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.     

LP-based online scheduling: from single to parallel machines

We study classic machine sequencing problems in an online setting. Specifically, we look at deterministic and randomized algorithms for the problem of scheduling jobs with release dates on identical parallel machines, to minimize the sum of weighted completion times: Both preemptive and non-preemptive versions of the problem are analyzed. Using linear programming techniques, borrowed from the single machine case, we are able to design a 2.62-competitive deterministic algorithm for the non-preemptive version of the problem, improving upon the 3.28-competitive algorithm of Megow and Schulz. Additionally, we show how to combine randomization techniques with the linear programming approach to obtain randomized algorithms for both versions of the problem with competitive ratio strictly smaller than 2 for any number of machines (but approaching two as the number of machines grows). Our algorithms naturally extend several approaches for single and parallel machine scheduling. We also present a brief computational study, for randomly generated problem instances, which suggests that our algorithms perform very well in practice. A preliminary version of this work appears in the Proceedings of the 11th conference on integer programming and combinatorial optimization (IPCO), Berlin, 8–10 June 2005.     

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.     

Online scheduling with machine cost and rejection

In this paper we define and investigate a new scheduling model. In this new model the number of machines is not fixed; the algorithm has to purchase the used machines, moreover the jobs can be rejected. We show that the simple combinations of the algorithms used in the area of scheduling with rejections and the area of scheduling with machine cost are not constant competitive. We present a 2.618-competitive algorithm called OPTCOPY.     

具有等级约束的三台机排序问题的可中断在线算法

研究具有等级约束的三台机在线排序问题．机器和工件的等级数均为1或2,工件只能在等级数不超过自身等级的机器上加工,且加工允许中断,目标是极小化最大工件完工时间．如果有两台机器等级为1,给出竞争比为3／2的在线算法,并证明算法是最好可能的;如果只有一台等级为1的机器,也给出竞争比为3／2的在线算法．     

A discrete artificial bee colony algorithm for the no-idle permutation flowshop scheduling problem with the total tardiness criterion

In this paper, we present a discrete artificial bee colony algorithm to solve the no-idle permutation flowshop scheduling problem with the total tardiness criterion. The no-idle permutation flowshop problem is a variant of the well-known permutation flowshop scheduling problem where idle time is not allowed on machines. In other words, the start time of processing the first job on a given machine must be delayed in order to satisfy the no-idle constraint. The paper presents the following contributions: First of all, a discrete artificial bee colony algorithm is presented to solve the problem on hand first time in the literature. Secondly, some novel methods of calculating the total tardiness from makespan are introduced for the no-idle permutation flowshop scheduling problem. Finally, the main contribution of the paper is due to the fact that a novel speed-up method for the insertion neighborhood is developed for the total tardiness criterion. The performance of the discrete artificial bee colony algorithm is evaluated against a traditional genetic algorithm. The computational results show its highly competitive performance when compared to the genetic algorithm. Ultimately, we provide the best known solutions for the total tardiness criterion with different due date tightness levels for the first time in the literature for the Taillard’s benchmark suit.     

Decomposition approaches in permutation scheduling problems with application to the M-machine flow shop scheduling problems

In this article, we consider three decomposition techniques for permutation scheduling problems. We introduce a general iterative decomposition algorithm for permutation scheduling problems and apply it to the permutation flow shop scheduling problem. We also develop bounds needed for this iterative decomposition approach and compare its computational requirements to that of the traditional branch and bound algorithms. Two heuristic algorithms based on the iterative decomposition approach are also developed. extensive numerical study indicates that the heuristic algorithms are practical alternatives to very costly exact algorithms for large flow shop scheduling problems.     

An efficient heuristic based on machine workload for the flowshop scheduling problem with setup and removal

We are concerned in this paper with solving ann jobs,M machines flowshop scheduling problem where the objective function is the minimization of the makespan. We take into account setup, processing and removal times separately. After drawing up a synthesis of existing work which addresses this type of problems, we propose a new heuristic algorithm which is based on the machine workload to find an efficient permutation schedule. Computational experiences show that our algorithm yields excellent results, particularly when bottleneck machines are present.     

异时排序问题的算法复杂性

我们将限制某些工件不能同时处理的平行机排序问题称为异时排序问题．本文我们讨论工件加工时间相同、目标为总完工时间最小的异时排序问题．我们证明了当机器台数为2时,该问题等价于图上的最大匹配问题,因此存在组合强多项式时间算法;但量当机器台数为3或者多于3时,该问题是强NP困难的．     

Heuristics for Flowshop Scheduling Problems Minimizing Mean Tardiness

Several heuristics are presented for the flowshop scheduling problem with the objective of minimizing mean tardiness. We consider the cases in which job sequences on all machines are the same (permutation flowshop) and in which they may be different. For the former case, the various methods that have been devised for minimizing the makespan are modified for our objective, while the list scheduling algorithm is used for the latter case. These heuristics are tested and compared with each other on randomly-generated test problems.     

