9 Conclusions
We can draw the following conclusions from the literature. First, in spite of their dominance in practice,
pickers-to-parts order-picking systems have received less research attention compared to parts-to-picker
order-picking systems. Less than 30 percent of the about 140 papers we considered concerns pickers-to-part
order-picking systems. The reasons for this may have something to do with the complexity and diversity of
picker-to-parts order-picking systems. Furthermore, parts-to-picker systems are often fully or partly
automated, thus catch the attention of researchers.
Second, although the number of publications in the areas of layout, batching, zoning, storage strategies (like
forward-reserve allocation, family grouping, and dynamic storage), and accumulation and sorting is still
limited, their number is growing. Particularly, the areas of storage assignment and routing appear to have
matured the last decade. Few authors address combinations of the decision problems. Yet, this is necessary as
there is obvious interdependency in their impact on the order picking objectives. New developments in
practice yielding unprecedented picker productivities like dynamic storage and put (order distribution)
systems have not yet led to attention from academics.
Third, existing studies in picker-to-parts order-picking systems mainly focus on random storage assignments.
Analytical models for optimising dedicated and class-based storage assignment manual-pick order-picking
systems are still lacking. Furthermore, storage assignment has an impact on the performance of the routing
method. However, this effect seems to be largely neglected in the literature. Instead, many authors focus on
random storage assignment to discuss about the performance of routing methods.
Fourth, almost all research in order picking treats demand as given (or known in advance). Certainly, this is
not true, especially in fast picking environments (e.g. small orders arrive on line and need to be shipped within
a tight time window). These order-picking situations are becoming more and more daily practice, particularly
for mail order companies which sell products online. Optimisation problems arising from these order-picking
systems, therefore, should be considered as stochastic optimisation problems, not deterministic ones.
Finally, most of the research focuses on a specific order picking situation or decision problem. However, it is
not straight forward to apply methods developed for a specific situation to another situation. ‘General’ design
procedures and ‘global’ optimisation models for order picking are still lacking.
9 ConclusionsWe can draw the following conclusions from the literature. First, in spite of their dominance in practice,pickers-to-parts order-picking systems have received less research attention compared to parts-to-pickerorder-picking systems. Less than 30 percent of the about 140 papers we considered concerns pickers-to-partorder-picking systems. The reasons for this may have something to do with the complexity and diversity ofpicker-to-parts order-picking systems. Furthermore, parts-to-picker systems are often fully or partlyautomated, thus catch the attention of researchers.Second, although the number of publications in the areas of layout, batching, zoning, storage strategies (likeforward-reserve allocation, family grouping, and dynamic storage), and accumulation and sorting is stilllimited, their number is growing. Particularly, the areas of storage assignment and routing appear to havematured the last decade. Few authors address combinations of the decision problems. Yet, this is necessary asthere is obvious interdependency in their impact on the order picking objectives. New developments inpractice yielding unprecedented picker productivities like dynamic storage and put (order distribution)systems have not yet led to attention from academics.Third, existing studies in picker-to-parts order-picking systems mainly focus on random storage assignments.Analytical models for optimising dedicated and class-based storage assignment manual-pick order-pickingsystems are still lacking. Furthermore, storage assignment has an impact on the performance of the routingmethod. However, this effect seems to be largely neglected in the literature. Instead, many authors focus onrandom storage assignment to discuss about the performance of routing methods.Fourth, almost all research in order picking treats demand as given (or known in advance). Certainly, this isnot true, especially in fast picking environments (e.g. small orders arrive on line and need to be shipped withina tight time window). These order-picking situations are becoming more and more daily practice, particularlyfor mail order companies which sell products online. Optimisation problems arising from these order-pickingsystems, therefore, should be considered as stochastic optimisation problems, not deterministic ones.Finally, most of the research focuses on a specific order picking situation or decision problem. However, it isnot straight forward to apply methods developed for a specific situation to another situation. ‘General’ designprocedures and ‘global’ optimisation models for order picking are still lacking.
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