This paperpresentsa travel time model for multi-deep AS/RS, which determines the average travel times during a single command storage or retrieval and a dual command cycle. The model can determine the relocation probability, the number of expected relocations and the travel times in storage channels itself exactly dependingon the stock filling level. The travel time model assumes random storage policies. Travel time determination is trivial for single-deep AS/RS, because no relocation necessity applies and it gets more complicated with an increasing depth of the storage racks. The deeper goods can be stored, the more goods can potentially be stored in front of each other in one storage channel. This leads to relocation operations of blocking goods and causes higher total travel times. The higher the stock filling level, the higher is the relocation probability and the number of necessary relocations. The calculation of relocation probabilities in this work is based on a homogeneousstorage good allocation structure which leads to a symmetric allocation of storage goods and enables an easy modelling of travel times. This paper presents a travel time model with a continuousstorage rack approximation of a multi-deepAS/RS in closed-form expression. Furthermore, the storage channel allocation probabilities are mathematically proven. The relocation probability for storage operations and retrieval operations are the same. Finally, the derived travel time models and relocation probabilities are verified by simulation.
{"title":"Travel time model for multi-deep automated storage and retrieval system with a homogeneous allocation structure","authors":"T. Lehmann, Jakob Hußmann","doi":"10.23773/2021_5","DOIUrl":"https://doi.org/10.23773/2021_5","url":null,"abstract":"This paperpresentsa travel time model for multi-deep AS/RS, which determines the average travel times during a single command storage or retrieval and a dual command cycle. The model can determine the relocation probability, the number of expected relocations and the travel times in storage channels itself exactly dependingon the stock filling level. The travel time model assumes random storage policies. Travel time determination is trivial for single-deep AS/RS, because no relocation necessity applies and it gets more complicated with an increasing depth of the storage racks. The deeper goods can be stored, the more goods can potentially be stored in front of each other in one storage channel. This leads to relocation operations of blocking goods and causes higher total travel times. The higher the stock filling level, the higher is the relocation probability and the number of necessary relocations. The calculation of relocation probabilities in this work is based on a homogeneousstorage good allocation structure which leads to a symmetric allocation of storage goods and enables an easy modelling of travel times. This paper presents a travel time model with a continuousstorage rack approximation of a multi-deepAS/RS in closed-form expression. Furthermore, the storage channel allocation probabilities are mathematically proven. The relocation probability for storage operations and retrieval operations are the same. Finally, the derived travel time models and relocation probabilities are verified by simulation.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73552596","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Fottner, D. Clauer, Fabian Hormes, M. Freitag, Thies Beinke, L. Overmeyer, S. Gottwald, R. Elbert, T. Sarnow, T. Schmidt, Karl-Benedikt Reith, Hartmut Zadek, Franziska Thomas
{"title":"Autonomous Systems in Intralogistics - State of the Art and Future Research Challenges","authors":"J. Fottner, D. Clauer, Fabian Hormes, M. Freitag, Thies Beinke, L. Overmeyer, S. Gottwald, R. Elbert, T. Sarnow, T. Schmidt, Karl-Benedikt Reith, Hartmut Zadek, Franziska Thomas","doi":"10.23773/2021_02","DOIUrl":"https://doi.org/10.23773/2021_02","url":null,"abstract":"","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88499031","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"A Systematic Evaluation of Extensions for the Shared Customer Collaboration Vehicle Routing Problem","authors":"B. Himstedt, F. Meisel","doi":"10.23773/2021_04","DOIUrl":"https://doi.org/10.23773/2021_04","url":null,"abstract":"","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87137090","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The mobile supply chain (MSC) is a new concept that allows companies more adaptability and flexibility. In MSCs, a product family can be produced, distributed, and delivered by a mobile factory, carried by trucks, and shared among different customers. In this paper, to optimize production scheduling and the mobile factory routing problem under uncertainty, a robust decentralized decision-making approach (RDDMA) based on the Analytical Target Cascading (ATC) approach is developed. The RDDMA is a bi-level hierarchical optimization method that divides an all-in-one model into sub-problems and aims to address each agent’s target. It is a 4-phase procedure, including time window determination, robust mobile factory routing, actual production scheduling, and adjustment. In real-world applications, the service time at each site is uncertain. Therefore, a scenario-based robust optimization approach is utilized to manage the uncertainties of the problem. Finally, the RDDMA performance is evaluated using several instances. The results suggest the proposed approach can provide robust solutions for such a multi-agent problem.