No-wait or no-idle permutation flowshop scheduling with dominating machines

In this paper we study the no-wait or no-idle permutation flowshop scheduling problem with an increasing and decreasing series of dominating machines. The objective is to minimize one of the five regular performance criteria, namely, total weighted completion time, maximum lateness, maximum tardiness, number of tardy jobs and makespan. We establish that these five cases are solvable by presenting a polynomial-time solution algorithm for each case.     

带服务器的三台平行机排序问题的复杂性和近似算法

本文研究了带服务器的三台平行机排序问题的复杂性，并给出了一个最好的在线近似算法．     

On-line scheduling with forbidden zones

In various manufacturing and computing contexts there may be a certain period in each time interval, during which processing may continue but may not be initiated. We examine the problem of on-line scheduling in the presence of such forbidden zones, whose complements are starting time windows. We show that no on-line algorithm is better than [frac97]-competitive, when minimizing the number of intervals used (essentially the makespan), whereas list scheduling is shown to be 2-competitive. We also investigate adaptations of the first fit, next fit and harmonic bin packing algorithms and test all four empirically.     

Minimizing the Number of Tardy Jobs in a Permutation Flowshop Scheduling Problem with Setup Times and Time Lags Constraints

This paper studies the permutation flowshop scheduling problem with sequence dependent setup times and time lags constraints minimizing the number of tardy jobs. Dependent setup times are defined as the work to prepare the machines between two successive jobs. Time lags are defined as intervals of time that must exist between every couple of successive operations of the same job. Two mathematical programming formulations are proposed for the considered problem. A simulated annealing algorithm is also developed to solve the problem. Computational experiments are presented and discussed.     

具有优势机器的无空闲同顺序Flow Shop排序问题

本文讨论具有优势机器的无空闲同顺序Flow Shop排序问题的两种特殊情况,第一种情况是具有增减系列优势机器,第二种情况是具有减增系列优势机器．对于目标函数是最大完工时间,加权完工时间和,最大延误和延误工件数等问题,给出了求解最优排序的有效方法．     

Parallel machine scheduling problems with a single server

In this paper, we consider the problem of scheduling jobs on parallel machines with setup times. The setup has to be performed by a single server. The objective is to minimize the schedule length (makespan), as well as the forced idle time. The makespan problem is known to be NP-hard even for the case of two identical parallel machines. This paper presents a pseudopolynomial algorithm for the case of two machines when all setup times are equal to one. We also show that the more general problem with an arbitrary number of machines is unary NP-hard and analyze some list scheduling heuristics for this problem. The problem of minimizing the forced idle time is known to be unary NP-hard for the case of two machines and arbitrary setup and processing times. We prove unary NP-hardness of this problem even for the case of constant setup times. Moreover, some polynomially solvable cases are given.     

机器具有优势关系下的工件加工时间可控的流水作业排序问题

本文主要研究机器具有优势关系下的工件加工时间可控的流水作业排序问题.我们主要对以下两种情形进行了讨论:工件加工时间为线性恶化和线性学习.对于每一种加工模型,我们分别研究了几类不同的优势机器,并且对每种情况均给出了多项式时间算法.     

A New Heuristic Method for the Permutation Flow Shop Scheduling Problem

A new heuristic method for the permutation flow shop scheduling problem is presented and compared with two other heuristics named NEH and SPIRIT. The new heuristic method is based on a property of the scheduling problem that provides an upper bound on the idle time of the last machine between any two adjacent jobs regardless of their position in the sequence of jobs. The results from computational experience have shown that the new heuristic outperforms, in solution quality, all others for problems having up to 50 jobs and 30 machines.     

An Application of a Multi-Objective Tabu Search Algorithm to a Bicriteria Flowshop Problem

This paper proposes a new tabu search algorithm for multi-objective combinatorial problems with the goal of obtaining a good approximation of the Pareto-optimal or efficient solutions. The algorithm works with several paths of solutions in parallel, each with its own tabu list, and the Pareto dominance concept is used to select solutions from the neighborhoods. In this way we obtain at each step a set of local nondominated points. The dispersion of points is achieved by a clustering procedure that groups together close points of this set and then selects the centroids of the clusters as search directions. A nice feature of this multi-objective algorithm is that it introduces only one additional parameter, namely, the number of paths. The algorithm is applied to the permutation flowshop scheduling problem in order to minimize the criteria of makespan and maximum tardiness. For instances involving two machines, the performance of the algorithm is tested against a Branch-and-Bound algorithm proposed in the literature, and for more than two machines it is compared with that of a tabu search algorithm and a genetic local search algorithm, both from the literature. Computational results show that the heuristic yields a better approximation than these algorithms.     

带复合信息的三台平行机半在线排序问题

本文讨论在已知加工工件总长度(sum)以及机器带一个缓冲区(buffer)两个复合信息下的同型平行机半在线排序问题． Dosa和He研究了当机器数m=2时的情形,设计出竞争比为5／4的最优半在线算法．本文将其情况推广到三台机器,给出竞争比为4／3的半在线算法,并得到一个11／9的问题下界．