Single-machine Scheduling Model Research Articles

Composite heuristics and water wave optimality algorithms for tri-criteria multiple job classes and customer order scheduling on a single machine

Among the well-known scheduling problems, the customer order scheduling problem (COSP) has always been of great importance in manufacturing. To reflect the reality of COSPs as much as possible, this study considers that jobs from different orders are classified in various classes. This paper addresses a tri-criteria single-machine scheduling model with multiple job classes and customer orders on which the measurement minimizes a linear combination of the sum of the ranges of all orders, the tardiness of all orders, and the total completion times of all jobs. Due to the NP-hard complexity of the problem, a lower bound and a property are developed and utilized in a branch-and-bound for solving an exact solution. Afterward, four heuristics with three local improved searching methods each and a water wave optimality algorithm with four variants of wavelengths are proposed. The tested outputs report the performances of the proposed methods.

Scheduling with variable-length calibrations: Two agreeable variants

Machines usually require maintenance after running a fixed period. A calibration at a cost has to be performed during the process. Finding a feasible schedule minimizing the total cost of calibrations is of great importance. In this paper, we deal with a single machine scheduling model with K types of calibrations. A calibration of type k∈{1,…,K} can be made instantaneously at any time point, which incurs a cost fk and can keep the machine active for a length Tk. Given a set of n jobs with release times, deadlines, and processing times, the goal is to minimize the total cost of calibrations by assigning all jobs in the calibrated state, where job preemption is allowed.We investigate two agreeable settings. Regarding agreeable jobs, later release times imply later deadlines. We establish a pseudo-polynomial time optimal algorithm and a (3+ε)-approximation algorithm. Moreover, if the largest job processing time is no more than any calibration length, it admits a (2+ε)-approximation algorithm. As for agreeable calibrations, where the cost of each calibration is proportional to its length, a 2-approximation algorithm is presented.

Tri-criteria single machine scheduling model with release times and learning factor

In this paper, we consider the single machine scheduling problem with release times and a learning effect. The actual processing time of a job is a function that is the total processing time of the jobs already processed. The objective is to minimize the weighted sum of makespan, total completion time and maximum tardiness. This problem is NP-hard, some heuristic algorithms and branch-and-bound algorithm based on the Depth-first Search algorithm are proposed to solve the problem. Computational experiments are conducted to evaluate the performance of the heuristic and branch-and-bound algorithms.

Task scheduling with progress control

ABSTRACTTasks with long durations often face the requirement of having to periodically report their progress to process controllers. Under this requirement, working teams that simultaneously process multiple tasks need to schedule their work carefully in order to demonstrate satisfactory progress on each unfinished task. We present a single-machine scheduling model that reflects this requirement. Our model has multiple milestones at which the tasks are penalized if their progress is below a satisfactory level. We develop polynomial solution methods for the general case with convex nonlinear penalty functions and for the special case with linear penalty functions. Extensions of our model are also discussed.

Stochastic single-machine scheduling with random resource arrival times

Scheduling in an uncertain environment remains a meaningful yet challenging direction of research. In this paper, we consider a new scheduling setting from practical complex business applications, where resources (e.g., raw materials) used for processing jobs arrive randomly, due to reasons such as unstable transportation service caused by extreme weather conditions, unreliable suppliers, unpredictable industrial actions, etc. Further, jobs must be processed one by one and preemption is not allowed. The processing times of jobs are not known but their distribution. We incorporate these factors into a stochastic single-machine scheduling model and examine two different common types of objectives: minimizing total expected weighted completion time and minimizing total expected weighted squared completion time. We derive and prove a natural and intuitive optimal policy for the model with the first objective. Besides, we find that, under some mild conditions, the well-known policy in stochastic scheduling, WSEPT (weighted shortest expected processing time), still holds optimal for achieving either of objectives. The numerical example further supports and illustrates our results, which provide decision-makers insights into tricky uncertain scheduling problems.

Adjusting scheduling model with release and due dates in production planning

Motivated by the conjecture that an interaction between scheduling and pre-scheduling phases in production planning may give certain benefits, we conduct a detailed study of the optimality conditions and dominance relations for a strongly NP-hard single-machine scheduling model when jobs have release and due-dates and the objective is to minimize maximum job lateness. By exploring the inherent structure of the problem, we establish the optimality conditions when the problem can be efficiently solved. We come to an NP-hard special case of the problem with only two possible job release times, that as we show allows stricter dominance rules and optimality conditions verifiable in polynomial time. The established properties give a potential of a beneficial interaction between scheduling and pre-scheduling phases in production planning, and also provide basic theoretical background for the construction of efficient heuristic and implicit enumerative algorithms.

