Farzad Avishan, M. Elyasi, Ihsan Yanikoglu, Ali Ekici, O. Ö. Özener
Management of humanitarian logistics operations is one of the most critical planning problems to be addressed immediately after a disaster. The response phase covers the first 12 hours after the disaster and is prone to uncertainties because of debris and gridlock traffic influencing the dispatching operations of relief logistics teams in the areas affected. Moreover, the teams have limited time and resources, and they must provide equitable distribution of supplies to affected people. This paper proposes an adjustable robust optimization approach for the associated humanitarian logistics problem. The approach creates routes for relief logistics teams and decides the service times of the visited sites to distribute relief supplies by taking the uncertainty in travel times into account. The associated model allows relief logistics teams to adjust their service decisions according to the revealed information during the process. Hence, our solutions are robust for the worst-case realization of travel times, but still more flexible and less conservative than those of static robust optimization. We propose novel reformulation techniques to model these adjustable decisions. The resulting models are computationally challenging optimization problems to be solved by exact methods, and, hence, we propose heuristic algorithms. The state-of-the-art heuristic, which is based on clustering and a dedicated decision-rule algorithm, yields near-optimal results for medium-sized instances and is scalable even for large-sized instances. We have also shown the effectiveness of our approach in a case study using a data set obtained from an earthquake that hit the Van province of Turkey in 2011. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1204 .
{"title":"Humanitarian Relief Distribution Problem: An Adjustable Robust Optimization Approach","authors":"Farzad Avishan, M. Elyasi, Ihsan Yanikoglu, Ali Ekici, O. Ö. Özener","doi":"10.1287/trsc.2023.1204","DOIUrl":"https://doi.org/10.1287/trsc.2023.1204","url":null,"abstract":"Management of humanitarian logistics operations is one of the most critical planning problems to be addressed immediately after a disaster. The response phase covers the first 12 hours after the disaster and is prone to uncertainties because of debris and gridlock traffic influencing the dispatching operations of relief logistics teams in the areas affected. Moreover, the teams have limited time and resources, and they must provide equitable distribution of supplies to affected people. This paper proposes an adjustable robust optimization approach for the associated humanitarian logistics problem. The approach creates routes for relief logistics teams and decides the service times of the visited sites to distribute relief supplies by taking the uncertainty in travel times into account. The associated model allows relief logistics teams to adjust their service decisions according to the revealed information during the process. Hence, our solutions are robust for the worst-case realization of travel times, but still more flexible and less conservative than those of static robust optimization. We propose novel reformulation techniques to model these adjustable decisions. The resulting models are computationally challenging optimization problems to be solved by exact methods, and, hence, we propose heuristic algorithms. The state-of-the-art heuristic, which is based on clustering and a dedicated decision-rule algorithm, yields near-optimal results for medium-sized instances and is scalable even for large-sized instances. We have also shown the effectiveness of our approach in a case study using a data set obtained from an earthquake that hit the Van province of Turkey in 2011. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1204 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48354875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In ride-sharing systems, platform providers aim to distribute the drivers in the city to meet current and potential future demand and to avoid service cancellations. Ensuring such distribution is particularly challenging in the case of a crowdsourced fleet, as drivers are not centrally controlled but are free to decide where to reposition when idle. Thus, providers look for alternative ways to ensure a vehicle distribution that benefits users, drivers, and the provider. We propose an intuitive mean to improve idle ride-sharing vehicles’ repositioning: repositioning heatmaps. These heatmaps highlight driver-specific earning opportunities approximated based on the expected future demand, current and expected future fleet distribution, and the location of the specific driver. Based on the heatmaps, drivers make decentralized yet better-informed repositioning decisions. As our heatmap policy changes the driver distribution in the future, we propose an adaptive learning algorithm for designing our heatmaps in large-scale ride-sharing systems. We simulate the system and generate heatmaps based on the previously learned policy in every iteration. We then update the policy based on the simulation’s outcome and use it in the next iteration. We test our heatmap design in a comprehensive case study on New York ride-sharing data. We show that carefully designed heatmaps reduce service cancellations and therefore, revenue loss for the platform and drivers significantly while leading to a better service level for the users and to a fairer treatment of drivers. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This research is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) [Grant 494812908]. M. W. Ulmer’s work is funded by the German Research Foundation Emmy Noether Programme [Grant 444657906]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1202 .
