A note to due-window assignment and single machine scheduling with deteriorating jobs and a rate-modifying activity

This note considers a single machine scheduling and due-window assignment problem, in which the processing time of a job is a linear function of its starting time and the job-independent deterioration rates are identical for all jobs. We allow an option for performing a rate-modifying activity for changing the normal processing times of the jobs following this activity. The objective is to schedule the jobs, the due-window and the rate-modifying activity so as to minimize the sum of earliness, tardiness and due-window starting time and due-window size costs. We introduce a polynomial solution for the problem.     

Scheduling with a common due-window: Polynomially solvable cases

The single machine scheduling problem to minimize maximum weighted absolute deviations of job completion times from a common due-date, is known to be NP-hard. However, two special cases have been shown to have polynomial time solutions: the case of unit processing time jobs, and the case of due-date assignment for a given job sequence. We extend both cases to a setting of a common due-window. We show that the unit-job problem includes 12 different sub-cases, depending on the size and location of the (given) due-window. Scheduling and due-window assignment for a given job sequence is solved for a single machine, for parallel identical machines and for flow-shops. For each of the above cases, an appropriate special-structured linear program is presented.     

Minmax scheduling problems with a common due-window

This paper focuses on a minmax due-window assignment problem. The goal is to schedule the jobs and the due-window such that the highest cost among all jobs is minimized. The objective function contains four cost components: for earliness, tardiness, due-window starting time and due-window size. We present a polynomial time solution for the case of a single machine and for a two-machine flow-shop. The cases of parallel identical machines and uniform machines are NP-hard, and simple heuristics and lower bounds are introduced and tested numerically.     

最小化提前和延误惩罚的批处理问题

考虑机器容量有限的同时加工排序问题, 为享有公共交货期窗口[e; d]的n个工件分批并排序以最小化总的赋权提前/延误惩罚. 本文把窗时排序与同时加工排序结合起来研究, 假设每个批的容量是b(< n, 其中n为工件的个数), 而且最早交货期e 和最晚交货期d已知. 但该问题是NP–完备的, 首先给出最优排序的几条性质, 进而解决了两类特殊情况.     

Single-machine scheduling with a variable maintenance activity

We consider the problem of scheduling a maintenance activity and jobs on a single machine, where the maintenance activity must start before a given deadline and the maintenance duration increases with its starting time. We provide polynomial-time algorithms to solve the problems to minimize the makespan, sum of completion times, maximum lateness, and number of tardy jobs.     

Single machine common flow allowance scheduling with a rate-modifying activity

In this paper we consider single machine SLK due date assignment scheduling problem with a rate-modifying activity. In this model, the 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 activity, the optimal common flow allowance and the sequence of jobs to minimize total earliness, tardiness and common flow allowance cost. We introduce an efficient (polynomial time) solution for this problem.     

Single-machine due-window assignment and scheduling with resource allocation,aging effect,and a deteriorating rate-modifying activity

We consider single-machine scheduling with a common due-window and a deteriorating rate-modifying activity. We assume that the processing time of a job is a function of the amount of a resource allocated to it, its position in the processing sequence, and its aging effect. The objective is to minimize the total cost, which is a function of earliness, tardiness, due-window starting time, due-window size, and resource consumption. We consider two models of the job processing time function and provide polynomial-time solution algorithms for the corresponding problems. We also give a more efficient solution algorithm for a special case of the second problem.     

Scheduling jobs under increasing linear machine maintenance time

Although scheduling problems with machine availability have attracted many researchers’ attention, most of the past studies are mainly focused on one or several prefixed machine maintenance activities. In this research, we assume that the time needed to perform one maintenance activity is an increasing linear function of the total processing time of the jobs that are processed after the machine’s last maintenance activity. We consider two scheduling problems with such maintenance requirement in this paper. The first problem is a parallel machine scheduling problem where the length of the time interval between any two consecutive maintenance activities is between two given positive numbers. The objective is to minimize the maintenance makespan, i.e., the completion time of the last finished maintenance. The second problem is a single machine scheduling problem where the length of the time interval between any two consecutive maintenance activities is fixed and the objective is to minimize the makespan, i.e., the completion time of the last finished job. We propose two approximation algorithms for the considered problems and analyze their performances.     

