no / No / Scheduling systems based on traditional heuristic rules, which deal with the complexities of manufacturing systems, have been used by researchers for the past six decades. These heuristics rules prioritise all jobs that are waiting to be processed on a resource. In this paper, a novel Index Based Heuristic (IBH) solution for the Job Shop Scheduling Problem (JSSP) is presented with the objective of minimising the overall Makespan (Cmax). The JSSP is still a challenge to researchers and is far from being completely solved due to its combinatorial nature. JSSP suits the challenges of current manufacturing environments. The proposed IBH calculates the indices of candidate jobs and assigns the job with the lower index value to the available machine. To minimise the gap between jobs, a swap technique is introduced. The swap technique takes candidate jobs for a machine and swaps them without violating the precedence constraint. Several benchmark problems are solved from the literature to test the validity and effectiveness of the proposed heuristic. The results show that the proposed IBH based algorithm outperforms the traditional heuristics and is a valid methodology for JSSP optimization.

Drought, which is an important result of the climate change, is one of the most serious natural hazards globally. It has been agreed all over the world that it has adverse impacts on the production of agriculture, which plays a major role in the economy of a country. Studies showed that the results of the drought directly affected the crop yields, and it seems that this negative impact will continue drastically soon. Moreover, many researches revealed that, Turkey will be affected from the results of climate change in many aspects, especially the agricultural production will encounter dry seasons after the rapid changes in the precipitation amount. Insurance is a well-established method, which is used to share the risk based on natural disasters by people and organizations. Furthermore, a new way of insuring against the weather shocks is designing index-based insurance, and it has gained special attention in many developing countries. In this study, our aim is to model weather index based drought insurance product to help the small holder farmers in the Cental Anatolia Region under different models. At first, time series techniques were applied to forecast the wheat yield relying on the past data. Then, the AMS (AgroMetShell) software outputs, NDVI (Normalized Difference Vegetation Index) values were used, and SPI values for distinct time steps were chosen to develop a basic threshold based drought insurance for each province. Linear regression equations were used to calculate the trigger points for weather index, afterwards based on these trigger levels / pure premium and indemnity calculations were made for each province separately. In addition to this, Panel Data Analysis were used to construct an alternative linear model for drought insurance. It can be helpful to understand the direct and actual effects of selected weather index measures on wheat yield and also reduce the basis risks for constructed contracts. A simple ratio was generated to compare the basis risk of the different index-based insurance contracts.

Sustainable manufacturing is becoming increasingly important due to scarcity of natural resources, stricter regulations and increasing customer demand for sustainable products. Sustainable manufacturing involves the use of sustainable processes and systems to produce more sustainable products. In order to meet these demands for sustainable products, manufacturing companies have to adopt numerous strategies to achieve sustainable manufacturing. The approach for evaluating sustainable products and processes have been investigated in previous work where product/process sustainability indices were proposed. However, no comprehensive methods are available for sustainable manufacturing performance evaluation at the system level. This work aims to develop two alternate methods for evaluating sustainable manufacturing performance at enterprise, plant and production line levels. First, requirements for a sustainability metrics framework are identified through studying and reviewing existing literature where the three pillars of sustainability, total life-cycle stages, and 6R concepts are concurrently addressed. Then index-and value-based methods are proposed to evaluate sustainable manufacturing performance by conducting assessment on economic, environmental and societal aspects. Finally, the application of these two methods is illustrated for a representative enterprise producing consumer electronics at the enterprise level; a case study for a satellite television dish production is used to demonstrate the application of these methods at the production line level. Results obtained from these two methods are compared and analyzed at the enterprise level. The proposed methods can provide information to a company to identify improvement strategies and for decision making for sustainable development.