{"title":"Heatmap-Based Decision Support for Repositioning in Ride-Sharing Systems","authors":"Jarmo Haferkamp, M. Ulmer, J. Ehmke","doi":"10.1287/trsc.2023.1202","DOIUrl":"https://doi.org/10.1287/trsc.2023.1202","url":null,"abstract":"In ride-sharing systems, platform providers aim to distribute the drivers in the city to meet current and potential future demand and to avoid service cancellations. Ensuring such distribution is particularly challenging in the case of a crowdsourced fleet, as drivers are not centrally controlled but are free to decide where to reposition when idle. Thus, providers look for alternative ways to ensure a vehicle distribution that benefits users, drivers, and the provider. We propose an intuitive mean to improve idle ride-sharing vehicles’ repositioning: repositioning heatmaps. These heatmaps highlight driver-specific earning opportunities approximated based on the expected future demand, current and expected future fleet distribution, and the location of the specific driver. Based on the heatmaps, drivers make decentralized yet better-informed repositioning decisions. As our heatmap policy changes the driver distribution in the future, we propose an adaptive learning algorithm for designing our heatmaps in large-scale ride-sharing systems. We simulate the system and generate heatmaps based on the previously learned policy in every iteration. We then update the policy based on the simulation’s outcome and use it in the next iteration. We test our heatmap design in a comprehensive case study on New York ride-sharing data. We show that carefully designed heatmaps reduce service cancellations and therefore, revenue loss for the platform and drivers significantly while leading to a better service level for the users and to a fairer treatment of drivers. History: This paper has been accepted for the Transportation Science Special Issue on Machine Learning Methods and Applications in Large-Scale Route Planning Problems. Funding: This research is funded by the German Research Foundation (Deutsche Forschungsgemeinschaft) [Grant 494812908]. M. W. Ulmer’s work is funded by the German Research Foundation Emmy Noether Programme [Grant 444657906]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1202 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47929631","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Logistics service providers use transportation assets to offer services to their customers. To cope with demand variability, they may acquire additional assets on a one-off (spot) basis. The planner’s problem is to determine the optimal level of assets acquired upfront, such that their cost is minimized, for a given planning horizon. Our formulation captures a nontrivial complication: Although ordering quantities are pertinent to asset acquisition, customer demand is in the form of service requests. Not only does each request have a stochastic duration, but also the total number of requests per customer is uncertain. We introduce a two-stage newsvendor model where demand for spot assets is derived through optimal cutting-stock patterns. Leveraging results from bin-packing, we propose polynomial algorithms that have worst-case guarantees for upper and lower bounds. Our method finds optimal solutions to instances intractable by commercial solvers. We investigate demand variability by means of a factorial experiment. We find that, whereas variability in the number of requests leads to higher costs, variability in each request’s duration can reduce costs. Finally, we demonstrate the modularity of our approach with two extensions: asset routing and outsourcing. Our results provide a practical approach to transportation asset acquisition and offer insights on the differing impact of demand uncertainty on the total acquisition cost. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1201 .
