In this study, we consider a logistics system, in which a single supplier delivers a product to multiple retailers over a finite time horizon. Supplier decides on the amount to order in each period and services retailers facing deterministic dynamic demand via a fleet of vehicles having limited capacity. Each retailer has specific minimum and maximum levels of inventory in an order-up-to level inventory policy setting. The problem is to simultaneously determine the quantity of product to order to the supplier, retailers to be visited, the quantity of product to be delivered to retailers and routes of vehicles in each period so as to minimize system-wide costs. We present a mathematical formulation for the problem, for which we develop several Lagrangian relaxation based solution procedures providing both upper and lower bounds to the problem. We implement these solution procedures on test instances and present the results. Computational study shows that our solution procedures generate good feasible solutions in reasonable time.

Recently, products of Unmanned Aerial System (UAS) integrated through SIFT algorithm and dense cloud matching using structure from motion has gained prominence with tree-level inventory maintenance in forestry. Various studies have been carried out by using UAS imagery to quantify and map forest structure of simple coniferous stands. However, most of the previous works employ methodologies that require manual inputs and lack of reproducibility to other forest systmes. Manual detection of trees and calculation of their attributes can be a time-consuming and complicated process which can be overcome with an automated technique applied by forest managers and/or landowners is highly desired to take full advantage of the readily available UAS remote sensing images. This study presents a methodology for automated measurements of tree height, crown area and crown diameter of hardwood species using UAS images. Different UAS platforms were employed to gather digital data of two hardwood plantations at Martell, Indiana. The resulting aerial images were used to generate the Digital Surface Model (DSM) and Digital Elevation Model (DEM) for the forest stand from which the Crown Height Model (CHM) was derived. The canopy height model can be inputted to the web platform deployed through shiny server (https://feilab.shinyapps.io/Crown/) to derive individual tree parameters automatically. The results show that this automated method provides a high accuracy in individual tree identification (F-score> 90%) and tree-level measurements (RMSEht<1.2m and RMSEcrn<1m). Moreover, tree-level parameter estimation for 4,600 trees were calculated in less than 30 minutes based on a post-processed DSM from UAS-SfM derived images with minimal manual inputs. This study demonstrates the feasibility of automated inventory and measure of tree-level attributes in hardwood plantations with UAS images.

En metod som används för lagerstyrning, och som fått ökat intresse inom forskningen de senaste åren, är klassificering med multipla kriterier (MCABC). Däremot har forskningen hittills tenderat att koncentrera sig på utveckling av klassificeringsmodeller snarare än på vägledning för tillämpning av modellerna. Studien påvisade med en litteraturstudie att forskningsområdet klassificering med multipla kriterier hade en begränsad mängd vägledning för tillämpning relativt mängden klassificeringsmodeller som utformats. Därför undersökte studien en modifierad lagerstyrningsstrategi. Syftet med studien var att finna produkter som lämpar sig för lagring av produktionskapacitet och att undersöka effekterna på färdigvarulagret om dessa produkter används för lageruppbyggnad inför säsongen. För att finna sådana produkter utformades en klassificeringsmodell med multipla kriterier, GTS-modellen (Good to Store). Modellen testades genom att ett antal scenarier simulerades på ett urval av produkter av det egentillverkade sortimentet i en fabrik. Detta gjordes i en fallstudie av ett företag inom processindustrin vars sortiment har säsongsvariationer. Resultatet av simuleringarna visade att lageruppbyggnad med GTS-modellen inför säsongen innebar att det genomsnittliga lagervärdet och antalet omställningar för det egentillverkade sortimentet minskade med ungefär 14 % vardera, jämfört med nuläget. Samtidigt kunde servicenivån bibehållas på i princip identisk nivå. Görs motsvarande jämförelse enbart på den simulerade delen av sortimentet blir minskningarna ungefär 35 % vardera. / Multi criteria classification (MCABC) is a method used for inventory management which has gained interest in research in recent years. However, research has tended to focus on the development of classification models rather than guidance on the application of the models. This study demonstrated, using a literature review, that the multi criteria classification field had a limited amount of guidance for application compared to the amount of classification models that were developed. Therefore, this study examined a modified inventory management strategy. The purpose of the study was to find products suitable for storing production capacity and to examine the effects on the finished goods inventory if these products are used for stock build-up prior to the season. A multi criteria classification model, the GTS (Good to Store) model, was developed to find such products. The model was tested by simulating several scenarios on a selection of products from the factory-made range. This was conducted in a case study regarding a company in the process industry whose range has seasonal variations. The simulations showed that stock build-up with the GTS model prior to the season resulted in an approximately 14 % decrease in average inventory value and number of setups for the factory-made range, compared to the current situation. At the same time, the service level was almost completely maintained. If the comparison is made exclusively on the simulated part of the assortment, the decrease will be approximately 35 %.