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Record W2263798772

Mathematical Models and Solution Procedures in the Design and Scheduling of Manufacturing Systems with Distributed Layouts

2015· dissertation· en· W2263798772 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueThe Atrium (University of Guelph) · 2015
Typedissertation
Languageen
FieldEngineering
TopicAdvanced Manufacturing and Logistics Optimization
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsScheduling (production processes)Computer scienceIndustrial engineeringManufacturing engineeringDistributed computingMathematical optimizationEngineeringMathematics
DOInot available

Abstract

fetched live from OpenAlex

This thesis addresses two distinct problems in facility design and scheduling for manufacturing firms operating in volatile environments and producing multiple batches of products. In regards to the facility layout problem, a new comprehensive mathematical model that integrates layout configuration and production planning in the design of dynamic distributed layouts is formulated. The model incorporates a number of important manufacturing attributes such as demand fluctuation, system reconfiguration, lot splitting, work load balancing, alternative routings, machine capability and tooling requirements. In addition, the model allows the optimization of several cost elements in an integrated manner. These include material handling, machine relocation, setup, inventory carrying, in-house production and subcontracting costs. With respect to the scheduling problem, a mathematical formulation for scheduling of manufacturing systems with distributed layouts is developed. The objective of scheduling model is the minimization of the weighted sum of makespan and total traveling distance by the products. Thus on one hand, the problem is to find a schedule of operations on machines (the sequence and starting times of the various operations) which minimizes the overall finishing time or makespan. On the other hand, the problem is to find assignment of jobs to the machines such that total distance traveled by parts is minimized. Optimal solutions for the proposed mathematical models can only be found for small size problems due to NP-complexity. To solve both models for larger-size problems, two hybrid metaheuristics for solving the facility design model and a genetic algorithm for the scheduling model have been developed. All proposed algorithms are thoroughly examined with an emphasis on solution convergence, solution quality and algorithm robustness. For both cases, we provide numerical results to support various managerial insights. In particular in facility design problem, we draw a managerial insight as to how high product variety and high volatility in the production environment can be accommodated without harm to operational efficiency or cost. Similarly in the scheduling study, we show that linking scheduling and material handling performance can contribute to the development of accurate models to obtain a schedule that can also greatly enhance system performance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.632
Threshold uncertainty score0.374

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.022
GPT teacher head0.215
Teacher spread0.193 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it