Abstract
This paper considers a rich vehicle routing problem in which a combination of transportation costs and customer-perceived waiting times should be minimized and a differentiation is made between priority and non-priority customers. We illustrate the problem using a case study of a wholesaler with its own last-mile delivery network where customers can have pickup and delivery demand and are served by a heterogeneous fleet of vehicles. We propose a bi-objective mathematical problem formulation, minimizing the combination of transportation costs and customer dissatisfaction. We model customer dissatisfaction using a non-linear function that approximates the perceived waiting time of the customers. To be able to solve realistically-sized problems in reasonable time, we propose a Simulated Annealing heuristic, Variable Neighborhood Search, and a combination of these. We perform various experiments considering different customer preferences (visit as soon as possible or at a specific time) and problem settings. For the combined objective, we see an average costs reduction for the dissatisfaction function approach compared to the standard time window approach of 48% over all experiments. Furthermore, we observe an average reduction in perceived waiting time of 48% and 20% for priority and non-priority customers, respectively.
Key in this approach is the interplay between the degree of autonomy of logistic systems and their degree of cooperativeness. On these two pillars, a unifying framework is presented, distinguishing four fundamental categories of self-organizing logistics. To illustrate the working of the framework in practice, we present four real-life case studies, one per each category. The case studies are positioned as-is, and concrete directions for (more) self-organization are presented for each case. Moreover, possible additional dimensions of the framework, e.g., control hierarchy, system intelligence, connectivity, and predictability are discussed.
The usefulness of the framework established is two-fold: (i) it provides a common ground for researchers to position their work and to identify potential future directions for research and (ii) it serves as a practical and understandable starting point for practitioners on investigating how self-organization may affect their business and where their limited resources should be focused upon.
Key in this approach is the interplay between the degree of autonomy of logistic systems and their degree of cooperativeness. On these two pillars, a unifying framework is presented, distinguishing four fundamental categories of self-organizing logistics. To illustrate the working of the framework in practice, we present four real-life case studies, one per each category. The case studies are positioned as-is, and concrete directions for (more) self-organization are presented for each case. Moreover, possible additional dimensions of the framework, e.g., control hierarchy, system intelligence, connectivity, and predictability are discussed.
The usefulness of the framework established is two-fold: (i) it provides a common ground for researchers to position their work and to identify potential future directions for research and (ii) it serves as a practical and understandable starting point for practitioners on investigating how self-organization may affect their business and where their limited resources should be focused upon.
Original language | English |
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Number of pages | 1 |
Publication status | Published - 2020 |
Event | 11th International Conference on Computational Logistics, ICCL 2020 - Online conference, Enschede, Netherlands Duration: 28 Sept 2020 → 30 Sept 2020 Conference number: 11 https://iccl2020.nl/ |
Conference
Conference | 11th International Conference on Computational Logistics, ICCL 2020 |
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Abbreviated title | ICCL |
Country/Territory | Netherlands |
City | Enschede |
Period | 28/09/20 → 30/09/20 |
Internet address |
Keywords
- Vehicle routing problem
- Customer satisfaction
- Simulated annealing
- Variable neighborhood search
- Time windows