scholarly journals Solution Algorithms for Single-Machine Group Scheduling with Learning Effect and Convex Resource Allocation

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Wanlei Wang ◽  
Jian-Jun Wang ◽  
Ji-Bo Wang
Keyword(s):  
Resource Allocation ◽  
Single Machine ◽  
Learning Effect ◽  
Group Technology ◽  
Due Date ◽  
Optimal Sequence ◽  
Due Date Assignment ◽  
Solution Algorithms ◽  
Special Cases ◽  
Optimal Resource

This paper deals with a single-machine resource allocation scheduling problem with learning effect and group technology. Under slack due-date assignment, our objective is to determine the optimal sequence of jobs and groups, optimal due-date assignment, and optimal resource allocation such that the weighted sum of earliness and tardiness penalties, common flow allowances, and resource consumption cost is minimized. For three special cases, it is proved that the problem can be solved in polynomial time. To solve the general case of problem, the heuristic, tabu search, and branch-and-bound algorithms are proposed.

scholarly journals Optimization for Due-Date Assignment Single-Machine Scheduling under Group Technology

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Li-Yan Wang ◽  
Mengqi Liu ◽  
Ji-Bo Wang ◽  
Yuan-Yuan Lu ◽  
Wei-Wei Liu
Keyword(s):  
Single Machine ◽  
Group Technology ◽  
Machine Scheduling ◽  
Time Algorithm ◽  
Single Machine Scheduling ◽  
Due Date ◽  
Optimal Sequence ◽  
Due Date Assignment ◽  
The Common ◽  
Slk Due Date Assignment

In this paper, the single-machine scheduling problem is studied by simultaneously considering due-date assignment and group technology (GT). The objective is to determine the optimal sequence of groups and jobs within groups and optimal due-date assignment to minimize the weighted sum of the absolute value in lateness and due-date assignment cost, where the weights are position dependent. For the common (CON) due-date assignment, slack (SLK) due-date assignment, and different (DIF) due-date assignment, an O n    log    n time algorithm is proposed, respectively, to solve the problem, where n is the number of jobs.

Single-Machine Due-Window Assignment Scheduling with Resource Allocation and Generalized Earliness/Tardiness Penalties

2021 ◽  
pp. 2150041
Author(s):  
Yu Tian
Keyword(s):  
Resource Allocation ◽  
Single Machine ◽  
Window Size ◽  
Single Machine Scheduling ◽  
Start Time ◽  
Optimal Sequence ◽  
Optimal Resource ◽  
Due Window ◽  
Weighted Earliness ◽  
Due Window Assignment

In this study, the due-window assignment single-machine scheduling problem with resource allocation is considered, where the processing time of a job is controllable as a linear or convex function of amount of resource allocated to the job. Under common due-window and slack due-window assignments, our goal is to determine the optimal sequence of all jobs, the due-window start time, due-window size, and optimal resource allocation such that a sum of the scheduling cost (including weighted earliness/tardiness penalty, weighted number of early and tardy job, weighted due-window start time, and due-window size) and resource consumption cost is minimized. We analyze the optimality properties, and provide polynomial time solutions to solve the problem under four versions of due-window assignment and resource allocation function.

A Bicriteria Approach for Single Machine Scheduling with Resource Allocation, Learning Effect and a Deteriorating Maintenance Activity

2017 ◽  
Vol 34 (04) ◽  
pp. 1750011 ◽  
Author(s):  
Zhusong Liu ◽  
Zhenyou Wang ◽  
Yuan-Yuan Lu
Keyword(s):  
Resource Allocation ◽  
Single Machine ◽  
Learning Effect ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Maintenance Activity ◽  
Optimal Resource ◽  
Deteriorating Maintenance ◽  
Polynomially Solvable ◽  
Completion Times

This paper considers the single machine scheduling with learning effect, resource allocation and deteriorating maintenance activity simultaneously. For the convex resource allocation consumption function, we provide a bicriteria analysis where the first (schedule) criterion is to minimize the total weighted sum of makespan, total completion time and total absolute differences in completion times, and the second (resource) criterion is to minimize the total weighted resource consumption. Our aim is to find the optimal resource allocations and job sequence that minimize the three different models of considering the two criterion. We show that these three models are polynomially solvable respectively.

