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Many studies show that the human energy-related behaviors have a significant impact on the return of Energy Efficiency Programs (EEPs). However, studies that aimed at increasing the energy savings from the EEPs are still limited. In this paper, a Genetic Agent-Based (GAB) framework has been proposed to enhance the return of a typical EEP by simulating social network and energy behavior attributes and finding the best participants among a target community. Several attributes are considered for creating the agent-based model of households and numerically representing their interactions with the EEP or within their social network. The improvement of the EEP using the GAB framework is tested on a social network consisting of 56 households. The simulation results show that by accurately selecting participants using the presented framework, the amount of energy saving could increase up to ten times. This ultimately indicates the considerable impact of the social network on the EEP performance. In other words, to have an efficient EEP in the long term, the social network attributes such as network degree and strength of connections should be also considered in decision-making along with the energy-related attributes.

ABSTRACTAppropriate planning and strategies in Municipal Solid Waste (MSW) management could significantly decrease environmental and economic costs imposing on both management authorities and local communities. MSW landfill siting has been a growing challenge since unsuitability of landfills could cause destructive impacts on the environment and human health in addition to economic costs. In this study, Analytic Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) are utilised in an integrated decision-making framework to develop an efficient Suitability Index (SI) to assess the suitability of landfills of New South Wales, Australia, with respect to a comprehensive set of evaluation criteria. A preliminary factors map and a concluding composite SI map are developed in the geographic information system environment, and a solid ranking scheme is proposed to assess the landfills. A wide range of statistical analysis is conducted to obtain practical and specific results for different divisions in three different managerial scenarios. Comprehensiveness of the applied decision-making model, efficiency of the developed SI and adequacy of the performed statistical analysis provide a competent supporting tool for the authorities for ongoing landfills replacement plans, rehabilitation plans and future landfill siting programs.

One of the initial phases in the applications dealing with data processing on GPS trajectory data is to generate the time-stamped Sequence of Visited Locations (SVLs) of the mobile objects. The sequence is constructed by labeling each of the GPS observations of the trajectory using the ID of their intersecting Geometries of Interest (GOIs). In this paper, we enhance the performance of the state-of-the-art scheme for constructing the GOIs of a mobile object by proposing a data aggregation and outlier detection method. Our experimental results using geometric similarity metrics show that our improved GOI construction method outperforms the baseline methods by constructing the GOIs remarkably more geometrically similar to the real world GOIs. The geometric similarity metrics are only applicable when we have access to the geometries of the real world GOIs (ground truth). To be able to analyse the performance of the GOI extraction methods in environments which we do not have access to the ground truth, we propose two useful spatio-temporal metrics to measure the quality of GOIs based on the quality of the generated SVLs based on them. Our experimental results show that these two metrics are able to discriminate between the results of our different outlier detection methods and select the best scheme without using any external knowledge about the geometries of the real world GOIs.

AbstractThe Ready Mixed Concrete Dispatching Problem (RMCDP) is a generalised version of the Vehicle Routing Problem (VRP). In RMCDP however, given the particular properties of concrete, a set of additional constraints must be considered. The perishability of fresh material, for example, has to be contemplated, together with other technical issues, such as a truck can only serve a single customer and two or more customers cannot be supplied using a truck in a single trip. RMCDP can be modelled as a mixed integer problem (with soft time window) and it can be optimally solved for small and medium sized variants of the problem. However, the objective cost and similar constraints are usually calculated using spatial distances (Euclidian distance) and similar approximate travel times are used. In this paper, we propose a more realistic method for calculating travel times based on real travel distances obtained from Google Maps API. The accurate travel time information is used as input for the RMCDP, to provide results that are more convincing. This paper compares  RMCDP with two different objective functions; (i) spatial distance and (ii) real distance. This research will clarify the influence that the quality of this information has on the decision-making process and whether it is relevant to regard the accurate travel time to achieve a more realistic result.

