Scheduling linear deteriorating jobs with rejection on a single machine

We consider several single machine scheduling problems in which the processing time of a job is a linear function of its starting time and jobs can be rejected by paying penalties. The objectives are to minimize the makespan, the total weighted completion time and the maximum lateness/tardiness plus the total penalty of the rejected jobs. We show that these problems are NP-hard, and design algorithms based on dynamic programming (including pseudo-polynomial time optimal algorithms and fully polynomial time approximation schemes) to solve them.     

SPT optimality (mostly) via linear programming

One of the oldest results in scheduling theory is that the Shortest Processing Time (SPT) rule finds an optimal solution to the problem of scheduling jobs on identical parallel machines to minimize average job completion times. We present a new proof of correctness of SPT based on linear programming (LP). Our proof relies on a generalization of a single-machine result that yields an equivalence between two scheduling problems. We first identify and solve an appropriate variant of our problem, then map its solutions to solutions for our original problem to establish SPT optimality. Geometric insights used therein may find further uses; we demonstrate two applications of the same principle in generalized settings.     

极小化总完工时间的同时加工排序

讨论了并行工件同时加工排序问题,即n个同时到达的工件在m台批处理机上排序的问题.批处理机一次最多能加工B个工件.每批的加工时间等于该批中所含工件的加工时间的最大者.主要考虑B n的特殊情况,即每批可包含任意多个工件,目标函数是极小化总完工时间.首先对同型批处理机的情况给出了动态规划算法,算法的运行时间为O(m nm+1),并进一步将结论推广到同类批处理机的情况.     

有关单机两代理排序问题的两个结果

研究了两个单机两代理排序问题. 在第一个两代理排序问题中, 代理A的目标函数为极小化所有工件的加权完工时间总和, 代理B的目标函数为极小化最大工件费用. 在第二个两代理排序问题中, 代理A的目标函数为极小化所有工件的加权完工时间总和, 代理B的目标函数为极小化所有工件的最大完工时间. 证明了第一个问题是强NP-难的, 改进了已有的一般意义NP-难的结果; 对第二个问题给出了一个与现有的动态规划算法不同的动态规划算法.     

Johnson’s rule,composite jobs and the relocation problem

Two-machine flowshop scheduling to minimize makespan is one of the most well-known classical scheduling problems. Johnson’s rule for solving this problem has been widely cited in the literature. We introduce in this paper the concept of composite job, which is an artificially constructed job with processing times such that it will incur the same amount of idle time on the second machine as that incurred by a chain of jobs in a given processing sequence. This concept due to Kurisu first appeared in 1976 to deal with the two-machine flowshop scheduling problem involving precedence constraints among the jobs. We show that this concept can be applied to reduce the computational time to solve some related scheduling problems. We also establish a link between solving the two-machine flowshop makespan minimization problem using Johnson’s rule and the relocation problem introduced by Kaplan. We present an intuitive interpretation of Johnson’s rule in the context of the relocation problem.     

具有老化效应的单机共同工期安排和工件可拒绝排序问题

研究共同工期安排和具有老化效应的单机排序问题。在整个加工过程中,工件的实际加工时间是与其所在位置和工件本身老化率相关的函数,生产商可以通过支付一定的处罚费用而拒绝加工某些工件。鉴于生产过程中出现老化效应,通过采取维修活动来提高生产率。目标是划分接受工件集和拒绝工件集,确定接受工件集中工件的加工次序和维修活动安排的位置,以极小化接受工件的提前、延误、工期与拒绝工件的总处罚费用的加权和。对这一问题,首先将其转化为指派问题并构造了最优多项式时间算法;其次,证明了目标函数满足一定条件下的问题的更一般形式能够在多项式时间内得到最优解;最后,对本文问题的一个特殊情况,设计了具有更低时间复杂度的多项式动态规划算法。     

具有可变配送费用和固定配送时刻的单机排序问题

研究了单机环境下生产与配送的协同排序问题.有多个工件需要在一台机器上进行加工,加工完的工件需要分批配送到一个客户.每批工件只能在固定的几个配送时刻出发,不同的配送时刻对应着不同的配送费用.我们的目标是找到生产与配送的协同排序,极小化排序的时间费用与配送费用的加权和.研究了排序理论中主要的四个目标函数,构建了单机情况下的具体模型,分析了问题的复杂性,对于配送费用单调非增的情况给出了它们的最优算法.     

