Scheduling with controllable release dates and processing times: Total completion time minimization

The single machine scheduling problem with two types of controllable parameters, job processing times and release dates, is studied. It is assumed that the cost of compressing processing times and release dates from their initial values is a linear function of the compression amounts. The objective is to minimize the sum of the total completion time of the jobs and the total compression cost. For the problem with equal release date compression costs we construct a reduction to the assignment problem. We demonstrate that if in addition the jobs have equal processing time compression costs, then it can be solved in O(n²) time. The solution algorithm can be considered as a generalization of the algorithm that minimizes the makespan and total compression cost. The generalized version of the algorithm is also applicable to the problem with parallel machines and to a range of due-date scheduling problems with controllable 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.     

NP-hardness of the single-variable-resource scheduling problem to minimize the total weighted completion time

Baker and Nuttle [K.R. Baker, H.L.W. Nuttle, Sequencing independent jobs with a single resource, Naval Research Logistics Quarterly 27 (1980) 499–510] studied the following single-variable-resource scheduling problem: sequencing n jobs for processing by a single resource to minimize a function of job completion times, when the availability of the resource varies over time. When the objective function to be minimized is the total weighted completion time, Baker and Nuttle conjectured that the problem is NP-hard. We show in this note that the conjecture is true.     

Single-machine scheduling of multi-operation jobs without missing operations to minimize the total completion time

We consider the problem of scheduling multi-operation jobs on a singe machine to minimize the total completion time. Each job consists of several operations that belong to different families. In a schedule each family of job operations may be processed as batches with each batch incurring a set-up time. A job is completed when all of its operations have been processed. We first show that the problem is strongly NP-hard even when the set-up times are common and each operation is not missing. When the operations have identical processing times and either the maximum set-up time is sufficiently small or the minimum set-up time is sufficiently large, the problem can be solved in polynomial time. We then consider the problem under the job-batch restriction in which the operations of each batch is partitioned into operation batches according to a partition of the jobs. We show that this case of the problem can be solved in polynomial time under a certain condition.     

An algorithm for single machine sequencing with release dates to minimize total weighted completion time

Each of n jobs is to be processed without interruption on a single machine which can handle only one job at a time. Each job becomes available for processing at its release date, requires a processing time and has a positive weight. Given a processing order of the jobs, the earliest completion time for each job can be computed. The objective is to find a processing order of the jobs which minimizes the sum of weighted completion times. In this paper a branch and bound algorithm for the problem is derived. Firstly a heuristic is presented which is used in calculating the lower bound. Then the lower bound is obtained by performing a Lagrangean relaxation of the release date constraints; the Lagrange multipliers are chosen so that the sequence generated by the heuristic is an optimum solution of the relaxed problem thus yielding a lower bound. A method to increase the lower bound by deriving improved constraints to replace the original release date constraints is given. The algorithm, which includes several dominance rules, is tested on problems with up to fifty jobs. The computational results indicate that the version of the lower bound using improved constraints is superior to the original version.     

Single-machine scheduling to minimize earliness and number of tardy jobs

This paper considers the problem of assigning a common due-date to a set of simultaneously available jobs and sequencing them on a single machine. The objective is to determine the optimal combination of the common due-date and job sequence that minimizes a cost function based on the assigned due-date, job earliness values, and number of tardy jobs. It is shown that the optimal due-date coincides with one of the job completion times. Conditions are derived to determine the optimal number of nontardy jobs. It is also shown that the optimal job sequence is one in which the nontardy jobs are arranged in nonincreasing order of processing times. An efficient algorithm of O(n logn) time complexity to find the optimal solution is presented and an illustrative example is provided. Finally, several extensions of the model are discussed.This research was supported in part by the Natural Sciences and Engineering Research Council of Canada under Grant OPG0036424. The authors are thankful to two anonymous referees for their constructive comments.     

Single-machine scheduling with both deterioration and learning effects

This paper considers a single-machine scheduling problem with both deterioration and learning effects. The objectives are to respectively minimize the makespan, the total completion times, the sum of weighted completion times, the sum of the kth power of the job completion times, the maximum lateness, the total absolute differences in completion times and the sum of earliness, tardiness and common due-date penalties. Several polynomial time algorithms are proposed to optimally solve the problem with the above objectives.     

Parallel-machine scheduling with simple linear deterioration to minimize total completion time

We consider the parallel-machine scheduling problem in which the processing time of a job is a simple linear increasing function of its starting time. The objective is to minimize the total completion time. We give a fully polynomial-time approximation scheme (FPTAS) for the case with m identical machines, where m is fixed. This study solves an open problem that has been posed in the literature for ten years.     