{"title":"A robust decentralized decision-making approach for mobile supply chains under uncertainty","authors":"Hani Shahmoradi-Moghadam, Jörn Schönberger","doi":"10.23773/2021_06","DOIUrl":"https://doi.org/10.23773/2021_06","url":null,"abstract":"The mobile supply chain (MSC) is a new concept that allows companies more adaptability and flexibility. In MSCs, a product family can be produced, distributed, and delivered by a mobile factory, carried by trucks, and shared among different customers. In this paper, to optimize production scheduling and the mobile factory routing problem under uncertainty, a robust decentralized decision-making approach (RDDMA) based on the Analytical Target Cascading (ATC) approach is developed. The RDDMA is a bi-level hierarchical optimization method that divides an all-in-one model into sub-problems and aims to address each agent’s target. It is a 4-phase procedure, including time window determination, robust mobile factory routing, actual production scheduling, and adjustment. In real-world applications, the service time at each site is uncertain. Therefore, a scenario-based robust optimization approach is utilized to manage the uncertainties of the problem. Finally, the RDDMA performance is evaluated using several instances. The results suggest the proposed approach can provide robust solutions for such a multi-agent problem.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79541821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk-aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp-up consideration can be based on a cost-benefit analysis.
{"title":"A model of supply-chain decisions for resource sharing with an application to ventilator allocation to combat COVID-19.","authors":"Sanjay Mehrotra, Hamed Rahimian, Masoud Barah, Fengqiao Luo, Karolina Schantz","doi":"10.1002/nav.21905","DOIUrl":"10.1002/nav.21905","url":null,"abstract":"<p><p>We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk-averse condition. The model is applied to study the allocation of ventilator inventory in the COVID-19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non-COVID-19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non-COVID-19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top-most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst-case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk-aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp-up consideration can be based on a cost-benefit analysis.</p>","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":1.9,"publicationDate":"2020-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7267382/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140866946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-04-03DOI: 10.1101/2020.04.02.20051078
Sanjay Mehrotra, Hamed Rahimian, Masoud Barah, Fengqiao Luo, K. Schantz
We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk‐averse condition. The model is applied to study the allocation of ventilator inventory in the COVID‐19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non‐COVID‐19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non‐COVID‐19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top‐most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst‐case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk‐aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp‐up consideration can be based on a cost‐benefit analysis.
{"title":"A model of supply‐chain decisions for resource sharing with an application to ventilator allocation to combat COVID‐19","authors":"Sanjay Mehrotra, Hamed Rahimian, Masoud Barah, Fengqiao Luo, K. Schantz","doi":"10.1101/2020.04.02.20051078","DOIUrl":"https://doi.org/10.1101/2020.04.02.20051078","url":null,"abstract":"We present a stochastic optimization model for allocating and sharing a critical resource in the case of a pandemic. The demand for different entities peaks at different times, and an initial inventory for a central agency are to be allocated. The entities (states) may share the critical resource with a different state under a risk‐averse condition. The model is applied to study the allocation of ventilator inventory in the COVID‐19 pandemic by FEMA to different U.S. states. Findings suggest that if less than 60% of the ventilator inventory is available for non‐COVID‐19 patients, FEMA's stockpile of 20 000 ventilators (as of March 23, 2020) would be nearly adequate to meet the projected needs in slightly above average demand scenarios. However, when more than 75% of the available ventilator inventory must be reserved for non‐COVID‐19 patients, various degrees of shortfall are expected. In a severe case, where the demand is concentrated in the top‐most quartile of the forecast confidence interval and states are not willing to share their stockpile of ventilators, the total shortfall over the planning horizon (until May 31, 2020) is about 232 000 ventilator days, with a peak shortfall of 17 200 ventilators on April 19, 2020. Results are also reported for a worst‐case where the demand is at the upper limit of the 95% confidence interval. An important finding of this study is that a central agency (FEMA) can act as a coordinator for sharing critical resources that are in short supply over time to add efficiency in the system. Moreover, through properly managing risk‐aversion of different entities (states) additional efficiency can be gained. An additional implication is that ramping up production early in the planning cycle allows to reduce shortfall significantly. An optimal timing of this production ramp‐up consideration can be based on a cost‐benefit analysis.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46966397","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Order picking systems are confronted with a volatile demand and short delivery time requirements. Manufacturing companies face the increasing variability requirements with Heijunka-levelling, one method of the Toyota Production System. The objectives of this publication are to develop a levelling concept for order picking systems, to analyse its performance based on a discrete-time analytical model and to develop a staffing algorithm determining the required workforce level in an order picking system with levelled order release. The levelling concept for order picking systems results from the existing models of Heijunka-levelling in the literature, which are adopted and expanded regarding the specific requirements of order picking systems. The order picking system with levelled order release is depicted as a discrete-time Markov chain. To analyse its performance, we derive several performance measures, such as service level, backlog duration and system utilisation, from the steady-state distribution of the Markov chain. The s taffing a lgorithm i s a binary search algorithm based on the Markov chain. The models developed in this publication enable a quantitative evaluation of the impact of several system parameters, such as variability of customer demand, workforce level and traffic intensity, on the performance measures of the order picking system. Furthermore, the staffing algorithm determines the workforce level which is required to guarantee a certain system performance, such as a service level of 99%, in an order picking system with levelled order release. By comparing levelled order release to FCFS-based order release strategies in a numerical example, we show the benefits of levelled order release.
