scholarly journals Single Machine Vector Scheduling with General Penalties

Mathematics ◽  
2021 ◽  
Vol 9 (16) ◽  
pp. 1965
Author(s):  
Xiaofei Liu ◽  
Weidong Li ◽  
Yaoyu Zhu
Keyword(s):  
Approximation Algorithm ◽  
Single Machine ◽  
Polynomial Function ◽  
Maximum Load ◽  
Subgradient Method ◽  
Dimensional Vector ◽  
Positive Polynomial ◽  
Set Function ◽  
Special Case ◽  
Rejection Penalty

In this paper, we study the single machine vector scheduling problem (SMVS) with general penalties, in which each job is characterized by a d-dimensional vector and can be accepted and processed on the machine or rejected. The objective is to minimize the sum of the maximum load over all dimensions of the total vector of all accepted jobs and the rejection penalty of the rejected jobs, which is determined by a set function. We perform the following work in this paper. First, we prove that the lower bound for SMVS with general penalties is α(n), where α(n) is any positive polynomial function of n. Then, we consider a special case in which both the diminishing-return ratio of the set function and the minimum load over all dimensions of any job are larger than zero, and we design an approximation algorithm based on the projected subgradient method. Second, we consider another special case in which the penalty set function is submodular. We propose a noncombinatorial ee−1-approximation algorithm and a combinatorial min{r,d}-approximation algorithm, where r is the maximum ratio of the maximum load to the minimum load on the d-dimensional vector.

Optimal Order Picking in Carousels Storage System

2012 ◽  
Vol 601 ◽  
pp. 347-353
Author(s):  
Xiong Zhi Wang ◽  
Guo Qing Wang
Keyword(s):  
Approximation Algorithm ◽  
General Problem ◽  
Storage System ◽  
Order Picking ◽  
Total Order ◽  
Experimental Results ◽  
Optimal Order ◽  
Np Hard ◽  
Special Case

We study the order picking problem in carousels system with a single picker. The objective is to find a picking scheduling to minimizing the total order picking time. After showing the problem being strongly in NP-Hard and finding two characteristics, we construct an approximation algorithm for a special case (two carousels) and a heuristics for the general problem. Experimental results verify that the solutions are quickly and steadily achieved and show its better performance.

scholarly journals Single-Machine Parallel-Batch Scheduling with Nonidentical Job Sizes and Rejection

Mathematics ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 258
Author(s):  
Miaomiao Jin ◽  
Xiaoxia Liu ◽  
Wenchang Luo
Keyword(s):  
Integer Programming ◽  
Single Machine ◽  
Heuristic Algorithms ◽  
Batch Processing ◽  
Batch Scheduling ◽  
Scheduling Problem ◽  
Programming Formulation ◽  
Processing Times ◽  
Batch Processing Machine ◽  
Rejection Penalty

We investigate the single-machine parallel-batch scheduling problem with nonidentical job sizes and rejection. In this problem, a set of jobs with different processing times and nonidentical sizes is given to be possibly processed on a parallel-batch processing machine. Each job is either accepted and then processed on the machine or rejected by paying its rejection penalty. Preemption is not allowed. Our task is to choose the accepted jobs and schedule them as batches on the machine to minimize the makespan of the accepted jobs plus the total rejection penalty of the rejected jobs. We provide an integer programming formulation to exactly solve our problem. Then, we propose three fast heuristic algorithms to solve the problem and evaluate their performances by using a small numerical example.

Single Machine Scheduling with an Availability Constraint and Rejection

2014 ◽  
Vol 31 (05) ◽  
pp. 1450037 ◽  
Author(s):  
Chuanli Zhao ◽  
Hengyong Tang
Keyword(s):  
Single Machine ◽  
Approximation Scheme ◽  
Dynamic Programming Algorithm ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Programming Algorithm ◽  
Time Interval ◽  
Availability Constraint ◽  
Np Hard Problem ◽  
Rejection Penalty

This paper considers single machine scheduling with an availability constraint and rejection. It is assumed that the machine is not available for processing during a given time interval. A job is either rejected, in which case a rejection penalty has to be paid, or accepted and processed on the machine. The objective is to minimize the sum of the weighted total completion time of the accepted jobs and the total rejection penalty of the rejected jobs. For this NP-hard problem, we present a pseudo-polynomial dynamic programming algorithm and a fully polynomial-time approximation scheme (FPTAS).

scholarly journals A Primal-Dual Approximation Algorithm for Min-Sum Single-Machine Scheduling Problems

2017 ◽  
Vol 31 (2) ◽  
pp. 825-838 ◽  
Author(s):  
Maurice Cheung ◽  
Julián Mestre ◽  
David B. Shmoys ◽  
José Verschae
Keyword(s):  
Approximation Algorithm ◽  
Single Machine ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Scheduling Problems ◽  
Primal Dual ◽  
Machine Scheduling Problems

The Supplying Chain Scheduling with Outsourcing and Transportation

2017 ◽  
Vol 34 (02) ◽  
pp. 1750009 ◽  
Author(s):  
Jianfeng Ren ◽  
Guo Sun ◽  
Yuzhong Zhang
Keyword(s):  
Supply Chain ◽  
Approximation Algorithm ◽  
Single Machine ◽  
Complexity Analysis ◽  
Weighted Sum ◽  
Objective Functions ◽  
Supply Chain Scheduling ◽  
Total Cost ◽  
Scheduling Model ◽  
Selection Of

