in urban rail transit systems serving urban distribution and passenger transport.

  • 9.
    Zhang, Cevin
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Baalsrud Hauge, Jannicke
    KTH, School of Industrial Engineering and Management (ITM), Sustainable production development. Bremer Institut fuer Produktion und Logistik Bremen Germany.
    Pukk Härenstam, Karin
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    A Serious Logistical Game of Paediatric Emergency Medicine: Proposed Scoring Mechanism and Pilot Test2019In: Games and Learning Alliance / [ed] Antonios Liapis; Georgios N. Yannakakis; Manuel Gentile; Manuel Ninaus, Springer Publishing Company, 2019, p. 468-478Chapter in book (Refereed)
    Abstract [en]

    Outcomes of care for various diseases and urgent conditions in an emergency department are dependent on balancing the patient’s need and available resources through management and coordination under often rapidly changing preconditions. However, although it is central to resilient operations, decision-making in dynamic resource management is rarely visible to managers. Sometimes the identification of successful strategies is apparent only through adverse event reports. A simulation game could be helpful for the acquisition of non-technical skills in addressing operational conundrums that could threaten the defence ability of a paediatric emergency department under care production pressures. This contribution presents a Sandtable serious logistical game of the care production system and, in particular, proposes its scoring mechanism, which was tested in a set of logistical experiments. The results show that through gamification, participants were challenged in terms of their intrinsic self-interest when it came to approaching the work. More importantly, the proposed extrinsic reward system allows all parallel functional roles to be equally rewarded as the game evolves. Anticipatory human resource management is identified as a successful strategy for achieving a sustainable working environment if the organizational resilience is confronted with patient inflow surges during the busiest hours of the busiest day.

  • 10.
    Zhang, Cevin
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Sebastiaan, Meijer
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems.
    A Simulation Game of Patient Transportation2019In: Neo-Simulation and Gaming Toward Active Learning / [ed] Ryoju Hamada;Songsri Soranastaporn; Hidehiko Kanegae; Pongchai Dumrongrojwatthana; Settachai Chaisanit; Paola Rizzi; Vinod Dumblekar, Springer, 2019, 1, p. 53-66Chapter in book (Refereed)
    Abstract [en]

    The handling of patients is a complex process. The training and education of patient transportation workers are meant to ensure efficiency and health outcomes. A simulation game, joined by personnel with working experience or prospective professionals in the healthcare system, is a life-like medium for improving decision makings in non-rational operation management. However, few examples are known in regard to synthesizing complex systems, such as clinical facilities, into healthcare simulation games. In order to fill this gap, this work proposes the adopt theory and reports the development of a simulation game that reconciles patient handling with the support of different types of simulation techniques. The simulation game has a physical entity simulator as its back-end and a panel of command and control for each player as its front end. The physical entity simulator is based on the interactions of mobile agents. Agent-based modeling targets the correct level of representation of the operative environment. The simulation game is tested with managers who have more than 10-years of working experience with patient flow management in pediatric care. Reflections from players indicate that modeling and abstraction using an agent model is an efficient synthesis of complex systems. The theory, methods, and results of this study are expected to contribute to the development of simulation games that can be applied in health service provision, in general, and in patient transportation, in particular.

  • 11.
    Zhang, Chen
    et al.
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    Grandits, Thomas
    KTH, School of Technology and Health (STH).
    Härenstam, Karin
    Karolinska Institutet.
    Hauge, Jannicke
    KTH, School of Industrial Engineering and Management (ITM).
    Meijer, Sebastiaan
    KTH, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), Biomedical Engineering and Health Systems, Health Informatics and Logistics.
    A systematic literature review of simulation models for non-technical skill training in healthcare logistics2018In: Advances in Simulation, ISSN 2059-0628, p. 1-15Article in journal (Refereed)
    Abstract [en]

    Resource allocation in patient care relies heavily on individual judgements of healthcare professionals. Such professionals perform coordinating functions by managing the timing and execution of a multitude of care processes for multiple patients. Based on advances in simulation, new technologies that could be used for establishing realistic representations have been developed. These simulations can be used to facilitate understanding of various situations, coordination training and education in logistics, decision-making processes, and design aspects of the healthcare system. However, no study in the literature has synthesized the types of simulations models available for non-technical skills training and coordination of care. A systematic literature review, following the PRISMA guidelines, was performed to identify simulation models that could be used for training individuals in operative logistical coordination that occurs on a daily basis. This article reviewed papers of simulation in healthcare logistics presented in the Web of Science Core Collections, ACM digital library, and JSTOR databases. We conducted a screening process to gather relevant papers as the knowledge foundation of our literature study. The screening process involved a query-based identification of papers and an assessment of relevance and quality. Two hundred ninety-four papers met the inclusion criteria. The review showed that different types of simulation models can be used for constructing scenarios for addressing different types of problems, primarily for training and education sessions. The papers identified were classified according to their utilized paradigm and focus areas. (1) Discrete-event simulation in single-category and single-unit scenarios formed the most dominant approach to developing healthcare simulations and dominated all other categories by a large margin. (2) As we approached a systems perspective (cross-departmental and cross-institutional), discrete-event simulation became less popular and is complemented by system dynamics or hybrid modeling. (3) Agent-based simulations and participatory simulations have increased in absolute terms, but the share of these modeling techniques among all simulations in this field remains low. An extensive study analyzing the literature on simulation in healthcare logistics indicates a growth in the number of examples demonstrating how simulation can be used in healthcare settings. Results show that the majority of studies create situations in which non-technical skills of managers, coordinators, and decision makers can be trained. However, more system-level and complex system-based approaches are limited and use methods other than discrete-event simulation.

  • 12.
    Zhang, Chen
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Hanchi, Hamza
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Meijer, Sebastiaan
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Evaluating the Effect of Centralized Administration on Health Care Performances Using Discrete-Event Simulation2017In: 2017 PORTLAND INTERNATIONAL CONFERENCE ON MANAGEMENT OF ENGINEERING AND TECHNOLOGY (PICMET) / [ed] Kocaoglu, DF Anderson, TR Daim, TU Kozanoglu, DC Niwa, K Perman, G Steenhuis, HJ, IEEE , 2017Conference paper (Refereed)
    Abstract [en]

    Patient flow management is increasingly motivated by the request to improve system performance. The improvements on local departments are expected with minimal negative effects on the upstream and downstream departments which are integral parts of the care pathway. Although it is widely debated that hospital buildings are expensively constructed and operated, we observe a lack of efforts on the logistical efficiency of care provision within facilities in hospitals, especially in developing areas. This askes for more research attentions towards the knowledge gap between health care supply and demand. Our work presented a simulation-based approach to study the impact of centralized administrative works by evaluating waiting times of services and resource utilizations. A discrete-event simulation (DES) model was constructed in reference to a hospital complex in Jiangsu, China. The results showed that the centralized administration benefited patients regarding a reduced total length of stay and waiting times of administration; however, reorganizing administration also influenced waiting times of medical services and resource utilizations of different types of facilities. Neglecting administration in care pathway might yield to unclear knowledge of their impacts. This article can also support the inclusion of simulation in the strategic planning phase of health care projects.

  • 13.
    Zhang, Chen
    et al.
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Hanchi, Hamza
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Meijer, Sebastiaan
    KTH, School of Technology and Health (STH), Health Systems Engineering, Health Care Logistics.
    Identifying Influential Factors of Patient Length of Stay In a Surgery Center: a Simulation Modelling Approach2017Conference paper (Refereed)
1 - 13 of 13
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