Single-machine group scheduling with processing times dependent on position,starting time and allotted resource

This article considers scheduling problems on a single machine with learning effect, deteriorating jobs and resource allocation under group technology (GT) assumption. We assume that the actual processing time of a job depends on the job position, the group position, the starting time and the amount of resource allocated to them concurrently, and the actual setup times of groups depend on the group position and the amount of resource allocated to them concurrently. Two resource allocation functions are examined for minimizing the weighted sum of makespan and total resource cost. We prove that the problems have polynomial solutions under the condition that the number of jobs in each group are the same.     

Single-machine group scheduling with deteriorating jobs and allotted resource

In this paper, we consider single-machine scheduling problems with deteriorating jobs and resource allocation in a group technology environment. In the proposed model of this paper the actual processing time of a job depend on its starting time and the amount of resource allocated to it, and the actual setup time of a group depend on its starting time and the amount of resource allocated. Deterioration effect and two resource allocation functions are examined for minimizing the weighted sum of makespan and total resource cost. For the linear resource allocation function and the convex resource allocation function, we show that the problem remains polynomially solvable under certain conditions.     

Bi-criterion single-machine scheduling and due-window assignment with common flow allowances and resource-dependent processing times

This paper addresses single-machine scheduling and due-window assignment with common flow allowances and resource-dependent processing times. Due-window assignment with common flow allowances means that each job has a job-dependent due window, the start time and finish time of which are equal to its actual processing time plus individual job-independent parameters shared by all the jobs, respectively. The processing time of each job can be controlled by extra resource allocation as a linear function of the amount of a common continuously divisible resource allocated to the job. Two criteria are considered, where one criterion is an integrated cost consisting of job earliness, weighted number of tardy jobs, and due-window assignment cost, while the other criterion is the resource consumption cost. Four different models are considered for treating the two criteria. It is shown that the problem under the model where the two criteria are integrated into a single criterion is polynomially solvable, while the problems under the other three models are all NP-hard and an optimal solution procedure is developed for them. Two polynomially solvable cases are also identified and investigated. Finally, numerical studies with randomly generated instances are conducted to assess the performance of the proposed algorithms.     

Single machine batch scheduling with resource dependent setup and processing times

Jobs are processed by a single machine in batches. A batch is a set of jobs processed contiguously and completed together when the processing of all jobs in the batch is finished. Processing of a batch requires a machine setup time common for all batches. Both the job processing times and the setup time can be compressed through allocation of a continuously divisible resource. Each job uses the same amount of the resource. Each setup also uses the same amount of the resource, which may be different from that for the jobs. Polynomial time algorithms are presented to find an optimal batch sequence and resource values such that either the total weighted resource consumption is minimized, subject to meeting job deadlines, or the maximum job lateness is minimized, subject to an upper bound on the total weighted resource consumption. The algorithms are based on linear programming formulations of the corresponding problems.     

带凸资源和恶化效应的单机松弛窗口排序

研究带凸资源和恶化效应的单机窗口指派排序问题,其中窗口指的是松弛窗口,凸资源和恶化效应指的是工件的实际加工时间是其开始加工时间的线性函数,是其资源消耗量的凸函数。目标是确定工件的加工顺序,资源分配量以及窗口的开始加工时间和长度使其在总资源消耗费用(与窗口有关的排序费用)有上界限制的条件下,极小化与窗口有关的排序费用(总资源消耗费用)。获得了求解上述问题的最优算法,证明了该问题是多项式时间可解的。     

Scheduling jobs with time-resource tradeoff via nonlinear programming

We consider a scheduling problem where the processing time of any job is dependent on the usage of a discrete renewable resource, e.g. personnel. An amount of k units of that resource can be allocated to the jobs at any time, and the more of that resource is allocated to a job, the smaller its processing time. The objective is to find a resource allocation and a schedule that minimizes the makespan. We explicitly allow for succinctly encodable time-resource tradeoff functions, which calls for mathematical programming techniques other than those that have been used before. Utilizing a (nonlinear) integer mathematical program, we obtain the first polynomial time approximation algorithm for the scheduling problem, with performance bound (3+ε) for any ε>0. Our approach relies on a fully polynomial time approximation scheme to solve the nonlinear mathematical programming relaxation. We also derive lower bounds for the approximation.     

