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.

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 Due-Window Assignment and Resource Allocation Scheduling with Truncated Learning Effect and Position-Dependent Weights

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Shan-Shan Lin
Keyword(s):  
Resource Allocation ◽  
Learning Effect ◽  
Window Size ◽  
Scheduling Problems ◽  
Polynomial Time Algorithms ◽  
Consumption Cost ◽  
Optimality Properties ◽  
Due Window ◽  
Due Window Assignment ◽  
Time Resource

This paper studies single-machine due-window assignment scheduling problems with truncated learning effect and resource allocation simultaneously. Linear and convex resource allocation functions under common due-window (CONW) assignment are considered. The goal is to find the optimal due-window starting (finishing) time, resource allocations and job sequence that minimize a weighted sum function of earliness and tardiness, due window starting time, due window size, and total resource consumption cost, where the weight is position-dependent weight. Optimality properties and polynomial time algorithms are proposed to solve these problems.

Bicriteria scheduling for due date assignment with total weighted tardiness

2018 ◽  
Vol 52 (2) ◽  
pp. 359-370
Author(s):  
Hao Lin ◽  
Cheng He ◽  
Yixun Lin
Keyword(s):  
Performance Criterion ◽  
Total Weighted Tardiness ◽  
Due Date ◽  
Due Date Assignment ◽  
Bicriteria Scheduling ◽  
Weighted Tardiness ◽  
Polynomial Time Algorithms ◽  
Weighted Number ◽  
Tardy Jobs ◽  
Scheduling Models

In the due date assignment, the bicriteria scheduling models are motivated by the trade-off between the due date assignment cost and a performance criterion of the scheduling system. The bicriteria scheduling models related to the maximum tardiness and the weighted number of tardy jobs have been studied in the literature. In this paper we consider a new model with criteria of the due date assignment cost and the total weighted tardiness. The main results are polynomial-time algorithms for the linear combination version, the constraint version, and the Pareto optimization version of bicriteria scheduling.

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