A Reactive GRASP Heuristic Algorithm for Vehicle Routing Problem with Release Date and Due Date Incurring Inventory Holding Cost and Tardiness Cost In this paper, vehicle routing problems with release dates and due dates (VRPRDD) are considered. Each customer order will incur an inventory holding cost if the order is held back at the depot after its release date and a tardiness cost if the delivery happens after its due date. The mathematical model is proposed and solved using Reactive GRASP heuristics. The results from the heuristics are compared with that of the mathematical model solved by CPLEX. Results of various data sets are used to analyze the effect of cost and due date flexibility on the routes generated. The proposed heuristic method outperforms the MILP solver of CPLEX in terms of quality of solution and the computation time taken. We study the managerial implications and insights subject to flexibility in due dates, and cost structure.

A stochastic multiitem replenishment and delivery problem with leadtime reduction initiatives and the solving methodologies Abstract   With the intensification of timebased competition, the importance of reducing leadtime by rapidly delivering multiorders has been underscored in the process of replenishmentstoragetransportation. This perception has prompted enterprises to increase expenditure on purchasing modern timetracing technologies (e.g. RFID), and equipping facilities for item fast movement (e.g. highrack automatic shelves) to retain customers. In this study, we explore a novel extension of the multiitem joint replenishment problem (JRP) with leadtime compressing initiatives. By assuming controllable leadtime, we construct a stochastic periodicreview joint replenishment and delivery (JRD) model to investigate impacts of capital investment in leadtime reduction to the decisions of multiitem joint replenishment and delivery. To solve the proposed JRD, two heuristics and a differential evolutionary algorithm are presented based on the model property analyses. The experimental results reveal the performance differences (e.g., searching speed, robustness and searching effectiveness) of three algorithms. Furthermore, our findings have managerial implications that proper investment in leadtime reduction not only helps shorten replenishment time and cut major ordering cost, but can also reduce the system cost.