Total Completion Time Of Jobs Research Articles

Minimizing total completion time and total deviation of job completion times from a restrictive due-date

We study a single-machine scheduling problem with the objective of minimizing a linear combination of total job completion times and total deviation of job completion times from a common due-date. The due-date is assumed to be restrictive, i.e., it may be sufficiently small to have an impact on the optimal sequence. When more weight is allocated to total job completion times, the problem is shown to have a polynomial time solution. When more weight is allocated to total completion time deviations from the due-date, the problem is NP-hard in the ordinary sense. For the latter case, we introduce an efficient dynamic programming algorithm, which is shown numerically to perform well in all our tests.

Developing Standard Times for Repair Activities for Transit Vehicles

This paper presents a systematic method for determining repair time standards for transit vehicles. Most approaches typically used for time and motion study are designed mainly for manufacturing processes and thus find limited applications in transit environments. The methodology developed in this study is comprehensive and incorporates relevant factors such as maintenance procedures and work bay components and design, which are particular to the transit industry. This methodology has been employed on two systems: brake repair and preventive maintenance on buses. A major component of the methodology pertains to restructuring procedures, not only to improve work flow but also to normalize the operative assignments across transit shops. That focus facilitates productivity control and allows managers to assess training needs better. Outcomes reveal that the proposed methodology serves as a framework to develop valid standards that could have an impact on the efficiency of technicians while reducing total job completion time. Results are enhanced by the development of an information system that allows tracking of the time technicians take to complete the tasks and that represents, graphically, the performance rating based on the time standards developed. This tool assists supervisors during complex management decisions for resource allocation and scheduling. This project was initiated by the Florida Department of Transportation and the Center of Urban Transportation Research (CUTR). The research is part of the Florida Maintenance Training and Technical Assistance Program in CUTR and is guided by the committee of Transit Maintenance Consortium. The committee includes the managers and supervisors of the transit agencies throughout Florida.

Shortest path to nonpreemptive schedules of unit-time jobs on two identical parallel machines with minimum total completion time

Ideal schedules reach both minimum maximum completion time and minimum total completion time of jobs. It is known that there exist computable in polynomial time ideal nonpreemptive two-machine schedules of unit-time operation jobs with equal release dates and arbitrary precedence constraints on identical parallel machines, in flow shops and open shops. In this paper, we study the possibility of extending these results to the case where release dates can be different. We establish the complexity status of P2|prec,r j ,p j =1|∑C j and F2|prec,r j ,p ij =1|∑C j showing that optimal schedules for these problems can also be found in polynomial time and conjecture that all such schedules are ideal indeed. On the other hand, we show that the ideal schedules in open shops do not always exist.

Simultaneous minimization of total completion time and total deviation of job completion times

This paper addresses a single-machine scheduling problem with the objective of minimizing a linear combination of total job completion times and total deviation of job completion times from a common due-date. The due-date is assumed to be non-restrictive, i.e., sufficiently large to have no impact on the optimal sequence. When the weights are job-independent, the problem is shown to have a polynomial time solution, and the optimal schedule is fully characterized as a function of the different parameters. When job-dependent weights are assumed, the problem is known to be NP-hard. We introduce a pseudo-polynomial dynamic programming algorithm, indicating that this case is NP-hard in the ordinary sense. The algorithm is shown experimentally to perform extremely well when tested on high-multiplicity instances with up to 1000 jobs.

Single machine batch scheduling with jointly compressible setup and processing times

A single machine batch scheduling problem is addressed. 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. Two external resources can be used to linearly compress setup and job processing times. The setup times are jointly compressible by one resource and the job processing times are jointly compressible by another resource, i.e., the amount of resource consumption for setup time compression is the same for all setups and the amount of resource consumption for job processing time compression is the same for all jobs. Polynomial time algorithms are presented to find an optimal batch sequence and optimal amounts of resource consumption such that either total job completion time is minimized, subject to an upper bound on total weighted resource consumption, or total weighted resource consumption is minimized, subject to an upper bound on total job completion time. The algorithms are based on results from linear programming and from batch scheduling with fixed setup and processing times.

Batch scheduling with controllable setup and processing times to minimize total completion time

We study a problem of scheduling n jobs on 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 dependent on the position of this batch in the batch sequence. Setup times and job processing times are continuously controllable, that is, they are real-valued variables within their lower and upper bounds. A deviation of a setup time or job processing time from its upper bound is called a compression. The problem is to find a job sequence, its partition into batches, and the values for setup times and job processing times such that (a) total job completion time is minimized, subject to an upper bound on total weighted setup time and job processing time compression, or (b) a linear combination of total job completion time, total setup time compression, and total job processing time compression is minimized. Properties of optimal solutions are established. If the lower and upper bounds on job processing times can be similarly ordered or the job sequence is fixed, then O(n3 log n) and O(n5) time algorithms are developed to solve cases (a) and (b), respectively. If all job processing times are fixed or all setup times are fixed, then more efficient algorithms can be devised to solve the problems.