Solution algorithms for single-machine resource allocation scheduling with deteriorating jobs and group technology

2019 ◽  
Vol 52 (7) ◽  
pp. 1184-1197 ◽  
Author(s):  
Xi-Xi Liang ◽  
Mengqi Liu ◽  
Yu-Bo Feng ◽  
Ji-Bo Wang ◽  
Li-Shu Wen
Keyword(s):  
Resource Allocation ◽  
Single Machine ◽  
Group Technology ◽  
Deteriorating Jobs ◽  
Solution Algorithms

Single-Machine Scheduling with Resource-Dependent Learning Effect and Deteriorating Jobs

2013 ◽  
Vol 423-426 ◽  
pp. 2224-2227
Author(s):  
Yan Peng Fan ◽  
Chuan Li Zhao
Keyword(s):  
Resource Allocation ◽  
Single Machine ◽  
Learning Effect ◽  
Operation Time ◽  
Deteriorating Jobs ◽  
Polynomial Time Algorithm ◽  
Time Algorithm ◽  
Single Machine Scheduling ◽  
Optimal Sequence ◽  
Dependent Learning

This paper considers single-machine due-window assignment and scheduling with learning effect and resource-dependent processing times. The processing time of a job is a function of its position in a sequence, its starting time, and its resource allocation. The objective is to determine the optimal sequence of jobs and optimal resource allocation so as to minimize the sum of earliness, tardiness, due-windows and resource and operation time cost, the considered problem is molded as an assignment problem and can be solved with a polynomial time algorithm.

Single-Machine Scheduling with Learning Effect, Deteriorating Jobs and Convex Resource Dependent Processing Times

2015 ◽  
Vol 32 (05) ◽  
pp. 1550033 ◽  
Author(s):  
Xin-Jun Li ◽  
Jian-Jun Wang ◽  
Xue-Ru Wang
Keyword(s):  
Resource Allocation ◽  
Single Machine ◽  
Learning Effect ◽  
Waiting Times ◽  
Deteriorating Jobs ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Optimal Sequence ◽  
Processing Times ◽  
Convex Resource Allocation

This paper considers single-machine scheduling with learning effect, deteriorating jobs and convex resource dependent processing times, i.e., the processing time of a job is a function of its starting time, its position in a sequence and its convex resource allocation. The objective is to find the optimal sequence of jobs and the optimal convex resource allocation separately to minimize a cost function containing makespan, total completion (waiting) time, total absolute differences in completion (waiting) times and total resource cost. It is proved that the problem can be solved in polynomial time.

Optimal due date assignment and resource allocation in a group technology scheduling environment

2010 ◽  
Vol 37 (12) ◽  
pp. 2218-2228 ◽  
Author(s):  
Dvir Shabtay ◽  
Yisrael Itskovich ◽  
Liron Yedidsion ◽  
Daniel Oron
Keyword(s):  
Resource Allocation ◽  
Group Technology ◽  
Due Date ◽  
Due Date Assignment

Flow Shop Resource Allocation Scheduling with Due Date Assignment, Learning Effect and Position-Dependent Weights

2020 ◽  
Vol 37 (03) ◽  
pp. 2050014
Author(s):  
Wei-Wei Liu ◽  
Chong Jiang
Keyword(s):  
Resource Allocation ◽  
Learning Effect ◽  
Flow Shop ◽  
Due Date ◽  
Due Date Assignment ◽  
Polynomial Time Algorithms ◽  
Optimality Properties ◽  
No Wait ◽  
Special Case ◽  
Order Algorithm

In this paper, the flow shop resource allocation scheduling with learning effect and position-dependent weights on two-machine no-wait setting is considered. Under common due date assignment and slack due date assignment rules, a bi-criteria analysis is provided. The optimality properties and polynomial time algorithms are developed to solve four versions of the problem. For a special case of the problem, it is proved that the problem can be optimally solved by a lower order algorithm.

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