Data analytics and optimization for assessing a ride sharing system

Ride-sharing schemes attempt to reduce road traffic by matching prospective passengers to drivers with spare seats in their cars. To be successful, such schemes require a critical mass of drivers and passengers. In current deployed implementations, the possible matches are based on heuristics, rather than real route times or distances. In some cases, the heuristics propose infeasible matches; in others, feasible matches are omitted. Poor ride matching is likely to deter participants from using the system. We develop a constraint-based model for acceptable ride matches which incorporates route plans and time windows. Through data analytics on a history of advertised schedules and agreed shared trips, we infer parameters for this model that account for 90% of agreed trips. By applying the inferred model to the advertised schedules, we demonstrate that there is an imbalance between riders and passengers. We assess the potential benefits of persuading existing drivers to switch to becoming passengers if appropriate matches can be found, by solving the inferred model with and without switching. We demonstrate that flexible participation has the potential to reduce the number of unmatched participants by up to 80%

An Optimization Approach to the Ordering Phase of an Attended Home Delivery Service

Attended Home Delivery (AHD) systems are used whenever a supplying company offers online shopping services that require that customers must be present when their deliveries arrive. Therefore, the supplying company and the customer must both agree on a time window, which ideally is rather short, during which delivery is guaranteed. Typically, a capacitated Vehicle Routing Problem with Time Windows forms the underlying optimization problem of the AHD system. In this work, we consider an AHD system that runs the online grocery shopping service of an international grocery retailer. The ordering phase, during which customers place their orders through the web service, is the computationally most challenging part of the AHD system. The delivery schedule must be built dynamically as new orders are placed. We propose a solution approach that allows to (non-stochastically) determine which delivery time windows can be offered to potential customers. We split the computations of the ordering phase into four key steps. For performing these basic steps we suggest both a heuristic approach and a hybrid approach employing mixed-integer linear programs. In an experimental evaluation we demonstrate the efficiency of our approaches

Modelling home care organisations from an operations management perspective

Home Care (HC) service consists of providing care to patients in their homes. During the last decade, the HC service industry experienced significant growth in many European countries. This growth stems from several factors, such as governmental pressure to reduce healthcare costs, demographic changes related to population ageing, social changes, an increase in the number of patients that suffer from chronic illnesses, and the development of new home-based services and technologies. This study proposes a framework that will enable HC service providers to better understand HC operations and their management. The study identifies the main processes and decisions that relate to the field of HC operations management. Hence, an IDEF0 (Integrated Definition for Function Modelling) activity-based model describes the most relevant clinical, logistical and organisational processes associated with HC operations. A hierarchical framework for operations management decisions is also proposed. This analysis is derived from data that was collected by nine HC service providers, which are located in France and Italy, and focuses on the manner in which operations are run, as well as associated constraints, inputs and outputs. The most challenging research areas in the field of HC operations management are also discussed

Drivers and technology-related obstacles in moving to multichannel retailing

Today, multichannel retailing is a key strategic issue for most retailers. Yet, while there are many drivers associated with retailers going multichannel so too are there technology-related obstacles, however, few prior empirical studies explore these themes. In light of this, by using a multi-case approach to understand the key drivers and technology-related obstacles associated with retailers moving to multichannel retailing our study makes two key contributions. First, we extend prior theory by providing novel empirical insights into the main drivers underpinning retailers using a multichannel strategy. We find that meeting customer needs and increasing sales were the primary drivers behind retailers using the strategy, although there is diversity in the way retailers respond to these motives. Second, we provide empirical support for a proposed theoretical framework which summarises the key technology-related obstacles retailers encounter when going multichannel, by stage of implementation. The framework reveals that retailers face technology-related obstacles when implementing a multichannel strategy due to the need to switch/acquire resources and achieve channel integration. Furthermore, the framework highlights that these resource and channel integration issues are often interrelated with each other and with other staff engagement and cultural issues, vary by retailer and stage of implementation, and pose greater obstacles to retailers using new and multiple channels than the extant literature suggests

The impact of green labels on time slot choice and operational sustainability

In this paper, we study the effectiveness of incentives on delivery service time slot choices. In particular, we focus on the use of green labels that specify time slots as environmentally friendly and that intrinsically motivate customers to choose a specific delivery time slot in lieu of price incentives based on extrinsic motivation. We argue this is important since green labels' intrinsic nature affects customer choice in fundamentally different ways than price incentives. We conduct two experiments and two simulation studies to study the effects of using green labels. Our experimental findings suggest that: (1) green labels are an effective tool to steer shoppers toward a certain delivery option, (2) green labels are more effective for people who are more eco‐conscious, (3) green labels remain effective in the presence of price incentives, while price incentives offer little added value beyond that of just green labels, and (4) the effectiveness of green labels versus price discounts remains high when time slots are less appealing (i.e., longer). Our simulation findings suggest that green slots, compared to price incentives or no incentives, offer providers a way to effectively steer consumer time slot choices to yield shorter routes, fewer delivery vehicles used, and more per‐customer revenue. We thus conclude that steering individuals to select delivery time slots through intrinsic motivation via green labels may be a promising, no‐cost direction for (online) retailers and an important topic for further research. LIACS-Managemen