Work Plan Scheduling Using Simplified Job Lists
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Solution Overview
Problem
Existing methods for solving job shop scheduling problems require excessive computation time, making it impractical to find optimal solutions within a reasonable timeframe.
Innovation Solution
A method involving setting processing conditions, creating a simplified job list by dividing set-up work times with a time divisor, and solving the problem as a 0-1 integer programming problem to obtain a final solution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the job shop scheduling problem is solved using exact methods to obtain optimal solutions, then solution optimality is improved, but computation time increases exponentially
Solution Approach 1:
The patent segments the original job shop scheduling problem into multiple subproblems by dividing the time horizon into discrete time slots. This segmentation allows the problem to be solved using dynamic programming or other efficient algorithms that process time slots sequentially rather than attempting to solve the entire scheduling problem at once, thereby reducing computation time while maintaining solution quality.
Solution Approach 2:
The patent transforms the scheduling problem into a 0-1 integer programming problem by changing the representation of decision variables. Instead of using continuous time or complex scheduling parameters, the solution uses binary variables to represent job assignment decisions in discrete time slots, which enables more efficient computation while preserving the optimality of the schedule.
2Loss of time
If the problem is solved as a 0-1 integer programming problem with simplified job list, then computation time is reduced, but solution accuracy may be affected
Solution Approach 1:
The patent applies partial action by solving the integer programming problem on a simplified job list that contains only the essential scheduling decisions. This partial solution approach focuses computational effort on the most critical scheduling aspects while omitting less important details, thereby reducing computation time while maintaining sufficient solution accuracy for practical purposes.
Solution Approach 2:
The patent creates a simplified copy of the original job list that retains the essential scheduling information while removing redundant details. This simplified job list is then used to formulate the 0-1 integer programming problem, allowing for faster computation. The solution is subsequently mapped back to the original problem, preserving accuracy through the copying transformation.
Data Source
AI summary
A method of the present disclosure includes (a) setting a processing condition including a job list that defines set-up work times in a plurality of machines for each of a plurality of jobs, (b) obtaining a temporary solution for a work plan by dividing each of the set-up work times in the job list by a time divisor to create a simplified job list and solving a job shop scheduling problem relating to the simplified job list as a 0-1 integer programming problem, and (c) modifying work times of respective jobs in the temporary solution of the set-up work times and obtaining a final solution of the work plan.