Coordinating supply and demand on an on-demand service platform with impatient customers

We consider an on-demand service platform using earning-sensitive independent providers with heterogeneous reservation price (for work participation) to serve its time and price-sensitive customers with heterogeneous valuation of the service. As such, the supply and demand are “endogenously” dependent on the price the platform charges its customers and the wage the platform pays its independent providers. We present an analytical model with endogenous supply (number of participating agents) and endogenous demand (customer request rate) to study this on-demand service platform. To coordinate endogenous demand with endogenous supply, we include the steady-state waiting time performance based on a queueing model in the customer utility function to characterize the optimal price and wage rates that maximize the profit of the platform. We first analyze a base model that uses a fixed payout ratio (i.e., the ratio of wage over price), and then extend our model to allow the platform to adopt a time-based payout ratio. We find that it is optimal for the platform to charge a higher price when demand increases; however, the optimal price is not necessarily monotonic when the provider capacity or the waiting cost increases. Furthermore, the platform should offer a higher payout ratio as demand increases, capacity decreases or customers become more sensitive to waiting time. We also find that the platform should lower its payout ratio as it grows with the number of providers and customer demand increasing at about the same rate. We use a set of actual data from a large on-demand ride-hailing platform to calibrate our model parameters in numerical experiments to illustrate some of our main insights

Price and Time Competition for Service Delivery

Many service firms use delivery time guarantees to compete for customers in the marketplace. In this research we develop a stylized model to analyze the impact of using time guarantees on competition. Demands are assumed to be sensitive to both the price and delivery time guarantees, and the objective of each firm is to select the best price and time guarantee to maximize its operating profit. We first analyze the optimization problem for the individual firms and then study the equilibrium solution in a multiple-firm competition. Using a numerical study, we further illustrate how the different firm and market characteristics would affect the price and delivery time competition in the market. Our results suggest that the equilibrium price and time guarantee decisions in an oligopolistic market with identical firms behave in a similar fashion as the optimal solution in a monopolistic situation from a previous study. However, when there are heterogeneous firms in the market, these firms will exploit their distinctive firm characteristics to differentiate their services. Assuming all other factors being equal, the high capacity firms provide better time guarantees, while firms with lower operating costs offer lower prices, and the differentiation becomes more acute as demands become more time-sensitive. Furthermore, as time-attractiveness of the market increases, firms compete less on price, and the equilibrium prices of the firms increase as a result. Our findings provide important implications about firm behaviour under price and time competition.time-based competition, price competition, service guarantees, competitive games

Some Heuristics for Scheduling Jobs on Parallel Machines with Setups

This paper studies the problem of scheduling jobs on parallel machines with setup times. When a machine switches from processing one type of job to another type, setup times are incurred. The problem is to find a feasible schedule for each machine which maximizes the total reward. We study three heuristics for solving this problem. Analytical and empirical results of the heuristics are given.production scheduling: group technology, dynamic programming: applications, heuristics

Developing Cost-Effective Inspection Sampling Plans for Energy-Efficiency Programs at Southern California Edison

This paper summarizes the results of the development and implementation of a decision model for a major California utility company. The company’s program managers use the model to select the most cost-effective sampling inspection plan for managing a portfolio of energy-efficiency programs. The decision model can be used to evaluate the performance of possible sampling strategies based on historical data. We illustrate the application of our decision model to a specific program. We further highlight several key implementation success factors and discuss the benefits that the utility company accrued from using the decision model