Abstract In this paper we present a novel transshipment problem for a large apparel retailer that operates an extensive retail network. Our problem is inspired by the logistics operations of a very large fast fashion retailer in Turkey, LC Waikiki, with over 475 retail branches and thousands of products. The purpose of transshipments is to rebalance stocks across the retail network to better match supply with demand. We formulate this problem as a large mixed integer linear program and develop a Lagrangian relaxation with a primal–dual approach to find upper bounds and a simulated annealing based metaheuristic to find promising solutions, both of which have proven to be quite effective. While our metaheuristic does not always produce better solutions than a commercial optimizer, it has consistently produced solutions with optimality gaps lower than 7% while the commercial optimizer may produce very poor solutions with optimality gaps as high as almost 300%. We have also conducted a set of numerical experiments to uncover implications of various operational practices of LC Waikiki on its system’s performance and important managerial insights.
{"title":"A Deterministic Model for the Transshipment Problem of a Fast Fashion Retailer under Capacity Constraints","authors":"S. Naderi, K. Kilic, A. Dasci","doi":"10.2139/ssrn.3512800","DOIUrl":"https://doi.org/10.2139/ssrn.3512800","url":null,"abstract":"Abstract In this paper we present a novel transshipment problem for a large apparel retailer that operates an extensive retail network. Our problem is inspired by the logistics operations of a very large fast fashion retailer in Turkey, LC Waikiki, with over 475 retail branches and thousands of products. The purpose of transshipments is to rebalance stocks across the retail network to better match supply with demand. We formulate this problem as a large mixed integer linear program and develop a Lagrangian relaxation with a primal–dual approach to find upper bounds and a simulated annealing based metaheuristic to find promising solutions, both of which have proven to be quite effective. While our metaheuristic does not always produce better solutions than a commercial optimizer, it has consistently produced solutions with optimality gaps lower than 7% while the commercial optimizer may produce very poor solutions with optimality gaps as high as almost 300%. We have also conducted a set of numerical experiments to uncover implications of various operational practices of LC Waikiki on its system’s performance and important managerial insights.","PeriodicalId":432405,"journal":{"name":"Transportation Science eJournal","volume":"310 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116348484","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We are motivated by a common dilemma that exists when coupling new mobility services with existing transportation systems — the unbalanced nature of operations with regard to efficiency and equity objectives. To address this issue, we study the joint routing and resource allocation problem. Vehicles need to repeatedly and simultaneously choose the route and the resource (i.e., capacity) allocation policy with unknown demand. Efficiency is measured by the total travel distance, and equity is measured by the minimum service level. We propose a two-phase heuristic that solve the learn-and-optimize problem iteratively with small cumulative regret. In Phase 1, the algorithm selects the best demand estimator; In Phase 2, it finds the near optimal operational plan. We examine the effectiveness of the algorithm in a case study from the Miami Dade County that uses idle shuttle vehicles to deliver packages during off-peak hours. The results show that we can improve the minimum service level from 33% to approximately 68% while maintaining small incremental travel costs. This heuristic can provide a general guidance for practitioners and researchers on operating new mobility services in a stochastic network.
{"title":"A Heuristic for Learn-and-Optimize New Mobility Services with Equity and Efficiency Metrics","authors":"Fangzhou Yu, Qi Luo, T. Fabusuyi, R. Hampshire","doi":"10.2139/ssrn.3488466","DOIUrl":"https://doi.org/10.2139/ssrn.3488466","url":null,"abstract":"We are motivated by a common dilemma that exists when coupling new mobility services with existing transportation systems — the unbalanced nature of operations with regard to efficiency and equity objectives. To address this issue, we study the joint routing and resource allocation problem. Vehicles need to repeatedly and simultaneously choose the route and the resource (i.e., capacity) allocation policy with unknown demand. Efficiency is measured by the total travel distance, and equity is measured by the minimum service level. We propose a two-phase heuristic that solve the learn-and-optimize problem iteratively with small cumulative regret. In Phase 1, the algorithm selects the best demand estimator; In Phase 2, it finds the near optimal operational plan. We examine the effectiveness of the algorithm in a case study from the Miami Dade County that uses idle shuttle vehicles to deliver packages during off-peak hours. The results show that we can improve the minimum service level from 33% to approximately 68% while maintaining small incremental travel costs. This heuristic can provide a general guidance for practitioners and researchers on operating new mobility services in a stochastic network.","PeriodicalId":432405,"journal":{"name":"Transportation Science eJournal","volume":"97 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130020638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
W. Mao, Liu Ming, Ying Rong, Christopher S. Tang, Huan Zheng
Academic/Practical Relevance: Our intent is to identify the underlying factors and develop an order assignment policy that can help an on-demand meal delivery service platform to grow. Methodology: By analyzing transactional data obtained from an online meal delivery platform in Hangzhou (China) over a two-month period in 2015, we examine the impact of meal delivery performance on a customer's future orders. Through a simulation study, we illustrate the importance of incorporating our empirical results into the development of a smarter "order assignment policy". Results: We find empirical evidence that an "early delivery'' is positively correlated with customer retention: a 10-minute earlier delivery is associated with an increase of one order per month from each customer. However, we find that the negative effect on future orders associated with "late deliveries'' is much stronger than the positive effect associated with "early deliveries". Moreover, we show empirically that a driver's individual local area knowledge and prior delivery experience can reduce late deliveries significantly. Finally, through a simulation study, we illustrate how one can incorporate our empirical results in the development of an order assignment policy that can help a platform to grow its business through customer retention. Managerial Implications: Our empirical results and our simulation study suggest that to increase future customer orders, an on-demand service platform should address the issues arising from both the supply side (i.e., driver's local area knowledge and delivery experience) and the demand side (i.e., asymmetric impacts of early and late deliveries on future customer orders) into their operations.
