Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

Co-Scheduling of Energy and Production in Discrete Manufacturing Considering Decision-Dependent Uncertainties

Yiyuan Pan, Zhaojian Wang

Abstract

Modern discrete manufacturing requires real-time energy and production co-scheduling to reduce business costs. In discrete manufacturing, production lines and equipment are complex and numerous, which introduces significant uncertainty during the production process. Among these uncertainties, decision-dependent uncertainties (DDUs) pose additional challenges in finding optimal production strategies, as the signature or the shape of the uncertainty set cannot be determined before solving the model. However, existing research does not account for decision-dependent uncertainties (DDUs) present in discrete manufacturing; moreover, current algorithms for solving models with DDUs suffer from high computational complexity, making them unsuitable for the real-time control requirements of modern industry. To this end, we proposed an energy-production co-scheduling model for discrete manufacturing, taking into account decision-dependent uncertainties (DDUs). Subsequently, we devised a method for transforming the constraints associated with DDUs into linear form ones. Finally, we designed a novel algorithm based on the column-and-constraint generation (C&CG) algorithm and undertook a theoretical analysis of its performance of convergence and algorithmic complexity. A real-world engine assembly line was used to test our model and algorithm. Simulation results demonstrate that our method significantly reduces production costs and achieves better frequency regulation performance.

Co-Scheduling of Energy and Production in Discrete Manufacturing Considering Decision-Dependent Uncertainties

Abstract

Modern discrete manufacturing requires real-time energy and production co-scheduling to reduce business costs. In discrete manufacturing, production lines and equipment are complex and numerous, which introduces significant uncertainty during the production process. Among these uncertainties, decision-dependent uncertainties (DDUs) pose additional challenges in finding optimal production strategies, as the signature or the shape of the uncertainty set cannot be determined before solving the model. However, existing research does not account for decision-dependent uncertainties (DDUs) present in discrete manufacturing; moreover, current algorithms for solving models with DDUs suffer from high computational complexity, making them unsuitable for the real-time control requirements of modern industry. To this end, we proposed an energy-production co-scheduling model for discrete manufacturing, taking into account decision-dependent uncertainties (DDUs). Subsequently, we devised a method for transforming the constraints associated with DDUs into linear form ones. Finally, we designed a novel algorithm based on the column-and-constraint generation (C&CG) algorithm and undertook a theoretical analysis of its performance of convergence and algorithmic complexity. A real-world engine assembly line was used to test our model and algorithm. Simulation results demonstrate that our method significantly reduces production costs and achieves better frequency regulation performance.

Paper Structure

This paper contains 31 sections, 2 theorems, 34 equations, 9 figures, 3 tables, 1 algorithm.

Table of Contents

  1. Abbreviations
  2. Variables
  3. Parameters
  4. Introduction
  5. Backgroound
  6. Related Works
  7. Methodology
  8. Problem Formulation
  9. Problem Overview
  10. Objective Function
  11. Production Model and Constraint
  12. Energy Model and Constraints
  13. Modeling of DDUs
  14. Classification of DDUs
  15. Definition and Simplification of DDUs
  16. ...and 16 more sections

Key Result

Proposition 1

If a decision-dependent two-stage robust optimization problem with both $W_X$ and $W_Y$ types of DDU variables satisfies Definition ass1, the DDCCG algorithm can converge in finite steps.

Figures (9)

  • Figure 1: Schematic diagram of the overall model architecture.
  • Figure 2: Difference in single-use time for different equipment.
  • Figure 3: DDCCG algorithm diagram.
  • Figure 4: Petri net for engine assembly lines.
  • Figure 5: Historiy of electricity cost and RTP for different models.
  • ...and 4 more figures

Theorems & Definitions (11)

  • Remark 1
  • Remark 2
  • Remark 3
  • Definition 1
  • Definition 2
  • Definition 3
  • Proposition 1
  • proof
  • Proposition 2
  • proof
  • ...and 1 more