{"title":"Transportation Asset Acquisition under a Newsvendor Model with Cutting-Stock Restrictions: Approximation and Decomposition Algorithms","authors":"J. Wagenaar, I. Fragkos, W. L. C. Faro","doi":"10.1287/trsc.2023.1201","DOIUrl":"https://doi.org/10.1287/trsc.2023.1201","url":null,"abstract":"Logistics service providers use transportation assets to offer services to their customers. To cope with demand variability, they may acquire additional assets on a one-off (spot) basis. The planner’s problem is to determine the optimal level of assets acquired upfront, such that their cost is minimized, for a given planning horizon. Our formulation captures a nontrivial complication: Although ordering quantities are pertinent to asset acquisition, customer demand is in the form of service requests. Not only does each request have a stochastic duration, but also the total number of requests per customer is uncertain. We introduce a two-stage newsvendor model where demand for spot assets is derived through optimal cutting-stock patterns. Leveraging results from bin-packing, we propose polynomial algorithms that have worst-case guarantees for upper and lower bounds. Our method finds optimal solutions to instances intractable by commercial solvers. We investigate demand variability by means of a factorial experiment. We find that, whereas variability in the number of requests leads to higher costs, variability in each request’s duration can reduce costs. Finally, we demonstrate the modularity of our approach with two extensions: asset routing and outsourcing. Our results provide a practical approach to transportation asset acquisition and offer insights on the differing impact of demand uncertainty on the total acquisition cost. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2023.1201 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46089835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Rui Jiang, Xiao Han, Xiao-Yan Sun, Kaijia Sun, Wen-Xu Wang, H. M. Zhang, Boaz Zhang, Ziyou Gao
Car use restrictions have been adopted in some mega cities that experience rapid car ownership increase and worsening traffic congestion. Although easy to implement and considered fair, most implementations of this travel demand management policy do not offer travelers the flexibility to choose the days that they cannot use their cars. In this paper, we study a flexible car use restriction policy under which a private car cannot be driven on a certain day of a week, but the day can be chosen by its owner. Under this flexible policy, individuals face a dilemma between driving in congestion and traveling without a car, each incurring a cost of its own. The resulting equilibrium solutions under these two competing choices were derived, and a series of laboratory experiments were carried out to validate the theoretical results. The experimental results are found to be in agreement with the theoretical results. Moreover, our analysis shows that the flexible car use restriction policy reduces the average travel cost with a lesser increase in average driving cost when compared with the traditional car use restriction policy. Funding: Z.-Y. Gao was supported by the National Natural Science Foundation of China [Grant 71621001]. W.-X. Wang was supported by the National Natural Science Foundation of China [Grant 71631002]. R. Jiang was supported by the National Natural Science Foundation of China [Grant 71931002]. X.-Y. Sun was supported by the National Natural Science Foundation of China [Grant 71961002]. B.-Y. Zhang was supported by the National Natural Science Foundation of China [Grants 71922004 and 72131003]. X. Han was supported by the National Natural Science Foundation of China [Grant 71801011] and by the China Postdoctoral Science Foundation [Grant 2018M631331]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.1200 .
{"title":"On a Flexible Car Use Restriction Policy: Theory and Experiment","authors":"Rui Jiang, Xiao Han, Xiao-Yan Sun, Kaijia Sun, Wen-Xu Wang, H. M. Zhang, Boaz Zhang, Ziyou Gao","doi":"10.1287/trsc.2023.1200","DOIUrl":"https://doi.org/10.1287/trsc.2023.1200","url":null,"abstract":"Car use restrictions have been adopted in some mega cities that experience rapid car ownership increase and worsening traffic congestion. Although easy to implement and considered fair, most implementations of this travel demand management policy do not offer travelers the flexibility to choose the days that they cannot use their cars. In this paper, we study a flexible car use restriction policy under which a private car cannot be driven on a certain day of a week, but the day can be chosen by its owner. Under this flexible policy, individuals face a dilemma between driving in congestion and traveling without a car, each incurring a cost of its own. The resulting equilibrium solutions under these two competing choices were derived, and a series of laboratory experiments were carried out to validate the theoretical results. The experimental results are found to be in agreement with the theoretical results. Moreover, our analysis shows that the flexible car use restriction policy reduces the average travel cost with a lesser increase in average driving cost when compared with the traditional car use restriction policy. Funding: Z.-Y. Gao was supported by the National Natural Science Foundation of China [Grant 71621001]. W.-X. Wang was supported by the National Natural Science Foundation of China [Grant 71631002]. R. Jiang was supported by the National Natural Science Foundation of China [Grant 71931002]. X.-Y. Sun was supported by the National Natural Science Foundation of China [Grant 71961002]. B.-Y. Zhang was supported by the National Natural Science Foundation of China [Grants 71922004 and 72131003]. X. Han was supported by the National Natural Science Foundation of China [Grant 71801011] and by the China Postdoctoral Science Foundation [Grant 2018M631331]. