Robust Design of Public Storage Warehouses

We apply robust optimization and revenue management in public storage warehouses. We optimize the expected revenue of public storage warehouses against the worst cases with a max-min revenue objective, and the decision variables are mainly the number of storage units for each storage type. With the robust design, we can observe worst-case revenue improvement

Performance Comparison of Various Order Picking Methods in Different Behavioral Contexts

Three manual picker-to-parts order picking methods (parallel picking, zone picking, and dynamic zone picking) are employed in an experimental warehouse setup and compared in terms of productivity, quality, and job satisfaction. Participants worked in teams and were subject to either an individual- based, or a team-based incentive scheme. Furthermore, the influence of individual participants’ dominant regulatory focus (promotion or prevention) was taken into account. The outcomes show that in parallel picking an incentive system focused on individual performance is beneficial for productivity and quality compared to an incentive system focused on team performance, whereas team-based incentives are more productive in zone picking. These results were more explicitly present for participants with a dominant promotion focus. Participants with a dominant prevention focus picked more productively with team-based incentives in all picking methods. In addition to this, team-based incentives led to a relatively high quality in zone-picking, but a relatively low quality in dynamic zone picking. Our study shows that assigning the right people to the right picking task with a fitting incentive system can substantially cut wage costs without simultaneously harming productivity, quality, or job satisfaction

A Two-level Stochastic Model to Estimate Vessel Throughput Time

A good estimate of the vessel sojourn time is essential for better planning and scheduling Of container terminal resources,such as berth scheduling,quay crane(qc)assignment And scheduling,and fleet size planning. However,estimating the expected vessel so journ Time is a complex exercise because the time is dependent on several terminal operating Parameters such as the size of the vessel,the number of containers to be loaded and Unloaded,and the through put of the qcs. The through put of the qcs in turn depends On the type and number of transport vehicles,number of stack blocks,the topology of The vehicle travel path,the layout of the terminal,and several event uncertainties. To Address the modelling complexity, we propose a two-level stochastic model to estimate The expected vessel so journ time. The higher level model consists of a continuous-time Markovchain(ctmc)that captures the effect of qc assignment and scheduling on vessel Sojourn time. The lower level model is a multi class closed queuing network(cqn)that Models the dynamic interactions among the terminal resources and provides an estimate Of the transition rate input parameters to the higher level ctmc model. We estimate the Expected vessel sojourn times for several container load and unload profiles and discuss The effect of terminal layout parameters on vessel so journ times

Modeling Conveyor Merges in Zone Picking Systems

In many order picking and sorting systems conveyors are used to transport products through the system and to merge multiple flows of products into one single flow. In practice, conveyor merges are potential points of congestion, and consequently can lead to a reduced throughput. In this paper, we study merges in a zone picking system. The performance of a zone picking system is, for a large part, determined by the performance of the merge locations. We model the system as a closed queueing network that describes the conveyor, the pick zones, and the merge locations. The resulting model does not have a product-form stationary queue-length distribution. This makes exact analysis practically infeasible. Therefore, we approximate the behavior of the model using the aggregation technique, where the resulting subnetworks are solved using matrix-geometric methods. We show that the approximation model allows us to determine very accurate estimates of the throughput when compared with simulation. Furthermore, our model is in particular well suited to evaluate many design alternatives, in terms of number of zones, zone buffer lengths, and maximum number of totes in the systems. It also can be used to determine the maximum throughput capability of the system and, if needed, modify the system in order to meet target performance levels

Analysis of Class-based Storage Strategies for the Mobile Shelf-based Order Pick System

Mobile Shelf-based Order Pick (MSOP) systems are gaining significant in- terest for e-commerce fulfillment due to their rapid deployment capability and dynamic organization of storage pods based on item demand profiles. In this research, we model the MSOP system with class-based storage strategies and alternate pod storage policies using multi-class closed queuing networks. We observe that though closest-open location pod storage policy do not allow to efficiently use the storage spaces in comparison to random location pod storage policy in an aisle, it increases the system throughput for all item classes

Evaluating battery charging and swapping strategies in a robotic mobile fulfilment system

Robotic mobile fulfillment systems (RMFS) have seen many implementations in recent years, due to their high flexibility and low operational cost. Such a system stores goods in movable shelves and uses movable robots to transport the shelves. The robot is battery powered and the battery depletes during operations, which can seriously affect the performance of the system. This study focuses on battery management problem in an RMFS, considering a battery swapping and a battery charging strategy with plug-in or inductive charging. We build a semi-open queueing network (SOQN) to estimate system performance, modeling the battery charging process as a single queue and the battery swapping process as a nested SOQN. We develop a decomposition method to solve the analytical models and validate them through simulation. Our models can be used to optimize battery recovery strategies and compare their cost and throughput time performance. The results show that throughput time performance can be significantly affected by the battery recovery policy, that inductive charging performs best, and that battery swapping outperforms plug-in charging by as large as 4.88%, in terms of retrieval transaction throughput time. However, the annual cost of the RMFS using the battery swapping strategy is generally higher than that of the RMFS using the plug-in charging strategy. In the RMFS that uses the inductive charging strategy, a critical price of a robot can be found, for a lower robot price and a small required retrieval transaction throughput time, inductive charging outperforms both plug-in charging and battery swapping strategies in terms of annual cost. We also find that ignoring the battery recovery will underestimate the number of robots required and the system cost for more than 15%