Disabled Adult Transit Service: Shift Design Optimization and Demand Forecasting Team: Mike Clay Yvan Fortier Chris Samuel

Agenda PART A: Shift Design Optimization Problem Solution Methodology Analysis Results Risk PART B: Demand Forecasting Problem Solution Methodology Analysis Risk Results Problem – Solution – Methodology – Analysis – Results - Risks

Executive Summary

Executive Summary: Part A Part-Time Shifts Full-Time Shifts Daily Cost and An increase in the amount of part time shifts and less full time shifts will reduce overtime hours paid out. It will be effective in reducing daily operator cost while still meeting same level of demand Productivity

Executive Summary: Part B 1. Redirecting 2. Optimizing 3. Centralizing Cost Containment Strategies: Operating Expenses: 197% Number of Employees: 268% Number of Vehicles: 215% Demand for DATS: 234% Forecast: 2010 - 2030

Shift Design Optimization Part A: Shift Design Optimization

Problem

Review the Effectiveness Overtime Reduction Perspective Problem Review the Effectiveness of Part-Time Shifts Maintain or Increase Service Level with a Greater Mix of Shifts Overtime Reduction Perspective in Reducing Daily Operator Cost Problem – Solution – Methodology – Analysis – Results - Risks Problem – Solution – Methodology – Analysis – Results - Risks

Solution

Solution Problem – Solution – Methodology – Analysis – Results - Risks Minimize Labour Cost Increase Operator Utilization Optimal Shift Design Model Efficacy of Part-Time Shifts Minimize labour cost and increase/maintain operator utilization Lowering workers scheduled during peak times Problem – Solution – Methodology – Analysis – Results - Risks Problem – Solution – Methodology – Analysis – Results - Risks

Methodology

Methodology Shift Scheduling Model built in Excel Queueing ToolPak (QTP) Review Current Shift Schedule Demand Supply Constraints Utilization Input Various Shift Combinations Cost Benefit Analysis Excel based model Queueing toolpak Cost benefit analysis of current shift design vs potential shifts Problem – Solution – Methodology – Analysis – Results - Risks Problem – Solution – Methodology – Analysis – Results - Risks

Analysis

Daily Wheelchair Demand Problem – Solution – Methodology – Analysis – Results - Risks

WC Demand vs. Scheduled Operators 1.9872….. .4986 Problem – Solution – Methodology – Analysis – Results - Risks

FT Shifts vs. PT Shifts 10.61% of staff PT.. 14 of 132 Problem – Solution – Methodology – Analysis – Results - Risks

Current Operator Utilization Avg 56%, max 126, min 5% Problem – Solution – Methodology – Analysis – Results - Risks

Monthly OT Costs (2007-2008) Problem – Solution – Methodology – Analysis – Results - Risks

Results

Results Problem – Solution – Methodology – Analysis – Results - Risks Current = 62 8, 17 10, 9PT ………FT <79…. 30 10, 45, 8, 8 5…83tot Problem – Solution – Methodology – Analysis – Results - Risks

Utilization: Old vs. New Avg 56%, max 126, min 5% Newbies- avg 61%. Not as steep of slope, smoothed Problem – Solution – Methodology – Analysis – Results - Risks

Chosen Shift Blend Maximum daily cost savings of $1093. savings of nearly $560 ($200 000 yrly)…. Employ 10 less ppl on an avg basis…..Raise wages by 3.92%, $14,258.28 $14,052.98 Problem – Solution – Methodology – Analysis – Results - Risks

Risks

Risks Higher utilization = More work for operators decreased worker quality of life Average trips made per shift will decrease Employees prefer full-time shifts and overtime hours Problem – Solution – Methodology – Analysis – Results - Risks

Part B: Demand Forecasting

Problem

Problem Population of Edmonton: increasing changing Demand for DATS: Resulting in: planning challenges fiscal constraints Requiring: forecast of future demand forecast of impacts Problem – Solution – Methodology – Analysis – Risks – Results

Solution

Solution Cost Containment Strategies Population of Edmonton: age gender Disabled Population of Edmonton Impacts on DATS: vehicles employees operating expenses Demand for DATS Cost Containment Strategies Problem – Solution – Methodology – Analysis – Risks – Results