Nowadays large amounts of GPS trajectory data is continuously collected by GPS-enabled devices such as vehicles navigation systems and mobile phones. GPS trajectory data is useful for applications such as traffic management, location forecasting, and itinerary planning. Such applications often need to extract the time-stamped sequence of visited locations (SVLs) of the mobile objects. The nearest neighbor query (NNQ) is the most applied method for labeling the visited locations based on the IDs of the points of interests (POIs) in the process of SVL generation. NNQ in some scenarios is not accurate enough. To improve the quality of the extracted SVLs, instead of using NNQ, we label the visited locations as the IDs of the POIs which geometrically intersect with the GPS observations. Intersection operator requires the accurate geometries of the POIs which we refer to them as the geometries of interest (GOIs). In some application domains (e.g. movement trajectories of animals), adequate information about the POIs and their geometries may not be available a priori, or they may not be publicly accessible and, therefore, they need to be derived from GPS trajectory data. In this paper we propose a novel method for estimating the GOIs, which consists of three phases: (1) extracting the geometries of the stay regions; (2) constructing the geometry of destination regions based on the extracted stay regions; and (3) constructing the GOIs based on the geometries of the destination regions. Using the geometric similarity to known GOIs as the major evaluation criterion, the experiments we performed using long-term GPS trajectory data show that our method outperforms the state-of-the-art.

This paper aims to propose an integrated municipal solid waste management network covering multiple types of wastes concurrently and utilise a location-routing problem framework to minimise the establishment cost of interrelated facilities (i.e., transfer stations; treatment, recycling and disposal centres) in the network and the transportation cost of wastes in the entire network. The defined problem consists of the concurrent site selection of the locations of the system's all facilities among the candidate locations and the determination of routes and amount of shipments among the selected facilities to minimise the total cost of transportation and facility establishment. As the addressed problem exhibits the non-deterministic polynomial-time hardness (NP-hardness), an adaptation of the simulated annealing algorithm is proposed in this paper. The experiment results, when compared with the exact solutions obtained by mixed-integer programming in terms of solution fitness and computing time, imply that the employed algorithm works effectively and efficiently.

The pavement performance model is a basic part of the pavement management system. The prediction accuracy of the model depends on the number of effective variables and the type of mathematical method that is used for modeling the pavement performance. In this paper, the capability of the support vector machine (SVM) method is analyzed for predicting the future of the pavement condition. Five kernel types of SVM algorithm are formed and nine input variables of the proposed models are extracted from the range of effective variables on the pavement condition. The international roughness index is used as the pavement performance index. The results show that the Pearson VII Universal kernel can accurately predict pavement performance in its life cycle.

The pavement performance model is a basic part of the pavement management system. The prediction accuracy of the model depends on the number of effective variables and the type of mathematical method that is used for modeling the pavement performance. In this paper, the capability of the support vector machine (SVM) method is analyzed for predicting the future of the pavement condition. Five kernel types of SVM algorithm are formed and nine input variables of the proposed models are extracted from the range of effective variables on the pavement condition. The international roughness index is used as the pavement performance index. The results show that the Pearson VII Universal kernel can accurately predict pavement performance in its life cycle.

The logistics and planning problem of delivering ready mixed concrete (RMC) to a set of demand customers from multiple depots is addressed. The RMC dispatching problem (RMCDP) is closely related to the vehicle routing problem, with the difference that a truck may visit demand nodes in the RMCDP more than once. This class of routing problems can be represented by using mixed-integer programming (MIP) and is known to be NP-hard. Solving RMC delivery problems is often achieved through heuristics and metaheuristic-based methods as exact solution approaches are often unable to find optimal solutions efficiently, in particular when multiple depots are represented in the model. Although a variety of methods are available to solve MIP models, in this paper an attempt is made to solve the RMCDP by using a Lagrangian relaxation technique. Namely, a solution algorithm based on Lagrangian relaxation is derived to reduce the complexity of the initial MIP model and show that the proposed relaxation is able to provide promising computation results on a realistic data set representative of an active RMCDP in the region of Adelaide, Australia.

The logistics and planning problem of delivering ready mixed concrete (RMC) to a set of demand customers from multiple depots is addressed. The RMC dispatching problem (RMCDP) is closely related to the vehicle routing problem, with the difference that a truck may visit demand nodes in the RMCDP more than once. This class of routing problems can be represented by using mixed-integer programming (MIP) and is known to be NP-hard. Solving RMC delivery problems is often achieved through heuristics and metaheuristic-based methods as exact solution approaches are often unable to find optimal solutions efficiently, in particular when multiple depots are represented in the model. Although a variety of methods are available to solve MIP models, in this paper an attempt is made to solve the RMCDP by using a Lagrangian relaxation technique. Namely, a solution algorithm based on Lagrangian relaxation is derived to reduce the complexity of the initial MIP model and show that the proposed relaxation is able to provide promising computation results on a realistic data set representative of an active RMCDP in the region of Adelaide, Australia.