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In this study, we tackle an integrated fleet sizing and replenishment planning problem in a vendor managed inventory system. There is a set of customers whose inventory must be replenished using a given set of predetermined frequencies for visits, and the customers have deterministic and constant demand. The vehicle fleet consists of multiple types of vehicles that differ in carrying capacity, cost per kilometer, and ownership costs. The main decision we need to make in this problem is the triple assignment of vehicle-frequency-customer. As a result of these assignment decisions, we obtain the annual total cost, which consists of vehicle ownership cost, routing cost, inventory holding, and fixed replenishment costs. A key simplification in the model is the use of linear approximation for the routing cost based on the number of customers visited in a route. The developed model in this study, which is new in the literature, integrates fleet sizing and replenishment planning decisions. We showed that our problem is NP-hard. In order to solve large problems efficiently, we proposed two versions of a fix and optimize type heuristic; with and without a moving sub problem window. We showed the effectiveness of the suggested heuristic solution procedure on a large set of randomly generated problems.

We address a real strategic fleet sizing problem for a furniture and home accessory distributor. The current practice in the real case is to keep the inventory of items in self-owned branch showrooms. The distributor plans to go to a new system where the inventory is centrally held in a distribution centre. In the new system, the area that the distributor serves is split into regions with their assigned group of vehicles. Showrooms in each region only showcase the products, and pass the orders to the distributor and the distributor transports the required items from the central depot to the customers directly. We propose a mixed integer linear program to determine the total number and types of owned and rented vehicles for each region under seasonal demand. We study the effects of different scenarios on cost and demand parameters, and suggest some managerial insights based on a set of numerical analysis. [Received: 16 February 2016; Revised: 31 May 2016; Accepted: 20 June 2016]

Maintenance, repair, and overhaul (MRO) activities for the airline sector are generally subject to some regulations to ensure the safety and the continuity of flights. The critical equipment on aircraft must go through MRO at regulated intervals for the continuing permission of use. Thus, the strict deadlines constraint overhaul activities. Several systems on aircrafts are of so-called rotable module type. These expensive rotable modules are overhauled by MRO companies and used repeatedly. MRO companies usually perform exchange programs with customer airlines regarding the expensive rotable modules. When an airplane comes for an MRO service involving rotable module, a ready-to-use module from the inventory of MRO company is exchanged with the rotable module extracted from the airplane so that the service time for the aircraft is minimized. The extracted module is overhauled in the MRO shop with a limited workforce capacity and the overhauled module is rotated back to the inventory for a future exchange. We tackle the overhaul and exchange scheduling problem together with the workforce planning for MRO companies with an expedited overhaul option. We propose a mixed integer programming formulation of the problem as a finite planning horizon model where we assume that there are multiple types of rotables handled by the MRO company, and we minimize the sum of inventory holding and workforce-related costs. The salient features of the model are that we allow exchanges to be carried out earlier than their due dates, if it makes sense cost wise, up to a certain earliness limit, and we assume that there is an expedited overhaul option. The model uses two types of time buckets, small and big, for overhaul scheduling and workforce planning, respectively. Both the problem and its model are new in the literature. We show that this planning problem is NP-Hard. We provide extensive numerical tests on a set of randomly generated problems and propose some managerial insights based on the results obtained.