Unmanned Aerial Vehicles (UAVs) may develop cracks, erosion, delamination or other damages due to aging, fatigue or extreme loads. Identifying these damages is critical for the safe and reliable operation of the systems. Structural Health Monitoring (SHM) is capable of determining the conditions of systems automatically and continually through processing and interpreting the data collected from a network of sensors embedded into the systems. With the desired awareness of the systems’ health conditions, SHM can greatly reduce operational cost and speed up maintenance processes. The purpose of this study is to develop an effective, low-cost, flexible and fault tolerant structural health monitoring system. The proposed Index Based Reasoning (IBR) system started as a simple look-up-table based diagnostic system. Later, Fast Fourier Transformation analysis and neural network diagnosis with self-learning capabilities were added. The current version is capable of classifying different health conditions with the learned characteristic patterns, after training with the sensory data acquired from the operating system under different status. The proposed IBR systems are hierarchy and distributed networks deployed into systems to monitor their health conditions. Each IBR node processes the sensory data to extract the features of the signal. Classifying tools are then used to evaluate the local conditions with health index (HI) values. The HI values will be carried to other IBR nodes in the next level of the structured network. The overall health condition of the system can be obtained by evaluating all the local health conditions. The performance of IBR systems has been evaluated by both simulation and experimental studies. The IBR system has been proven successful on simulated cases of a turbojet engine, a high displacement actuator, and a quad rotor helicopter. For its application on experimental data of a four rotor helicopter, IBR also performed acceptably accurate. The proposed IBR system is a perfect fit for the low-cost UAVs to be the onboard structural health management system. It can also be a backup system for aircraft and advanced Space Utility Vehicles.

This extended abstract addresses index-based pricing models between shippers and carriers in strategic freight procurement. The increasingly unpredictable transportation cost developments, caused by a variety of different international crises, led to the need for shippers and carriers to find an equitable form of collaboration. Index-based pricing models are one way to regulate the price adjustment between shipper and carrier during the contract period due to cost developments. However, one obstacle to agreeing on such models is to find the correct index for the respective cost component that is adequate for both shipper and carrier and reflects the actual cost development of the carrier. In semi-structured expert interviews, we investigated which strategies are used to reflect volatile cost developments, how index-based pricing models work, and where problems are seen in this context. Furthermore, a broader study can lead to confirmation or further classification of the results. The abstract describes index-based pricing models with focus on road freight, but the mechanism will work for other transportation modes as well.

Many applications are migrating or beginning to make use native XML data. We anticipate that queries will emerge that emphasize the structural semantics of XML query languages like XPath and XQuery. This brings a need for an efficient query engine and database management system tailored for XML data similar to traditional relational engines. While mapping large XML documents into relational database systems while possible, poses difficulty in mapping XML queries to the less powerful relational query language SQL and creates a data model mismatch between relational tables and semi-structured XML data. Hence native solutions to efficiently store and query XML data are being developed recently. However, most of these systems thus far fail to demonstrate scalability with large document sizes, to provide robust support for the XPath query language nor to adequately address costing with respect to query optimization. In this thesis, we propose a novel cost-driven XPath engine to support the scalable evaluation of ad-hoc XPath expressions called VAMANA. VAMANA makes use of an efficient XML repository for storing and indexing large XML documents called the Multi-Axis Storage Structure (MASS) developed at WPI. VAMANA extensively uses indexes for query evaluation by considering index-only plans. To the best of our knowledge, it is the only XML query engine that supports an index plan approach for large XML documents. Our index-oriented query plans allow queries to be evaluated while reading only a fraction of the data, as all tuples for a particular context node are clustered together. The pipelined query framework minimizes the cost of handing intermediate data during query processing. Unlike other native solutions, VAMANA provides support for all 13 XPath axes. Our schema independent cost model provides dynamically calculated statistics that are then used for intelligent cost-based transformations, further improving performance. Our optimization strategy for increasing execution time performance is affirmed through our experimental studies on XMark benchmark data. VAMANA query execution is significantly faster than leading available XML query engines.

Rainfed agriculture is inherently risky, with climate change expected to intensify its variability. In the West African Sahel, where agriculture is crucial not only for subsistence but for national and household incomes through cotton production, the need to safeguard farmers' livelihoods against risk is essential. Formal crop insurance providers in such contexts cannot easily rely on traditional models, where indemnifications are based on realized losses, and have instead proposed a stream of index-based insurance products which indemnify clients based on a predefined, and yet objective parameter (the index). One promising product for Burkinabe cotton farmers is, the Double-Trigger Index-Based Insurance (2TIC), whose two-tier triggering mechanism has the potential of reducing moral hazard and minimizing basis risk. This dissertation uses three essays to consider a farmer-centric approach to assessing the implications of this double trigger mechanism for index-based insurance. The first essay explores cotton farmers' judgments of fairness vis-à-vis the 2TIC indemnification system by using Principal Component Analysis (PCA) and Logistic Regression Analyses, and examines if and how these judgments affect decisions to subscribe. The second essay assesses the impact of 2TIC on farmers' cotton-derived net income by employing Coarsened Exact Matching (CEM). The third essay compares the actuarially fair premium of the 2TIC with the commercial premium paid by cotton farmers, by using statistical approaches. The study provides important evidence-based insights into how 2TIC can be improved and promoted by incorporating farmers' needs and perspectives.

