Sample of parcel locker literature
| Article | Location (context) | Method | Focus | Outcome | Assumptions about the network | Assumptions about consumers |
|---|---|---|---|---|---|---|
| Weltevreden (2008) | Netherlands | Survey | Consumer Adoption | Consumer use of attended service points | Existing locker network (the Netherlands) | Respondents shop online |
| Oliveira et al. (2017) | Brazil | Stated Choice Experiment | Consumer Adoption | Preference for Automatic Delivery Stations | Potential locker network characteristics | Residents of Belo Horizonte, Brazil |
| Deutsch and Golany (2018) | Survey | Consumer Adoption | Customer intent to use parcel lockers | Existing network | Prior online shopping experience | |
| Wang et al. (2019) | Singapore | Survey | Consumer Adoption | Customer satisfaction | ||
| Yuen et al. (2019) | China | Survey | Consumer Adoption | Intent to use Lockers | Smart locker users | |
| Tsai and Tiwasing (2021) | Thailand | Survey | Consumer Adoption | Customer attitude and intent to use lockers | Thai citizens with online shopping experience | |
| Merkert et al. (2022) | Australia | Stated Choice Experiment | Consumer Adoption | Consumer Decision to use Locker | Potential locker network characteristics | Respondents used home delivery in prior 12 months |
| Edwards et al. (2010) | United Kingdom | Case Study | Locker Network | Carbon Emissions | Routes are set at 120 deliveries and 50 miles | Combined collection and other activities |
| Lachapelle et al. (2018) | Australia | Case Study | Locker Network | Locker network description | Existing locker network (Sydney, Australia) | |
| Veenstra et al. (2018) | Netherlands | Location and Routing Problem | Locker Network | Travel costs consumer Facility costs | Random locker locations | Travel distance limit |
| Hong et al. (2019) | Created Instances | Traveling Salesman Problem | Locker Network | Distance (Cost) | Random locker locations | Locker delivery only Travel distance limit |
| Ji et al. (2019) | Created Instances | Location Assignment Problem | Locker Network | Total cost (locker purchase and rental costs)Total energy consumption | Random locker locations Time windows | Randomly generated demand |
| Jiang et al. (2019) | China | Traveling Salesman Problem | Locker Network | Total cost (delivery, pickup and locker opening cost) | Random locker locations | Locker delivery vs. store pickup |
| Ulmer and Streng (2019) | Germany | Location and Delivery Problem | Locker Network | Total delivery time | Random locker locations | Locker delivery only Travel time limit |
| Schwerdfeger and Boysen (2020) | None | Facility Location Problem | Locker Network | Total Profit | Random mobile locker locations | Locker delivery only Travel distance limit |
| Peppel and Spinler (2022) | “European Country” | Network Optimization | Locker Network | Economic and Environmental Cost | Random locker locations | Actual demand data Locker vs. home delivery Travel distance limit |
| Seghezzi et al. (2022) | Italy | Simulation | Locker Network | Average cost per delivery | Random locker locations | Randomly generated demand |
| Leung et al. (2023) | China | Simulation | Locker Network | Customer delivery time Route distance | Random locker locations | Randomly generated demand |
| Mohri et al. (2024) | Australia | Simulation | Locker Network | Operational Cost | Existing locker network (Melbourne, Australia) | Demand based on market observations |
| Peppel et al. (2024) | Global | Location Routing Problem | Locker Network | Economic and Environmental Cost | Existing locker network (15 global cities) | Actual demand data Locker vs. home delivery Travel distance limit |
| Article | Location (context) | Method | Focus | Outcome | Assumptions about the network | Assumptions about consumers |
|---|---|---|---|---|---|---|
| Netherlands | Survey | Consumer Adoption | Consumer use of attended service points | Existing locker network (the Netherlands) | Respondents shop online | |
| Brazil | Stated Choice Experiment | Consumer Adoption | Preference for Automatic Delivery Stations | Potential locker network characteristics | Residents of Belo Horizonte, Brazil | |
| Survey | Consumer Adoption | Customer intent to use parcel lockers | Existing network | Prior online shopping experience | ||
| Singapore | Survey | Consumer Adoption | Customer satisfaction | |||
| China | Survey | Consumer Adoption | Intent to use Lockers | Smart locker users | ||
| Thailand | Survey | Consumer Adoption | Customer attitude and intent to use lockers | Thai citizens with online shopping experience | ||
| Australia | Stated Choice Experiment | Consumer Adoption | Consumer Decision to use Locker | Potential locker network characteristics | Respondents used home delivery in prior 12 months | |
| United Kingdom | Case Study | Locker Network | Carbon Emissions | Routes are set at 120 deliveries and 50 miles | Combined collection and other activities | |
| Australia | Case Study | Locker Network | Locker network description | Existing locker network (Sydney, Australia) | ||
| Netherlands | Location and Routing Problem | Locker Network | Travel costs consumer | Random locker locations | Travel distance limit | |
| Created Instances | Traveling Salesman Problem | Locker Network | Distance (Cost) | Random locker locations | Locker delivery only | |
| Created Instances | Location Assignment Problem | Locker Network | Total cost (locker purchase and rental costs)Total energy consumption | Random locker locations | Randomly generated demand | |
| China | Traveling Salesman Problem | Locker Network | Total cost (delivery, pickup and locker opening cost) | Random locker locations | Locker delivery vs. store pickup | |
| Germany | Location and Delivery Problem | Locker Network | Total delivery time | Random locker locations | Locker delivery only | |
| None | Facility Location Problem | Locker Network | Total Profit | Random mobile locker locations | Locker delivery only | |
| “European Country” | Network Optimization | Locker Network | Economic and Environmental Cost | Random locker locations | Actual demand data | |
| Italy | Simulation | Locker Network | Average cost per delivery | Random locker locations | Randomly generated demand | |
| China | Simulation | Locker Network | Customer delivery time | Random locker locations | Randomly generated demand | |
| Australia | Simulation | Locker Network | Operational Cost | Existing locker network (Melbourne, Australia) | Demand based on market observations | |
| Global | Location Routing Problem | Locker Network | Economic and Environmental Cost | Existing locker network (15 global cities) | Actual demand data |
Source(s): Table created by authors
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