Route specification can be a time consuming process in flight planning, and at the same time, it should be done very diligently for the sake of a safe flight and to reach the target on time. An autonomous decision support system can save time spent on finding the most accurate and shortest route from departure to destination. Different kinds of external sources are used as input for making intelligent decisions by taking advantage of a rule based reasoning approach. In this regard, a rule based decision support concept based on Drools rule engine is proposed in order to ease the workload of pilots during route specification. / Flygruttsplanering är en tidskrävande process vid uppdragsplanering. Den bör göras med stor omsorg för att garantera en säker flygning som når målet på utsatt tid. Ett autonomt beslutstödssystem kan spara tid genom att hitta den säkraste och kortaste rutten mellan avgång och destination. I detta sammanhang används olika externa källor som indata och de ligger till grund för att fatta intelligenta beslut med hjälp av regelbaserat resonerande. Konceptet regelbaserat beslutsstöd är baserat på Drools regelmotor för att minska arbetsbördan för piloter under ruttplanering.

Supply chain operations of construction industry including road projects in disaster regions results in exceeding project budget and timelines. In road construction projects, supply chain with poor performance can affect efficiency and completion time of the project. This is also the case of the road projects in disaster areas. Disaster areas consider both natural and man-made disasters. Few examples of disaster zones are; Pakistan, Afghanistan, Iraq, Sri Lanka, India, Japan, Haiti and many other countries with similar environments. The key factors affecting project performance and execution are insecurity, uncertainties in demand and supply, poor communication and technology, poor infrastructure, lack of political and government will, unmotivated organizational staff, restricted accessibility to construction materials, legal hitches, multiple challenges of hiring labour force and exponential construction rates due to high risk environment along with multiple other factors. The managers at all tiers are facing challenges of overrunning time and budget of supply chain operations during planning as well as execution phase of development projects. The aim of research is to develop a Multiple Linear Regression Model (MLRM) and a Rule Based Decision Support System by incorporating various factors affecting supply chain management of road projects in disaster areas in the order of importance. This knowledge base (KB) (importance / coefficient of each factor) will assist infrastructure managers (road projects) and practitioners in disaster regions in decision making to minimize the effect of each factor which will further help them in project improvement. Conduct of Literature Review in the fields of disaster areas, supply chain operational environments of road project, statistical techniques, Artificial Intelligence (AI) and types of research approaches has provided deep insights to the researchers. An initial questionnaire was developed and distributed amongst participants as pilot project and consequently results were analysed. The results’ analysis enabled the researcher to extract key variables impacting supply chain performance of road project. The results of questionnaire analysis will facilitate development of Multiple Linear Regression Model, which will eventually be verified and validated with real data from actual environments. The development of Multiple Linear Regression Model and Rule Based Decision Support System incorporating all factors which affect supply chain performance of road projects in disastrous regions is the most vital contribution to the research. The significance and novelty of this research is the methodology developed that is the integration of those different methods which will be employed to measure the SCM performance of road projects in disaster areas.