Makespan minimization for parallel machines scheduling with multiple availability constraints

The problem of makespan minimization for parallel machines scheduling with multiple planned nonavailability periods in the case of resumable jobs is considered. In the current state of the literature, there is a limited number of models and algorithms dealing with this problem and only for very small problem size, and nonavailability limited to some machines. The problem is first formulated as a mixed integer linear programming model and optimally solved using CPLEX for small to moderately large size problems with multiple availability constraints on all machines. An implicit enumeration algorithm using the lexicographic order is then designed to solve large-scale problems. Numerical results are obtained for several experiments and they show the validity and performance improvements procured by both the MILP model and the new enumeration algorithm.     

带机器故障的两台机带权误工数排序问题

讨论机器带故障中断的两台平行机排序问题,工件加工时间均为单位时间,目标是极小化带权误工工件数.当转移时间t=0时给出了最优的算法.当t≠0时,给出了一个多项式时间的近似算法,并证明算法解与最优解至多相差一个带权误工数.     

Minimizing the number of machines for scheduling jobs with equal processing times

In this paper, we consider a parallel machine environment when all jobs have the same processing time and arbitrary release dates and deadlines of the jobs are given. We suppose that the available number of machines, which can be used simultaneously, may vary over time. The aim is to construct a feasible schedule in such a way that the maximal number of simultaneously used machines is minimal. We give a polynomial algorithm for this problem.     

Metaheuristics for scheduling jobs with incompatible families on parallel batching machines

In this paper, we discuss the scheduling of jobs with incompatible families on parallel batching machines. The performance measure is total weighted tardiness. This research is motivated by a scheduling problem found in the diffusion and oxidation areas of semiconductor wafer fabrication where the machines can be modelled as parallel batch processors. Given that this scheduling problem is NP-hard, we suggest an ant colony optimization (ACO) and a variable neighbourhood search (VNS) approach. Both metaheuristics are hybridized with a decomposition heuristic and a local search scheme. We compare the performance of the two algorithms with that of a genetic algorithm (GA) based on extensive computational experiments. The VNS approach outperforms the ACO and GA approach with respect to time and solution quality.     

Customer order scheduling to minimize the number of late jobs

In the order scheduling problem, every job (order) consists of several tasks (product items), each of which will be processed on a dedicated machine. The completion time of a job is defined as the time at which all its tasks are finished. Minimizing the number of late jobs was known to be strongly NP-hard. In this note, we show that no FPTAS exists for the two-machine, common due date case, unless P = NP. We design a heuristic algorithm and analyze its performance ratio for the unweighted case. An LP-based approximation algorithm is presented for the general multicover problem. The algorithm can be applied to the weighted version of the order scheduling problem with a common due date.     

两台平行机完工时间平方和最小的排序问题

在两个竞争公司进行零和博弈过程中, 最大化两个公司收益的乘积, 在两台平行机的离线排序问题中相当于最小化两台机器完工时间的平方和. 给出了该问题修改的延缓开始\ LPT\ 算法: 首先, 将工件按照加工时间$\p_j\ $的\ LPT\ 序重新标记; 若加工时间最长的前\ $2m$\ 个工件的总加工时间\ $P(2m)< (2m+1)p_{2m+1}$, 最优的安排加工前\ $2m+1$\ 个工件, 一旦有机器空闲, 依次从第\ $2m+2$\ 个工件安排加工; 否则,\ $P(2m)\geq (2m+1)p_{2m+1}$, 最优的安排加工前\ $2m$\ 个工件, 一旦有机器空闲, 依次从第\ $2m+1$\ 个工件安排加工. 证明了该算法的最差性能比不超过\ $1+ ( \frac{1}{2m+2} )^2$, 且界是紧的.     

Online scheduling of parallel jobs on two machines is 2-competitive

We consider online scheduling of parallel jobs on parallel machines. For the problem with two machines and the objective of minimizing the makespan, we show that 2 is a tight lower bound on the competitive ratio. For the problem with m machines, we derive lower bounds using an ILP formulation.     

Soft due window assignment and scheduling of unit-time jobs on parallel machines

We study problems of scheduling n unit-time jobs on m identical parallel machines, in which a common due window has to be assigned to all jobs. If a job is completed within the due window, then no scheduling cost incurs. Otherwise, a job-dependent earliness or tardiness cost incurs. The job completion times, the due window location and the size are integer valued decision variables. The objective is to find a job schedule as well as the location and the size of the due window such that a weighted sum or maximum of costs associated with job earliness, job tardiness and due window location and size is minimized. We establish properties of optimal solutions of these min-sum and min-max problems and reduce them to min-sum (traditional) or min-max (bottleneck) assignment problems solvable in O(n ⁵/m ²) and O(n ^4.5log^0.5 n/m ²) time, respectively. More efficient solution procedures are given for the case in which the due window size cost does not exceed the due window start time cost, the single machine case, the case of proportional earliness and tardiness costs and the case of equal earliness and tardiness costs.     

