Earliness Tardiness Research Articles

Machine scheduling with earliness, tardiness and non-execution penalties

The study of scheduling problems with earliness–tardiness (E/T) penalties is motivated by the just-in-time (JIT) philosophy, which supports the notion that earliness, as well as tardiness, should be discouraged. In this work, we consider several scheduling problems. We begin by generalizing a known polynomial time algorithm that calculates an optimal schedule for a given sequence of tasks, on a single machine, assuming that the tasks have distinct E/T penalty weights, distinct processing times and distinct due dates. We then present new results to problems, where tasks have common processing times. We also introduce a new concept in E/T scheduling problems, where we allow the non-execution of tasks and consequently, are penalized for each non-executed task. The notion of task's non-execution, coincides with the JIT philosophy in that every violation or a breach of an agreement, should be penalized. We develop polynomial time algorithms for special cases in E/T scheduling problems with non-execution penalties.

Progress payments in project scheduling problems

In this paper we study the unconstrained project scheduling problem with discounted cash flows where the net cash flows are assumed to be dependent on the completion times of the corresponding activities. Cash outflows occur when an activity is completed whereas cash inflows are incurred as progress payments at the end of some time period. The objective is to schedule the activities in order to maximize the net present value of the project subject to the precedence constraints and a fixed deadline. This paper extends the ever-growing amount of research concerning the financial aspects in project scheduling in which cash flows are time-dependent. We introduce a branch-and-bound algorithm which computes upper bounds by making piecewise linear overestimations. In doing so, the algorithm transforms the problem into a weighted earliness–tardiness project scheduling problem. The algorithm is extended with two new rules in order to reduce the size of the branch-and-bound tree. Computational results are reported.

Parallel machine scheduling with earliness–tardiness penalties and additional resource constraints

This research considers the problem of scheduling jobs on parallel machines with noncommon due dates and additional resource constraints. The objective is to minimize the total absolute deviation of job completion times about the corresponding due dates. All job processing times are assumed to be the same. This problem is motivated by restrictions that occur in the handling and processing of jobs in certain phases of semiconductor manufacturing and other production systems. We examine two special cases. For the first of these, the number of additional resource types and the resource requirements per job are arbitrary. The problem is formulated as a zero–one integer linear program and the Lagrangian relaxation approach is used to obtain tight lower bounds. In the second case, there exist one single type of additional resource and the resource requirements per job are zero or one. This problem is shown to be equivalent to the asymmetric assignment problem. Scope and purpose This paper considers the problem of scheduling jobs on parallel machines where jobs have different due dates and may require, besides machines, certain additional limited resources for their handling and processing. The objective is to minimize the total absolute deviation of job completion times about the corresponding due dates. This objective function is consistent with the just-in-time production philosophy which espouses the notion that earliness as well as tardiness should be penalized. Two problems are addressed. In the first of these, the number of different types of additional resources and resource requirements per job are arbitrary. The problem is formulated as a zero–one integer linear program and the Lagrangian relaxation approach is used. In the second problem, there exists one single type of additional resource and the resource requirements per job are zero or one. This problem can be reformulated as an assignment problem.

PERT scheduling with convex cost functions

This paper deals with the problem of finding a minimum cost schedule for a set of dependent activities when a convex cost function is attached to the starting time of each activity. A first optimality necessary and sufficient condition bearing on the head and tail blocks of a schedule is first established. A second such condition that uses the spanning active equality trees of a schedule leads to design a generic algorithm for the general case. When the cost function is the usual earliness–tardiness linear function with assymetric and independent penalty coefficients, the problem is shown to be solved in O(n max{n,m}) . Finally, the special cases when the precedence graph is an intree or a family of chains are then also shown to be solved by efficient polynomial algorithms.

Single machine earliness–tardiness scheduling with resource-dependent release dates

This paper deals with the single machine earliness and tardiness scheduling problem with a common due date and resource-dependent release dates. It is assumed that the cost of resource consumption of a job is a non-increasing linear function of the job release date, and this function is common for all jobs. The objective is to find a schedule and job release dates that minimize the total resource consumption, and earliness and tardiness penalties. It is shown that the problem is NP-hard in the ordinary sense even if the due date is unrestricted (the number of jobs that can be scheduled before the due date is unrestricted). An exact dynamic programming (DP) algorithm for small and medium size problems is developed. A heuristic algorithm for large-scale problems is also proposed and the results of a computational comparison between heuristic and optimal solutions are discussed.

Approximation algorithms for common due date assignment and job scheduling on parallel machines

We consider the problem of assigning a common due date to a set of jobs and scheduling the jobs on a set of parallel machines so that the weighted sum of the due date, total earliness, and total tardiness is minimized. A heuristic is developed to solve this problem, and an absolute performance ratio is provided for this heuristic. Another heuristic with a better worst-case performance bound is presented for the case with a zero earliness penalty. A fully polynomial approximation scheme is also developed.

