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Record W2902347415 · doi:10.1002/nav.21812

On the optimality of the sequential approach for network design problems of service operations

2018· article· en· W2902347415 on OpenAlexaffabout
Opher Baron, Oded Berman, Yael Deutsch

Bibliographic record

VenueNaval Research Logistics (NRL) · 2018
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFacility Location and Emergency Management
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsQueueing theoryComputer scienceService (business)Network planning and designMathematical optimizationBlocking (statistics)Operations researchNode (physics)Service levelComputer networkMathematicsEngineeringBusiness

Abstract

fetched live from OpenAlex

Abstract We consider the problem of service network design: choosing the optimal number, locations, and service capacities of facilities, taking into account that facilities may have a finite or an infinite waiting room. Accordingly, our service measure is either the percentage of blocked customers or the percentage of customers who need to wait in line. The goal is to minimize the total cost, which consists of costs associated with traveling, blocking or queueing delay, service capacities, and operating (fixed) costs. We derive structural results when facilities are on a two‐node network, and then use them to study the problem for a general network. We prove that the cost of service capacity and the cost of blocking or queueing delay are independent of the number of opened facilities as long as all facilities are identical in terms of their design parameters c and K (or only c in the case of an infinite waiting room). We use our results to develop an efficient algorithm that solves the problem for general networks. Finally, to demonstrate the applicability of our results and tractability of our algorithm, we discuss as an example an industrial‐size problem, which considers the drive‐through operations of McDonald's in the Toronto metropolitan area.

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.

How this classification was reachedexpand

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)0.005
metaresearch head score (Gemma)0.003
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.985
Threshold uncertainty score0.521

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.359
GPT teacher head0.375
Teacher spread0.016 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes2
Has abstractyes

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