Minimizing the weighted number of early and tardy jobs in a stochastic single machine scheduling problem

We study a static stochastic single machine scheduling problem in which jobs have random processing times with arbitrary distributions, due dates are known with certainty, and fixed individual penalties (or weights) are imposed on both early and tardy jobs. The objective is to find an optimal sequence that minimizes the expected total weighted number of early and tardy jobs. The general problem is NP-hard to solve; however, in this paper, we develop certain conditions under which the problem is solvable exactly. An efficient heuristic is also introduced to find a candidate for the optimal sequence of the general problem. Our illustrative examples and computational results demonstrate that the heuristic performs well in identifying either optimal sequences or good candidates with low errors. Furthermore, we show that special cases of the problem studied here reduce to some classical stochastic single machine scheduling problems including the problem of minimizing the expected weighted number of early jobs and the problem of minimizing the expected weighted number of tardy jobs which are both solvable by the proposed exact or heuristic methods.     

Algorithms for two-machine flow-shop sequencing with precedence constraints

This paper considers the problem of minimizing the maximum completion time in a two-machine flow-shop for which precedence constraints on the jobs are specified. If one job has precedence over another, then the second of these jobs cannot be processed on a machine until the first of them is completed on that machine. A powerful new lower bounding rule which uses Lagrangean relaxation is developed. Then several dominance theorems are presented which are used to eliminate some jobs from the problem. The lower bound is used in three branch and bound algorithms, two of which employ well-known branching rules while the third uses a new branching rule. The algorithms are compared and tested on problems with up to 80 jobs.     

Minimizing number of tardy jobs on a batch processing machine with incompatible job families

In this paper we consider the problem of minimizing number of tardy jobs on a single batch processing machine. The batch processing machine is capable of processing up to B jobs simultaneously as a batch. We are given a set of n jobs which can be partitioned into m incompatible families such that the processing times of all jobs belonging to the same family are equal and jobs of different families cannot be processed together. We show that this problem is NP-hard and present a dynamic programming algorithm which has polynomial time complexity when the number of job families and the batch machine capacity are fixed. We also show that when the jobs of a family have a common due date the problem can be solved by a pseudo-polynomial time procedure.     

Minimum weighted number of tardy jobs on an m-machine flow-shop with a critical machine

We study a flow-shop problem, where each of the jobs is limited to no more than two operations. One of these operations is common for all the jobs, and is performed on the same (”critical”) machine. Reflecting many applications, jobs are assumed to be processed in blocks on the critical machine. All the jobs share a common due-date, and the objective is minimum weighted number of tardy jobs. We prove that the problem is NP-hard. Then we formulate the problem as an integer program, and introduce a pseudo-polynomial dynamic programming algorithm, proving that the problem is NP-hard in the ordinary sense. We also propose an efficient heuristic, which is shown numerically to produce very close-to-optimal schedules. Finally, we show that the special case of identical weights is polynomially solvable.     

Sequencing with general precedence constraints

Consider a set of jobs where an arbitrary precedence relationship exists among the jobs and a cost is associated with every permutation of the jobs. The objective is to find a minimum-cost permutation which is consistent with the precedence relations. A class of problems is studied which includes the least-cost fault detection problem, the one-machine total weighted completion time problem, and the two-machine maximum flow-time problem.Transformations are developed which systematically reduce the size of the general precedence-constrained problem. This process continues until either the problem is solved or no further reductions are possible. The worst-case effectiveness of these transformations is analyzed in detail. These results generalize the majority of work previously done on efficient sequencing with precedence constraints.     

Routing open shop and flow shop scheduling problems

We consider a generalization of the classical open shop and flow shop scheduling problems where the jobs are located at the vertices of an undirected graph and the machines, initially located at the same vertex, have to travel along the graph to process the jobs. The objective is to minimize the makespan. In the tour-version the makespan means the time by which each machine has processed all jobs and returned to the initial location. While in the path-version the makespan represents the maximum completion time of the jobs. We present improved approximation algorithms for various cases of the open shop problem on a general graph, and the tour-version of the two-machine flow shop problem on a tree. Also, we prove that both versions of the latter problem are NP-hard, which answers an open question posed in the literature.     

带固定工件的单机排序问题1｜FB, rj,pmtn｜∑j Uj的多项式算法

研究具有若干固定工件和自由工件,其中固定工件必须在指定时间窗内加工,而自由工件具有不同交工的时间,并且其加工可以中断的单机排序问题,其目标是极小化工件的误工数.该问题可以表示为1|FB,rj,pmtn|∑j Uj.首先讨论了问题的几个重要性质,以此为基础建立了求解该问题的动态规划算法,其时间复杂度为O(n4+m log m),其中m和n分别是固定工件数和自由工件数.     

