Less-Than-Truckload (LTL) carriers generally serve geographical regions that are more localized than the inter-city routes served by truckload carriers. That localization can lead to urban freight transportation routes that overlap. If trucks are traveling with less than full loads there may exist opportunities for carriers to collaborate over such routes. That is, Carrier A will also deliver one or more shipments of Carrier B. This will improve vehicle asset utilization and reduce asset-repositioning costs, and may also lead to reduced congestion and pollution in cities. We refer to the above coordination as “collaborative routing”. In our framework for collaboration, we also propose that carriers exchange goods at logistics platforms located at the entry point to a city. This is referred to as “entry-point collaboration”. One difficulty in collaboration is the lack of facilities to allow transfer of goods between carriers. We highlight that the reduction in pollution and congestion under our proposed framework will give the city government an incentive to support these initiatives by providing facilities. Further, our analysis has shown that contrary to the poor benefits reported by previous work on vehicle routing with transshipment, strategic location of transshipment facilities in urban areas may solve this problem and lead to large cost savings from transfer of loads between carriers. We also present a novel integrated three-phase solution method. Our first phase uses either a modified tabu search, or a guided local search, to solve the vehicle routing problems with time windows that result from entry-point collaboration. The preceding methods use a constraint programming engine for feasibility checks. The second phase uses a quad-tree search to locate facilities. Quad-tree search methods are popular in computer graphics, and for grid generation in fluid simulation. These methods are known to be efficient in partitioning a two-dimensional space for storage and computation. We use this efficiency to search a two-dimensional region and locate possible transshipment facilities. In phase three, we employ an integrated greedy local search method to build collaborative routes, using three new transshipment-specific moves for neighborhood definition. We utilize an optimization module within local search to combine multiple moves at each iteration, thereby taking efficient advantage of information from neighborhood exploration. Extensive computational tests are done on random data sets which represent a city such as Toronto. Sensitivity analysis is performed on important parameters to characterize the situations when collaboration will be beneficial. Overall results show that our proposal for collaboration leads to 12% and 15% decrease in route distance and time, respectively. Average asset utilization is seen to increase by about 5% as well.

Less-Than-Truckload (LTL) carriers generally serve geographical regions that are more localized than the inter-city routes served by truckload carriers. That localization can lead to urban freight transportation routes that overlap. If trucks are traveling with less than full loads there may exist opportunities for carriers to collaborate over such routes. That is, Carrier A will also deliver one or more shipments of Carrier B. This will improve vehicle asset utilization and reduce asset-repositioning costs, and may also lead to reduced congestion and pollution in cities. We refer to the above coordination as “collaborative routing”. In our framework for collaboration, we also propose that carriers exchange goods at logistics platforms located at the entry point to a city. This is referred to as “entry-point collaboration”. One difficulty in collaboration is the lack of facilities to allow transfer of goods between carriers. We highlight that the reduction in pollution and congestion under our proposed framework will give the city government an incentive to support these initiatives by providing facilities. Further, our analysis has shown that contrary to the poor benefits reported by previous work on vehicle routing with transshipment, strategic location of transshipment facilities in urban areas may solve this problem and lead to large cost savings from transfer of loads between carriers. We also present a novel integrated three-phase solution method. Our first phase uses either a modified tabu search, or a guided local search, to solve the vehicle routing problems with time windows that result from entry-point collaboration. The preceding methods use a constraint programming engine for feasibility checks. The second phase uses a quad-tree search to locate facilities. Quad-tree search methods are popular in computer graphics, and for grid generation in fluid simulation. These methods are known to be efficient in partitioning a two-dimensional space for storage and computation. We use this efficiency to search a two-dimensional region and locate possible transshipment facilities. In phase three, we employ an integrated greedy local search method to build collaborative routes, using three new transshipment-specific moves for neighborhood definition. We utilize an optimization module within local search to combine multiple moves at each iteration, thereby taking efficient advantage of information from neighborhood exploration. Extensive computational tests are done on random data sets which represent a city such as Toronto. Sensitivity analysis is performed on important parameters to characterize the situations when collaboration will be beneficial. Overall results show that our proposal for collaboration leads to 12% and 15% decrease in route distance and time, respectively. Average asset utilization is seen to increase by about 5% as well.