In this paper, we address a supply chain scheduling model with outsourcing and transportation. A job can be scheduled either on a single machine at a manufacturer’s plant or outsourced to a subcontractor. We assume that there are a sufficient number of vehicles at the manufacturer and the subcontractor such that each completed job can be transported to its customer immediately. For a given set of jobs, the decisions we need to make include the selection of jobs to be outsourced and the schedule of all the jobs. When the objective functions are to minimize the weighted sum of common scheduling measures and the total cost, we present their complexity analysis and a [Formula: see text]-approximation algorithm for the second problem.

scholarly journals A Combinatorial 2-Approximation Algorithm for the Parallel-Machine Scheduling with Release Times and Submodular Penalties

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 61
Author(s):  
Wencheng Wang ◽  
Xiaofei Liu
Keyword(s):  
Approximation Algorithm ◽  
Single Machine ◽  
Machine Scheduling ◽  
Parallel Machine Scheduling ◽  
Parallel Machine ◽  
Single Machine Scheduling ◽  
Release Time ◽  
Release Dates ◽  
Scheduling Problem ◽  
Release Times

In this paper, we consider parallel-machine scheduling with release times and submodular penalties (P|rj,reject|Cmax+π(R)), in which each job can be accepted and processed on one of m identical parallel machines or rejected, but a penalty must paid if a job is rejected. Each job has a release time and a processing time, and the job can not be processed before its release time. The objective of P|rj,reject|Cmax+π(R) is to minimize the makespan of the accepted jobs plus the penalty of the rejected jobs, where the penalty is determined by a submodular function. This problem generalizes a multiprocessor scheduling problem with rejection, the parallel-machine scheduling with submodular penalties, and the single machine scheduling problem with release dates and submodular rejection penalties. In this paper, inspired by the primal-dual method, we present a combinatorial 2-approximation algorithm to P|rj,reject|Cmax+π(R). This ratio coincides with the best known ratio for the parallel-machine scheduling with submodular penalties and the single machine scheduling problem with release dates and submodular rejection penalties.

scholarly journals Stochastic Single Machine JIT Scheduling with Geometric Processing Times and Due Dates

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yuncheng Luo
Keyword(s):  
Single Machine ◽  
Optimal Schedule ◽  
Dynamic Programming Algorithm ◽  
Programming Algorithm ◽  
Scheduling Problem ◽  
Due Dates ◽  
Common Mean ◽  
Processing Times ◽  
Tardy Jobs ◽  
Special Case

In this paper, we investigate a static stochastic single machine JIT scheduling problem in which the jobs’ processing times are stochastically independent and follow geometric distributions whose mean is provided, due dates are geometrically distributed with a common mean, and both the unit penalty of earliness/tardiness and the fixed penalty of earliness/tardiness are deterministic and different. The objective is to minimize the expected total penalties for quadratic earliness, quadratic tardiness, and early and tardy jobs. We prove that the optimal schedule to minimize this problem is V-shaped with respect to the ratio of mean processing time to unit tardiness penalty under the specific condition. Also, we show a special case and two theorems related to this JIT scheduling problem under specific situations where the optimal solutions exist. Finally, based on the V-shaped characteristic, a dynamic programming algorithm is designed to achieve an optimal V-shaped schedule in pseudopolynomial time.

scholarly journals Application of Divisors on a Hyperelliptic Curve in Python

2021 ◽  
Vol 3 ◽  
pp. 11-24
Author(s):  
Denys Boiko
Keyword(s):  
Programming Language ◽  
Hyperelliptic Curve ◽  
Polynomial Function ◽  
Hyperelliptic Curves ◽  
Unique Form ◽  
Python Programming Language ◽  
Definition Of ◽  
Mumford Representation ◽  
Python Programming ◽  
Special Case

The paper studies hyperelliptic curves of the genus g > 1, divisors on them and their applications in Python programming language. The basic necessary definitions and known properties of hyperelliptic curves are demonstrated, as well as the notion of polynomial function, its representation in unique form, also the notion of rational function, norm, degree and conjugate to a polynomial are presented. These facts are needed to calculate the order of points of desirable functions, and thus to quickly and efficiently calculate divisors. The definition of a divisor on a hyperelliptic curve is shown, and the main known properties of a divisor are given. There are also an example of calculating a divisor of a polynomial function, reduced and semi-reduced divisors are described, theorem of the existence of such a not unique semi-reduced divisor, and theorem of the existence of a unique reduced divisor, which is equivalent to the initial one, are proved. In particular, a semi-reduced divisor can be represented as an GCD of divisors of two polynomial functions. It is also demonstrated that each reduced divisor can be represented in unique form by pair of polynomials [a(x), b(x)], which is called Mumford representation, and several examples of its representation calculation are given. There are shown Cantor’s algorithms for calculating the sum of two divisors: its compositional part, by means of which a not unique semi-reduced divisor is formed, and the reduction part, which gives us a unique reduced divisor. In particular, special case of the compositional part of Cantor’s algorithm, doubling of the divisor, is described: it significantly reduces algorithm time complexity. Also the correctness of the algorithms are proved, examples of applications are given. The main result of the work is the implementation of the divisor calculation of a polynomial function, its Mumford representation, and Cantor’s algorithm in Python programming language. Thus, the aim of the work is to demonstrate the possibility of e↵ective use of described algorithms for further work with divisors on the hyperelliptic curve, including the development of cryptosystem, digital signature based on hyperelliptic curves, attacks on such cryptosystems.

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