{"title":"Faster Deliveries and Smarter Order Assignments for an On-Demand Meal Delivery Platform","authors":"W. Mao, Liu Ming, Ying Rong, Christopher S. Tang, Huan Zheng","doi":"10.2139/ssrn.3469015","DOIUrl":"https://doi.org/10.2139/ssrn.3469015","url":null,"abstract":"Academic/Practical Relevance: Our intent is to identify the underlying factors and develop an order assignment policy that can help an on-demand meal delivery service platform to grow. \u0000 \u0000Methodology: By analyzing transactional data obtained from an online meal delivery platform in Hangzhou (China) over a two-month period in 2015, we examine the impact of meal delivery performance on a customer's future orders. Through a simulation study, we illustrate the importance of incorporating our empirical results into the development of a smarter \"order assignment policy\". \u0000 \u0000Results: We find empirical evidence that an \"early delivery'' is positively correlated with customer retention: a 10-minute earlier delivery is associated with an increase of one order per month from each customer. However, we find that the negative effect on future orders associated with \"late deliveries'' is much stronger than the positive effect associated with \"early deliveries\". Moreover, we show empirically that a driver's individual local area knowledge and prior delivery experience can reduce late deliveries significantly. Finally, through a simulation study, we illustrate how one can incorporate our empirical results in the development of an order assignment policy that can help a platform to grow its business through customer retention. \u0000 \u0000Managerial Implications: Our empirical results and our simulation study suggest that to increase future customer orders, an on-demand service platform should address the issues arising from both the supply side (i.e., driver's local area knowledge and delivery experience) and the demand side (i.e., asymmetric impacts of early and late deliveries on future customer orders) into their operations.","PeriodicalId":432405,"journal":{"name":"Transportation Science eJournal","volume":"165 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116131366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We consider a dynamic, stochastic extension to the transportation problem. For the deterministic problem, there are known necessary and sufficient conditions under which a greedy algorithm achieves the optimal solution. We define a distribution-free type of optimality and provide analogous necessary and sufficient conditions under which a greedy policy achieves this type of optimality in the dynamic, stochastic setting. These results are used to prove that a greedy algorithm is optimal when planning a type of air-traffic management initiative. We also provide weaker conditions under which it is possible to strengthen an existing policy. These results can be applied to the problem of matching passengers with drivers in an on-demand taxi service. They specify conditions under which a passenger and driver should not be left unassigned.
{"title":"Monge Properties, Optimal Greedy Policies, and Policy Improvement for the Dynamic Stochastic Transportation Problem","authors":"Alexander S. Estes, M. Ball","doi":"10.2139/ssrn.3067130","DOIUrl":"https://doi.org/10.2139/ssrn.3067130","url":null,"abstract":"We consider a dynamic, stochastic extension to the transportation problem. For the deterministic problem, there are known necessary and sufficient conditions under which a greedy algorithm achieves the optimal solution. We define a distribution-free type of optimality and provide analogous necessary and sufficient conditions under which a greedy policy achieves this type of optimality in the dynamic, stochastic setting. These results are used to prove that a greedy algorithm is optimal when planning a type of air-traffic management initiative. We also provide weaker conditions under which it is possible to strengthen an existing policy. These results can be applied to the problem of matching passengers with drivers in an on-demand taxi service. They specify conditions under which a passenger and driver should not be left unassigned.","PeriodicalId":432405,"journal":{"name":"Transportation Science eJournal","volume":"5 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115570217","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shaik Mastan, U. Balakrishnan, G. Sankar Sekhar Raju
The voyaging sales rep issue is a fit perceived enhancement issue. Ideal answers for humble cases can be found in sensible time by straight programming. Be that as it may, since the TSP is NP-hard, it will be very tedious to determine bigger occurrences with ensured optimality. Circumstance optimality aside, there's a lot of calculations opening equivalently quick activity time and still delicate close ideal arrangements.