Forest is a resource that is increasingly utilized for multiple purposes. The balance between energy demands and the long-term capacity of ecosystems to support biodiversity and other ecosystem services is crucial. The aim of this project was to increase the knowledge on and to develop methods and tools for trade-offs and synergies analysis among forest ecosystem services based on different forest management policies. Paper I provides an overview of existing models for integrated energy-environment assessment. A literature review was conducted on assessment models and their ability to integrate energy with environmental aspects. Missing environmental aspects concern land use, landscapes and biodiversity. In Paper II a modelling framework was set up to link a landscape simulator with a habitat network model for integrated assessment of bioenergy feedstock and biodiversity related impacts in Kronoberg County. In Paper III we continued with the same management scenarios, while the analysis was expanded to five ecosystem services by developing the Landscape simulation and Ecological Assessment (LEcA) tool: industrial wood, bioenergy, forest carbon stock, recreation areas and habitat networks. In Paper IV we present two heuristic methods for spatial optimization – simulated annealing (SA) and genetic algorithm (GA) – to find optimal solutions for allocating harvest activities, in order to minimize the impacts on habitat networks. In Paper V, as response to the findings in Paper I, we linked the energy model MESSAGE with our LEcA tool for forest bioenergy demand assessment while applying environmental and transport restrictions, in a study of Lithuania. We found trade-offs between industrial wood production and bioenergy on one side, and recreation values, biodiversity, and to some extent carbon storage on the other side. The LEcA tool integrated forest simulation and management with assessment of ecosystem services, which is promising for integrated sustainability assessment of forest management policies. / <p>QC 20171023</p>

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. <br /><br /> Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts. <br ><br /> We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases. <br /><br /> This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas. <br /><br /> Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc.

Layout Decision Analysis and Design is a ubiquitous problem in a variety of work domains that is important from both strategic and operational perspectives. It is largely a complex, vague, difficult, and ill-structured problem that requires intelligent and sophisticated decision analysis and design support. <br /><br /> Inadequate information availability, combinatorial complexity, subjective and uncertain preferences, and cognitive biases of decision makers often hamper the procurement of a superior layout configuration. Consequently, it is desirable to develop an intelligent decision support system for layout design that could deal with such challenging issues by providing efficient and effective means of generating, analyzing, enumerating, ranking, and manipulating superior alternative layouts. <br ><br /> We present a research framework and a functional prototype for an interactive Intelligent System for Decision Support and Expert Analysis in Multi-Attribute Layout Optimization (IDEAL) based on soft computing tools. A fundamental issue in layout design is efficient production of superior alternatives through the incorporation of subjective and uncertain design preferences. Consequently, we have developed an efficient and Intelligent Layout Design Generator (ILG) using a generic two-dimensional bin-packing formulation that utilizes multiple preference weights furnished by a fuzzy Preference Inferencing Agent (PIA). The sub-cognitive, intuitive, multi-facet, and dynamic nature of design preferences indicates that an automated Preference Discovery Agent (PDA) could be an important component of such a system. A user-friendly, interactive, and effective User Interface is deemed critical for the success of the system. The effectiveness of the proposed solution paradigm and the implemented prototype is demonstrated through examples and cases. <br /><br /> This research framework and prototype contribute to the field of layout decision analysis and design by enabling explicit representation of experts? knowledge, formal modeling of fuzzy user preferences, and swift generation and manipulation of superior layout alternatives. Such efforts are expected to afford efficient procurement of superior outcomes and to facilitate cognitive, ergonomic, and economic efficiency of layout designers as well as future research in related areas. <br /><br /> Applications of this research are broad ranging including facilities layout design, VLSI circuit layout design, newspaper layout design, cutting and packing, adaptive user interfaces, dynamic memory allocation, multi-processor scheduling, metacomputing, etc.