Profit Maximizing Distributed Service System Design with Congestion and Elastic Demand
Bibliographic record
Abstract
In this paper we develop a service network design model that explicitly takes into account the elasticity of customer demand with respect to travel distance and congestion delays. The model incorporates a feedback loop between customer demand and congestion at the facilities. The problem is to determine the number of facilities, their locations, their service capacity, and the assignment of customers to facilities so as to maximize the overall profit of the system. Two versions of the problem are presented. In one, each facility is modeled as an M/M/1 queuing system where the service rate is a decision variable; in the other one, the facility is modeled as an M/M/k queuing model where the service rate is given, but the number k is a decision variable. An exact algorithm and heuristics are developed and tested via computational experiments. Although our model is of the “directed choice” type where the assignment of customers to facilities is controlled by the decision maker, computational results show that in the vast majority of cases the customers are assigned to the utility-maximizing facility, indicating that there is no conflict between the customers' and decision makers' goals. A case study of locating preventive medicine clinics in Toronto, Ontario, illustrates the model.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".