Single-machine scheduling with convex resource dependent processing times and deteriorating jobs

In this study, we consider scheduling problems with convex resource dependent processing times and deteriorating jobs, in which the processing time of a job is a function of its starting time and its convex resource allocation. The objective is to find the optimal sequence of jobs and the optimal convex resource allocation separately. This paper focus on the single-machine problems with objectives of minimizing a cost function containing makespan, total completion time, total absolute differences in completion times and total resource cost, and a cost function containing makespan, total waiting time, total absolute differences in waiting times and total resource cost. It shows that the problems remain polynomially solvable under the proposed model.     

Due-window assignment with identical jobs on parallel uniform machines

A scheduling problem with a common due-window, earliness and tardiness costs, and identical processing time jobs is studied. We focus on the setting of both (i) job-dependent earliness/tardiness job weights and (ii) parallel uniform machines. The objective is to find the job allocation to the machines and the job schedule, such that the total weighted earliness and tardiness cost is minimized. We study both cases of a non-restrictive (i.e. sufficiently late), and a restrictive due-window. For a given number of machines, the solutions of the problems studied here are obtained in polynomial time in the number of jobs.     

A branch and bound algorithm for scheduling jobs with controllable processing times on a single machine to meet due dates

In most deterministic scheduling problems, job-processing times are regarded as constant and known in advance. However, in many realistic environments, job-processing times can be controlled by the allocation of a common resource to jobs. In this paper, we consider the problem of scheduling jobs with arbitrary release dates and due dates on a single machine, where job-processing times are controllable and are modeled by a non-linear convex resource consumption function. The objective is to determine simultaneously an optimal processing permutation as well as an optimal resource allocation, such that no job is completed later than its due date, and the total resource consumption is minimized. The problem is strongly NP\mathcal{NP}-hard. A branch and bound algorithm is presented to solve the problem. The computational experiments show that the algorithm can provide optimal solution for small-sized problems, and near-optimal solution for medium-sized problems in acceptable computing time.     

Multi-agent single machine scheduling

We consider the scheduling problems arising when several agents, each owning a set of nonpreemptive jobs, compete to perform their respective jobs on one shared processing resource. Each agent wants to minimize a certain cost function, which depends on the completion times of its jobs only. The cost functions we consider in this paper are maximum of regular functions (associated with each job), number of late jobs and total weighted completion time. The different combinations of the cost functions of each agent lead to various problems, whose computational complexity is analysed in this paper. In particular, we investigate the problem of finding schedules whose cost for each agent does not exceed a given bound for each agent.     

Single Machine Batch Scheduling Problem with Resource Dependent Setup and Processing Time in the Presence of Fuzzy Due Date

We consider a batch scheduling problem on a single machine which processes jobs with resource dependent setup and processing time in the presence of fuzzy due-dates given as follows:1. There are n independent non-preemptive and simultaneously available jobs processed on a single machine in batches. Each job j has a processing time and a due-date.2. All jobs in a batch are completed together upon the completion of the last job in the batch. The batch processing time is equal to the sum of the processing times of its jobs. A common machine setup time is required before the processing of each batch.3. Both the job processing times and the setup time can be compressed through allocation of a continuously divisible resource. Each job uses the same amount of the resource. Each setup also uses the same amount of the resource.4. The due-date of each job is flexible. That is, a membership function describing non-decreasing satisfaction degree about completion time of each job is defined.5. Under above setting, we find an optimal batch sequence and resource values such that the total weighted resource consumption is minimized subject to meeting the job due-dates, and minimal satisfaction degree about each due-date of each job is maximized. But usually we cannot optimize two objectives at a time. So we seek non-dominated pairs i.e. the batch sequence and resource value, after defining dominance between solutions.A polynomial algorithm is constructed based on linear programming formulations of the corresponding problems.     

Approximation algorithms for scheduling on multi-core processor with shared speedup resources

We consider a joint resource partition and scheduling problem. We are given $m$ identical cores and discrete resources of total size $k$. We need to partition the resources among these cores. A set of jobs must be processed non-preemptively on these cores after the resource partition. The processing time of a job on a core depends on the size of resources allocated to that corresponding core. The resource allocation scheme is static, i.e., we cannot change the amount of resources that was allocated to a core during the whole scheduling. Hassidim et al. (2013) investigated this problem with a general processing time function, i.e., the processing time of a job is an arbitrary function of the level of resources allocated to that core. They provided an algorithm with approximation ratio of 36. In this paper, we improve the approximation ratio to 8 by presenting a new resource partition scheme. Next, we consider a special model where the core’s speed is proportional to its allocated resource, then we present two algorithms with improved approximation ratios.     

