Ambulance Locations in a Tiered Emergency Medical System in a City

This paper deals with optimizing the location of ambulance stations in a two-tiered emergency medical system in an urban environment. Several variants of station distribution are calculated by different mathematical programming models and are evaluated by a detailed computer simulation model. A new modification of the modular capacitated location model is proposed. Two ways of demand modelling are applied; namely, the aggregation of the ambient population and the aggregation of permanent residents at the street level. A case study of the city of Prešov, Slovakia is used to assess the models. The performance of the current and proposed sets of locations is evaluated using real historical data on ambulance trips. Computer simulation demonstrates that the modular capacitated location model, with the ambient population demand, significantly reduces the average response time to high-priority patients (by 79 s in the city and 62 s in the district) and increases the percentage of high-priority calls responded to within 8 min (by almost 4% in the city and 5% in the district). Our findings show that a significant improvement in the availability of the service can be achieved when ambulances are not accumulated at a few stations but rather spread over the city territory.

Heuristic algorithm based on Ant Colony Optimization for the Capacitated Location-Routing problem with Homogeneous Fleet

This paper considers the Capacitated Location-Routing Problem with Homogeneous Fleet (CLRP). The objective is to minimize the sum of the open depots' costs, the costs for the used vehicles, and the costs associated with the distances traveled. A metaheuristic algorithm of two phases for the CLRP is proposed. In the first phase, customers establish the clusters to be subsequently heuristically assigned to the depots. In the second phase, the initial routes are improved using an algorithm based on Ant Colony. The obtained results show the efficiency of the proposed approach.

Capacitated Location-Allocation-Routing Problem with Time Windows for On-Demand Urban Air Taxi Operation

Urban air taxi (UAT) operation has gained traction with the advancements in distributed electric propulsion and the emergence of electric vertical take-off and landing aircraft. Start-up companies and aircraft manufacturers are pursuing the possibility of operating UAT at scale in urban and suburban areas and at an affordable price. However, considerable uncertainties remain about several strategic, tactical, and operational aspects that affect UAT adoption. We envision a mature state of UAT operation in which the UAT operator offers door-to-door, multimodal, on-demand, and per-seat service. We propose the concept of flexible meeting points for UAT operation where passengers are flexible about the location of the UAT pads for boarding and deboarding, and could therefore be pooled together to share an aircraft. Consequently, we model UAT fleet operation as a capacitated location-allocation-routing problem with time windows and present a mixed integer programming formulation. The formulation addresses decisions on request acceptance and rejection, allocation of requests to flights, and aircraft routing and scheduling. Additionally, it allows for consolidating the demand to increase the aircraft’s utilization and service rate. The numerical results indicate that the demand consolidation scheme could significantly decrease the number of rejected requests and the aerial mileage. Depending on the operator’s business model, the proposed formulation could be used offline in a static and deterministic setting when all requests are known in advance, or it could be employed online by sequentially solving the static and deterministic snapshot problems with no knowledge about future requests.

Capacitated location routing problem with ‎simultaneous pickup and delivery under the risk of ‎disruption

This paper develops a new mathematical model to study a location-routing problem with simultaneous pickup and delivery under the risk of disruption. A remarkable number of previous studies have assumed that network components (e.g., routes, production factories, depots, etc.) are always available and can permanently serve the customers. This assumption is no longer valid when the network faces disruptions such as flood, earthquake, tsunami, terrorist attacks and workers strike. In case of any disruption in the network, tremendous cost is imposed on the stockholders. Incorporating disruption in the design phase of the network will alleviate the impact of these disasters and let the network resist disruption. In this study, a mixed integer programming (MIP) model is proposed that formulates a reliable capacitated location-routing problem with simultaneous pickup and delivery (RCLRP-SPD) services in supply chain distribution network. The objective function attempts to minimize the sum of location cost of depots, routing cost of vehicles and cost of unfulfilled demand of customers. Since the model is NP-Hard, three meta-heuristics are tailored for large-sized instances and the results show the outperformance of hybrid algorithms comparing to classic genetic algorithm. Finally, the obtained results are discussed and the paper is concluded.

An approach based on Clustering AH and VND for Supply Chain Management

In this work, we present a new approach to solve the Capacitated Location-Routing Problem (CLRP). The aim of this method is to determine the depot locations, to assign customers to facilities and to define routes for each depot to serve the associated clients. The proposed approach contains two phases, which are the constructive phase and the improvement phase. In the first phase, we select the depots to be opened, allocate the customers to open depots using a Hierarchical Ascendant (HA) method and we solve the vehicle routing problem for each depot using Sweep algorithm. In the second phase, we apply a Variable Neighborhood Descent (VND) with three structures in order to optimize the cost obtained by the first phase. Two sets of well-known instances from the literature are used to evaluate the performance of this method, and the numerical results obtained are compared with the experimental results of other methods. Results show that our method is competitive with respect to the best-known solutions (BKSs) and demonstrate its efficiency in comparison with other approaches.