Scheduling jobs with piecewise linear decreasing processing times

AbstractWe study the problems of scheduling a set of nonpreemptive jobs on a single or multiple machines without idle times where the processing time of a job is a piecewise linear nonincreasing function of its start time. The objectives are the minimization of makespan and minimization of total job completion time. The single machine problems are proved to be NP‐hard, and some properties of their optimal solutions are established. A pseudopolynomial time algorithm is constructed for makespan minimization. Several heuristics are derived for both total completion time and makespan minimization. Computational experiments are conducted to evaluate their efficiency. NP‐hardness proofs and polynomial time algorithms are presented for some special cases of the parallel machine problems. © 2003 Wiley Periodicals, Inc. Naval Research Logistics 50: 531–554, 2003

The complexity of two group scheduling problems

The problems of scheduling groups of jobs under the group technology assumption are studied. The two remaining open questions posed in the literature a decade ago about the computational complexity of these problems (J. Oper. Res. Soc., 1992; 43:395–406), are answered. The parallel machine problem to minimize the total job completion time is proved to be NP-hard in the strong sense, even if group set-up times are equal to zero. A dynamic programming algorithm is presented to solve this problem, which is polynomial if the number of machines is fixed. The two-machine open-shop problem to minimize the makespan is proved to be NP-hard in the ordinary sense, even if there are zero group set-up times and equal job processing times on both machines. It is shown that the latter problem under the condition that any group can be split into batches does not reduce to the problem where the splittings are not allowed, i.e. the group technology assumption is satisfied. Copyright © 2002 John Wiley & Sons, Ltd.

Non-Approximability Results for Scheduling Problems with Minsum Criteria

We provide several non-approximability results for deterministic scheduling problems whose objective is to minimize the total job completion time. Unless 𝒫 =𝒩𝒫, none of the problems under consideration can be approximated in polynomial time within arbitrarily good precision. Most of our results are derived by APX-hardness proofs. We show that, whereas scheduling on unrelated machines with unit weights is polynomially solvable, the problem becomes APX-hard if release dates or weights are added. We further show APX-hardness for scheduling in flow shops, job shops, and open shops. We also investigate the problems of scheduling on parallel machines with precedence constraints and unit processing times, and two variants of the latter problem with unit communication delays; for these problems we provide lower bounds on the worst-case behavior of any polynomial-time approximation algorithm through the gap-reduction technique.

Scheduling the maintenance on a single machine

This paper considers a single machine scheduling problem with preventive maintenance. In many cases, a machine must be maintained after it continuously works for a period of time. But most papers in the literature ignore non-availability of the machine. For this reason, this paper studies the problem of scheduling processing of jobs and maintenance of machine simultaneously. The objective is to minimise total completion time of jobs. The problem is proved to be NP-hard in the strong sense. Three heuristic algorithms and a branch and bound algorithm are proposed. Computational experiments are done to evaluate the effectiveness of the algorithms.

Parallel Machine Scheduling with Batch Setup Times

We consider a problem of scheduling several batches of jobs on two identical parallel machines to minimize the total completion time of jobs. A setup time is incurred whenever there is a switch from processing a job in one batch to a job in another batch. When the number of jobs is arbitrary, the computational complexity of the problem is posed as an open problem in the literature. We show in this note that the problem is binary NP-hard even when the setup times are sequence independent and all processing times are equal.

Scheduling with resource-dependent release dates—a comparison of two different resource consumption functions

We consider the problem of scheduling a set of jobs on a single machine where the release time of a job is related to the amount of resource consumed. The objective is to minimize the total resource consumption with a control on the completion times of the jobs. Four different variants of the problem are studied: (i) minimization of the total resource consumption subject to a common deadline for all jobs, (ii) minimization of the total resource consumption subject to a constraint on the total completion time of the jobs, (iii) minimization of the weighted total resource consumption and maximum job completion time, and (iv) minimization of the weighted total resource consumption and the total job completion time. We compare the common resource consumption function with the function where the resource consumed is proportional to the processing time of the job. We show that these two different resource consumption functions can give rise to very different solution methods and different computational complexities for the problem. © 1994 John Wiley & Sons, Inc.