Group sequencing around a common due date

The problem of scheduling groups of jobs on a single machine under the group technology assumption is studied. Jobs of the same group are processed contiguously and a sequence independent setup time precedes the processing of each group. All jobs have a common fixed due date, which can be either unrestrictively large or restrictively small. The objective is to minimize the total weighted earliness–tardiness. Properties of optimal solutions are established, and dynamic programming algorithms are derived to solve several special cases of this problem. Computational experiments show that the algorithms can easily solve problems with 500 groups of jobs and each group has 10 to 50 jobs on a standard PC.     

A heuristic for scheduling in a two-stage hybrid flowshop with renewable resources shared among the stages

In this paper we propose a heuristic for solving the problem of resource constrained preemptive scheduling in the two-stage flowshop with one machine at the first stage and parallel unrelated machines at the second stage, where renewable resources are shared among the stages, so some quantities of the same resource can be used at different stages at the same time. Availability of every resource at any moment is limited and resource requirements of jobs are arbitrary. The objective is minimization of makespan. The problem is NP-hard. The heuristic first sequences jobs on the machine at stage 1 and then solves the preemptive scheduling problem at stage 2. Priority rules which depend on processing times and resource requirements of jobs are proposed for sequencing jobs at stage 1. A column generation algorithm which involves linear programming, a tabu search algorithm and a greedy procedure is proposed to minimize the makespan at stage 2. A lower bound on the optimal makespan in which sharing of the resources between the stages is taken into account is also derived. The performance of the heuristic evaluated experimentally by comparing the solutions to the lower bound is satisfactory.     

Minimizing mean tardiness subject to unspecified minimum number tardy for a single machine

In this paper we propose a hybrid branch and bound algorithm for solving the problem of minimizing mean tardiness for a single machine problem subject to minimum number of tardy jobs. Although the minimum number of tardy jobs is known, the subset of tardy job is not known. The proposed algorithm uses traditional branch and bound scheme where lower bounds on mean tardiness are calculated coupled with using the information that the number of tardy jobs is known. It also uses an insertion algorithm which determines the optimal mean tardiness once the subset of tardy jobs is specified. An example is solved to illustrate the developed procedure.     

需要安装时间的平行多功能机排序问题的启发式算法

考虑需要安装时间的平行多功能机排序问题。在该模型中,每个工件对应机器集合的一个子集,其只能在这个子集中的任一台机器上加工,称这个子集为该工件的加工集合;工件分组,同组工件具有相同的加工时间和加工集合,不同组中的工件在同一台机器上连续加工需要安装时间,目标函数为极小化最大完工时间。对该问题NP-难的一般情况设计启发式算法:首先按照特定的规则将所有工件组都整组地安排到各台机器上,然后通过在各机器间转移工件不断改进当前最大完工时间。通过与下界的比较检验算法的性能,大量的计算实验表明,算法是实用而有效的。     

A permutation flow-shop scheduling problem with convex models of operation processing times

The paper is an extension of the classical permutation flow-shop scheduling problem to the case where some of the job operation processing times are convex decreasing functions of the amounts of resources (e.g., financial outlay, energy, raw material) allocated to the operations (or machines on which they are performed). Some precedence constraints among the jobs are given. For this extended permutation flow-shop problem, the objective is to find a processing order of the jobs (which will be the same on each machine) and an allocation of a constrained resource so as to minimize the duration required to complete all jobs (i.e., the makespan). A computational complexity analysis of the problem shows that the problem is NP-hard. An analysis of the structure of the optimal solutions provides some elimination properties, which are exploited in a branch-and-bound solution scheme. Three approximate algorithms, together with the results of some computational experiments conducted to test the effectiveness of the algorithms, are also presented. This revised version was published online in June 2006 with corrections to the Cover Date.     

Some Generalisations of the Time Minimising Assignment Problem

The time minimising assignment problem is the problem of finding an assignment of n jobs to n facilities, one to each, which minimises the total time for completing all the jobs. The usual assumption made in these problems is that all the jobs are commenced simultaneously. In this paper two generalisations of this assumption are considered, and algorithms are presented to solve these general problems. Numerical examples are worked out illustrating the algorithms.     

求带释放时间的半导体煅烧排序的最短交付时间的一个高效PTAS

本文研究一个目标是最小化最大交付时间的能分批处理的非中断单机排序问题．这个问题来源于半导体制造过程中对芯片煅烧工序的排序．煅烧炉可以看成一个能同时最多加工B（〈n）个工件的处理机．此外，每个工件有一个可以允许其加工的释放时间和一个完成加工后的额外交付时间．该问题就是将工件分批后再依批次的排序加工，使得所有工件都交付后所需的时间最短．我们设计了一个用时O（f（l／ε）n^5/2）的多项式时间近似方案，其中关于1／ε的指数函数厂（1／ε）对固定的ε是个常数.     