Energy-Efficient Scheduling Problem Using an Effective Hybrid Multi-Objective Evolutionary Algorithm

Nowadays, manufacturing enterprises face the challenge of just-in-time (JIT) production and energy saving. Therefore, study of JIT production and energy consumption is necessary and important in manufacturing sectors. Moreover, energy saving can be attained by the operational method and turn off/on idle machine method, which also increases the complexity of problem solving. Thus, most researchers still focus on small scale problems with one objective: a single machine environment. However, the scheduling problem is a multi-objective optimization problem in real applications. In this paper, a single machine scheduling model with controllable processing and sequence dependence setup times is developed for minimizing the total earliness/tardiness (E/T), cost, and energy consumption simultaneously. An effective multi-objective evolutionary algorithm called local multi-objective evolutionary algorithm (LMOEA) is presented to tackle this multi-objective scheduling problem. To accommodate the characteristic of the problem, a new solution representation is proposed, which can convert discrete combinational problems into continuous problems. Additionally, a multiple local search strategy with self-adaptive mechanism is introduced into the proposed algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by instances with comparison to other multi-objective meta-heuristics such as Nondominated Sorting Genetic Algorithm II (NSGA-II), Strength Pareto Evolutionary Algorithm 2 (SPEA2), Multiobjective Particle Swarm Optimization (OMOPSO), and Multiobjective Evolutionary Algorithm Based on Decomposition (MOEA/D). Experimental results demonstrate that the proposed LMOEA algorithm outperforms its counterparts for this kind of scheduling problems.

A two-agent single machine scheduling problem with due-window assignment and a common flow-allowance

We study a single-machine scheduling model combining two competing agents and due-date assignment. The basic setting involves two agents who need to process their own sets of jobs, and compete on the use of a common processor. Our goal is to find the joint schedule that minimizes the value of the objective function of one agent, subject to an upper bound on the value of the objective function of the second agent. The scheduling measure considered in this paper is minimum total (earliness, tardiness and due-date) cost, based on common flow allowance, i.e., due-dates are defined as linear functions of the job processing times. We introduce a simple polynomial time solution for this problem (linear in the number of jobs), as well as to its extension to a multi-agent setting. We further extend the model to that of a due-window assignment based on common flow allowance.

MP or not MP: that is the question

It is well known that in the twentieth century, mathematical programming (MP) modeling and particularly linear programming (LP) modeling, even though strongly applied to combinatorial optimization, were not too successful when directed to scheduling problems. The purpose of this paper is to show that the field of successful applications of LP/MP modeling is still growing and includes also scheduling topics. We first focus on single machine scheduling. We consider a single machine scheduling model where a quadratic programming (QP) formulation handled by means of a QP solver is shown to be competitive with the state of the art approaches. Also, we discuss a single machine bicriterion scheduling problem and show that a standard LP formulation based on positional completion times performs reasonably well when handled by means of a LP solver. Then, we show how LP can be used to tighten bounds for approximation results in sequencing problems. Finally, we show how to enhance the complexity bounds of branch-and-reduce exact exponential algorithms by means of the so-called measure-and-conquer paradigm requiring always the solution of a specific MP model.

Some Scheduling Problems on a Single Machine with General Job Effects of Position-Dependent Learning and Start-Time-Dependent Deterioration

Job learning and deterioration coexist in many realistic machine-job scheduling situations. However, in literature, the constructed forms of the machine scheduling models with job learning and/or deteriorating effects were specific types of functions, which constrained their applicability in practice. This paper introduces a new single-machine scheduling model, where the actual processing time of a job is a general function of its starting time as well as scheduled position, which shows a broad generalization in contrast to that of certain existing models. For three objectives corresponding to the single-machine scheduling problem–total weighted completion time, discounted total weighted completion time, and maximum lateness — this paper presents their respective approximation result on the basis of the worst-case bound analysis from the optimal algorithm. The results demonstrate that under our proposed model, minimization of scheduling operations such as the makespan, sum of the kth power of completion times, and total lateness are polynomially solvable. Moreover, under some feasible conditions for the scheduling parameters, the minimum optimization problems of the total weighted completion time, discounted total weighted completion time, maximum lateness, and total tardiness are all recognized as polynomial forms and their solutions are provided.

Mathematical model applied to single-track line scheduling problem in Brazilian railways

In this paper, a single machine scheduling model is used to schedule trains in order to minimize the total weighted delay time. The railway studied, is composed of a part of two joined railways in Brazil, which belongs to VALE company and to Ferrovia Centro-Atlântica (FCA). This railway is a single-track line composed by single-tracks and it is shared by these railways (VALE and FCA). The trains travel in both directions on this single-track line. These railways have a bottleneck caused by a heavy traffic. Therefore, mathematical equations are developed to determine the shortest time interval admissible between two trains, ensuring that they will not overlap. These equations enable us to simplify the mathematical representation of the problem and define it as a single machine scheduling problem. Later, two single machine scheduling formulations are proposed and analyzed; one based on “Arc-Time-Indexed” variables and other, on “Binary” variables. Each formulation reflects a specific concept on how the variables and parameters are defined and require particular settings and definitions. Extensive computational experiments are performed considering various instances to capture several aspects of practical situations. Based on the results of experiments, these mathematical formulations are evaluated as decision-support tools.

