An improved on-line algorithm for scheduling on two unrestrictive parallel batch processing machines

We consider the problem of on-line scheduling a set of n jobs on two parallel batch processing machines. The objective is to minimize the makespan. We provide an algorithm for the problem that is better than one given in the literature, improving the competitive ratio from to .     

Randomized on-line scheduling on three processors

We consider a randomized on-line scheduling problem where each job has to be scheduled on any of m identical processors. The objective is to minimize the expected makespan. We show that the competitive ratio of any randomized algorithm for m=3 processors must be strictly greater than .     

On-line scheduling on a batch machine to minimize makespan with limited restarts

We study the on-line scheduling on an unbounded batch machine to minimize makespan. In this model, jobs arrive over time and batches are allowed limited restarts. Any batch that contains a job which has already been restarted once cannot be restarted any more. We provide a best possible on-line algorithm for the problem with a competitive ratio .     

Online C-benevolent job scheduling on multiple machines

We consider scheduling a sequence of C-benevolent jobs on multiple homogeneous machines. For two machines, we propose a 2-competitive Cooperative Greedy algorithm and provide a lower bound of 2 for the competitive ratio of any deterministic online scheduling algorithms on two machines. For multiple machines, we propose a Pairing-m Greedy algorithm, which is deterministic 2-competitive for even number of machines and randomized \((2+2/{\hbox {m}})\)-competitive for odd number of machines. We provide a lower bound of 1.436 for the competitive ratio of any deterministic online scheduling algorithms on three machines, which is the best known lower bound for competitive ratios of deterministic scheduling algorithms on three machines.     

A class of on-line scheduling algorithms to minimize total completion time

We consider the problem of scheduling jobs on-line on a single machine and on identical machines with the objective to minimize total completion time. We assume that the jobs arrive over time. We give a general 2-competitive algorithm for the single machine problem. The algorithm is based on delaying the release time of the jobs, i.e., making the jobs artificially later available to the on-line scheduler than the actual release times. Our algorithm includes two known algorithms for this problem that apply delay of release times. The proposed algorithm is interesting since it gives the on-line scheduler a whole range of choices for the delays, each of which leading to 2-competitiveness.We also show that the algorithm is 2α competitive for the problem on identical machines where α is the performance ratio of the Shortest Remaining Processing Time first rule for the preemptive relaxation of the problem.     

On-line scheduling to minimize max flow time: an optimal preemptive algorithm

We investigate the maximum flow time minimization problem of on-line scheduling jobs on m identical parallel machines. When preemption is allowed, we derive an optimal algorithm with competitive ratio 2-1/m. When preemption is not allowed and m=2, we show that the First In First Out heuristic achieves the best possible competitive ratio.     

Minimizing total tardiness in an unrelated parallel-machine scheduling problem

We consider a problem of scheduling n independent jobs on m unrelated parallel machines with the objective of minimizing total tardiness. Processing times of a job on different machines may be different on unrelated parallel-machine scheduling problems. We develop several dominance properties and lower bounds for the problem, and suggest a branch and bound algorithm using them. Results of computational experiments show that the suggested algorithm gives optimal solutions for problems with up to five machines and 20 jobs in a reasonable amount of CPU time.     

clever or smart: Strategies for the online target date assignment problem

In this paper, we consider the Online Target Date Assignment Problem (OnlineTDAP) for general downstream problems, where the downstream cost are nonnegative, additive and satisfy the triangle inequality.We analyze algorithm smart, which was introduced by Angelelli et al. [3] and give its exact competitive ratio depending on the number of requests. Since the obtained competitive ratio is at most we answer the question posed in Angelelli et al. [4] if smart has a competitive ratio strictly less than 2.Moreover, we introduce a new algorithm called clever and show that this strategy has a competitive ratio of 3/2. We show that this is asymptotically optimal by proving that no online algorithm can perform better than 3/2−ε.     

Online and semi-online scheduling of two machines under a grade of service provision

We consider the online scheduling of two machines under a grade of service (GoS) provision and its semi-online variant where the total processing time is known. Respectively for the online and semi-online problems, we develop algorithms with competitive ratios of and which are shown to be optimal.     

