Single-machine scheduling problems with an aging effect

This paper considers single machine scheduling problems where the processing time of a job increases as a function of its position in the sequence. In this model, the later a given job is scheduled in the sequence, the longer its processing time. It is shown that the optimal schedule may be very different from that of the classical version of the problem. We introduce polynomial solutions for the makespan minimization problem, the sum of completion times minimization problem and the sum of earliness penalties minimization problem. For two resource constrained problems, based on the analysis of the problems, the optimal resource allocation methods are presented, respectively.     

Single-machine scheduling problems with a learning effect

In many situations, the skills of workers continuously improve when repeating the same or similar tasks. This phenomenon is known as the “learning effect” in the literature. In most studies, the learning phenomenon is implemented by assuming the actual job processing time is a function of its scheduled position [D. Biskup, Single-machine scheduling with learning considerations, Eur. J. Oper. Res. 115 (1999) 173–178]. Recently, a new model is proposed where the actual job processing time depends on the sum of the processing times of jobs already processed [C. Koulamas, G.J. Kyparisis, Single-machine and two-machine flowshop scheduling with general learning functions, Eur. J. Oper. Res. 178 (2007) 402–407]. In this paper, we extend their models in which the actual job processing time not only depends on its scheduled position, but also depends on the sum of the processing times of jobs already processed. We then show that the single-machine makespan and the total completion time problems remain polynomially solvable under the proposed model. In addition, we show that the total weighted completion time has a polynomial optimal solution under certain agreeable solutions.     

The strong NP-hardness of the maximum lateness minimization scheduling problem with the processing-time based aging effect

In this paper, we analyse the single machine maximum lateness minimization scheduling problem with the processing time based aging effect, where the processing time of each job is described by a non-decreasing function dependent on the sum of the normal processing times of preceded jobs. The computational complexity of this problem was not determined. However, we show it is strongly NP-hard by proving the strong NP-hardness of the single machine maximum completion time minimization problem with this aging model and job deadlines. Furthermore, we determine the boundary between polynomially solvable and NP-hard cases.     

Single-machine group scheduling problems with deteriorated and learning effect

In many realistic situation, a job processed later consumes more time than the same job when it is processed earlier, this phenomenon is known as deteriorated effect. The skills of workers continuously improve when repeating the same or similar tasks, this phenomenon is as the “learning effect” in the literature. However, most studies considering the deteriorated and learning effect ignore the fact that production efficiency can be increased by grouping various parts and products with similar designs and/or production processes. This phenomenon is known “group technology” in the literature. In this paper, we propose a new group scheduling with deteriorated and learning model where the learning effect not only depends on job position, but also depends on the group position; the deteriorated effect depends on its starting time of the job. We then show that the single-machine makespan and the total completion time problems remain polynomial optimal solvable under the proposed model. In addition, we show the maximum lateness have a polynomial optimal solution under certain agreeable restriction.     

Some scheduling problems with time-dependent learning effect and deteriorating jobs

Although machine scheduling problems with learning and deteriorating effects consideration have received increasing attention in the recent years, most studies have seldom considered the two phenomena simultaneously. However, learning and deteriorating effects might co-exist in many realistic scheduling situations. Thus, in this article, a model which takes the effects of time-dependent learning and deterioration simultaneously is proposed and applied into some scheduling problems. Under the proposed model, the processing time of a job is determined by a function of its corresponding starting time and positional sequence in each machine. We show that some single machine and flowshop scheduling problems are polynomially solvable with the certain performance measures such as makespan, total completion time, and weighted completion time.     

Single-machine scheduling problems with actual time-dependent and job-dependent learning effect

In this study, we introduce an actual time-dependent and job-dependent learning effect into single-machine scheduling problems. We show that the complexity results of the makespan minimization problem and the sum of weighted completion time minimization problem are all NP-hard. The complexity result of the maximum lateness minimization problem is NP-hard in the strong sense. We also provide three special cases which can be solved by polynomial time algorithms.     

A revision of machine scheduling problems with a general learning effect

In this paper, we show that the main results in a recent paper by Zhang and Yan [X. Zhang and G. Yan, Machine scheduling problems with a general learning effect, Mathematical and Computer Modelling 51 (2010) 84–90] are incorrect as an important reason is missing, that is, the processing time of a job is variable according to a general learning effect. Here we point out these wrong results by a counter-example. In addition, we give a revised model with a general learning effect. We show that some single machine scheduling problems are still polynomially solvable under the revised model. We also show that some special cases of the flowshop scheduling problems can be solved in polynomial time under the revised model.     

基于加工时间之和学习效应下的单机成组排序问题

研究具有加工时间之和学习效应下的一个新型成组排序问题,工件的学习效应是之前工件加工时间之和的函数,组学习效应是成组加工所在的位置的函数. 考虑最大完工时间和总完工时间两个问题,证明了这两个问题都是多项式时间可解的,并提出了相应的多项式时间算法.     

A note on single-machine group scheduling problems with position-based learning effect

In many situations, the skills of workers continuously improve when repeating the same or similar tasks. This phenomenon is known as the “learning effect” in the literature. However, most studies considering the learning effect ignore the fact that production efficiency can be increased by grouping various parts and products with similar designs and/or production processes. This phenomenon is known as “group technology” in the literature. In this paper, we propose a new group scheduling learning model where the learning effect not only depends on the job position, but also depends on the group position. We then show that the makespan and the total completion time problems remain polynomially solvable under the proposed model.     