Mixed Binary Integer Programming Formulation for the Two-Echelon Supply Chain System with Two Customer Locations

Coordinating works between production and delivery is a crucial issue for the management of production and logistics. This study developed a single machine scheduling model that incorporated delivery vehicle routing decisions and serves two customer locations. The objective was to minimize the mean arrival time. The problem is NP-hard in the strong sense in general. A mixed binary integer programming (BIP) model was developed to optimally solve this problem. In addition, computational results of the illustrative example are reported using the mixed BIP model to solve the proposed problem.

Some single-machine scheduling problems with past-sequence-dependent setup times and a general learning effect

This paper considers a single-machine scheduling model with past-sequence-dependent setup times and a general learning effect. It develops a general model with setup times and learning effect considerations where the actual processing time of a job is not only a function of the total actual processing time of the jobs already processed, but also a function of the job’s scheduled position. The paper shows that the single-machine scheduling problems to minimize the makespan and the sum of the kth power of completion times are polynomially solvable under the proposed model. It further shows that the problems to minimize the total weighted completion time and the maximum lateness are polynomially solvable under certain conditions.

Scheduling problems on tardiness penalty and earliness award with simply linear processing time

In this paper, a single-machine scheduling model with a given common due date and simple linear processing times was considered. The objective is the total weighted tardiness penalty and earliness award. Some polynomial time solvable cases for this problem are given. A dynamic programming algorithm was provided and a branch and bound algorithm for general case of the problem was provided based on a rapid method for estimating the lower bound.

A dynamic programming algorithm for scheduling problems on earliness award and tardiness penalty with time-dependent processing time

In this paper, a single-machine scheduling model with a given common due date is considered. Job processing time is a linear decreasing function of its starting time. The objective function is to minimize the total weighted earliness award and tardiness penalty. Our aim is to find an optimal schedule so as to minimize the objective function. As the problem is NP-hard, some properties and polynomial time solvable cases of this problem are given. A dynamic programming algorithm for the general case of the problem is provided.

The single-machine scheduling problems with deteriorating jobs and learning effect

In this paper we consider a single-machine scheduling model with deteriorating jobs and simultaneous learning, and we introduce polynomial solutions for single machine makespan minimization, total flow times minimization and maximum lateness minimization corresponding to the first and second special cases of our model under some agreeable conditions. However, corresponding to the third special case of our model, we show that the optimal schedules may be different from those of the classical version for the above objective functions.

Machine scheduling with deliveries to multiple customer locations

One important issue in production and logistics management is the coordination of activities between production and delivery. In this paper, we develop a single-machine scheduling model that incorporates routing decisions of a delivery vehicle which serves customers at different locations. The objective is to minimize the sum of job arrival times. The problem is NP-hard in the strong sense in general. We develop a polynomial time algorithm for the case when the number of customers is fixed. More efficient algorithms are developed for several special cases of the problem. In particular, an algorithm is developed for the single-customer case with a complexity lower than the existing ones.

Using real time information for effective dynamic scheduling

In many production processes real time information may be obtained from process control computers and other monitoring systems, but most existing scheduling models are unable to use this information to effectively influence scheduling decisions in real time. In this paper we develop a general framework for using real time information to improve scheduling decisions, which allows us to trade off the quality of the revised schedule against the production disturbance which results from changing the planned schedule. We illustrate how our framework can be used to select a strategy for using real time information for a single machine scheduling model and discuss how it may be used to incorporate real time information into scheduling the complex production processes of steel continuous caster planning.

A time-dependent multiple criteria single-machine scheduling problem

We introduce a nonpreemptive single-machine scheduling model with time-dependent multiple criteria. We formulate the problem as a knapsack problem and propose a dynamic programming (DP)-based algorithm to finding all efficient schedules. An illustrative example is enclosed.

A branch-and-bound algorithm for the single machine earliness and tardiness scheduling problem

This paper addresses the problem of scheduling a given set of independent jobs on a single machine to minimize the sum of weighted earliness and weighted tardiness without considering machine idle time. Efficient lower and upper bounds are developed. The lower bound is obtained based on a Lagrangian relaxation that decomposes the problem into two subproblems, and an efficient lower bounding procedure for each of the subproblems. The upper bound is obtained using a two-phase heuristic procedure that combines a priority dispatching rule with a local improvement procedure. A branch-and-bound algorithm incorporating these bounds and some dominance rules is proposed. Computational experiments on problems with up to 50 jobs show that both the lower and upper bounds are very tight and the branch-and-bound algorithm performs very well. Scope and purpose Single-machine scheduling models involving both earliness and tardiness costs have received significant attention in recent years. These models are consistent with the just-in-time (JIT) production philosophy in the sense that they force jobs to be completed as close to their due dates as possible. This paper examines a single-machine scheduling model where the objective is to minimize the sum of weighted earliness and weighted tardiness subject to the constraint that no machine idle time is allowed. The assumption of no machine idle time represents a type of production setting where the machine idle cost is larger than the job earliness cost, or the machine capacity is limited compared to demand. The problem is known to be NP-hard. A branch-and-bound algorithm based on powerful lower and upper bounding procedures together with some dominance rules is presented. Computational experience is also reported.