Sentimental causal rule discovery from Twitter

Social media, especially Twitter is now one of the most popular platforms where people can freely express their opinion. However, it is difficult to extract important summary information from many millions of tweets sent every hour. In this work we propose a new concept, sentimental causal rules, and techniques for extracting sentimental causal rules from textual data sources such as Twitter which combine sentiment analysis and causal rule discovery. Sentiment analysis refers to the task of extracting public sentiment from textual data. The value in sentiment analysis lies in its ability to reflect popularly voiced perceptions that are stated in natural language. Causal rules on the other hand indicate associations between different concepts in a context where one (or several concepts) cause(s) the other(s). We believe that sentimental causal rules are an effective summarization mechanism that combine causal relations among different aspects extracted from textual data as well as the sentiment embedded in these causal relationships. In order to show the effectiveness of sentimental causal rules, we have conducted experiments on Twitter data collected on the Kurdish political issue in Turkey which has been an ongoing heated public debate for many years. Our experiments on Twitter data show that sentimental causal rule discovery is an effective method to summarize information about important aspects of an issue in Twitter which may further be used by politicians for better policy making.     

Self-regularized causal structure discovery for trajectory-based networks

Trajectory-based networks exhibit strong heterogeneous patterns amid human behaviors. We propose a notion of causal time-varying dynamic Bayesian network (cTVDBN) to efficiently discover such patterns. While asymmetric kernels are used to make the model better adherence to causal principles, the variations of network connectivities are addressed by an adaptive over-fitting control. Compact regularization paths are obtained by approximate homotopy to make the solution tractable. In our experiments, cTVDBN structure discovery has successfully revealed the evolution of time-varying relationships in a ring road system, and provided insights for plausible road structure improvements from a traffic flow dataset.     

A new heuristic for inferring regular grammars

Modifications to a grammatical inference scheme by Feldman et al. are presented. A comparison of the relative performance of the original and modified schemes is made using the complexity measures of Feldman and Wharton. The case where a complex model is used to generate the sample set is then analyzed. A set of 104 samples was found that trained the program to infer the grammar that corresponded to the original model. The results of a study of the performance of this algorithm when there is a large number of samples is then presented. The major conclusion of this study is that the modified scheme has a superior performance on small sample sets but is highly unsuitable for large ones.     

A new fast heuristic for labeling points

In the point site labeling problem, we are given a set P={p₁,p₂,…,pn} of point sites in the plane. The label of a point pi is an axis-parallel rectangle of specified size. The objective is to label the maximum number of points on the map so that the placed labels are mutually non-overlapping. Here, we investigate a special class of the point site labeling problem where (i) height of the labels of all the points are same but their lengths may differ, (ii) the label of a point pi touches the point at one of its four corners, and (iii) the label of one point does not obscure any other point in P. We describe an efficient heuristic algorithm for this problem which runs in time in the worst case. We run our algorithm as well as the algorithm Rules proposed by Wagner et al. on randomly generated point sets and on the available benchmarks. The results produced by our algorithm are almost the same as Rules in most of the cases. But our algorithm is faster than Rules in dense instance. We have also computed the optimum solutions for all the examples we have considered by designing an algorithm, which performs an exhaustive search in the worst case. We found that the exhaustive search algorithm runs reasonably fast for most of the examples we have considered.     

A new heuristic algorithm for rectangle packing

The rectangle packing problem often appears in encasement and cutting as well as very large-scale integration design. To solve this problem, many algorithms such as genetic algorithm, simulated annealing and other heuristic algorithms have been proposed. In this paper, a new heuristic algorithm is recommended based on two important concepts, namely, the corner-occupying action and caving degree. Twenty-one rectangle-packing instances are tested by the algorithm developed, 16 of which having achieved optimum solutions within reasonable runtime. Experimental results demonstrate that the algorithm developed is fairly efficient for solving the rectangle packing problem.     

A new heuristic approach for non-convex optimization problems

In this work a new optimization method, called the heuristic Kalman algorithm (HKA), is presented. This new algorithm is proposed as an alternative approach for solving continuous, non-convex optimization problems. The principle of HKA is to explicitly consider the optimization problem as a measurement process designed to give an estimate of the optimum. A specific procedure, based on the Kalman estimator, was developed to improve the quality of the estimate obtained through the measurement process. The main advantage of HKA, compared to other metaheuristics, lies in the small number of parameters that need to be set by the user. Further, it is shown that HKA converges almost surely to a near-optimal solution. The efficiency of HKA was evaluated in detail using several non-convex test problems, both in the unconstrained and constrained cases. The results were then compared to those obtained via other metaheuristics. The numerical experiments show that HKA is a promising approach for solving non-convex optimization problems, particularly in terms of computation time and success ratio.     

