Tim Lamballais Tessensohn
Research Subjects
Tim Lamballais Tessensohn has a total of 9 co-authors. Here are the top co-authors by number of shared publications:
Debjit Roy
Operations Management, Indian Institute of Management Ahmedabad, Ahmedabad, India
2 shared publicationsD. Roy
1 shared publicationK. Postek
1 shared publicationM. B. M. de Koster
Erasmus University Rotterdam (EUR) - Department of Technology and Operations Management
1 shared publication- Martijn van Ee
Netherlands Defence Academy
1 shared publication - Mette Wagenvoort
Econometric Institute, Erasmus School of Economics, Erasmus University Rotterdam, The Netherlands
1 shared publication
Latest Publications
- Open Access
- 19 August 2024
- Published by Elsevier in European Journal of Operational Research
- Vol. 320 (1) , 115-131
Exact and heuristic approaches for the ship-to-shore problem
- 19 August 2024
- Published by Elsevier in European Journal of Operational Research
- Vol. 320 (1) , 115-131
Open AccessExact and heuristic approaches for the ship-to-shore problem
- 13 May 2019
- Published by Taylor & Francis in IISE Transactions
- Vol. 52 (1) , 1-17
Inventory allocation in robotic mobile fulfillment systems
A Robotic Mobile Fulfillment System is a recently developed automated, parts-to-picker material handling system. Robots can move storage shelves, also known as inventory pods, between the storage area and the workstations and can continually reposition them during operations. This paper shows how to optimize three key decision variables: (1) the number of pods per SKU (2) the ratio of the number of pick stations to replenishment stations, and (3) the replenishment level per pod. Our results show that throughput performance improves substantially when inventory is spread across multiple pods, when an optimum ratio between the number of pick stations to replenishment stations is achieved and when a pod is replenished before it is completely empty. This paper contributes methodologically by introducing a new type of Semi-Open Queueing Networks (SOQN): cross-class matching multi-class SOQN, by deriving necessary stability conditions, and by introducing a novel interpretation of the classes.
- 13 May 2019
- Published by Taylor & Francis in IISE Transactions
- Vol. 52 (1) , 1-17
Inventory allocation in robotic mobile fulfillment systems
A Robotic Mobile Fulfillment System is a recently developed automated, parts-to-picker material handling system. Robots can move storage shelves, also known as inventory pods, between the storage area and the workstations and can continually reposition them during operations. This paper shows how to optimize three key decision variables: (1) the number of pods per SKU (2) the ratio of the number of pick stations to replenishment stations, and (3) the replenishment level per pod. Our results show that throughput performance improves substantially when inventory is spread across multiple pods, when an optimum ratio between the number of pick stations to replenishment stations is achieved and when a pod is replenished before it is completely empty. This paper contributes methodologically by introducing a new type of Semi-Open Queueing Networks (SOQN): cross-class matching multi-class SOQN, by deriving necessary stability conditions, and by introducing a novel interpretation of the classes.
- Top Cited Papers
- 1 February 2017
- Published by Elsevier in European Journal of Operational Research
- Vol. 256 (3) , 976-990
Estimating performance in a Robotic Mobile Fulfillment System
- 1 February 2017
- Published by Elsevier in European Journal of Operational Research
- Vol. 256 (3) , 976-990
Top Cited PapersEstimating performance in a Robotic Mobile Fulfillment System
- Open Access
- 9 January 2017
- Published by Elsevier in SSRN Electronic Journal
Inventory Allocation in Robotic Mobile Fulfillment Systems
A Robotic Mobile Fulfillment System is a recently developed automated, parts-to-picker material handling system. Robots can move storage shelves, also known as inventory pods, between the storage area and the workstations and can continually reposition them during operations. This paper shows how to optimize three key decision variables: (1) the number of pods per product (2) the ratio of the number of pick stations to replenishment stations, and (3) the replenishment level per pod. Our results show that throughput performance improves substantially when inventory is spread across multiple pods, when an optimum ratio between the number of pick stations to replenishment stations is achieved and when a pod is replenished before it is completely empty. This paper contributes methodologically by introducing a new type of Semi-Open Queueing Networks (SOQN): cross-class matching multi-class SOQN, by deriving necessary stability conditions, and by introducing a novel interpretation of the classes.
- 9 January 2017
- Published by Elsevier in SSRN Electronic Journal
Open AccessInventory Allocation in Robotic Mobile Fulfillment Systems
A Robotic Mobile Fulfillment System is a recently developed automated, parts-to-picker material handling system. Robots can move storage shelves, also known as inventory pods, between the storage area and the workstations and can continually reposition them during operations. This paper shows how to optimize three key decision variables: (1) the number of pods per product (2) the ratio of the number of pick stations to replenishment stations, and (3) the replenishment level per pod. Our results show that throughput performance improves substantially when inventory is spread across multiple pods, when an optimum ratio between the number of pick stations to replenishment stations is achieved and when a pod is replenished before it is completely empty. This paper contributes methodologically by introducing a new type of Semi-Open Queueing Networks (SOQN): cross-class matching multi-class SOQN, by deriving necessary stability conditions, and by introducing a novel interpretation of the classes.