An exact exponential time algorithm for counting bipartite cliques

We present a simple exact algorithm for counting bicliques of given size in a bipartite graph on n   vertices. We achieve running time of O(1.2491ⁿ)$O({1.2491}^n)$, improving upon known exact algorithms for finding and counting bipartite cliques.     

Single-machine scheduling with periodic maintenance to minimize makespan

We consider a single-machine scheduling problem with periodic maintenance activities. Although the scheduling problem with maintenance has attracted researchers’ attention, most of past studies considered only one maintenance period. In this research several maintenance periods are considered where each maintenance activity is scheduled after a periodic time interval. The objective is to find a schedule that minimizes the makespan, subject to periodic maintenance and nonresumable jobs. We first prove that the worst-case ratio of the classical LPT   algorithm is 2. Then we show that there is no polynomial time approximation algorithm with a worst-case ratio less than 2 unless P=NP$P=\text{<italic>NP</italic>}$, which implies that the LPT algorithm is the best possible.     

Online vector scheduling and generalized load balancing

We give a polynomial time reduction from the vector scheduling problem (VS) to the generalized load balancing problem (GLB). This reduction gives the first non-trivial online algorithm for VS where vectors come in an online fashion. The online algorithm is very simple in that each vector only needs to minimize the _Lln(md)$L_{ln\left(md\right)}$ norm of the resulting load when it comes, where m$m$ is the number of partitions and d$d$ is the dimension of vectors. It has an approximation bound of elog(md)$elog\left(md\right)$, which is in O(ln(md))$O(ln\left(md\right))$, so it also improves the O(ln²d)$O\left({ln}^{2}d\right)$ bound of the existing polynomial time algorithm for VS. Additionally, the reduction shows that GLB does not have constant approximation algorithms that run in polynomial time unless P=NP$P=NP$.     

Scheduling parallel CNC machines with time/cost trade-off considerations

When the processing times of jobs are controllable, selected processing times affect both the manufacturing cost and the scheduling performance. A well-known example for such a case that this paper specifically deals with is the turning operation on a CNC machine. Manufacturing cost of a turning operation is a nonlinear convex function of its processing time. We also know that scheduling decisions are quite sensitive to the processing times. Therefore, this paper considers minimizing total manufacturing cost (_F1)$(F_1)$ and total completion time (_F2)$(F_2)$ objectives simultaneously on identical parallel CNC turning machines. Since decreasing processing time of a job increases its manufacturing cost, we cannot minimize both objectives at the same time, so the problem is to generate non-dominated solutions. We consider the problem of minimizing _F1$F_1$ subject to a given _F2$F_2$ level and give an effective formulation for the problem. For this problem, we prove some optimality properties which facilitated designing an efficient heuristic algorithm to generate approximate non-dominated solutions. Computational results show that proposed algorithm performs almost equal with the GAMS/MINOS commercial solver although it spends much less computation time.     

A new self-stabilizing maximal matching algorithm

The maximal matching problem has received considerable attention in the self-stabilizing community. Previous work has given several self-stabilizing algorithms that solve the problem for both the adversarial and the fair distributed daemon, the sequential adversarial daemon, as well as the synchronous daemon. In the following we present a single self-stabilizing algorithm for this problem that unites all of these algorithms in that it has the same time complexity as the previous best algorithms for the sequential adversarial, the distributed fair, and the synchronous daemon. In addition, the algorithm improves the previous best time complexities for the distributed adversarial daemon from O(n²)$O\left(n^2\right)$ and O(δm)$O\left(\delta m\right)$ to O(m)$O\left(m\right)$ where n$n$ is the number of processes, m$m$ is the number of edges, and δ$\delta$ is the maximum degree in the graph.     

On the connectivity preserving minimum cut problem

In this paper, we study a generalization of the classical minimum cut problem, called Connectivity Preserving Minimum Cut (CPMC) problem, which seeks a minimum cut to separate a pair (or pairs) of source and destination nodes and meanwhile ensure the connectivity between the source and its partner node(s). For this problem, we consider two variants, connectivity preserving minimum node cut (CPMNC) and connectivity preserving minimum edge cut (CPMEC). For CPMNC, we show that it cannot be approximated within α logn for some constant α   unless P=NP$P=\mathit{NP}$, and cannot be approximated within any poly(logn)$\mathit{poly}(\mathit{logn})$ unless NP has quasi-polynomial time algorithms. The hardness results hold even for graphs with unit weight and bipartite graphs. For CPMEC, we show that it is in P for planar graphs.     

An efficient algorithm for minimizing earliness,tardiness, and due-date costs for equal-sized jobs

We consider a single-machine scheduling problem with equal-sized jobs. The objective is to minimize the maximum weighted earliness–tardiness and due-date costs. We present an algorithm to solve this problem. Our algorithm makes use of bottleneck jobs and priority queues, and has a computational complexity of O(n⁴logn)$O(n^4\log n)$. This complexity is a significant improvement of the existing algorithm in the literature.     

A hybrid algorithm for the single-machine total tardiness problem

We propose a hybrid algorithm based on the ant colony optimization (ACO) meta-heuristic, in conjunction with four well-known elimination rules, to tackle the NP$\mathit{NP}$-hard single-machine scheduling problem to minimize the total job tardiness. The hybrid algorithm has the same running time as that of ACO. We conducted extensive computational experiments to test the performance of the hybrid algorithm and ACO. The computational results show that the hybrid algorithm can produce optimal or near-optimal solutions quickly, and its performance compares favourably with that of ACO for handling standard instances of the problem.     

On-line scheduling of parallel machines to minimize total completion times

In this paper, we consider the scheduling problem on identical parallel machines, in which jobs are arriving over time and preemption is not allowed. The goal is to minimize the total completion times. According to the idea of the Delayed-SPT Algorithm proposed by Hoogeven and Vestjens [Optimal on-line algorithms for single-machine scheduling. In: Proceedings 5th international conference on integer programming and combinatorial optimization (IPCO). Lecture notes in computer science, vol. 1084. Berlin: Springer; 1996. p. 404–14], we give an on-line algorithm for the scheduling problem on m$m$ identical parallel machines. We show that this algorithm is 2-competitive and the bound is tight.