A new heuristic approach to assembly line balancing

The main feature of the method suggested in this paper is the assignment of priority to elements and priority elements are preferred to non-priority elements when assigning elements to stations. It gives the minimum number of stations under a predetermined cycle time. The work element time is considered to be invariant. This method has been tested by solving nearly all the problems available in the pertinent literature. This method yields better or similar results as available in the literature. A computer program incorporating the new heuristic method is presented in the paper.     

A new mapping heuristic based on mean field annealing

A new mapping heuristic is developed, based on the recently proposed Mean Field Annealing (MFA) algorithm. An efficient implementation scheme, which decreases the complexity of the proposed algorithm by asymptotical factors, is also given. Performance of the proposed MFA algorithm is evaluated in comparison with two well-known heuristics: Simulated Annealing and Kernighan-Lin. Results of the experiments indicate that MFA can be used as an alternative heuristic for solving the mapping problem. The inherent parallelism of the MFA is exploited by designing an efficient parallel algorithm for the proposed MFA heuristic.     

A new energy landscape paving heuristic for satellite module layouts

This article describes a study of the satellite module layout problem (SMLP), which is a three-dimensional (3D) layout optimization problem with performance constraints that has proved to be non-deterministic polynomial-time hard (NP-hard). To deal with this problem, we convert it into an unconstrained optimization problem using a quasi-physical strategy and the penalty function method. The energy landscape paving (ELP) method is a class of Monte-Carlo-based global optimization algorithm that has been successfully applied to solve many optimization problems. ELP can search for low-energy layouts via a random walk in complex energy landscapes. However, when ELP falls into the narrow and deep valleys of an energy landscape, it is difficult to escape. By putting forward a new update mechanism of the histogram function in ELP, we obtain an improved ELP method which can overcome this drawback. By incorporating the gradient method with local search into the improved ELP method, a new global search optimization method, nELP, is proposed for SMLP. Two representative instances from the literature are tested. Computational results show that the proposed nELP algorithm is an effective method for solving SMLP with performance constraints.     

Conformant planning via heuristic forward search: A new approach

Conformant planning is the task of generating plans given uncertainty about the initial state and action effects, and without any sensing capabilities during plan execution. The plan should be successful regardless of which particular initial world we start from. It is well known that conformant planning can be transformed into a search problem in belief space, the space whose elements are sets of possible worlds. We introduce a new representation of that search space, replacing the need to store sets of possible worlds with a need to reason about the effects of action sequences. The reasoning is done by implication tests on propositional formulas in conjunctive normal form (CNF) that capture the action sequence semantics. Based on this approach, we extend the classical heuristic forward-search planning system FF to the conformant setting. The key to this extension is an appropriate extension of the relaxation that underlies FF's heuristic function, and of FF's machinery for solving relaxed planning problems: the extended machinery includes a stronger form of the CNF implication tests that we use to reason about the effects of action sequences. Our experimental evaluation shows the resulting planning system to be superior to the state-of-the-art conformant planners MBP, KACMBP, and GPT in a variety of benchmark domains.     

A new heuristic algorithm for the operating room scheduling problem

Due to the great importance of operating rooms in hospitals, this paper studies an operating room scheduling problem with open scheduling strategy. According to this strategy, no time slot is reserved for a particular surgeon. The surgeons can use all available time slots. Based on Fei et al.’s model which is considered to be close to reality, we develop a heuristic algorithm to solve it. The idea of this heuristic algorithm is from dynamic programming by aggregating states to avoid the explosion of the number of states. The objective of this paper is to design an operating program to maximize the operating rooms’ use efficiency and minimize the overtime cost. Computational results show that our algorithm is efficient, especially for large size instances where our algorithm always finds feasible solutions while the algorithm of Fei et al. does not.     

A new pruning heuristic based on variance analysis of sensitivityinformation

Architecture selection is a very important aspect in the design of neural networks (NNs) to optimally tune performance and computational complexity. Sensitivity analysis has been used successfully to prune irrelevant parameters from feedforward NNs. This paper presents a new pruning algorithm that uses the sensitivity analysis to quantify the relevance of input and hidden units. A new statistical pruning heuristic is proposed, based on the variance analysis, to decide which units to prune. The basic idea is that a parameter with a variance in sensitivity not significantly different from zero, is irrelevant and can be removed. Experimental results show that the new pruning algorithm correctly prunes irrelevant input and hidden units. The new pruning algorithm is also compared with standard pruning algorithms.     

