Stochastic bounds for order flow times in parts-to-picker warehouses with remotely located order-picking workstations

\u3cp\u3eThis paper focuses on the mathematical analysis of order flow times in parts-to-picker warehouses with remotely located order-picking workstations. To this end, a polling system with a new type of arrival process and service discipline is introduced as a model for an order-picking workstation. Stochastic bounds are deduced for the cycle time, which corresponds to the order flow time. These bounds are shown to be adequate and aid in setting targets for the throughput of the storage area. The paper thus complements existing literature, which mainly focuses on optimizing the operations in the storage area.\u3c/p\u3

Stochastic bounds for order flow times in parts-to-picker warehouses with remotely located order-picking workstations

\u3cp\u3eThis paper focuses on the mathematical analysis of order flow times in parts-to-picker warehouses with remotely located order-picking workstations. To this end, a polling system with a new type of arrival process and service discipline is introduced as a model for an order-picking workstation. Stochastic bounds are deduced for the cycle time, which corresponds to the order flow time. These bounds are shown to be adequate and aid in setting targets for the throughput of the storage area. The paper thus complements existing literature, which mainly focuses on optimizing the operations in the storage area.\u3c/p\u3

Stochastic bounds for order flow times in parts-to-picker warehouses with remotely located order-picking workstations

This paper focuses on the mathematical analysis of order flow times in parts-to-picker warehouses with remotely located order-picking workstations. To this end, a polling system with a new type of arrival process and service discipline is introduced as a model for an order-picking workstation. Stochastic bounds are deduced for the cycle time, which corresponds to the order flow time. These bounds are shown to be adequate and aid in setting targets for the throughput of the storage area. The paper thus complements existing literature, which mainly focuses on optimizing the operations in the storage area

Forward-reserve storage strategies with order picking: When do they pay off?

Customer order response time and system throughput capacity are key performance measures in warehouses. They depend strongly on the storage strategies deployed. One popular strategy is to split inventory into a bulk storage and a pick stock, or Forward-Reserve (FR) storage. Managers often use a rule of thumb: when the ratio m of average picks per replenishment is larger than a certain factor, it is beneficial to split inventory. However, research that systematically quantifies the benefits is lacking. We quantify the benefits analytically by developing response travel time models for FR storage in an Automated Storage/Retrieval system combined with order picking. We compare performance of FR storage with turnover class-based storage, and find when it pays off. Our findings illustrate that, in FR storage systems where forward and reserve stocks are stored in the same rack, FR storage usually pays off, as long as m is sufficiently larger than 1. The response time savings can go up to 50% when m is larger than 10. We validate these results using real data from a wholesale distributor

Estimation des distances lors de la préparation de commande

RÉSUMÉ : Le système de préparation de commande est l’un des processus les plus importants dans le fonctionnement d’un entrepôt. Dans un contexte où les entreprises cherchent à optimiser chaque fonctionnement, le processus de préparation de commande ne fait pas l’exception et les entreprises doivent constamment réévaluer la performance des systèmes établis. Plusieurs axes d’amélioration se posent. Les quatre plus pertinents ont été listés dans ce travail : (1) l’implantation de la zone d’entreposage, (2) la méthode de préparation de commande, (3) le modèle de circulation et (4) la politique de localisation des stocks. Le concepteur se trouvera finalement en face d’un système de préparation de commande dont il doit mesurer la performance. La revue de littérature présente plusieurs indicateurs qui ont été regroupés en six classes : indicateurs du temps, de distance, de rendement, de coût, techniques et orientés-clients. Le choix entre ces indicateurs se fait de plusieurs façons qui ne se basent pas sur une méthodologie précise. En plus, il n’y a pas de relations claires entre le choix des indicateurs et l’axe d’amélioration retenu. Par conséquent, le suivi de plusieurs indicateurs à la fois semble nécessaire. À travers ce mémoire, nous présentons un outil analytique pour estimer la distance parcourue et suivre l’un des plus importants indicateurs de performance. À cette fin, un exemple d’entrepôt, que nous jugeons représentatif de plusieurs entrepôts, a été choisi avec un jeu de données générées aléatoirement. Finalement, les résultats donnés par l’outil analytique ont été comparés avec des résultats de simulation.----------ABSTRACT : The order picking system is one of the most important processes in the operation of a warehouse. In a context where companies seek to optimize each operation, the order picking process is no exception and companies must constantly re-evaluate the performance of established systems. There are several areas for improvement. The four most relevant ones are listed in this work: (1) warehouse layout, (2) order preparation method, (3) circulation model and (4) location policy stocks. The designer is finally faced with a command preparation system whose performance must be evaluated. The literature review presents several indicators that have been grouped into six classes: time, distance, performance, cost, technical and customer-oriented indicators. The choice between these indicators is made in several ways that are not based on a specific methodology. In addition, there is no clear relationship between the choice of indicators and the improvement axis. Therefore, the monitoring of several indicators at a time seems necessary. Through this paper, we present an analytical tool to estimate the distance traveled and to follow one of the most important performance indicators. To this end, an example of a warehouse, which we consider representative of warehouses found in the industry, was selected with a randomly generated data set. Finally, the results given by the analytical tool were compared with simulation results