Single operation earliness—tardiness scheduling with machine activation costs

We consider a static, single operation, non-pre-emptive, deterministic scheduling problem in which a set of n jobs is to be processed on k identical machines. Jobs assigned to each machine have a common due date. The number of machines (k) is unknown. Activating a machine will require additional costs to be incurred. The objective is to find an optimal sequence, the optimal number of machines (k), and the respective due dates to minimize the weighted sum of earliness, tardiness, and machine activation costs. We propose a polynomial time algorithm to solve the problem.

Parallel machine scheduling with a common due window

In this paper, we consider a machine scheduling problem where jobs should be completed at times as close as possible to their respective due dates, and hence both earliness and tardiness should be penalized. Specifically, we consider the problem with a set of independent jobs to be processed on several identical parallel machines. All the jobs have a given common due window. If a job is completed within the due window, then there is no penalty. Otherwise, there is either a job-dependent earliness penalty or a job-dependent tardiness penalty depending on whether the job is completed before or after the due window. The objective is to find an optimal schedule with minimum total earliness–tardiness penalty. The problem is known to be NP-hard. We propose a branch and bound algorithm for finding an optimal schedule of the problem. The algorithm is based on the column generation approach in which the problem is first formulated as a set partitioning type formulation and then in each branch and bound iteration the linear relaxation of this formulation is solved by the standard column generation procedure. Our computational experiments show that this algorithm is capable of solving problems with up to 40 jobs and any number of machines within a reasonable computational time.

Approximation algorithms for common due date assignment and job scheduling on parallel machines

We consider the problem of assigning a common due date to a set of jobs and scheduling the jobs on a set of parallel machines so that the weighted sum of the due date, total earliness, and total tardiness is minimized. A heuristic is developed to solve this problem, and an absolute performance ratio is provided for this heuristic. Another heuristic with a better worst-case performance bound is presented for the case with a zero earliness penalty. A fully polynomial approximation scheme is also developed.

Single operation earliness—tardiness scheduling with machine activation costs

We consider a static, single operation, non-pre-emptive, deterministic scheduling problem in which a set of n jobs is to be processed on k identical machines. Jobs assigned to each machine have a common due date. The number of machines (k) is unknown. Activating a machine will require additional costs to be incurred. The objective is to find an optimal sequence, the optimal number of machines (k), and the respective due dates to minimize the weighted sum of earliness, tardiness, and machine activation costs. We propose a polynomial time algorithm to solve the problem.

Single machine weighted earliness–tardiness penalty problem with a common due date

In this paper, we have considered a class of single machine job scheduling problems where the objective is to minimize the weighted sum of earliness–tardiness penalties of jobs. The weights are job-independent but they depend on whether a job is early or tardy. The restricted version of the problem where the common due date is smaller than a critical value, is known to be NP-complete. While dynamic programming formulation runs out of memory for large problem instances, depth-first branch-and-bound formulation runs slow for large problems since it uses a tree search space. In this paper, we have suggested an algorithm to optimally solve large instances of the restricted version of the problem. The algorithm uses a graph search space. Unlike dynamic programming, the algorithm can output optimal solutions even when available memory is limited. It has been found to run faster than dynamic programming and depth-first branch-and-bound formulations and can solve much larger instances of the problem in reasonable time. New upper and lower bounds have been proposed and used. Experimental findings are given in detail. Scope and purpose A class of single machine problems arising out of scheduling jobs in JIT environment has been considered in this paper. The objective is to minimize the total weighted earliness–tardiness penalties of jobs. In this paper, we have presented a new algorithm and conducted extensive empirical runs to show that the new algorithm performs much better than the existing approaches in solving large instances of the problem.

Minmax earliness–tardiness costs with unit processing time jobs

The problem addressed in this paper is defined by M parallel identical machines, N jobs with identical (unit) processing time, job-dependent weights, and a common due-date for all jobs. The objective is of a minmax type, i.e. we are interested in minimizing the cost of the worst scheduled job. In the case of a non-restrictive (i.e., sufficiently large) common due-date, the problem is shown to have a solution that is polynomial in the number of jobs. The solution in the case of a restrictive due-date remains polynomial in the number of jobs, but is exponential in the number of machines. We introduce a lower bound on the optimal cost and an efficient heuristic. We show that the worst case relative error of the heuristic is bounded by 2 and that this bound is tight. We also prove that the heuristic is asymptotically optimal under very general assumptions. Finally, we provide an extensive numerical study demonstrating that in most cases the heuristic performs extremely well.

An Exact Procedure for the Resource-Constrained Weighted Earliness–Tardiness Project Scheduling Problem

In this paper we study the resource-constrained project scheduling problem with weighted earliness–tardinesss penalty costs. Project activities are assumed to have a known deterministic due date, a unit earliness as well as a unit tardiness penalty cost and constant renewable resource requirements. The objective is to schedule the activities in order to minimize the total weighted earliness–tardinesss penalty cost of the project subject to the finish–start precedence constraints and the constant renewable resource availability constraints. With these features the problem becomes highly attractive in just-in-time environments. We introduce a depth-first branch-and-bound algorithm which makes use of extra precedence relations to resolve resource conflicts and relies on a fast recursive search algorithm for the unconstrained weighted earliness–tardinesss problem to compute lower bounds. The procedure has been coded in Visual C++, version 4.0 under Windows NT. Both the recursive search algorithm and the branch-and-bound procedure have been validated on a randomly generated problem set.