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.1200 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48713718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Given a set of customer orders each comprising one or more individual items to be picked, the order batching problem (OBP) in warehousing consists of designing a set of picking batches such that each customer order is assigned to exactly one batch, all batches satisfy the capacity restriction of the pickers, and the total distance traveled by the pickers is minimal. In order to collect the items of a batch, the pickers traverse the warehouse using a predefined routing strategy. We propose a branch-price-and-cut (BPC) algorithm for the exact solution of the OBP investigating the routing strategies traversal, return, midpoint, largest gap, combined, and optimal. The column-generation pricing problem is modeled as a shortest path problem with resource constraints (SPPRC) that can be adapted to handle the implications from nonrobust valid inequalities and branching decisions. The SPPRC pricing problem is solved by means of an effective labeling algorithm that relies on strong completion bounds. Capacity cuts and subset-row cuts are used to strengthen the lower bounds. Furthermore, we derive two BPC-based heuristics to identify high-quality solutions in short computation times. Extensive computational results demonstrate the effectiveness of the proposed methods. The BPC is faster by two orders of magnitude compared with the state-of-the-art exact approach and can solve to optimality hundreds of instances that were previously unsolved. The BPC-based heuristics are able to significantly improve the gaps reported for the state-of-the-art heuristic and provide hundreds of new best-known solutions. Funding: This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [Grant 418727865]. This support is gratefully acknowledged. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.1198 .
{"title":"Branch-Price-and-Cut-Based Solution of Order Batching Problems","authors":"Julia Wahlen, Timo Gschwind","doi":"10.1287/trsc.2023.1198","DOIUrl":"https://doi.org/10.1287/trsc.2023.1198","url":null,"abstract":"Given a set of customer orders each comprising one or more individual items to be picked, the order batching problem (OBP) in warehousing consists of designing a set of picking batches such that each customer order is assigned to exactly one batch, all batches satisfy the capacity restriction of the pickers, and the total distance traveled by the pickers is minimal. In order to collect the items of a batch, the pickers traverse the warehouse using a predefined routing strategy. We propose a branch-price-and-cut (BPC) algorithm for the exact solution of the OBP investigating the routing strategies traversal, return, midpoint, largest gap, combined, and optimal. The column-generation pricing problem is modeled as a shortest path problem with resource constraints (SPPRC) that can be adapted to handle the implications from nonrobust valid inequalities and branching decisions. The SPPRC pricing problem is solved by means of an effective labeling algorithm that relies on strong completion bounds. Capacity cuts and subset-row cuts are used to strengthen the lower bounds. Furthermore, we derive two BPC-based heuristics to identify high-quality solutions in short computation times. Extensive computational results demonstrate the effectiveness of the proposed methods. The BPC is faster by two orders of magnitude compared with the state-of-the-art exact approach and can solve to optimality hundreds of instances that were previously unsolved. The BPC-based heuristics are able to significantly improve the gaps reported for the state-of-the-art heuristic and provide hundreds of new best-known solutions. Funding: This research was supported by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) [Grant 418727865]. This support is gratefully acknowledged. Supplemental Material: The e-companion is available at https://doi.org/10.1287/trsc.2023.1198 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-03-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46241154","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Stefan Voigt, Markus Frank, Pirmin Fontaine, Heinrich Kuhn
In business-to-consumer (B2C) parcel delivery, the presence of the customer at the time of delivery is implicitly required in many cases. If the customer is not at home, the delivery fails—causing additional costs and efforts for the parcel service provider as well as inconvenience for the customer. Parcel service providers typically report high failed-delivery rates, as they have limited possibilities to arrange a delivery time with the recipient. We address the failed-delivery problem in B2C parcel delivery by considering customer-individual availability profiles (APs) that consist of a set of time windows, each associated with a probability that the delivery is successful if conducted in the respective time window. To assess the benefit of APs for delivery tour planning, we formulate the vehicle routing problem with availability profiles (VRPAP) as a mixed integer program, including the trade-off between transportation and failed-delivery costs. We provide analytical insights concerning the model’s cost-savings potential by determining lower and upper bounds. In order to solve larger instances, we develop a novel hybrid adaptive large neighborhood search (HALNS). The HALNS is highly adaptable and also able to solve related time-constrained vehicle routing problems (i.e., vehicle routing problems with hard, multiple, and soft time windows). We show its performance on these related benchmark instances and find a total of 20 new best-known solutions. We additionally conduct various experiments on self-generated VRPAP instances to generate managerial insights. In a case study using real-world data, despite little information on the APs, we were able to reduce failed deliveries by approximately 12% and overall costs by 5%. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1182 .