{"title":"Discrete-Time Analysis of Levelled Order Release and Staffing in Order Picking Systems","authors":"Uta Mohring, Marion Baumann, K. Furmans","doi":"10.23773/2020_9","DOIUrl":"https://doi.org/10.23773/2020_9","url":null,"abstract":"Order picking systems are confronted with a volatile demand and short delivery time requirements. Manufacturing companies face the increasing variability requirements with Heijunka-levelling, one method of the Toyota Production System. The objectives of this publication are to develop a levelling concept for order picking systems, to analyse its performance based on a discrete-time analytical model and to develop a staffing algorithm determining the required workforce level in an order picking system with levelled order release. The levelling concept for order picking systems results from the existing models of Heijunka-levelling in the literature, which are adopted and expanded regarding the specific requirements of order picking systems. The order picking system with levelled order release is depicted as a discrete-time Markov chain. To analyse its performance, we derive several performance measures, such as service level, backlog duration and system utilisation, from the steady-state distribution of the Markov chain. The s taffing a lgorithm i s a binary search algorithm based on the Markov chain. The models developed in this publication enable a quantitative evaluation of the impact of several system parameters, such as variability of customer demand, workforce level and traffic intensity, on the performance measures of the order picking system. Furthermore, the staffing algorithm determines the workforce level which is required to guarantee a certain system performance, such as a service level of 99%, in an order picking system with levelled order release. By comparing levelled order release to FCFS-based order release strategies in a numerical example, we show the benefits of levelled order release.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90189704","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This research article aims to identify sustainability performance indicators (SPIs) and provide practical guidance for assessing organizations and their supply chains’ sustainability-related performances. We conducted a systematic literature review to analyze scientific journal articles related to sustainable supply chains and performance measurement. We assessed sustainability performance by identifying 1054 indicators from selected scientific journal articles. In addition, in-depth analyses of selected journal articles, predefined attribute categories, and the text restructuring resulted in a unique and coherent list of 68 SPIs. Of these SPIs, 47% originated from the environmental sustainability dimension, 31% from the social sustainability dimension, and 22% from the economic sustainability dimension. The systematic literature review’s results identified a complete lack of agreement on how to measure organizations and their supply chains’ sustainability performances.
{"title":"Supply chain sustainability performance indicators - A systematic literature review","authors":"M. Saeed, W. Kersten","doi":"10.23773/2020_6","DOIUrl":"https://doi.org/10.23773/2020_6","url":null,"abstract":"This research article aims to identify sustainability performance indicators (SPIs) and provide practical guidance for assessing organizations and their supply chains’ sustainability-related performances. We conducted a systematic literature review to analyze scientific journal articles related to sustainable supply chains and performance measurement. We assessed sustainability performance by identifying 1054 indicators from selected scientific journal articles. In addition, in-depth analyses of selected journal articles, predefined attribute categories, and the text restructuring resulted in a unique and coherent list of 68 SPIs. Of these SPIs, 47% originated from the environmental sustainability dimension, 31% from the social sustainability dimension, and 22% from the economic sustainability dimension. The systematic literature review’s results identified a complete lack of agreement on how to measure organizations and their supply chains’ sustainability performances.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89560899","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Retailers can exploit the consumer willingness to substitute to improve their profit, service level and waste. This paper investigates to what extent such improvement can be realised by the replenishment decisions. Two order policies are compared: one policy neglecting product substitution, and a new policy that decides on order quantities for all products simultaneously meanwhile anticipating stock-outbased substitution. Both policies are analysed by simulation-based optimisation. Besides finding the optimal parameter values or a variety of settings by exact enumeration (as a benchmark), we present for the case of one-way substitution a heuristic search procedure. The heuristic finds (nearly) optimal parameter values quickly and turns out to find optimal parameter values in almost all settings. An average profit increase of almost 9% is obtained when anticipating on substitution, while waste levels can decrease with more than 35%. A clear trade-off between service levels and profit/waste levels is found. Assuming the retailer aims at profit maximisation, the service level of one product maybe very low or even zero. The results provide the following managerial insights in: (i) the service levels and waste levels that maximize the retailer’s profit, (ii) whether a product should be removed from the assortment, (iii) the profit loss and waste increase of setting a higher (sub optimal) service level, e.g. for strategic reasons. Reversely, one may learn from the results what the profit margin of a product should be to justify a certain service level to a profit maximizing retailer. These insights maybe useful to retailers whose primary objective is beyond profit maximisation.