Single-machine scheduling with a common due window

We study several single-machine non-preemptive scheduling problems to minimize the sum of weighted earliness–tardiness, weighted number of early and tardy jobs, common due window location, and flowtime penalties. We allow the due window location to be either a decision variable or a given parameter. We assume that the due window location has a tolerance and the window size is a given parameter. We further make the assumption that the ratios of the job processing times to the earliness–tardiness weights are agreeable for the first problem. We propose pseudo-polynomial dynamic programming algorithms to optimally solve the problems. We also provide polynomial time algorithms for several special cases. Scope and purpose The widespread use of Just-In-Time philosophy in manufacturing to eliminate inventories leads to a new class of scheduling problems in which the earliness and/or number of early jobs are penalized as well as the tardiness and/or tardy jobs. In this type of environments, the jobs are sometimes associated with a period of time within which they incur no penalty since the customers will generally allow a time interval for the delivery of the products. This time period is called a due window. There are a variety of applications with due windows in factory automation, production maintenance, and so on. In this paper, we consider the common due window problems to minimize the weighted earliness–tardiness, weighted number of early–tardy jobs and weighted flowtime on a single machine. The main contributions of this paper are identifying the computational complexity of the problems, developing dynamic programming algorithms to optimally solve them, and providing efficient and exact polynomial algorithms for the special cases.

Trade-off solutions in a single-machine scheduling problem for minimizing total earliness and maximum tardiness

This paper considers a single-machine scheduling problem involving minimization of the total earliness and the maximum tardiness. Four dominant properties for the precedence relationship between jobs in a search for an optimal solution are proposed. The lower bounds of the total earliness and the maximum tardiness of a subproblem are derived. The dominance properties and the lower bounds are implemented in the branchand-bound algorithm to facilitate the search for an optimal schedule. A heuristic algorithm is then developed to overcome the inefficiency of the branch-and-bound algorithm. Computational performance of the two algorithms is also investigated.

A Fully Polynomial Approximation Scheme for the Weighted Earliness–Tardiness Problem

A fully polynomial approximation scheme for the problem of scheduling n jobs on a single machine to minimize total weighted earliness and tardiness is presented. A new technique is used to develop the scheme. The main feature of this technique is that it recursively computes lower and upper bounds on the value of partial optimal solutions. Therefore, the scheme does not require any prior knowledge of lower and upper bounds on the value of a complete optimal solution. This distinguishes it from all the existing approximation schemes.

Multiple-machine scheduling with earliness, tardiness and completion time penalties

We consider the problem of scheduling n jobs on m parallel and identical machines. The goal is to find an optimal permutation of the jobs and an optimal due date which jointly minimize an objective function consisting of earliness tardiness and completion time penalties. We show that the problem is NP-hard and present a polynomially solvable case. Furthermore a heuristic is developed and computationally demonstrated to be efficient in obtaining near-optimal solutions. Scope and purpose This study deals with the problem of scheduling a number of jobs on parallel machines. We assume that companies are mainly interested in two objectives, namely achieving small deviations from a common due date and short flowtimes. Therefore earliness tardiness and completion time penalties for the jobs are introduced. We are able to show that the problem is intractable and consequently develop an efficient heuristic to obtain near-optimal solutions.

A Branch-and-Bound Algorithm for Single-Machine Earliness–Tardiness Scheduling with Idle Time

We address the NP-hard single-machine problem of scheduling n independent jobs so as to minimize the sum of α times total completion time and β times total earliness with β > α, which can be rewritten as an earliness–tardiness problem. Postponing jobs by leaving the machine idle may then be advantageous. The allowance of machine idle time between the execution of jobs singles out our problem from most concurrent research on problems with earliness penalties. Solving the problem to optimality poses a computational challenge, since the possibility of leaving the machine idle has a major effect on designing a branch-and-bound algorithm in general, and on computing lower bounds in particular. We present a branch-and-bound algorithm which is based upon many dominance rules and various lower bound approaches, including relaxation of the machine capacity, data manipulation, and Lagrangian relaxation. The algorithm is shown to solve small instances with up to 20 jobs.

Earliness–Tardiness Scheduling Problems, II: Deviation of Completion Times About a Restrictive Common Due Date

A companion paper (Part I) considers the problem of minimizing the weighted earliness and tardiness of jobs scheduled on a single machine around a common due date, d, which is unrestrictively late. This paper (Part II) considers the problem of minimizing the unweighted earliness and tardiness of jobs, allowing the possibility that d is early enough to constrain the scheduling decision. We describe several optimality conditions. The recognition version of the problem is shown to be NP-complete in the ordinary sense, confirming a well known conjecture. Moreover, this complexity definition is precise, since we describe a dynamic programming algorithm which runs in pseudopolynomial time. This algorithm is also extremely efficient computationally, providing an improvement over earlier procedures, of almost two orders of magnitude in the size of instance that can be solved. Finally, we describe a special case of the problem which is polynomially solvable.