{"title":"The Vehicle Routing Problem with Availability Profiles","authors":"Stefan Voigt, Markus Frank, Pirmin Fontaine, Heinrich Kuhn","doi":"10.1287/trsc.2022.1182","DOIUrl":"https://doi.org/10.1287/trsc.2022.1182","url":null,"abstract":"In business-to-consumer (B2C) parcel delivery, the presence of the customer at the time of delivery is implicitly required in many cases. If the customer is not at home, the delivery fails—causing additional costs and efforts for the parcel service provider as well as inconvenience for the customer. Parcel service providers typically report high failed-delivery rates, as they have limited possibilities to arrange a delivery time with the recipient. We address the failed-delivery problem in B2C parcel delivery by considering customer-individual availability profiles (APs) that consist of a set of time windows, each associated with a probability that the delivery is successful if conducted in the respective time window. To assess the benefit of APs for delivery tour planning, we formulate the vehicle routing problem with availability profiles (VRPAP) as a mixed integer program, including the trade-off between transportation and failed-delivery costs. We provide analytical insights concerning the model’s cost-savings potential by determining lower and upper bounds. In order to solve larger instances, we develop a novel hybrid adaptive large neighborhood search (HALNS). The HALNS is highly adaptable and also able to solve related time-constrained vehicle routing problems (i.e., vehicle routing problems with hard, multiple, and soft time windows). We show its performance on these related benchmark instances and find a total of 20 new best-known solutions. We additionally conduct various experiments on self-generated VRPAP instances to generate managerial insights. In a case study using real-world data, despite little information on the APs, we were able to reduce failed deliveries by approximately 12% and overall costs by 5%. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1182 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":"101 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135594070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Puzzle-based storage (PBS) systems store unit loads at very high density, without consuming space for transport aisles. In such systems, each load is stored on a moving device (conveyor module or transport vehicle), making these systems very expensive to build and maintain. This paper studies a new type of PBS system where loads are moved by a small number of autonomous mobile robots (AMRs). The AMRs (or vehicles) can travel freely underneath loads and lift a specific load and carry it to a neighboring vacant space. These systems are hard to analyze, as all the AMRs can move simultaneously with or without loads. We formulate an integer linear programming model that minimizes the retrieval time and the number of load and vehicle movements. The proposed model can handle single-load movements as well as block movements, multiple input/output points, and various constraints on simultaneous vehicle movements. The integer linear programming formulation can solve relatively small problems (a grid with up to about 50 cells) and a sufficient number of empty cells. For larger systems or those with few empty cells, a three-phase heuristic (3PH) is developed, which significantly outperforms the heuristic methods known to date and solves large instances sufficiently fast. The 3PH and an additional hybrid heuristic yield relatively small gaps from a lower bound provided by the integer linear programming model. We find that increasing the number of vehicles has a diminishing return effect on the retrieval times. Using a relatively small number of vehicles makes retrieval times only slightly longer than those obtained when having a vehicle under each load (which is equivalent to the traditional PBS systems). With single-load movement, more vehicles are needed compared with block movement to reach short retrieval times. Also, the marginal contribution of extra empty slots appears to decrease rapidly, which implies high storage densities can be obtained in practice.
