PurposeThe purpose of this article is to present a model to estimate and evaluate the operational costs of alternative layouts for large capacity warehouses or distribution centers with a large variety of goods.Design/methodology/approachThe proposed model is based on time and resources studies per each of the basic activities on a warehouse operation. For validation purposes, the proposed model was applied on a perishable goods warehouse in Mexico. The output data was compared to actual data. Performance measures were operational costs and average picking time.FindingsIt was found that the proposed model is robust, flexible, simple and easy to be implemented. The model was used to evaluate two new alternatives of layout and operations of the same warehouse. It was found that the option with the layout with docks on long opposite sides of the warehouse and the operation without a separate picking zone minimizes operational costs.Research limitations/implicationsThe richness of the model is strongly supported by the information the warehouse has about its operation. With knowledge of the process, it is required to distinguish deterministic from stochastic basic activities and develop distance computations that depend on the layout being studied.Practical implicationsThe approach used to model warehouse operations was to estimate the movements and resource consumption per commodity. This allows the model to be used in every operational context when the complexity of the system is strongly dependent on and proportional to the volume of operations. In addition, it is particularly adequate as a tool to compare average performance measures of different scenarios for the same system.Originality/valueThe model proposed here provides a simple way to estimate particularly operational resource consumptions and picking times as proxy measures for efficiency and efficacy of a warehouse. It uses distance computations, time information and unit occurrence frequencies of basic activities over a single commodity in the system.
The classical problem of order acceptance/rejection in make-to-order environments, when aiming to maximise profit with machine set-ups is extended in this paper to multiple set-ups depending on manufacturing batch size. In this case, if the manufacturing batch is larger than certain product-dependent bounds, not only is the initial set-up required but also periodic reset-ups are in order, generating sub-batches of the same order, such as tool resharpening and machine recalibration.Anetwork formulation provides the basis for identifying effective algorithms to obtain a solution to the problem. A binary programming model (BPM) and a dynamic programming formulation (DPF) are proposed to solve the problem to optimality. In addition, two heuristics are developed to obtain lower bounds on maximum profit: each attempt to maximise customer satisfaction under production time restrictions, and to provide an extension to the classical knapsack problem. Numerical experimentation shows that computational time is not an issue when BPM and heuristics are applied, but the cost of commercial solvers for BPM algorithms might be problematic. However, if the aim is to code the DPF in-house, the curse of dimensionality in dynamic programming must be addressed, although dynamic programming does yield a full sensitivity analysis, which is useful for decision-making.
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