Worst-case analysis for flow shop scheduling problems with an exponential learning effect

A real industrial production phenomenon, referred to as learning effects, has drawn increasing attention. However, most research on this issue considers only single machine problems. Motivated by this limitation, this paper considers flow shop scheduling problems with an exponential learning effect. By the exponential learning effect, we mean that the processing time of a job is defined by an exponent function of its position in a processing permutation. The objective is to minimize one of the four regular performance criteria, namely, the total completion time, the total weighted completion time, the discounted total weighted completion time, and the sum of the quadratic job completion times. We present heuristic algorithms by using the optimal permutations for the corresponding single-machine scheduling problems. We also analyse the worst-case bound of our heuristic algorithms.     

Strong NP-hardness of AC power flows feasibility

We prove that testing feasibility for an AC power flow system is a strongly NP-hard problem.     

Flow-shop scheduling with a learning effect

The paper is devoted to some flow-shop scheduling problems with a learning effect. The objective is to minimize one of the two regular performance criteria, namely, makespan and total flowtime. A heuristic algorithm with worst-case bound m for each criteria is given, where m is the number of machines. Furthermore, a polynomial algorithm is proposed for both of the special cases: identical processing time on each machine and an increasing series of dominating machines. An example is also constructed to show that the classical Johnson's rule is not the optimal solution for the two-machine flow-shop scheduling to minimize makespan with a learning effect. Some extensions of the problem are also shown.     

Strong NP-hardness of minimizing total deviation with generalized and periodic due dates

We consider a single-machine scheduling problem with generalized and periodic due dates such that each due date is assigned not to a specific job but to a position and the lengths of the intervals between consecutive due dates are identical. The objective is to minimize the total deviation, which is calculated as the sum of the earliness and tardiness of each job. We show that the problem is strongly NP-hard. We develop a heuristic and verify its performance via experiments.     

Several single-machine scheduling problems with general learning effects

In this paper we consider several single-machine scheduling problems with general learning effects. By general learning effects, we mean that the processing time of a job depends not only on its scheduled position, but also on the total normal processing time of the jobs already processed. We show that the scheduling problems of minimization of the makespan, the total completion time and the sum of the θ  th (θ?0$\theta ?0$) power of job completion times can be solved in polynomial time under the proposed models. We also prove that some special cases of the total weighted completion time minimization problem and the maximum lateness minimization problem can be solved in polynomial time.     

A note on proving the strong NP-hardness of some scheduling problems with start time dependent job processing times

In this paper, we show that the strong NP-hardness proofs of some scheduling problems with start time dependent job processing times presented in Gawiejnowicz (Eur J Oper Res 180:472–478, 2007) and Zhao and Tang (Optim Lett 5:183–190, 2011) are incorrect. Namely, the applied transformations from 4-Product problem to the considered scheduling problems are polynomial not pseudopolynomial. Thus, the related problems are NP-hard, but their complete computational status is still an open issue: ordinary or strongly NP-hard?     

Single-machine scheduling problems with both start-time dependent learning and position dependent aging effects under deteriorating maintenance consideration

In this paper we introduce a new model of joint start-time dependent learning and position dependent aging effects into single-machine scheduling problems. The machine may need maintenance to improve its production efficiency. The objectives are to find jointly the optimal maintenance position and the optimal sequence such that the makespan, the total completion time, and the total absolute deviation of completion times (TADC) are minimized. We also aim to determine jointly the optimal maintenance position, the optimal due-window size and location, and the optimal sequence to minimize the sum of earliness, tardiness and due-window related costs function. We show that all the studied problems can be optimally solved by polynomial time algorithms.     

Single-machine scheduling with a sum-of-actual-processing-time-based learning effect

In this paper we consider the single-machine scheduling problems with a sum-of-actual-processing-time-based learning effect. By the sum-of-actual-processing-time-based learning effect, we mean that the processing time of a job is defined by a function of the sum of the actual processing time of the already processed jobs. We show that even with the introduction of the sum-of-actual-processing-time-based learning effect to job processing times, the makespan minimization problem, the total completion time minimization problem, the total completion time square minimization problem, and some special cases of the total weighted completion time minimization problem and the maximum lateness minimization problem remain polynomially solvable, respectively.     

Parallel machine scheduling with a learning effect

The phenomenon of ‘learning’ has been extensively studied in many different areas of Operational Research. However, the ‘learning effect’ of the producer/processor has rarely been studied in the general context of production scheduling, and has never been investigated in multi-machine scheduling settings. We focus in this paper on flow-time minimization on parallel identical machines. We show that this problem has a polynomial time solution, although the computational effort required is much larger than the effort required for solving the classical version of the problem.     

Solving project scheduling problems with a heuristic learning algorithm

We have developed an approach to implement a real time admissible heuristic search algorithm to solve project scheduling problems. This algorithm is characterised by the complete heuristic learning process: state selection, heuristic learning, and search path review. This implementation approach is based on the dynamic nature of the activity status and the resource availability of a project. It consists of states, state transition operator, heuristic estimate, and the cost of transition between states. The performance analysis shows that the accumulation of heuristic learning during the search process has led to the re-scheduling of resource dominating activities, which is a major factor in controlling the overall project completion time.