Preemptive scheduling of independent jobs on parallel machines subject to financial constraints

The paper deals with the preemptive scheduling of independent jobs on parallel unrelated machines with the use of additional renewable resources (manpower, facilities) and the consumption of a nonrenewable resource (money). Money becomes available at different dates in specified amounts (financial constraints). Two scheduling criteria are considered: schedule length and total cost. The algorithm consists in solving a parametric linear program and using its results to construct a most satisfactory schedule in polynomial time. The reduction of job preemptions in a feasible schedule is considered.     

Minimizing the number of machines with limited workload capacity for scheduling jobs with interval constraints

In this paper, we consider a parallel machine scheduling problem in which machines have a limited workload capacity and jobs have deadlines and release dates. The problem is motivated by the operation of energy storage management systems for microgrids under emergency conditions and generalizes some problems that have already been studied in the literature for their theoretical value. In this work, we propose heuristic and exact algorithms to solve the problem. The heuristics are adaptations of classical bin packing heuristics in which additional conditions on the feasibility of a solution are imposed, whereas the exact method is a branch-and-price approach. The results show that the branch-and-price approach is able to optimally solve random instances with up to 250 jobs within a time limit of one hour, while the heuristic procedures provide near optimal solution within reduced running times. Finally, we also provide additional complexity results for a special case of the problem.     

Unrelated parallel machines scheduling with deteriorating jobs and resource dependent processing times

We consider unrelated parallel machines scheduling problems involving resource dependent (controllable) processing times and deteriorating jobs simultaneously, i.e., the actual processing time of a job is a function of its starting time and its resource allocation. Two generally resource consumption functions, the linear and convex resource, were investigated. The objective is to find the optimal sequence of jobs and the optimal resource allocation separately. This paper focus on the objectives of minimizing a cost function containing makespan, total completion time, total absolute differences in completion times and total resource cost, and a cost function containing makespan, total waiting time, total absolute differences in waiting times and total resource cost. If the number of unrelated parallel machines is a given constant, we show that the problems remain polynomially solvable under the proposed model.     

Single machine batch scheduling to minimize the weighted number of late jobs

The single machine batch scheduling problem to minimize the weighted number of late jobs is studied. In this problem,n jobs have to be processed on a single machine. Each job has a processing time, a due date and a weight. Jobs may be combined to form batches containing contiguously scheduled jobs. For each batch, a constant set-up time is needed before the first job of this batch is processed. The completion time of each job in the batch coincides with the completion time of the last job in this batch. A job is late if it is completed after its due date. A schedule specifies the sequence of jobs and the size of each batch, i.e. the number of jobs it contains. The objective is to find a schedule which minimizes the weighted number of late jobs. This problem isNP-hard even if all due dates are equal. For the general case, we present a dynamic programming algorithm which solves the problem with equal weights inO(n ³) time. We formulate a certain scaled problem and show that our dynamic programming algorithm applied to this scaled problem provides a fully polynomial approximation scheme for the original problem. Each algorithm of this scheme has a time requirement ofO(n ³/ +n ³ logn). A side result is anO(n logn) algorithm for the problem of minimizing the maximum weight of late jobs.Supported by INTAS Project 93-257.     

A graph-theoretic approach to interval scheduling on dedicated unrelated parallel machines

We study the problem of scheduling n non-preemptive jobs on m unrelated parallel machines. Each machine can process a specified subset of the jobs. If a job is assigned to a machine, then it occupies a specified time interval on the machine. Each assignment of a job to a machine yields a value. The objective is to find a subset of the jobs and their feasible assignments to the machines such that the total value is maximized. The problem is NP-hard in the strong sense. We reduce the problem to finding a maximum weight clique in a graph and survey available solution methods. Furthermore, based on the peculiar properties of graphs, we propose an exact solution algorithm and five heuristics. We conduct computer experiments to assess the performance of our and other existing heuristics. The computational results show that our heuristics outperform the existing heuristics.     

A comparison of branch-and-bound algorithms for a family scheduling problem with identical parallel machines

This paper investigates branch-and-bound algorithms for the problem of scheduling jobs with family setups on identical parallel machines to minimize the weighted sum of completion times. In particular, we propose a new branching scheme that appears to substantially outperform current procedures in terms of computation time and search tree size.     

Local search heuristics for single-machine scheduling with batching to minimize the number of late jobs

Local search heuristics are developed for a problem of scheduling jobs on a single machine. Jobs are partitioned into families, and a set-up time is necessary when there is a switch in processing jobs from one family to jobs of another family. The objective is to minimize the number of late jobs. Four alternative local search methods are proposed: multi-start descent, simulated annealing, tabu search and a genetic algorithm. The performance of these heuristics is evaluated on a large set of test problems. The best results are obtained with the genetic algorithm; multi-start descent also performs quite well.     

Optimal preemptive scheduling on a fixed number of identical parallel machines

In this paper, we consider the preemptive scheduling problem on a fixed number of identical parallel machines. We present a polynomial-time algorithm for finding a minimal length schedule for an order class which contains properly interval orders.