On scheduling around large restrictive common due windows

This paper deals with the problem of scheduling a number of jobs on a single machine around a large, restrictive common due window. We consider individual earliness and tardiness penalties for the jobs. The objective is to find an optimal schedule which jointly minimizes the sum of the earliness and tardiness penalties. This problem is intractable and hence no efficient procedure for solving large instances is expected to be found. For this reason we first introduced a mapping of the problem which takes advantage of the structural properties inherent to optimal solutions. Secondly we solved the problem under study by using this mapping and applying three meta-heuristics, namely evolutionary strategy, simulated annealing and threshold accepting. To validate the quality of these approaches, altogether 250 benchmark problems with different window sizes and positions of up to 200 jobs are examined. Furthermore small instances are solved to optimality by a mixed integer programming formulation.     

Minimizing the weighted number of tardy jobs with due date assignment and capacity-constrained deliveries for multiple customers in supply chains

In this paper, an integrated due date assignment and production and batch delivery scheduling problem for make-to-order production system and multiple customers is addressed. Consider a supply chain scheduling problem in which n orders (jobs) have to be scheduled on a single machine and delivered to K customers or to other machines for further processing in batches. A common due date is assigned to all the jobs of each customer and the number of jobs in delivery batches is constrained by the batch size. The objective is to minimize the sum of the total weighted number of tardy jobs, the total due date assignment costs and the total batch delivery costs. The problem is NP-hard. We formulate the problem as an Integer Programming (IP) model. Also, in this paper, a Heuristic Algorithm (HA) and a Branch and Bound (B&B) method for solving this problem are presented. Computational tests are used to demonstrate the efficiency of the developed methods.     

加工时间和尺寸成正比单机排序和工件运输的最小化最大完工时间问题

在单机排序和工件运输的最小化最大完工时间问题中,工件首先在一台机器上加工,然后被一辆有容量限制的汽车运送到一个顾客．当工件的加工时间和尺寸无关时, Chang和Lee已经证明该问题是强NP困难的．他们也给出了一个启发式算法,它的最差执行比为5／3,并且这个界是紧的．本文考虑工件的加工时间和尺寸成正比的情形,证明了Chang和Lee的算法有更好的最差执行比53／35,并提供了一个新的启发式算法,它的最差执行比是3／2,并且这个界是最好的．     

分配小于人数和任务数的指派问题的反点算法

摘要：本文对从 个人中派出 个人去完成 项任务中的 项任务使总效率最高这类指派问题给出了新算法,通过对这类指派问题引入了反点的概念,讨论了反点所具有的一些性质并证明了相关结论,利用这些结论找到了通过增加反点来解决此类指派问题的反点算法。     

Parallel machine scheduling with a convex resource consumption function

We consider some problems of scheduling jobs on identical parallel machines where job-processing times are controllable through the allocation of a nonrenewable common limited resource. The objective is to assign the jobs to the machines, to sequence the jobs on each machine and to allocate the resource so that the makespan or the sum of completion times is minimized. The optimization is done for both preemptive and nonpreemptive jobs. For the makespan problem with nonpreemptive jobs we apply the equivalent load method in order to allocate the resources, and thereby reduce the problem to a combinatorial one. The reduced problem is shown to be NP-hard. If preemptive jobs are allowed, the makespan problem is shown to be solvable in O(n²) time. Some special cases of this problem with precedence constraints are presented and the problem of minimizing the sum of completion times is shown to be solvable in O(n log n) time.     

Optimal clustering of frequency-constrained maintenance jobs with shared set-ups

Since maintenance jobs often require one or more set-up activities, joint execution or clustering of maintenance jobs is a powerful instrument to reduce shut-down costs. We consider a clustering problem for frequency-constrained maintenance jobs, i.e. maintenance jobs that must be carried out with a prescribed (or higher) frequency. For the clustering of maintenance jobs with identical, so-called common set-ups, several strong dominance rules are provided. These dominance rules are used in an efficient dynamic programming algorithm which solves the problem in polynomial time. For the clustering of maintenance jobs with partially identical, so-called shared set-ups, similar but less strong dominance rules are available. Nevertheless, a surprisingly well-performing greedy heuristic and a branch and bound procedure have been developed to solve this problem. For randomly generated test problems with 10 set-ups and 30 maintenance jobs, the heuristic was optimal in 47 out of 100 test problems, with an average deviation of 0.24% from the optimal solution. In addition, the branch and bound method found an optimal solution in only a few seconds computation time on average.