A mixed integer linear programming model to support e-fulfillment strategies in warehouse-based supermarket chains

Author:

Vazquez-Noguerol MarORCID,Comesaña-Benavides Jose A.ORCID,Riveiro-Sanroman SaraORCID,Prado-Prado J. CarlosORCID

Abstract

AbstractThe use of the online channel has greatly increased the logistics costs of supermarket chains. Even the difficulty of managing order picking and delivery processes has increased due to the short delivery times and the preservation of perishable products. Against that backdrop, the proposed approach presents a mathematical model for planning the e-fulfillment activities with the objective of ensuring maximum efficiency. The linear programming model has been designed for e-grocers that prepare their online orders at central warehouses. The mathematical model determines both the time windows during which picking and transport should take place and the assignment of trucks to delivery routes. The allocation of online orders is performed taking into account the conservation requirement of each type of product and the availability of means. Considering this planning tool, managers can improve the decision-making process guaranteeing the quality of service while reducing the e-fulfillment cost for joint picking and delivery point of view. Motivated by a cooperation with a supermarket chain, results bring great insight based on the simulation of different logistics alternatives. Companies and researchers can compare the strategy of leveling the workload and the strategy of reducing the number of means, a common alternative in logistics outsourced to third parties. In addition, the different scenarios developed make it possible to determine the substantial savings achieved by modifying the delivery services and advancing the order preparation. As a result, managerial insights are identified highlighting the importance of efficient order planning to improve the profitability of online sales.

Funder

Universidade de Vigo

Publisher

Springer Science and Business Media LLC

Subject

Management Science and Operations Research

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