Towards A Personal Shopper's Dilemma: Time vs Cost

TL;DR

This paper addresses the complex problem of optimizing shopping routes for personal shoppers balancing time and cost, proposing a heuristic solution that efficiently approximates optimal routes with promising accuracy and speed.

Contribution

It introduces a heuristic method for the NP-hard personal shopper's dilemma, along with new metrics for evaluating route optimality and coverage gaps.

Findings

Approach is two orders of magnitude faster than baseline methods.

Achieves low optimality and coverage gaps in realistic datasets.

Effectively balances time and cost in shopping route optimization.

Abstract

Consider a customer who needs to fulfill a shopping list, and also a personal shopper who is willing to buy and resell to customers the goods in their shopping lists. It is in the personal shopper's best interest to find (shopping) routes that (i) minimize the time serving a customer, in order to be able to serve more customers, and (ii) minimize the price paid for the goods, in order to maximize his/her potential profit when reselling them. Those are typically competing criteria leading to what we refer to as the Personal Shopper's Dilemma query, i.e., to determine where to buy each of the required goods while attempting to optimize both criteria at the same time. Given the query's NP-hardness we propose a heuristic approach to determine a subset of the sub-optimal routes under any linear combination of the aforementioned criteria, i.e., the query's approximate linear skyline set. In order to measure the effectiveness of our approach we also introduce two new metrics, optimality and coverage gaps w.r.t. an optimal, but computationally expensive, baseline solution. Our experiments, using realistic city-scale datasets, show that our proposed approach is two orders of magnitude faster than the baseline and yields low values for the optimality and coverage gaps.