Sevast'yanov's algorithm for the flow-shop scheduling problem

We consider the flow-shop scheduling problem. The objective is to schedule the jobs on the machines so that we minimize the time by which all jobs are completed. We studied and implemented different versions of the algorithm of Sevast'yanov based on linear programming to solve this problem. Using CPLEX, we did computational tests with random instances having up to 1000 jobs and 100 machines. If the size of the flow-shop scheduling problem is small or if the running time is not a critical factor, the Nawaz-Enscore-Ham approximation algorithm still performs better. But if the running time is an important factor, Sevast'yanov's algorithm can be a very good alternative especially in presence of very large scale instances with a relatively small number of machines.     

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.     

Multicut Benders decomposition algorithm for process supply chain planning under uncertainty

In this paper, we present a multicut version of the Benders decomposition method for solving two-stage stochastic linear programming problems, including stochastic mixed-integer programs with only continuous recourse (two-stage) variables. The main idea is to add one cut per realization of uncertainty to the master problem in each iteration, that is, as many Benders cuts as the number of scenarios added to the master problem in each iteration. Two examples are presented to illustrate the application of the proposed algorithm. One involves production-transportation planning under demand uncertainty, and the other one involves multiperiod planning of global, multiproduct chemical supply chains under demand and freight rate uncertainty. Computational studies show that while both the standard and the multicut versions of the Benders decomposition method can solve large-scale stochastic programming problems with reasonable computational effort, significant savings in CPU time can be achieved by using the proposed multicut algorithm.     

Minimizing makespan in a two-machine flowshop scheduling with batching and release time

This paper studies two-machine flowshop scheduling with batching and release time, whose objective is to minimize the makespan. We formulate the scheduling problem as a mixed integer programming model and show that it is a strongly NP-hard problem. We derive a lower bound and develop dynamic programming-based heuristic algorithms to solve the scheduling problem. Computational experiments are carried out to evaluate the performance of the heuristic algorithms. The numerical results show that some of the heuristic algorithms can indeed find effective solutions for the scheduling problem.     

A bicriteria flowshop scheduling with a learning effect

In many situations, a worker’s ability improves as a result of repeating the same or similar tasks; this phenomenon is known as the learning effect. In this paper the learning effect is considered in a two-machine flowshop. The objective is to find a sequence that minimizes a weighted sum of total completion time and makespan. Total completion time and makespan are widely used performance measures in scheduling literature. To solve this scheduling problem, an integer programming model with n² + 6n variables and 7n constraints where n is the number of jobs is formulated. Because of the lengthy computing time and high computing complexity of the integer programming model, the problem with up to 30 jobs can be solved. A heuristic algorithm and a tabu search based heuristic algorithm are presented to solve large size problems. Experimental results show that the proposed heuristic methods can solve this problem with up to 300 jobs rapidly. According to the best of our knowledge, no work exists on the bicriteria flowshop with a learning effect.     

Minimizing the weighted number of tardy jobs and maximum tardiness in relocation problem with due date constraints

The relocation problem addressed in this paper is to determine a reconstruction sequence for a set of old buildings, under a limited budget, such that there is adequate temporary space to house the residents decanted during rehabilitation. It can be regarded as a resource-constrained scheduling problem where there is a set of jobs to be processed on a single machine. Each job demands a number of resources for processing and returns probably a different number of resources on its completion. Given a number of initial resources, the problem seeks to determine if there is a feasible sequence for the successful processing of all the jobs. Two generalizations of the relocation problem in the context of single machine scheduling with due date constraints are studied in this paper. The first problem is to minimize the weighted number of tardy jobs under a common due date. We show that it is NP-hard even when all the jobs have the same tardy weight and the same resource requirement. A dynamic programming algorithm with pseudo-polynomial computational time is proposed for the general case. In the second problem, the objective is to minimize the maximum tardiness when each job is associated with an individual due date. We prove that it is strongly NP-hard. We also propose a pseudo-polynomial time dynamic programming algorithm for the case where the number of possible due dates is predetermined.     

Synchronizing production and air transportation scheduling using mathematical programming models

Traditional scheduling problems assume that there are always infinitely many resources for delivering finished jobs to their destinations, and no time is needed for their transportation, so that finished products can be transported to customers without delay. So, for coordination of these two different activities in the implementation of a supply chain solution, we studied the problem of synchronizing production and air transportation scheduling using mathematical programming models. The overall problem is decomposed into two sub-problems, which consists of air transportation allocation problem and a single machine scheduling problem which they are considered together. We have taken into consideration different constraints and assumptions in our modeling such as special flights, delivery tardiness and no delivery tardiness. For these purposes, a variety of models have been proposed to minimize supply chain total cost which encompass transportation, makespan, delivery earliness tardiness and departure time earliness tardiness costs.     