{"title":"Retailer replenishment policies with one-way consumer-based substitution to increase profit and reduce food waste","authors":"M. Buisman, R. Haijema, E. Hendrix","doi":"10.23773/2020_7","DOIUrl":"https://doi.org/10.23773/2020_7","url":null,"abstract":"Retailers can exploit the consumer willingness to substitute to improve their profit, service level and waste. This paper investigates to what extent such improvement can be realised by the replenishment decisions. Two order policies are compared: one policy neglecting product substitution, and a new policy that decides on order quantities for all products simultaneously meanwhile anticipating stock-outbased substitution. Both policies are analysed by simulation-based optimisation. Besides finding the optimal parameter values or a variety of settings by exact enumeration (as a benchmark), we present for the case of one-way substitution a heuristic search procedure. The heuristic finds (nearly) optimal parameter values quickly and turns out to find optimal parameter values in almost all settings. An average profit increase of almost 9% is obtained when anticipating on substitution, while waste levels can decrease with more than 35%. A clear trade-off between service levels and profit/waste levels is found. Assuming the retailer aims at profit maximisation, the service level of one product maybe very low or even zero. The results provide the following managerial insights in: (i) the service levels and waste levels that maximize the retailer’s profit, (ii) whether a product should be removed from the assortment, (iii) the profit loss and waste increase of setting a higher (sub optimal) service level, e.g. for strategic reasons. Reversely, one may learn from the results what the profit margin of a product should be to justify a certain service level to a profit maximizing retailer. These insights maybe useful to retailers whose primary objective is beyond profit maximisation.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87586436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Electric Vehicle Scheduling Problem (E-VSP) complicates traditional bus scheduling for public transport by restricting the range of the buses. To compensate for these limitations, detours to charging stations become necessary in order to charge the vehicle batteries. Charging is a nonlinear process with regard to real conditions, especially when taking partial and opportunity charging into account. However, within most existing solution methods for the E-VSP, the work of charging a vehicle battery is substantially simplified. In most cases, charging is assumed to be performed within linear or even constant time windows. In this paper, we analyze the impact of simplifying assumptions about charging times of electric buses on solutions of the E-VSP. Therefore, we propose charging models reflecting the nonlinear charging process precisely. Furthermore, we enhance an existing solution method for the E-VSP and provide an algorithm for incorporating partial and opportunity charging. Through a comprehensive computational study using real-world bus timetables, we identify major discrepancies between model assumptions and real charging behaviours of electric buses. On the one hand, we show that the assumption of constant charging times generally leads to overestimated time windows for charging, which increases the total costs. On the other hand, we demonstrate that assuming linear charging times underestimates the time windows actually required for charging, widely leading to infeasible vehicle rotations. We investigate this issue by using the technical data of lithium-ion batteries, which are mainly used in practice at present.
{"title":"Scheduling Electric Buses in Public Transport: Modeling of the Charging Process and Analysis of Assumptions","authors":"Nils Olsen, N. Kliewer","doi":"10.23773/2020_4","DOIUrl":"https://doi.org/10.23773/2020_4","url":null,"abstract":"The Electric Vehicle Scheduling Problem (E-VSP) complicates traditional bus scheduling for public transport by restricting the range of the buses. To compensate for these limitations, detours to charging stations become necessary in order to charge the vehicle batteries. Charging is a nonlinear process with regard to real conditions, especially when taking partial and opportunity charging into account. However, within most existing solution methods for the E-VSP, the work of charging a vehicle battery is substantially simplified. In most cases, charging is assumed to be performed within linear or even constant time windows. In this paper, we analyze the impact of simplifying assumptions about charging times of electric buses on solutions of the E-VSP. Therefore, we propose charging models reflecting the nonlinear charging process precisely. Furthermore, we enhance an existing solution method for the E-VSP and provide an algorithm for incorporating partial and opportunity charging. Through a comprehensive computational study using real-world bus timetables, we identify major discrepancies between model assumptions and real charging behaviours of electric buses. On the one hand, we show that the assumption of constant charging times generally leads to overestimated time windows for charging, which increases the total costs. On the other hand, we demonstrate that assuming linear charging times underestimates the time windows actually required for charging, widely leading to infeasible vehicle rotations. We investigate this issue by using the technical data of lithium-ion batteries, which are mainly used in practice at present.","PeriodicalId":49772,"journal":{"name":"Naval Research Logistics","volume":null,"pages":null},"PeriodicalIF":2.3,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79346882","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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