Single machine scheduling problems with controllable processing times and total absolute differences penalties

In this paper, we consider single machine scheduling problem in which job processing times are controllable variables with linear costs. We concentrate on two goals separately, namely, minimizing a cost function containing total completion time, total absolute differences in completion times and total compression cost; minimizing a cost function containing total waiting time, total absolute differences in waiting times and total compression cost. The problem is modelled as an assignment problem, and thus can be solved with the well-known algorithms. For the case where all the jobs have a common difference between normal and crash processing time and an equal unit compression penalty, we present an O(n log n) algorithm to obtain the optimal solution.     

Single machine scheduling with a general exponential learning effect

In this paper we consider the scheduling problem with a general exponential learning effect and past-sequence-dependent (p-s-d) setup times. By the general exponential learning effect, we mean that the processing time of a job is defined by an exponent function of the total weighted normal processing time of the already processed jobs and its position in a sequence, where the weight is a position-dependent weight. The setup times are proportional to the length of the already processed jobs. We consider the following objective functions: the makespan, the total completion time, the sum of the δ ? 0th power of completion times, the total weighted completion time and the maximum lateness. We show that the makespan minimization problem, the total completion time minimization problem and the sum of the quadratic job completion times minimization problem can be solved by the smallest (normal) processing time first (SPT) rule, respectively. We also show that the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time under certain conditions.     

Scheduling start time dependent jobs to minimize the total weighted completion time

This paper deals with a single machine scheduling problem with start time dependent job processing times. The job processing times are characterized by decreasing linear functions dependent on their start times. The problem is to find a schedule for which the total weighted completion time is minimized. It is proved that the problem is NP-hard. Some properties of special cases of the general problem are also given. Based on these results, two heuristic algorithms are constructed and their performance is compared.     

Single Machine Batch Scheduling Problem with Resource Dependent Setup and Processing Time in the Presence of Fuzzy Due Date

We consider a batch scheduling problem on a single machine which processes jobs with resource dependent setup and processing time in the presence of fuzzy due-dates given as follows:1. There are n independent non-preemptive and simultaneously available jobs processed on a single machine in batches. Each job j has a processing time and a due-date.2. All jobs in a batch are completed together upon the completion of the last job in the batch. The batch processing time is equal to the sum of the processing times of its jobs. A common machine setup time is required before the processing of each batch.3. 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.4. The due-date of each job is flexible. That is, a membership function describing non-decreasing satisfaction degree about completion time of each job is defined.5. Under above setting, we find an optimal batch sequence and resource values such that the total weighted resource consumption is minimized subject to meeting the job due-dates, and minimal satisfaction degree about each due-date of each job is maximized. But usually we cannot optimize two objectives at a time. So we seek non-dominated pairs i.e. the batch sequence and resource value, after defining dominance between solutions.A polynomial algorithm is constructed based on linear programming formulations of the corresponding problems.     

Two-parallel machines scheduling with rate-modifying activities to minimize total completion time

This paper considers the two-parallel machines scheduling problem with rate-modifying activities. In this model, each machine has a rate-modifying activity that can change the processing rate of machine under consideration. Hence the actual processing times of jobs vary depending on whether the job is scheduled before or after the rate-modifying activity. We need to make a decision on when to schedule the rate-modifying activities and the sequence of jobs to minimize some objective function. We provide polynomial and pseudo-polynomial time algorithms to solve the total completion time minimization problem and total weighted completion time minimization problem under agreeable ratio condition.     

An algorithm for single machine sequencing with deadlines to minimize total weighted completion time

Each of n jobs is to be processed without interruption on a single machine. Each job becomes available for processing at time zero, has a deadline by which it must be completed and has a positive weight. The objective is to find a processing order of the jobs which minimizes the sum of weighted completion times. In this paper a branch and bound algorithm for the problem is presented which incorporates lower bounds that are obtained using a new technique called the multiplier adjustment method. Firstly several dominance conditions are derived. Then a heuristic is described and sufficient conditions for its optimality are given. The lower bound is obtained by performing a Lagrangean relaxation of the deadline constraints; the Lagrange multipliers are chosen so that the sequence generated by the heuristic is an optimal solution of the relaxed problem, thus yielding a lower bound. The algorithm is tested on problems with up to fifty jobs.     