Online competitive algorithms for maximizing weighted throughput of unit jobs

We study an online unit-job scheduling problem arising in buffer management. Each job is specified by its release time, deadline, and a nonnegative weight. Due to overloading conditions, some jobs have to be dropped. The goal is to maximize the total weight of scheduled jobs. We present several competitive online algorithms for various versions of unit-job scheduling, as well as some lower bounds on the competitive ratios.We first give a randomized algorithm RMix with competitive ratio of e/(e−1)≈1.582. This is the first algorithm for this problem with competitive ratio smaller than 2.Then we consider s-bounded instances, where the span of each job (deadline minus release time) is at most s. We give a 1.25-competitive randomized algorithm for 2-bounded instances, matching the known lower bound. We also give a deterministic algorithm Edf_α, whose competitive ratio on s-bounded instances is 2−2/s+o(1/s). For 3-bounded instances its ratio is ≈1.618, matching the known lower bound.In s-uniform instances, the span of each job is exactly s. We show that no randomized algorithm can be better than 1.25-competitive on s-uniform instances, if the span s is unbounded. For s=2, our proof gives a lower bound of . Also, in the 2-uniform case, we prove a lower bound of for deterministic memoryless algorithms, matching a known upper bound.Finally, we investigate the multiprocessor case and give a -competitive algorithm for m processors. We also show improved lower bounds for the general and s-uniform cases.     

The bounded single-machine parallel-batching scheduling problem with family jobs and release dates to minimize makespan

We consider the problem of scheduling family jobs with release dates on a bounded batching machine to minimize the makespan. A polynomial-time approximation scheme for the identical job size model and an approximation algorithm with a worst-case ratio of for the non-identical job size model will be derived.     

平行机上带有前瞻区间的不相容工件组在线排序问题

研究当不相容工件组的个数与机器数相等时,具有前瞻区间的单位工件平行机无界平行分批在线排序问题.工件按时在线到达, 目标是最小化 最大完工时间. 具有前瞻区间是指在时刻t, 在线算法能预见到时间区间(t,t+\beta) 内到达的所有工件的信息.不可相容的工件组是指属于不同组的工件不能被安排在同一批中加工. \beta\geq 1 时, 提供了一个最优的在线算法; 当0\leq \beta < 1时, 提供了一个竞争比为1+\alpha 的最好可能的在线算法, 其中\alpha是方程\alpha^{2}+(1+\beta) \alpha+\beta-1=0的一个正根.最后, 给出了当\beta =0 时稠密算法竞争比的下界,并提供了达到该下界的最好可能的稠密算法.     

可拒绝的同类机在线排序(英文)

考虑了带拒绝费用的在线同类机排序模型.工件一个一个的到达,到达后或被接受,或以一定的费用被拒绝,目标是最小化最大完工时间与总的拒绝费用之和.我们提供了一个在线算法和分析了算法的竞赛比.     

Approximation algorithms for the minimum rainbow subgraph problem

We consider the minimum rainbow subgraph problem (MRS): given a graph G, whose edges are coloured with p colours. Find a subgraph F⊆G of G of minimum order and with p edges such that each colour occurs exactly once. For graphs with maximum degree Δ(G) there is a greedy polynomial-time approximation algorithm for the MRS problem with an approximation ratio of Δ(G). In this paper we present a polynomial-time approximation algorithm with an approximation ratio of for Δ≥2.     

Integrated production and delivery scheduling with disjoint windows

Consider a company that manufactures perishable goods. The company relies on a third party to deliver goods, which picks up delivery products at regular or irregular times. At each delivery time, there is a time window that products can be produced to be delivered at that delivery time. The time windows are disjoint. Suppose we have a set of jobs with each job specifying its delivery time, processing time and profit. The company can earn the profit if the job is produced and delivered at its specified delivery time. From the company point of view, we are interested in picking a subset of jobs to produce and deliver so as to maximize the total profit. The unpicked jobs will be discarded without penalty. We consider both the single machine case and the parallel and identical machine case.In this article we consider three kinds of profits: (1) arbitrary profit, (2) equal profit, and (3) profit proportional to its processing time. In the first case, we give a fully polynomial time approximation scheme (FPTAS) for a single machine with running time . Using the bound improvement technique of Kovalyov, the running time can be further reduced to . In the second case, we give an O(nlogn)-time optimal algorithm for a single machine. In the third case, we give an FPTAS for a single machine with running time . All of our algorithms can be extended to parallel and identical machines with a degradation of performance ratios.