{"title":"Heuristics Designed For the Traveling Salesman Problem","authors":"Shaik Mastan, U. Balakrishnan, G. Sankar Sekhar Raju","doi":"10.2139/ssrn.3497478","DOIUrl":"https://doi.org/10.2139/ssrn.3497478","url":null,"abstract":"The voyaging sales rep issue is a fit perceived enhancement issue. Ideal answers for humble cases can be found in sensible time by straight programming. Be that as it may, since the TSP is NP-hard, it will be very tedious to determine bigger occurrences with ensured optimality. Circumstance optimality aside, there's a lot of calculations opening equivalently quick activity time and still delicate close ideal arrangements.","PeriodicalId":432405,"journal":{"name":"Transportation Science eJournal","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128907479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
User toll means that drivers have to pay money to cover road cost, just like eaters pay money to cover food cost. This paper first shows that the U-shaped cost theory supports such toll, but the corresponding functions are neither demand nor supply. Then, it separates the U-shaped cost theory into two: A variable cost theory to uproot the user toll mindset and to derive a positive traffic supply; A fixed cost one to evaluate the worthiness of a road. After the correct assessment for public roads is derived, this paper disproves toll-cum-tax. It also corrects many economics misunderstandings.
{"title":"Disproving the User Toll Theory","authors":"Hak Choi","doi":"10.2139/ssrn.3117992","DOIUrl":"https://doi.org/10.2139/ssrn.3117992","url":null,"abstract":"User toll means that drivers have to pay money to cover road cost, just like eaters pay money to cover food cost. This paper first shows that the U-shaped cost theory supports such toll, but the corresponding functions are neither demand nor supply. Then, it separates the U-shaped cost theory into two: A variable cost theory to uproot the user toll mindset and to derive a positive traffic supply; A fixed cost one to evaluate the worthiness of a road. After the correct assessment for public roads is derived, this paper disproves toll-cum-tax. It also corrects many economics misunderstandings.","PeriodicalId":432405,"journal":{"name":"Transportation Science eJournal","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132829227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper proves that Lupton’s toll-elmimate-congestion theory applies only to area beyond the road capacity, or to the backward bending area of the flow chart, and the corresponding toll must be negative.
{"title":"Lupton's Negative Toll Theory","authors":"Hak Choi","doi":"10.2139/ssrn.3107680","DOIUrl":"https://doi.org/10.2139/ssrn.3107680","url":null,"abstract":"This paper proves that Lupton’s toll-elmimate-congestion theory applies only to area beyond the road capacity, or to the backward bending area of the flow chart, and the corresponding toll must be negative.","PeriodicalId":432405,"journal":{"name":"Transportation Science eJournal","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125586687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Anirudh Kishore Bhoopalam, Niels A. H. Agatz, R. Zuidwijk
A truck platoon is a set of virtually linked trucks that drive closely behind one another using automated driving technology. Benefits of truck platooning include cost savings, reduced emissions, and more efficient utilization of road capacity. To fully reap these benefits in the initial phases requires careful planning of platoons based on trucks’ itineraries and time schedules. This paper provides a framework to classify various new transportation planning problems that arise in truck platooning, surveys relevant operations research models for these problems in the literature and identifies directions for future research.