{"title":"Optimal Retrieval in Puzzle-Based Storage Systems Using Automated Mobile Robots","authors":"T. Raviv, Y. Bukchin, R. D. de Koster","doi":"10.1287/trsc.2022.1169","DOIUrl":"https://doi.org/10.1287/trsc.2022.1169","url":null,"abstract":"Puzzle-based storage (PBS) systems store unit loads at very high density, without consuming space for transport aisles. In such systems, each load is stored on a moving device (conveyor module or transport vehicle), making these systems very expensive to build and maintain. This paper studies a new type of PBS system where loads are moved by a small number of autonomous mobile robots (AMRs). The AMRs (or vehicles) can travel freely underneath loads and lift a specific load and carry it to a neighboring vacant space. These systems are hard to analyze, as all the AMRs can move simultaneously with or without loads. We formulate an integer linear programming model that minimizes the retrieval time and the number of load and vehicle movements. The proposed model can handle single-load movements as well as block movements, multiple input/output points, and various constraints on simultaneous vehicle movements. The integer linear programming formulation can solve relatively small problems (a grid with up to about 50 cells) and a sufficient number of empty cells. For larger systems or those with few empty cells, a three-phase heuristic (3PH) is developed, which significantly outperforms the heuristic methods known to date and solves large instances sufficiently fast. The 3PH and an additional hybrid heuristic yield relatively small gaps from a lower bound provided by the integer linear programming model. We find that increasing the number of vehicles has a diminishing return effect on the retrieval times. Using a relatively small number of vehicles makes retrieval times only slightly longer than those obtained when having a vehicle under each load (which is equivalent to the traditional PBS systems). With single-load movement, more vehicles are needed compared with block movement to reach short retrieval times. Also, the marginal contribution of extra empty slots appears to decrease rapidly, which implies high storage densities can be obtained in practice.","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46494123","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"In Memoriam: Bernard Gendron,1966–2022","authors":"T. Crainic, Andrea Frangioni, M. Gendreau","doi":"10.1287/trsc.2023.1197","DOIUrl":"https://doi.org/10.1287/trsc.2023.1197","url":null,"abstract":"","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-01-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46497872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Theresa Gattermann-Itschert, Laura Maria Poreschack, U. W. Thonemann
In crew scheduling, optimization models can become complex when a large number of penalty terms is included in the objective function to take planners’ preferences into account. Planners’ preferences often include nonmonetary aspects for which both the mathematical formulation and the assignment of appropriate penalty costs can be difficult. We address this problem by using machine learning to learn and predict planners’ preferences. We train a random forest classifier on planner feedback regarding duties from their daily work in railway crew scheduling. Our data set contains over 16,000 duties that planners labeled as good or bad. The trained model predicts the probability that a duty is perceived as bad by the planners. We present a novel approach to replace the large construct of penalty terms in a crew scheduling optimization model by a single term that penalizes duties proportionally to the predicted probability of being assessed as unfavorable by a planner. By integrating this probability into the optimization model, we generate schedules that include more duties with preferred characteristics. We increase the mean planner acceptance probability by more than 12% while only facing a marginal increase in costs compared with the original approach that utilizes a set of multiple penalty terms. Our approach combines machine learning to detect complex patterns regarding favorable duty characteristics and optimization to create feasible and cost-efficient crew schedules. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1196 .
