Resource-dependent Processing Times Research Articles

Resource Management in Machine Scheduling Problems: A Survey

The paper is a survey devoted to job scheduling problems with resource allocation. We present the results available in the scientific literature for commonly used models of job processing times and job release dates, i.e., the models in which the job processing time or the job release date is given as a linear or convex function dependent on the amount of the additional resource allotted to the job. The scheduling models with resource dependent processing times or resource dependent release dates extend the classical scheduling models to reflect more precisely scheduling problems that appear in real life. Thus, in this paper we present the computational complexity results and solution algorithms that have been developed for this kind of problems.

An approximation algorithm for identical parallel machine scheduling with resource dependent processing times

We present a ( 3.5 + ɛ ) -approximation algorithm for a scheduling problem on identical parallel machines with the objective to mimimize the makespan. The processing times depend on the usage of a single renewable resource where at any point of time at most k units from the resource are available.

Single machine scheduling problem with controllable processing times and resource dependent release times

We consider two single machine scheduling problems with resource dependent release times and processing times, in which the release times and processing times are linearly decreasing functions of the amount of resources consumed. The objective is to minimize the total cost of makespan and resource consumption function that is composed of release time reduction and processing time reduction. In the first problem, the cost of reducing a unit release time for each job is common. We show that the problem can be solved in polynomial time. The second problem assumes different reduction costs of job release times. We show that the problem can be reduced polynomially from the partition problem and thus, is NP-complete.

The no-wait two-machine flow shop scheduling problem with convex resource-dependent processing times

We extend the classical no-wait two-machine flow shop scheduling problem to the case where job-processing times are controllable through the allocation of a common, limited and nonrenewable resource. Our objective is to simultaneously determine the sequence of the jobs and the resource allocation for each job on both machines in order to minimize the makespan. By using the equivalent load method to obtain the optimal resource allocation on a series-parallel graph, we reduce the problem to a sequencing one and show that it is equivalent to a new special case of the Traveling Salesman Problem (TSP). We prove that although the reduced problem forms a subclass of the TSP on permuted Monge matrices, it is still strongly NP-hard. We provide an approximation result and present three special cases which are polynomially solvable. We have also tested two different subtour-patching heuristics in large-scale computational experiments on randomly generated instances of the problem. Both heuristics produced close-to-optimal solutions in most cases.

Machine scheduling with resource dependent processing times

We consider machine scheduling on unrelated parallel machines with the objective to minimize the schedule makespan. We assume that, in addition to its machine dependence, the processing time of any job is dependent on the usage of a discrete renewable resource, e.g. workers. A given amount of that resource can be distributed over the jobs in process at any time, and the more of that resource is allocated to a job, the smaller is its processing time. This model generalizes the classical unrelated parallel machine scheduling problem by adding a time-resource tradeoff. It is also a natural variant of a generalized assignment problem studied previously by Shmoys and Tardos. On the basis of an integer linear programming formulation for a relaxation of the problem, we use LP rounding techniques to allocate resources to jobs, and to assign jobs to machines. Combined with Graham’s list scheduling, we show how to derive a 4-approximation algorithm. We also show how to tune our approach to yield a 3.75-approximation algorithm. This is achieved by applying the same rounding technique to a slightly modified linear programming relaxation, and by using a more sophisticated scheduling algorithm that is inspired by the harmonic algorithm for bin packing. We finally derive inapproximability results for two special cases, and discuss tightness of the integer linear programming relaxations.

Single machine scheduling with resource dependent release times and processing times

We consider the single machine scheduling problem with resource dependent release times and processing times, in which both the release times and processing times are strictly linear decreasing functions of the amount of resources consumed. The objective is to minimize the makespan plus the total resource consumption costs. We propose a heuristic algorithm for the general problem by utilizing some derived optimal properties and analyze its performance bound. For some special cases, we propose another heuristic algorithm that achieves a tighter performance bound.

Single machine group scheduling with resource dependent setup and processing times

A single machine scheduling problem is studied. There is a partition of the set of n jobs into g groups on the basis of group technology. Jobs of the same group are processed contiguously. A sequence independent setup time precedes the processing of each group. Two external renewable resources can be used to linearly compress setup and job processing times. The setup times are jointly compressible by one resource, the job processing times are jointly compressible by another resource and the level of the resource is the same for all setups and all jobs. Polynomial time algorithms are presented to find an optimal job sequence and resource values such that the total weighted resource consumption is minimum, subject to meeting job deadlines. The algorithms are based on solving linear programming problems with two variables by geometric techniques.

Single machine scheduling with a variable common due date and resource-dependent processing times

The problem of scheduling n jobs with a variable common due date on a single machine is studied. It is assumed that the job processing times are non-increasing linear functions of an equal amount of a resource allocated to the jobs. The due date and resource values can be continuous or discrete. The objective is to minimize a linear combination of scheduling, due date assignment and resource consumption costs. The resource consumption cost function may be non-monotonous. Algorithms with O(n 2 log n) running times are presented for scheduling costs involving earliness/tardiness and number of tardy jobs. Computational experiments show that the algorithms can solve problems with n=5,000 in less than a minute on a standard PC. Scope and purpose We study a problem that combines scheduling, common due date assignment and resource allocation decisions. Due date scheduling is an important area of scheduling research because it focuses on customer satisfaction. Variable common due date applies to situations where several items constitute a single customer's order and the due date for the order can be negotiated. Resource-dependent processing times appear whenever resources can be employed to adjust processing requirements. Polynomial time algorithms are presented for minimizing a linear combination of scheduling, due date assignment and resource consumption costs. Computational experiments demonstrate their efficiency in solving large-scale problem instances.

Single machine batch scheduling with resource dependent setup and processing times

Jobs are processed by a single machine in batches. A batch is a set of jobs processed contiguously and completed together when the processing of all jobs in the batch is finished. Processing of a batch requires a machine setup time common for all batches. Both the job processing times and the setup time can be compressed through allocation of a continuously divisible resource. Each job uses the same amount of the resource. Each setup also uses the same amount of the resource, which may be different from that for the jobs. Polynomial time algorithms are presented to find an optimal batch sequence and resource values such that either the total weighted resource consumption is minimized, subject to meeting job deadlines, or the maximum job lateness is minimized, subject to an upper bound on the total weighted resource consumption. The algorithms are based on linear programming formulations of the corresponding problems.

Single machine scheduling subject to deadlines and resource dependent processing times

The problem of scheduling n jobs on a single machine is studied. Each job has a deadline and a processing time which is a linear decreasing function of the amount of a common resource allocated to the job. The objective is to find simultaneously a sequence of the jobs and a resource allocation so as the deadlines are satisfied and the total weighted resource consumption is minimized. The problem is shown to be solvable in O( n log n) time if the resource is continuously divisible. If the resource is discrete, then the problem is proved to be binary NP-hard. Some special cases are solvable in O( n log n) time. A fully polynomial approximation scheme is presented for the general problem with discrete resource.

Single machine batch scheduling with deadlines and resource dependent processing times

We consider the problem of scheduling n jobs on a single machine where each job has a deadline and a processing time that is a linear decreasing function of the amount of a common discrete resource allocated to the job. 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 the batch is processed. The completion time of each job in a batch coincides with the completion time of the last job in the batch. 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 simultaneously a resource allocation and a schedule which is feasible with respect to the deadlines so as to minimize the total weighted resource consumption. The problem is shown to be NP-hard even for the special case of common parameters. Two dynamic programming algorithms are presented for the general problem, as well as a fully polynomial approximation scheme.