{"title":"Planning of Truck Platoons: A Literature Review and Directions for Future Research","authors":"Anirudh Kishore Bhoopalam, Niels A. H. Agatz, R. Zuidwijk","doi":"10.2139/ssrn.2988195","DOIUrl":"https://doi.org/10.2139/ssrn.2988195","url":null,"abstract":"A truck platoon is a set of virtually linked trucks that drive closely behind one another using automated driving technology. Benefits of truck platooning include cost savings, reduced emissions, and more efficient utilization of road capacity. To fully reap these benefits in the initial phases requires careful planning of platoons based on trucks’ itineraries and time schedules. This paper provides a framework to classify various new transportation planning problems that arise in truck platooning, surveys relevant operations research models for these problems in the literature and identifies directions for future research.","PeriodicalId":432405,"journal":{"name":"Transportation Science eJournal","volume":"120 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2017-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122558597","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
At the beginning of the 20th Century, people endeavored into the skies for the first time. While this once audacious pursuit has become commonplace in 21st Century life, the safety of air travel has come at the expense of many aircraft accidents and incidents. Aviation safety analysis is traditionally retroactive, as new policies, procedures, practices, and regulation are driven by these accidents and incidents. The United States Federal Aviation Administration (FAA) is currently advocating a more proactive approach to aviation safety wherein certain factors, whether they be environmental, mechanical, human, or some combination thereof are analyzed in order to identify the level of risk of an operation. The intent of this paper is to demonstrate two models by which risk analysis can be conducted using data that contains external factors and the outcome of the flight.
{"title":"An Empirical Analysis of Risk Factors in Aviation: Evidence from 1995-2016 NTSB Data","authors":"R. Herman, Rossitza B. Wooster","doi":"10.2139/ssrn.2887620","DOIUrl":"https://doi.org/10.2139/ssrn.2887620","url":null,"abstract":"At the beginning of the 20th Century, people endeavored into the skies for the first time. While this once audacious pursuit has become commonplace in 21st Century life, the safety of air travel has come at the expense of many aircraft accidents and incidents. Aviation safety analysis is traditionally retroactive, as new policies, procedures, practices, and regulation are driven by these accidents and incidents. The United States Federal Aviation Administration (FAA) is currently advocating a more proactive approach to aviation safety wherein certain factors, whether they be environmental, mechanical, human, or some combination thereof are analyzed in order to identify the level of risk of an operation. The intent of this paper is to demonstrate two models by which risk analysis can be conducted using data that contains external factors and the outcome of the flight.","PeriodicalId":432405,"journal":{"name":"Transportation Science eJournal","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122450247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Problem definition: We consider an intermediary’s problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. Specifically, there are two disjoint sets of demand and supply types, and a reward for each possible matching of a demand type and a supply type. In each period, demand and supply of various types arrive in random quantities. The platform decides on the optimal matching policy to maximize the expected total discounted rewards, given that unmatched demand and supply may incur waiting or holding costs, and will be fully or partially carried over to the next period. Academic/practical relevance: The problem is crucial to many intermediaries who manage matchings centrally in a sharing economy. Methodology: We formulate the problem as a dynamic program. We explore the structural properties of the optimal policy and propose heuristic policies. Results: We provide sufficient conditions on matching rewards such that the optimal matching policy follows a priority hierarchy among possible matching pairs. We show that those conditions are satisfied by vertically and unidirectionally horizontally differentiated types, for which quality and distance determine priority, respectively. Managerial implications: The priority property simplifies the matching decision within a period, and the trade-off reduces to a choice between matching in the current period and that in the future. Then the optimal matching policy has a match-down-to structure when considering a specific pair of demand and supply types in the priority hierarchy.
{"title":"Dynamic Type Matching","authors":"Ming Hu, Yun Zhou","doi":"10.2139/ssrn.2592622","DOIUrl":"https://doi.org/10.2139/ssrn.2592622","url":null,"abstract":"Problem definition: We consider an intermediary’s problem of dynamically matching demand and supply of heterogeneous types in a periodic-review fashion. Specifically, there are two disjoint sets of demand and supply types, and a reward for each possible matching of a demand type and a supply type. In each period, demand and supply of various types arrive in random quantities. The platform decides on the optimal matching policy to maximize the expected total discounted rewards, given that unmatched demand and supply may incur waiting or holding costs, and will be fully or partially carried over to the next period. Academic/practical relevance: The problem is crucial to many intermediaries who manage matchings centrally in a sharing economy. Methodology: We formulate the problem as a dynamic program. We explore the structural properties of the optimal policy and propose heuristic policies. Results: We provide sufficient conditions on matching rewards such that the optimal matching policy follows a priority hierarchy among possible matching pairs. We show that those conditions are satisfied by vertically and unidirectionally horizontally differentiated types, for which quality and distance determine priority, respectively. Managerial implications: The priority property simplifies the matching decision within a period, and the trade-off reduces to a choice between matching in the current period and that in the future. Then the optimal matching policy has a match-down-to structure when considering a specific pair of demand and supply types in the priority hierarchy.","PeriodicalId":432405,"journal":{"name":"Transportation Science eJournal","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124820326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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