Resource-constrained job scheduling with recyclable resources

In resource-constrained project scheduling problems, resources are typically classified as being either renewable, non-renewable, or doubly-constrained. A new resource classification, recyclable, is introduced. Notation and a generalized problem formulation are developed for resource-constrained job scheduling problems where resources are recyclable. This foundation is then used for studying the single-machine scheduling problem with tooling constraints. For a given set of jobs, the problem is to find the job sequence, tool type quantities, and tool recycling schedule such that the sum of job completion times and quantity of tools allocated are both minimized. Two solution approaches are developed, and examples are used to compare and contrast the approaches. The results indicate that the ‘traditional’ job scheduling approach (i.e. schedule jobs first, then tools) can lead to sub-optimal solutions. Furthermore, by scheduling jobs and tools simultaneously, it may be possible to attain a given level of performance with fewer tools.     

Single-machine scheduling with time-and-resource-dependent processing times

We consider single-machine scheduling problems in which the processing time of a job is a function of its starting time and its resource allocation. The objective is to find the optimal sequence of jobs and the optimal resource allocation separately. We concentrate on two goals separately, namely, minimizing a cost function containing makespan, total completion time, total absolute differences in completion times and total resource cost; minimizing a cost function containing makespan, total waiting time, total absolute differences in waiting times and total resource cost. We show that the problems remain polynomially solvable under the proposed model.     

Unrelated parallel machines scheduling with deteriorating jobs and resource dependent processing times

We consider unrelated parallel machines scheduling problems involving resource dependent (controllable) processing times and deteriorating jobs simultaneously, i.e., the actual processing time of a job is a function of its starting time and its resource allocation. Two generally resource consumption functions, the linear and convex resource, were investigated. The objective is to find the optimal sequence of jobs and the optimal resource allocation separately. This paper focus on the objectives of minimizing a cost function containing makespan, total completion time, total absolute differences in completion times and total resource cost, and a cost function containing makespan, total waiting time, total absolute differences in waiting times and total resource cost. If the number of unrelated parallel machines is a given constant, we show that the problems remain polynomially solvable under the proposed model.     

Scheduling of Jobs in a Hypercube Processing System

When a job is processed in a hypercube multi-processor, it is allocated a cube of processing elements of the requisite size. There are three distinct costs involved in the hypercube scheduling problem: the cost of detecting a free cube (allocation), the cost of migrating jobs and merging the free spaces to accommodate a larger cube request (relocation) and the cost of not meeting the due date (tardiness). Traditionally, research in this area has focused on finding efficient algorithms for allocating a free cube (if any) in the hypercube system. The relocation cost has been treated as an independent cost metric. The role of scheduling has not received much attention and present subcube allocation methods assume a first-come-first-serve (FCFS) approach over the input job set.This paper considers the underlying scheduling issues in a hypercube processing system and shows how techniques other than FCFS scheduling of the incoming jobs can help in reducing the relocation cost and hence the overall subcube resource assignment cost. We discuss five simple and easily implementable dispatching heuristics, and compare their relative performance with the FCFS scheduling rule to demonstrate the advantages of scheduling in subcube allocation.     

Precedence constrained parallel-machine scheduling of position-dependent jobs

We consider parallel-machine job scheduling problems with precedence constraints. Job processing times are variable and depend on positions of jobs in a schedule. The objective is to minimize the maximum completion time or the total weighted completion time. We specify certain conditions under which the problem can be solved by scheduling algorithms applied earlier for fixed job processing times.     

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.     

Single machine scheduling subject to deadlines and resource dependent processing times

The problem of scheduling n jobs on a single machine is studied. Each job has a deadline and a processing time which is a linear decreasing function of the amount of a common resource allocated to the job. The objective is to find simultaneously a sequence of the jobs and a resource allocation so as the deadlines are satisfied and the total weighted resource consumption is minimized. The problem is shown to be solvable in O(n log n) time if the resource is continuously divisible. If the resource is discrete, then the problem is proved to be binary NP-hard. Some special cases are solvable in O(n log n) time. A fully polynomial approximation scheme is presented for the general problem with discrete resource.     

Scheduling jobs with release dates and tails on identical machines to minimize the makespan

This paper considers the problem of scheduling independent jobs with release dates and tails on m identical machines to minimize the makespan. This m-machines problem is NP-hard in the strong sense. Jackson's schedule is defined as the list schedule built by giving priority to the available job with the largest tail. It is proved that the deviation of Jackson's schedule from the optimum is smaller than twice the largest processing time.Next, a new branching scheme is proposed by associating with each job an interval of time during which it has to be processed. To branch, the interval for a particular job is divided into two smaller ones. This is a general scheme which can be applied to many scheduling problems.Finally, a branch and bound algorithm is explained in detail and computational results are given.