Unrelated parallel-machine scheduling with aging effects and multi-maintenance activities

Machine maintenance is often performed in manufacturing to prevent premature machine failures with a view to sustaining production efficiency. In this paper we study the parallel-machine scheduling problem with aging effects and multi-maintenance activities simultaneously. We assume that each machine may be subject to several maintenance activities over the scheduling horizon. A machine reverts to its initial condition after maintenance and the aging effects start anew. The objective is to find jointly the optimal maintenance frequencies, the optimal positions of the maintenance activities, and the optimal job sequences such that the total machine load is minimized. We apply the group balance principle to obtain the optimal positions of the maintenance activities and the number of jobs in each group in the scheduling sequence on each machine. We provide an efficient algorithm to solve the problem when the maintenance frequencies on the machines are given.     

Single machine scheduling with past-sequence-dependent setup times and deteriorating jobs

This paper considers single machine scheduling problems with setup times and deteriorating jobs. The setup times are proportional to the length of the already processed jobs, that is, the setup times are past-sequence-dependent (p-s-d). It is assumed that the job processing times are defined by functions dependent on their starting times. The following objectives are considered: the makespan, the total completion time, and the sum of earliness, tardiness, and due-window starting time and size penalties. We propose polynomial time algorithms to solve these problems.     

Single machine scheduling with past-sequence-dependent setup times and deteriorating jobs

This paper considers single machine scheduling problems with setup times and deteriorating jobs. The setup times are proportional to the length of the already processed jobs, that is, the setup times are past-sequence-dependent (p-s-d). It is assumed that the job processing times are defined by functions dependent on their starting times. The following objectives are considered: the makespan, the total completion time, and the sum of earliness, tardiness, and due-window starting time and size penalties. We propose polynomial time algorithms to solve these problems.     

Common due date assignment and scheduling with ready times

We consider the problem of scheduling a set of nonsimultaneously available jobs on one machine. Each job has a ready time only at or after which the job can be processed. All the jobs have a common due date, which needs to be determined. The problem is to determine a due date and a schedule so as to minimize a total penalty depending on the earliness, tardiness and due date. We show that this problem is strongly NP-hard and give an efficient algorithm that finds an optimal due date and schedule when either the job sequence is predetermined or all jobs have the same processing time. We also propose three approximation algorithms for the general and special cases together with their experimental analysis.

Scope and purpose

We consider the single machine due date assignment problem for scheduling jobs which are ready for processing at different times. The problem under consideration arises in production planning and scheduling concerning the setting of appropriate due dates for a number of customer orders arriving over time. Most of the earlier publications on this subject assumed that the jobs are ready for processing simultaneously. This assumption is too restrictive for real-life production systems where jobs arrive at different times. We show that the problem with unequal ready times is NP-hard and develop fast heuristic algorithms for it, and exact algorithms for two special cases.     

Minimizing the number of tardy jobs in a single-machine scheduling problem with periodic maintenance

This research focuses on the problem of scheduling jobs on a single machine that requires periodic maintenance with the objective of minimizing the number of tardy jobs. We present a two-phase heuristic algorithm in which an initial solution is obtained first with a method modified from Moore's algorithm for the problem without maintenance and then the solution is improved in the second phase. Performance of the proposed heuristic algorithm is evaluated through computational experiments on randomly generated problem instances and results show that the heuristic gives solutions close to those obtained from a commercial integer programming solver in much shorter time and works better than an existing heuristic algorithm in terms of the solution quality.     

Single machine scheduling with a generalized job-dependent cumulative effect

We consider a single machine scheduling problem with changing processing times. The processing conditions are subject to a general cumulative effect, in which the processing time of a job depends on the sum of certain parameters associated with previously scheduled jobs. In previous papers, these parameters are assumed to be equal to the normal processing times of jobs, which seriously limits the practical application of this model. We further generalize this model by allowing every job to respond differently to these cumulative effects. For the introduced model, we solve the problem of minimizing the makespan, with and without precedence constraints. For the problem without precedence constraints, we also consider a situation in which a maintenance activity is included in the schedule, which can improve the processing conditions of the machine, not necessarily to its original state. The resulting problem is reformulated as a variant of a Boolean programming problem with a quadratic objective, known as a half-product, which allows us to develop a fully polynomial-time approximation scheme with the best possible running time.     

