An uncertain furniture production planning problem with cumulative service levels

To investigate how the loss averse customer’s psychological satisfaction affects the company’s furniture production planning, we establish a furniture production planning model under uncertain environment, where customer demand and production costs are characterized by mutually independent uncertain...

Full description

Saved in:
Bibliographic Details
Published in:Soft computing (Berlin, Germany) Vol. 21; no. 4; pp. 1041 - 1055
Main Authors: Yang, Guoqing, Tang, Wansheng, Zhao, Ruiqing
Format: Journal Article
Language:English
Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.02.2017
Springer Nature B.V
Subjects:
Algorithms
Artificial Intelligence
Behavior
Chemical reactions
Computational Intelligence
Control
Customer satisfaction
Customer services
Customers
Decision making
Design
Engineering
Furniture industry
Genetic algorithms
Graph theory
Independent variables
Integer programming
Linear programming
Mathematical Logic and Foundations
Mathematical programming
Mechatronics
Methodologies and Application
Mixed integer
Optimization
Production costs
Production planning
Random variables
Robotics
Subject specialists
Prospect theory
Furniture production planning
Chemical reaction optimization
Cumulative service level
Uncertainty theory
Loss aversion
ISSN:1432-7643, 1433-7479
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:To investigate how the loss averse customer’s psychological satisfaction affects the company’s furniture production planning, we establish a furniture production planning model under uncertain environment, where customer demand and production costs are characterized by mutually independent uncertain variables. Based on prospect theory, customer’s psychological satisfaction about stockout performance is measured by cumulative service levels in our model. In the framework of uncertainty theory, the proposed uncertain model can be transformed into an equivalent deterministic form. However, the transformed model is a nonlinear mixed integer programming problem, which cannot be solved by conventional optimization algorithms. To cope with this difficulty, a chemical reaction optimization algorithm integrated with LINGO software is designed to solve the proposed production planning problem. In order to verify the effectiveness of the designed hybrid chemical reaction optimization (CRO) algorithm, we conduct several numerical experiments via an application example and compare with a spanning tree-based genetic algorithm (hst-GA). The computational results show that our proposed CRO algorithm achieves better performance than hst-GA, and the results also provide several interesting managerial insights in production planning problems.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1432-7643
1433-7479
DOI:10.1007/s00500-015-1839-6