Ruhe, GuentherLivani, Emadoddin2013-05-232013-11-122013-05-232013Livani, E. (2013). Decision Support for Strategic and Operational Planning of Logistic Services (Doctoral thesis, University of Calgary, Calgary, Canada). Retrieved from https://prism.ucalgary.ca. doi:10.11575/PRISM/25529http://hdl.handle.net/11023/723Logistics services are type of services that deal with industries like manufacturers, raw material suppliers, distributors, retailers, and shippers within the supply chain. Strategic planning for logistic services is a complex process that requires an understanding of how the different elements and activities of logistics. The decision-making process is considered as fundamental to strategic planning. The majority of the current studies focus only on optimizing the operational resources or model the strategic decisions without evaluating their impact on the operational resources. A decision support methodology, named SOPLS, is proposed in this thesis for Strategic and Operational Planning of Logistic Services. SOPLS consists of two layers: strategic and operational. The methods are integrated to form the strategic layer: model-based scenario analysis, search-based scenario generation, and multi-criteria scenario prioritization. The model-based scenario analysis works based on Bayesian belief networks (BBN) to provide decision support for cost-benefit analysis of the service scenarios. The search-based scenario generation method integrates BBN with genetic algorithms in order to generate new scenarios based on a given goal. A multi-criteria scenario prioritization method is proposed, by integrating BBN with analytical hierarchy process, to automatically prioritize the scenarios based on user preferences. Two methods are integrated in the operational layer. Firstly, a hybrid prediction method is proposed for estimating the demand by integrating k-means clustering with linear regression. Secondly, a vehicle routing algorithm is proposed to minimize the length of the routes, taken by the service vehicles. By integrating the strategic and operational levels, the impact of the strategic decisions on the operational resources, like staff and vehicles, will be estimated. The results will potentially trigger updating the strategies, or even the parameters in the cost-benefit model, which form an iterative decision making process. We evaluated the proposed decision support methodology through a collaboration project with the City of Calgary’s Waste and Recycling Services (WRS). The results showed the usefulness and applicability of the proposed solution in the area of waste management.engUniversity of Calgary graduate students retain copyright ownership and moral rights for their thesis. You may use this material in any way that is permitted by the Copyright Act or through licensing that has been assigned to the document. For uses that are not allowable under copyright legislation or licensing, you are required to seek permission.Computer ScienceDecision Support SystemsWaste and Recycling ManagementMachine LearningBayesian NetworksVehicle RoutingGenetic AlgorithmsOptimizationdoctoral thesis10.11575/PRISM/25529