HC_AB: A new heuristic clustering algorithm based on Approximate Backbone

Clustering is an important research area with numerous applications in pattern recognition, machine learning, and data mining. Since the clustering problem on numeric data sets can be formulated as a typical combinatorial optimization problem, many researches have addressed the design of heuristic algorithms for finding sub-optimal solutions in a reasonable period of time. However, most of the heuristic clustering algorithms suffer from the problem of being sensitive to the initialization and do not guarantee the high quality results. Recently, Approximate Backbone (AB), i.e., the commonly shared intersection of several sub-optimal solutions, has been proposed to address the sensitivity problem of initialization. In this paper, we aim to introduce the AB into heuristic clustering to overcome the initialization sensitivity of conventional heuristic clustering algorithms. The main advantage of the proposed method is the capability of restricting the initial search space around the optimal result by defining the AB, and in turn, reducing the impact of initialization on clustering, eventually improving the performance of heuristic clustering. Experiments on synthetic and real world data sets are performed to validate the effectiveness of the proposed approach in comparison to three conventional heuristic clustering algorithms and three other algorithms with improvement on initialization.     

A new heuristic algorithm for cuboids packing with no orientation constraints

The three-dimensional cuboids packing is NP-hard and finds many applications in the transportation industry. The problem is to pack a subset of cuboid boxes into a big cuboid container such that the total volume of the packed boxes is maximized. The boxes have no orientation constraints, i.e. they can be rotated by 90^°${90}^{°}$ in any direction. A new heuristic algorithm is presented that defines a conception of caving degree to judge how close a packing box is to those boxes already packed into the container, and always chooses a packing with the largest caving degree to do. The performance is evaluated on all the 47 related benchmarks from the OR-Library. Experiments on a personal computer show a high average volume utilization of 94.6% with an average computation time of 23 min for the strengthened A1 algorithm, which improves current best records by 3.6%. In addition, the top-10 A2 algorithm achieved an average volume utilization of 91.9% with an average computation time of 55 s, which also got higher utilization than current best records reported in the literature.     

A new heuristic for solving the p-median problem in the plane

This paper presents a new local search for solving the continuous p-median problem in the plane. The basic idea is to first find a good starting solution by overlaying the area containing the set of demand points with a grid and solving heuristically the location problem on this grid. The solution is then used as an initial point for running an improved version of Cooper's well-known alternating local search.     

A new heuristic for scheduling the two-stage flowshop with additional resources

This paper deals with the problem of preemptive scheduling in a two-stage flowshop with parallel unrelated machines at the first stage and a single machine at the second stage. At the first stage, jobs use some additional resources which are available in limited quantities at any time. The resource requirements are of 0–1 type. The objective is the minimization of makespan. The problem is NP-hard. Heuristic algorithms are proposed which solve to optimality the resource constrained scheduling problem at the first stage of the flowshop, and at the same time, minimize the makespan in the flowshop by selecting appropriate jobs for simultaneous processing. Several rules of job selection are considered. The performance of the proposed heuristic algorithms is analyzed by comparing solutions with the lower bound on the optimal makespan. The extensive computational experiment shows that the proposed heuristic algorithms are able to produce near-optimal solutions in short computational time.     

A new heuristic recursive algorithm for the strip rectangular packing problem

A fast and new heuristic recursive algorithm to find a minimum height for two-dimensional strip rectangular packing problem is presented. This algorithm is mainly based on heuristic strategies and a recursive structure, and its average running time is T(n)=θ(n³)$T(n)=\theta (n^3)$. The computational results on a class of benchmark problems have shown that this algorithm not only finds shorter height than the known meta-heuristic ones, but also runs in shorter time. Especially for large test problems, it performs better.     

A new heuristic to solve the one-warehouse N-retailer problem

We deal with a multi-echelon inventory system in which one warehouse supplies an item to multiple retailers. Customer demand arrives at each retailer at a constant rate. The retailers replenish their inventories from the warehouse that in turn orders from an outside supplier. It is assumed that shortages are not allowed and lead times are negligible. The goal is to determine replenishment policies that minimize the overall cost in the system. We develop a heuristic to compute efficient policies, which also can easily be used in a spreadsheet application. The main idea consists of finding a balance between the replenishment and the inventory holding costs at each installation. This new heuristic we compare with two other approaches proposed in the literature; the computational studies show that in most of the instances generated the new method provides lower costs.     

Nonparametric-copula-entropy and network deconvolution method for causal discovery in complex manufacturing systems

Journal of Intelligent Manufacturing - To clarify the causality among process parameters is a core issue of data-driven production performance analysis and product quality optimization. The...