Yes / Scheduling is an important element that has a major impact on the efficiency of all manufacturing processes. It plays an important role in optimising the manufacturing times and costs resulting in energy efficient processes. It has been estimated that more than 75% of manufacturing processes occur in small batches. In such environments, processes must be able to perform a variety of operations on a mix of different batches. Batch-job scheduling optimisation is the response to such low batch manufacturing problems. The optimisation of batch-job process scheduling problem is still a challenge to researchers and is far from being completely solved due to its combinatorial nature. In this paper, a novel hybrid heuristic (HybH) solution approach for batch-job scheduling problem is presented with the objective of optimising the overall Makespan (Cmax). The proposed HybH is the combination of Index Based Heuristic (IBH) and the Finished Batch-Job (FBJ) process schedule. The heuristic assigns the first operation to a batch-job using IBH and the remaining operations on the basis FBJ process schedule. The FBJ process schedule gives priority to the batch-job with early finished operations, without violating the constraints of process order. The proposed HybH is explained with the help of a detailed example. Several benchmark problems are solved from the literature to check the validity and effectiveness of the proposed heuristic. The presented HybH has achieved batch-job process schedules which have outperformed the traditional heuristics. The results are encouraging and show that the proposed heuristic is a valid methodology for batch process scheduling optimisation.

Die Beiträge der vorliegenden Dissertationsschrift untersuchen zum einen, inwieweit Wetterindexversicherungen einen Beitrag zur Stabilisierung von wetterbedingten Einkommensschwankungen in der Landwirtschaft leisten können. In der Landwirtschaft ist trotz bedeutender wetterbedingter Einkommensschwankungen bisher nur ein sehr verhaltener Einsatz von Indexversicherungen zu beobachten. Allerdings gibt es bislang kaum Studien, die Möglichkeiten zur Reduzierung des mit dem Einsatz von Wetterindexversicherungen verbundenen Basisrisikos untersuchen. Zum anderen wird untersucht, inwieweit Wetterrisiken das Rückzahlungsverhalten landwirtschaftlicher Mikrokreditnehmer beeinflussen. Das Risiko bei der Kreditvergabe an landwirtschaftliche Klein-Betriebe in Entwicklungs- und Schwellenländern gilt aus Bankensicht aufgrund der vergleichsweise hohen Einkommensschwankungen in der landwirtschaftlichen Produktion als besonders hoch. In der Literatur wird das wetterbedingte Einkommensrisiko als wesentlicher Grund für das vergleichsweise hohe Kreditrisiko von landwirtschaftlichen Mikrokrediten angeführt. Allerdings wurde dies bislang noch nicht empirisch verifiziert. Die Dissertationsschrift widmet sich dem Thema in vier Beiträgen, die unterschiedliche Aspekte der übergeordneten Problematik behandeln.

Thesis (M.Sc. Agriculture (Agricultural Economics)) -- University of Limpopo, 2022 / Climate change and variability on a global level is one of the most serious environmental challenges which exacerbates the well-known vulnerability of smallholder farmers. To cushion the shock of climate change, risk management plays a crucial role in farming, and in the same context, insurance positions itself as one of the main risk management instruments available to farmers. However, one of the key reasons to the struggles of smallholder farmers in South Africa is the inability to obtain effective insurance. To this end, the study identified and profiled the socio-economic features of smallholder cattle farmers, identified smallholder cattle farmers’ perceptions on components of climate change as sources of risk to cattle production and finally, analysed the determinants of willingness to adopt IBLI among smallholder cattle farmers. A purposive multistage sampling procedure was used to identify 110 smallholder cattle farmers in the Polokwane local Municipality. IBM SPSS version 26 and STATA version 15 was used to analyse the primary cross-sectional data collected through structured questionnaire interviews. Farmers’ perceptions on components of climate change as sources of risk were identified using the Principal Component Analysis (PCA). The PCA revealed three principal components (PCs) explaining almost 61% of the variance of the original variables (climate change components). Furthermore, about 89% of the sampled farmers were willing to adopt IBLI, while about 11% weren’t willing to adopt and the Probit regression model results revealed that marital status, farming experience, access to extension services, off- farm income and land ownership, farmers’ educational level, household size, experience of livestock loss and farm size significantly influenced willingness to adopt IBLI. Based on the findings of this study, the recommended interventions include workshops for raising IBLI awareness, government subsidy on insurance premiums and finally, increased extension service reach to smallholder cattle farmers. / AgriSETA