Approximability of single machine scheduling with fixed jobs to minimize total completion time

We consider the single machine scheduling problem to minimize total completion time with fixed jobs, precedence constraints and release dates. There are some jobs that are already fixed in the schedule. The remaining jobs are free to be assigned to any free-time intervals on the machine in such a way that they do not overlap with the fixed jobs. Each free job has a release date, and the order of processing the free jobs is restricted by the given precedence constraints. The objective is to minimize the total completion time. This problem is strongly NP-hard. Approximability of this problem is studied in this paper. When the jobs are processed without preemption, we show that the problem has a linear-time n-approximation algorithm, but no pseudopolynomial-time (1 − δ)n-approximation algorithm exists even if all the release dates are zero, for any constant δ > 0, if P ≠ NP, where n is the number of jobs; for the case that the jobs have no precedence constraints and no release dates, we show that the problem has no pseudopolynomial-time (2 − δ)-approximation algorithm, for any constant δ > 0, if P ≠ NP, and for the weighted version, we show that the problem has no polynomial-time 2^q(n)-approximation algorithm and no pseudopolynomial-time q(n)-approximation algorithm, where q(n) is any given polynomial of n. When preemption is allowed, we show that the problem with independent jobs can be solved in O(n log n) time with distinct release dates, but the weighted version is strongly NP-hard even with no release dates; the problems with weighted independent jobs or with jobs under precedence constraints are shown having polynomial-time n-approximation algorithms. We also establish the relationship of the approximability between the fixed job scheduling problem and the bin-packing problem.     

An improved branch and bound algorithm for single machine scheduling with deadlines to minimize total weighted completion time

We consider a scheduling problem in which n jobs with distinct deadlines are to be scheduled on a single machine. The objective is to find a feasible job sequence that minimizes the total weighted completion time. We present an efficient branch-and-bound algorithm that fully exploits the principle of optimality. Favorable numerical results are also reported on an extensive set of problem instances of 20-120 jobs.     

Flow shop scheduling algorithms for minimizing the completion time variance and the sum of squares of completion time deviations from a common due date

We consider two problems of m-machine flow shop scheduling in this paper: one, with the objective of minimizing the variance of completion times of jobs, and the other with the objective of minimizing the sum of squares of deviations of job completion times from a common due date. Lower bounds on the sum of squares of deviations of job completion times from the mean completion time of jobs for a given partial sequence are first presented. Using these lower bounds, a branch and bound algorithm based on breadth-first search procedure for scheduling n jobs on m-machines with the objective of minimizing completion time variance (CTV) is developed to obtain the best permutation sequence. We also present two lower bounds and thereafter, a branch and bound algorithm with the objective of minimizing the sum of squares of deviations of job completion times from a given common due date (called the MSD problem). The computational experience with the working of the two proposed branch and bound algorithms is also reported. Two heuristics, one for each of the two problems, are developed. The computational experience on the evaluation of the heuristics is discussed.     

A Heuristic for Common Due-date Assignment and Job Scheduling on Parallel Machines

In this paper we consider the problem of scheduling a set of simultaneously available jobs on several parallel and identical machines. The problem is to find the optimal due-date, assuming this to be the same for all jobs. We also seek to sequence the jobs such that some are early and some are late so as to minimize a penalty function. For the single-machine problem, we present a simple proof of the well-known optimality result that the optimal due-date coincides with one of the job completion times. We show that the optimal job sequence for the single-machine problem can be easily determined. We prove that the same optimal due-date result can be generalized to the parallel-machine problem. However, determination of the optimal job sequence for such a problem is much more complex, and we present a simple heuristic to find an approximate solution. On the basis of a limited experiment, we observe that the heuristic is very effective in obtaining near-optimal solutions.     

Minimizing total completion time on a single machine with step improving jobs

Production systems often experience a shock or a technological change, resulting in performance improvement. In such settings, job processing times become shorter if jobs start processing at, or after, a common critical date. This paper considers a single machine scheduling problem with step-improving processing times, where the effects are job-dependent. The objective is to minimize the total completion time. We show that the problem is NP-hard in general and discuss several special cases which can be solved in polynomial time. We formulate a Mixed Integer Programming model and develop an LP-based heuristic for the general problem. Finally, computational experiments show that the proposed heuristic yields very effective and efficient solutions.     

No-wait flowshops with bicriteria of makespan and total completion time

We consider the m-machine no-wait flowshop scheduling problem with the objective of minimizing a weighted sum of makespan and total completion time. For the two-machine problem, we develop a dominance relation and embed it within a proposed branch-and-bound algorithm. For the m-machine problem, we propose a heuristic. Computational experiments show that the proposed heuristic outperforms the best existing multi-criteria heuristics and the best single criterion heuristics for makespan and total completion time. The efficiency of the dominance relation and branch-and-bound algorithm is also investigated and shown to be effective.