具有凸资源约束的单机指数学习效应问题

研究在所有工件的正常加工时间均相同的情况下具有指数学习效应和凸资源约束的单机排序问题.给出了两种模型:在资源消耗总费用有限的情况下,以工件的最大完工时间为目标函数;在工件的最大完工时间有限的情况下,以资源消耗总费用为目标函数.求两种模型下的最优排序和最优资源分配,使得目标函数最小.证明这两个问题都是多项式时间可解的,并给出了相应的算法.     

Bicriteria hierarchical optimization of two-machine flow shop scheduling problem with time-dependent deteriorating jobs

We study a two-machine flowshop scheduling problem with time-dependent deteriorating jobs, i.e. the processing times of jobs are an increasing function of their starting time. The objective is to minimize the total completion time subject to minimum makespan. We propose a mixed integer programming model, and develop two pairwise interchange algorithms and a branch-and-bound procedure to solve the problem while using several dominance conditions to limit the size of the search tree. Several polynomial-time solvable special cases are discussed. Finally, numerical studies are performed to examine the effectiveness and the efficiency of the proposed algorithms.     

Rescheduling problems with deteriorating jobs under disruptions

In this paper, we consider two single-machine rescheduling problems with linear deteriorating jobs under disruption. By a deteriorating jobs, we mean that the actual processing time of the job is an increasing function of its starting time. The two problems correspond to two different increasing linear function. Rescheduling means a set of original jobs has already been scheduled to minimize some classical objective, then a new set of jobs arrives and creates a disruption. We consider the rescheduling problem to minimize the total completion time under a limit of the disruption from the original scheduling. For each problem, we consider two versions. For each version, the polynomial algorithms are proposed, respectively.     

Single-machine total completion time scheduling with a time-dependent deterioration

In this paper, we consider the single-machine scheduling problems with a time-dependent deterioration. By the time-dependent deterioration, we mean that the processing time of a job is defined by an increasing function of total normal processing time of jobs in front of it in the sequence. The objective is to minimize the total completion time. We develop a mixed integer programming formulation for the problem. The complexity status of this problem remains open. Hence, we use the smallest normal processing time (SPT) first rule as a heuristic algorithm for the general cases and analyze its worst-case error bound. Two heuristic algorithms utilize the V-shaped property are also proposed to solve the problem. Computational results are presented to evaluate the performance of the proposed algorithms.     

Scheduling with controllable release dates and processing times: Total completion time minimization

The single machine scheduling problem with two types of controllable parameters, job processing times and release dates, is studied. It is assumed that the cost of compressing processing times and release dates from their initial values is a linear function of the compression amounts. The objective is to minimize the sum of the total completion time of the jobs and the total compression cost. For the problem with equal release date compression costs we construct a reduction to the assignment problem. We demonstrate that if in addition the jobs have equal processing time compression costs, then it can be solved in O(n²) time. The solution algorithm can be considered as a generalization of the algorithm that minimizes the makespan and total compression cost. The generalized version of the algorithm is also applicable to the problem with parallel machines and to a range of due-date scheduling problems with controllable processing times.     

Scheduling with batch setup times and earliness-tardiness penalties

We consider a scheduling model in which several batches of jobs need to be processed by a single machine. During processing, a setup time is incurred whenever there is a switch from processing a job in one batch to a job in another batch. All the jobs in the same batch have a common due date that is either externally given as an input data or internally determined as a decision variable. Two problems are investigated. One problem is to minimize the total earliness and tardiness penalties provided that each due date is externally given. We show that this problem is NP-hard even when there are only two batches of jobs and the two due dates are unrestrictively large. The other problem is to minimize the total earliness and tardiness penalties plus the total due date penalty provided that each due date is a decision variable. We give some optimality properties for this problem with the general case and propose a polynomial dynamic programming algorithm for solving this problem with two batches of jobs. We also consider a special case for both of the problems when the common due dates for different batches are all equal. Under this special case, we give a dynamic programming algorithm for solving the first problem with an unrestrictively large due date and for solving the second problem. This algorithm has a running time polynomial in the number of jobs but exponential in the number of batches.