The quantity of medical waste produced by municipalities is on the rise, potentially presenting significant hazards to both the environment and human health. Developing a robust supply chain network for managing municipal medical waste is important for society, especially during a pandemic like COVID-19. In supply chain network design, factors such as the collection of non-infectious waste, transporting infectious waste from hospitals to disposal facilities, revenue generation from waste-to-energy initiatives, and the potential for pandemic outbreaks are often overlooked. Hence, in this study, we design a model incorporating COVID-19 parameters to mitigate the spread of the virus while designing an effective municipal medical waste supply chain network during a pandemic. The proposed model is multi-objective, multi-echelon, multi-commodity and involves coalition-based cooperation. The first objective function aims to minimize total costs, while the second objective pertains to minimizing the risk of a COVID-19 outbreak. We identify optimal collaboration among municipal medical waste collection centers to maximize cost savings. The COVID-19 prevalence risk level by the waste in each zone is calculated pursuant to their inhabitants. Additionally, we analyze a system dynamic simulation framework to forecast waste generation levels amid COVID-19 conditions. A metaheuristic based on the Non-dominated Sorting Genetic Algorithm II is used to solve the problem and is benchmarked against exact solutions. To illustrate our approach, we present a case study focused on Tehran, Iran. The results show that an increase in the amount of generated waste leads to an increase in the total costs of the supply chain.

Abstract Attended home deliveries (AHDs) are characterized by dynamic customer acceptance and narrow customer‐specific delivery time windows. Both impede efficient routing and thus make AHDs very costly. In this article, we explore how established horizontal collaborative transportation planning methods can be adapted to render AHDs more efficient. The general idea is to enable request reallocation between multiple collaborating carriers after the order capture phase. We use an established centralized reallocation framework that allows participating carriers to submit delivery requests for reallocation. We extend this framework for AHD specifics such as the dynamic arrival of customer requests and information about delivery time windows. Using realistic instances based on the city of Vienna, we quantify the collaboration savings by solving the underlying routing and reallocation problems. We show that narrow time windows can lower the savings obtainable by the reallocation by up to 15%. Therefore, we suggest enhancing the decision processes of request selection and request bundling using information about delivery time windows. Our findings demonstrate that adapting methods of request selection and bundle generation to environments with narrow time windows can increase collaboration savings by up to 25% and 35%, respectively in comparison to methods that work well only when no time windows are imposed.

Abstract The ongoing growth of e‐commerce deliveries has led to a significant increase in last‐mile delivery volumes. New technologies are being investigated to provide these deliveries efficiently and in a customer‐friendly manner. A common practice is to use fixed parcel lockers (FPLs) to make deliveries independent from the presence of the customer as is the case in attended home deliveries (AHDs). FPLs are usually installed at key locations in cities, and customers can collect their package at any time once it has been delivered to this particular location. Mobile parcel lockers (MPLs) represent a new idea: they can be parked for temporary collection of items at different locations, keeping the pickup distance to the customer short and avoiding high infrastructure costs. However, customers need to collect their parcels within a restricted time window. This service is supposed to become especially efficient through autonomously operating vehicles that can move MPLs at low costs. In this article, we introduce the heterogeneous locker location problem to study the effects of fleets combining two of the services—FPLs, MPLs, AHDs—within one framework. In our comparison, we consider that customers may have different expectations regarding their maximum pickup distance as well as their temporal flexibility in accepting deliveries. A fixed fleet is applied to maximize the number of customers served, respecting individual customer preferences in terms of pickup distances and time windows. We evaluate the different delivery services regarding managerial insights on service quality and efficiency. In the experiments, we analyze the impact of structural demand differences and different fleet sizes, as well as the operational fleet utilization and the individual customer experience. Results show the potential to increase the number of customers served by about 14%–19% through the use of MPLs while considering individual customer preferences.

Abstract In recent years, innovative ride‐sharing systems have gained significant attention. In such systems, dynamic fleet management covers demand and fulfillment control to determine which stochastically incoming requests are to be satisfied and how vehicle resources are utilized for their fulfillment, respectively. Demand and fulfillment control can be implemented ranging from straightforward myopic to more sophisticated anticipatory. In this paper, our aim is twofold: (1) we want to classify how policies implement demand and fulfillment control in the related literature on dynamic fleet management; (2) we want to explore the effectiveness of demand and fulfillment control under varying conditions in order to identify benefits and risks for ride‐sharing systems. To this end, we define policies that differ in the optimization of demand and/or fulfillment control through the exploitation of either confirmed or complete information. Our experimental results demonstrate that demand and fulfillment control affect the performance and service quality of ride‐sharing systems quite differently.

Abstract In this article, we investigate a two‐tier delivery network with robots operating on the second tier. We determine the optimal number of local robot hubs as well as the optimal number of robots to service all customers and compare the resulting operational cost to conventional truck‐based deliveries. Based on the well‐known p ‐median problem, we present mixed‐integer programs that consider the limited range of robots due to battery size. Compared with conventional truck‐based deliveries, robot‐based deliveries can save about 70% of operational cost and even more, up to 90%, for instances with customer time windows.

Travel itineraries between many origin‐destination (OD) pairs can require multiple legs, such as several trains, shared rides or flights, to arrive at the final destination. Travelers expect transparent reliability information to help improve decision‐making for multi‐leg itineraries. We focus on airline travel and making a priori decisions about flight itineraries based on the reliability of arriving at the destination within the travel time budget. We model the reliability of multi‐leg itineraries and, given publicly available data, create probability distributions of flight arrival and departure times. We use these values in our reliability calculations for the entire itinerary. We implement a stochastic network search algorithm that finds the most reliable flight itinerary (MRFI). We also implement several ideas to improve the efficiency of this network search. Computational experiments help identify characteristics of the MRFI for a diverse set of OD pairs.

Abstract As urbanization increases, municipalities across the world have become aware of the negative impacts of road‐based transportation, which include traffic congestion and air pollution. As a result, several cities have introduced tolling schemes to discourage vehicles from entering the inner city. However, little research has been done to examine the impact of tolling schemes on the routing of commercial fleets, especially on the resulting costs and emissions. In this study, we investigate a vehicle routing problem considering different congestion charge schemes for several city types. We design comprehensive computational experiments to investigate whether different types of tolling schemes work in the way municipalities expect and what factors affect the performance of the congestion charge schemes. We compare the impact on a company's total costs, fuel usage (which drives emissions), and delivery tour plans. Our experimental results demonstrate that some congestion pricing schemes may even increase the emissions in the city center, and higher congestion charges may not necessarily lead to lower emissions.