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Record W2081437561 · doi:10.1287/msom.1050.0094

Service System Design with Immobile Servers, Stochastic Demand, and Congestion

2006· article· en· W2081437561 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueManufacturing & Service Operations Management · 2006
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFacility Location and Emergency Management
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceQueueMathematical optimizationService (business)Network congestionHeuristicServerQueueing theoryLinearizationOperations researchSet (abstract data type)Computer networkNonlinear systemMathematicsNetwork packetEconomics

Abstract

fetched live from OpenAlex

The service system design problem seeks to locate a set of service facilities, allocate enough capacity, and assign stochastic customer demand to each of them, so as to minimize the fixed costs of opening facilities and acquiring service capacity, as well as the variable access and waiting costs. This problem is commonly known in the location literature as the facility location problem with immobile servers, stochastic demand, and congestion. It is often set up as a network of M/M/1 queues and modeled as a nonlinear mixed-integer program (MIP). Because of the complexity of the resulting model, the current literature focuses on approximate and/or heuristic solution methods. This paper proposes a linearization based on a simple transformation and piecewise linear approximations and an exact solution method based on cutting planes. This leads to the exact solution of models with up to 100 customers, 20 potential service facilities, and 3 capacity levels.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.283
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.012
GPT teacher head0.186
Teacher spread0.174 · 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