An interactive fuzzy physical programming for solving multiobjective skip entry problem

The multi-criteria trajectory planning for Space Manoeuvre Vehicle (SMV) is recognised as a challenging problem. Because of the nonlinearity and uncertainty in the dynamic model and even the objectives, it is hard for decision makers to balance all of the preference indices without violating strict path and box constraints. In this paper, to provide the designer an effective method and solve the trajectory hopping problem, an Interactive Fuzzy Physical Programming (IFPP) algorithm is introduced. A new multi-objective SMV optimal control problem is formulated and parameterized using an adaptive technique. By using the density function, the oscillations of the trajectory can be captured effectively. In addition, an interactive decision-making strategy is applied to modify the current designer’s preferences during optimization process. Two realistic decision-making scenarios are conducted by using the proposed algorithm; Simulation results indicated that without driving objective functions out of the tolerable region, the proposed approach can have better performance in terms of the satisfactory degree compared with other approaches like traditional weighted-sum method, Goal Programming (GP) and fuzzy goal programming (FGP). Also, the results can satisfy the current preferences given by the decision makers. Therefore, The method is potentially feasible for solving multi-criteria SMV trajectory planning problems

Travel Package Recommendation

Location Based SocialNetworks (LBSN) benefit the users by allowing them to share their locations and life moments with their friends. The users can also review the locations they have visited. Classical recommender systems provide users a ranked list of single items. This is not suitable for applications like trip planning,where the recommendations should contain multiple items in an appropriate sequence. The problem of generating such recommendations is challenging due to various critical aspects, which includes user interest, budget constraints and high sparsity in the available data used to solve the problem. In this paper, we propose a graph based approach to recommend a set of personalized travel packages. Each recommended package comprises of a sequence of multiple Point of Interests (POIs). Given the current location and spatio-temporal constraints, our goal is to recommend a package which satisfies the constraints. This approach utilizes the data collected fromLBSNs to learn user preferences and also models the location popularity

An Ontological formalization of the planning task

In this paper we propose a generic task ontology, which formalizes the space of planning problems. Although planning is one of the oldest researched areas in Artificial Intelligence and attempts have been made in the past at developing task ontologies for planning, these formalizations suffer from serious limitations: they do not exhibit the required level of formalization and precision and they usually fail to include some of the key concepts required for specifying planning problems. In con-trast with earlier proposals, our task ontology formalizes the nature of the planning task independently of any planning paradigm, specific domains, or applications and provides a fine-grained, precise and comprehensive characterization of the space of planning problems. Finally, in addition to producing a formal specification we have also operationalized the ontology into a set of executable definitions, which provide a concrete reusable resource for knowledge acquisition and system development in planning applications

Process Planning Based on User Preferences

Typical approaches to adaptive slicing in previous literature have typically used surface finish requirements to control the slicing process. As a result, slice schemes improve the part's surface quality, but do not enable explicit trade-offs between finish and build time. The purpose of this article is to present a process planning method that enables the preferences of the user for surface finish, build time, and accuracy to control how trade-offs are made in a process plan. A multiobjective goal formulation is used by this method to evaluate how well user preferences are met by a process plan. This method consists of three modules, for determining part orientation, for slicing the part, and for determining other parameter values. An example with several scenarios representing different user preferences is provided to illustrate the process planning method.Mechanical Engineerin

Operating theatre modelling: integrating social measures

Hospital resource modelling literature is primarily focussed on productivity and efficiency measures. In this paper, our focus is on the alignment of the most valuable revenue factor, the operating room (OR) with the most valuable cost factor, the staff. When aligning these economic and social decisions, respectively, into one sustainable model, simulation results justify the integration of these factors. This research shows that integrating staff decisions and OR decisions results in better solutions for both entities. A discrete event simulation approach is used as a performance test to evaluate an integrated and an iterative model. Experimental analysis show how our integrated approach can benefit the alignment of the planning of the human resources as well as the planning of the capacity of the OR based on both economic related metrics (lead time, overtime, number of patients rejected) and social related metrics (personnel preferences, aversions, roster quality)