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.     

Minimizing total completion time on a single machine with a flexible maintenance activity

A problem of jointly scheduling multiple jobs and a single maintenance activity on a single machine with the objective of minimizing total completion time is considered in this paper. It is assumed that the machine should be stopped for maintenance which takes a constant duration within a predefined period. The problem is generalized from the one with a fixed maintenance in that it relaxes the starting time of the maintenance from a fixed time point to a predefined period. Both resumable and nonresumable cases are studied. First, three properties of an optimal solution to each of the two cases are identified. Then it is shown that the proposed shortest processing time (SPT) algorithm is optimal for the resumable case. As for the nonresumable case, the conditions under which the SPT algorithm is optimal are also specified. Furthermore, it is shown that relaxing the starting time of the maintenance cannot improve the relative error bound of the SPT algorithm. The focus of the paper is presented afterwards, which is to develop a dynamic programming algorithm and a branch-and-bound algorithm to generate an optimal solution for this case. Experimental results show that these algorithms are effective and complementary in dealing with different instances of the problem.     

A dynamic-programming-based exact algorithm for general single-machine scheduling with machine idle time

This paper proposes an efficient exact algorithm for the general single-machine scheduling problem where machine idle time is permitted. The algorithm is an extension of the authors’ previous algorithm for the problem without machine idle time, which is based on the SSDP (Successive Sublimation Dynamic Programming) method. We first extend our previous algorithm to the problem with machine idle time and next propose several improvements. Then, the proposed algorithm is applied to four types of single-machine scheduling problems: the total weighted earliness-tardiness problem with equal (zero) release dates, that with distinct release dates, the total weighted completion time problem with distinct release dates, and the total weighted tardiness problem with distinct release dates. Computational experiments demonstrate that our algorithm outperforms existing exact algorithms and can solve instances of the first three problems with up to 200 jobs and those of the last problem with up to 80 jobs.     

Due-date assignment and machine scheduling in a low machine-rate situation with stochastic processing times

Due date assignment combined with shop floor scheduling has attracted enormous amount of research, in recent years. In many make-to-order situations, the processing times are not known exactly in advance. Further, machine rates are not constant at different times. We assume the case where the machine rate is low at the beginning of the scheduling horizon. However, it can be brought back to normal rate by performing a maintenance activity. The problem includes assigning due-dates and scheduling the jobs and maintenance activity on a single machine where the processing times are stochastic. The objective is minimizing the total cost of lengths of quoted due-dates and expected deviations of completion times from declared due-dates. The optimal solutions of medium-sized problems are found by solving some nonlinear programming models. For larger problems, robust metaheuristics are developed and their performances are statistically analyzed.     

Parallel machine scheduling with splitting jobs by a hybrid differential evolution algorithm

The problem of parallel machine scheduling for minimizing the makespan is an open scheduling problem with extensive practical relevance. It has been proved to be non-deterministic polynomial hard. Considering a job’s batch size greater than one in the real manufacturing environment, this paper investigates into the parallel machine scheduling with splitting jobs. Differential evolution is employed as a solution approach due to its distinctive feature, and a new crossover method and a new mutation method are brought forward in the global search procedure, according to the job splitting constraint. A specific local search method is further designed to gain a better performance, based on the analytical result from the single product problem. Numerical experiments on the performance of the proposed hybrid DE on parallel machine scheduling problems with splitting jobs covering identical and unrelated machine kinds and a realistic problem are performed, and the results indicate that the algorithm is feasible and efficient.     

Approximation algorithms for multi-agent scheduling to minimize total weighted completion time

We consider a multi-agent scheduling problem on a single machine in which each agent is responsible for his own set of jobs and wishes to minimize the total weighted completion time of his own set of jobs. It is known that the unweighted problem with two agents is NP-hard in the ordinary sense. For this case, we can reduce our problem to a Multi-Objective Shortest-Path (MOSP) problem and this reduction leads to several results including Fully Polynomial Time Approximation Schemes (FPTAS). We also provide an efficient approximation algorithm with a reasonably good worst-case ratio.