Conference Agenda

Due to the influence of indirect emissions in sustainability reporting, there is an incentive for industrial shippers to choose climate-friendly transport options. As a result, freight forwarders are faced with the long-term task of converting their fleets to lower-emission vehicles. This process involves a fluid change in the fleet, with conventionally powered and lower-emission vehicles being used in parallel. Route planning is now faced with the problem of reconciling this heterogeneous fleet with divergent customer preferences. This paper provides a planning model for the design and deployment of a mixed fleet of conventional and lower-emission vehicles. Transport services performed by lower-emission vehicles are priced higher but receive lower emission reports. Our model attends to increase the overall customer satisfaction. It is tested under varying customer preferences regarding cost and emissions and under changing compositions of fleets. Our computational results indicates that solutions beyond minimum cost or minimum emission plans can be more suitable in order to match heterogeneous customer preferences.

Companies for home appliances often offer after-sales services, especially repair services, to their customers. One of the key challenges in this context is the planning of allocation and routing of service technicians that satisfy customers' service requests. In this paper, we consider technicians with two different skill levels. Senior technicians with extensive experience, consistently handle the repair tasks successfully, whereas junior technicians, due to their limited experience, may fail to repair the appliances, necessitating a follow-up visit by a senior technician to complete the service. Stochasticity from random service results and new customer requests are incorporated. We formulate the problem as a sequential decision problem, where decisions regarding technician allocation and routing for each workday are made, based on the information of customer requests for the upcoming workday without knowing further future demands. The objective is the minimization of the overall expected costs associated with technicians' travel and customers' waiting times. Both myopic and anticipatory methods are introduced to address the multi-period problem. We conduct a comprehensive numerical study comparing the performance of the heuristic methods under different realistic problem settings and show that the anticipatory method leads to a reduction in long-term total costs in certain settings. Additionally, we provide managerial insights into strategic allocation operations in various scenarios.

Multi-agent path planning (MAPP) is the problem of finding an optimal set of paths for multiple agents and can be widely applied in the fleet management of different modes of mobility ranging from aircraft, ground vehicles to robots. The challenge faced in the problem lies in obtaining balanced paths for all agents when solving large-scale problems with numerous targets due to the NP-hardness. Additionally, the problem can become further complicated for the practical purpose of handling a situation where agents are expected to execute their tasks to moving targets at distant positions. To address these issues, this paper proposes a novel path planning algorithm that uses clustering and meta-heuristics. In this approach, the problem is divided into a combinatorial optimization problem of finding the target visitation orders for agents and a continuous optimization problem of determining the task execution positions of the agents. The first optimization involves clustering targets for the prioritization of agent target assignment with the aim of balancing the travel times of the agents while also accelerating the search for more promising solutions. In the second problem, our formulation allows solutions to satisfy practical constraints on the task execution with the target movements predicted. Numerical experiments conducted on simulated scenarios as well as real data examples examine the effectiveness of the proposed algorithm and demonstrate its potential for practical implementation.

All nations in the world were under tremendous economic and logistical strain as a result of the advent of COVID-19. Early in the epidemic, getting COVID-19 diagnostic tests was a significant difficulty. Furthermore, logistical challenges arose from the restricted transportation infrastructure and disruptions in international supply chains in the distribution of these testing kits. In the face of such obstacles, it is critical to give patients' needs top priority in order to provide fair access to testing. In order to manage contagious disease testing, this work proposes a bi-objective and multi-period mathematical model with an emphasis on mobile tester route plans and testing resource allocation. In order to optimize patient scores and reduce the likelihood of patients going untreated, the suggested team orienteering model takes into account issues like resource limitations, geographic clustering, and testing capacity limitations. To this aim, we present a comparison between quarantine and non-quarantine scenarios, introduce an equitable categorization based on disease backgrounds into “standard” and “risky” groups, and cluster geographical locations according to average age and contact rate. We use a variable neighborhood search (VNS) meta-heuristic, which has been applied for Vienna, Austria, with a case study. The results demonstrate that, over the course of several weeks, the average number of unserved risky patients in the prioritizing scenario is consistently lower than the usual number of patients. In the absence of prioritization, the average number of high-risk patients who remain untreated rises sharply and exceeds that of regular patients, though.