Methodology

Methodology Forecasting the Population of Edmonton Disabled Population of Edmonton Forecasting the Demand for DATS Forecasting the Impacts on DATS Drivers of Disability Problem – Solution – Methodology – Analysis – Risks – Results

Analysis

Population of Edmonton Growth: total = 29% 65+ = 99% Growth: total = 1% 65+ = 4% Growth: total = 17% 65+ = 93% Historical: 2001 - 2008 Forecast: 2010 - 2030 Problem – Solution – Methodology – Analysis – Risks – Results

Disabled Population of Edmonton Growth: total = 72% 65+ = 133% Growth: total = 62% 65+ = 125% Growth: total = 24% 65+ = 31% Historical: 2001 - 2008 Forecast: 2010 - 2030 Problem – Solution – Methodology – Analysis – Risks - Results

Demand For DATS Growth: total = 256% 65+ = 460% Growth: total = 234% 65+ = 422% Growth: total = 167% 65+ = 302% Historical: 2001 - 2008 Forecast: 2010 - 2030 Problem – Solution – Methodology – Analysis – Risks - Results

Number Of Vehicles Growth: 235% Growth: 29% Growth: 215% Historical: 2001 - 2008 Forecast: 2010 - 2030 Problem – Solution – Methodology – Analysis – Risks - Results

Number Of Employees Growth: 293% Growth: 66% Growth: 268% Historical: 2001 - 2008 Forecast: 2010 - 2030 Problem – Solution – Methodology – Analysis – Risks - Results

Total Operating Expenses Growth: 215% Growth: 52% Growth: 197% Historical: 2001 - 2008 Forecast: 2010 - 2030 Problem – Solution – Methodology – Analysis – Risks - Results

Risks

Demand For DATS Average Range = 29% Average Range = 29% Problem – Solution – Methodology – Analysis – Risks – Results

Number Of Vehicles Average Range per Range in Demand = 0.95 Problem – Solution – Methodology – Analysis – Risks - Results

Number Of Employees Average Range per Range in Demand = 1.08 Problem – Solution – Methodology – Analysis – Risks - Results

Total Operating Expenses Average Range per Range in Demand = 0.90 Average Range per Range in Demand = 0.90 Problem – Solution – Methodology – Analysis – Risks - Results

Results

Results Cost Containment Strategies Redirecting to Optimizing shift schedule Redirecting to other assisted transportation services Cost Containment Strategies Centralizing pick-ups and drop-offs Problem – Solution – Methodology – Analysis – Risks – Results

Key Findings

More PT Key Findings – Part A Less OT Higher Utilization With a greater balance of PT and FT shifts, OT will be reduced Demand will be more effectively met from a cost perspective with the same service level

Key Findings – Part B Demand for DATS will grow significantly: 234% - 256% Will cause significant impacts: operating expenses increase by: 197% - 215% Can be managed with cost containment strategies: redirecting optimizing centralizing

Questions

Appendix

Appendix – Part A Threshold Time – 30 min Service Level - .99 Arrival Rate – Demand from 2 periods ahead averaged Service Rate* – 1.9872 per hour.. .4986 per quarter hour Queue Capacity – infinite Lagg SIPP (stationary independent period by period) approach MMS – Poisson Distribution, inter-event times (no exact info about next pickup) *Service Rate = average(total passengers carried per operator / hours worked by operator)

Appendix – Part A ASSUMPTIONS Demand = SCHED_WC – CANCEL_WC Demand broken down into weekday and weekend demand No break time. Only current shift schedule includes split shifts Tours 0.5hrs than actual length

Appendix – Part A CONSTRAINTS 10 PT per day Avg. Wage

Appendix – Part A Labour Costs Different Costs of Shift Combinations

Appendix – Part A

Appendix – Part A

Appendix – Part B: Data Limitations and Assumptions Profiled only by age, not also by gender No data on disabled population of Edmonton Data only on disabled Alberta for 2001 and 2006 Two data sets are insufficient for MVR Two data sets only allows forecasting by SLR DATS only tracks registered clients, not inquiries Two data sets are insufficient for MVR Two data sets only allows forecasting by SLR Assumed: only cost driver is number of clients no intervention by decision makers no changes in productivity