{"title":"Using Machine Learning to Include Planners’ Preferences in Railway Crew Scheduling Optimization","authors":"Theresa Gattermann-Itschert, Laura Maria Poreschack, U. W. Thonemann","doi":"10.1287/trsc.2022.1196","DOIUrl":"https://doi.org/10.1287/trsc.2022.1196","url":null,"abstract":"In crew scheduling, optimization models can become complex when a large number of penalty terms is included in the objective function to take planners’ preferences into account. Planners’ preferences often include nonmonetary aspects for which both the mathematical formulation and the assignment of appropriate penalty costs can be difficult. We address this problem by using machine learning to learn and predict planners’ preferences. We train a random forest classifier on planner feedback regarding duties from their daily work in railway crew scheduling. Our data set contains over 16,000 duties that planners labeled as good or bad. The trained model predicts the probability that a duty is perceived as bad by the planners. We present a novel approach to replace the large construct of penalty terms in a crew scheduling optimization model by a single term that penalizes duties proportionally to the predicted probability of being assessed as unfavorable by a planner. By integrating this probability into the optimization model, we generate schedules that include more duties with preferred characteristics. We increase the mean planner acceptance probability by more than 12% while only facing a marginal increase in costs compared with the original approach that utilizes a set of multiple penalty terms. Our approach combines machine learning to detect complex patterns regarding favorable duty characteristics and optimization to create feasible and cost-efficient crew schedules. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1196 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44712144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Gourav Dwivedi, S. Chakraborty, Y. Agarwal, R. Srivastava
Additive manufacturing (AM) promises considerable advantages over conventional manufacturing to meet the growing demand for customized products and faster delivery times. Consider a mobile mini-factory, that is, a vehicle equipped with an AM facility, which can simultaneously produce and transport the final products to the customers. The overlapping of production and transportation processes allows potential savings on customer delivery lead times and inventory holding costs, thereby facilitating on-demand fulfillment of the orders of intricate products. Based on this situation and motivated by a recent Amazon patent, we introduce a novel routing optimization problem called Simultaneous Production and Transportation Problem (SPTP) in this study. Given a set of customers and their respective orders with associated production time and delivery due dates, SPTP minimizes the trip time for the AM installed vehicle while meeting the customers’ stipulated due dates for all deliveries. We formulate the problem using a mixed integer linear program, discuss several valid inequalities to strengthen the formulation, and discuss a cutting-plane-based exact solution approach. We also design a variable neighborhood search metaheuristic to solve larger instances of SPTP very efficiently. The effectiveness of the exact and heuristic solution approaches is demonstrated using extensive computational experiments. The study also explores the interaction between production and travel times in SPTP and how the problem compares with the traveling salesman problem and the single machine scheduling problem, each of which may be viewed as special cases of SPTP. Further, the problem involves a trade-off between the total trip time and the tardiness of the deliveries. Therefore, an extension of the proposed formulation is also proposed with interesting managerial insights on identifying appropriate trip time-tardiness combinations using an illustrative example. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1195 .
{"title":"Simultaneous Production and Transportation Problem: A Case of Additive Manufacturing","authors":"Gourav Dwivedi, S. Chakraborty, Y. Agarwal, R. Srivastava","doi":"10.1287/trsc.2022.1195","DOIUrl":"https://doi.org/10.1287/trsc.2022.1195","url":null,"abstract":"Additive manufacturing (AM) promises considerable advantages over conventional manufacturing to meet the growing demand for customized products and faster delivery times. Consider a mobile mini-factory, that is, a vehicle equipped with an AM facility, which can simultaneously produce and transport the final products to the customers. The overlapping of production and transportation processes allows potential savings on customer delivery lead times and inventory holding costs, thereby facilitating on-demand fulfillment of the orders of intricate products. Based on this situation and motivated by a recent Amazon patent, we introduce a novel routing optimization problem called Simultaneous Production and Transportation Problem (SPTP) in this study. Given a set of customers and their respective orders with associated production time and delivery due dates, SPTP minimizes the trip time for the AM installed vehicle while meeting the customers’ stipulated due dates for all deliveries. We formulate the problem using a mixed integer linear program, discuss several valid inequalities to strengthen the formulation, and discuss a cutting-plane-based exact solution approach. We also design a variable neighborhood search metaheuristic to solve larger instances of SPTP very efficiently. The effectiveness of the exact and heuristic solution approaches is demonstrated using extensive computational experiments. The study also explores the interaction between production and travel times in SPTP and how the problem compares with the traveling salesman problem and the single machine scheduling problem, each of which may be viewed as special cases of SPTP. Further, the problem involves a trade-off between the total trip time and the tardiness of the deliveries. Therefore, an extension of the proposed formulation is also proposed with interesting managerial insights on identifying appropriate trip time-tardiness combinations using an illustrative example. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2022.1195 .","PeriodicalId":51202,"journal":{"name":"Transportation Science","volume":" ","pages":""},"PeriodicalIF":4.6,"publicationDate":"2023-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44413281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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