This paper addresses a critical gap in circular economy (CE) research by introducing a novel methodological framework that integrates Network Data Envelopment Analysis (NDEA) with a chance-constrained programming. The proposed approach captures the interrelated dynamics of economic production and waste treatment subsystems, while accounting for stochastic variables and data uncertainties to provide robust CE efficiency estimates. Using data from 26 European (EU) countries from 2013 to 2020, our results reveal that achieving CE efficiency requires a balanced focus on economic production and waste management. Although strong economic output can support circularity, waste treatment efficiency often plays a decisive role in determining overall CE performance. Moreover, we find that economic size does not necessarily translate into circular efficiency, whilst large economies may face challenges with effective waste management and resource recovery despite their economic status. The proposed approach offers policymakers and practitioners a robust empirical framework to guide CE improvements, particularly in regions where environmental practices lag behind economic achievements. Stronger incentives and regulatory measures are recommended to enhance circular activities within the EU and foster greater circular efficiency across countries.
We seek to provide practicable approximations of the two-stage robust stochastic optimization model when its ambiguity set is constructed with an f-divergence radius. These models are known to be numerically challenging to various degrees, depending on the choice of the f-divergence function. The numerical challenges are even more pronounced under mixed-integer first-stage decisions. In this paper, we propose novel divergence functions that produce practicable robust counterparts, while maintaining versatility in modeling diverse ambiguity aversions. Our functions yield robust counterparts that have comparable numerical difficulties to their nominal problems. We also propose ways to use our divergences to mimic existing f-divergences without affecting the practicability. We implement our models in a realistic location-allocation model for humanitarian operations in Brazil. Our humanitarian model optimizes an effectiveness-equity trade-off, defined with a new utility function and a Gini mean difference coefficient. With the case study, we showcase (1) the significant improvement in practicability of the robust stochastic optimization counterparts with our proposed divergence functions compared to existing f-divergences, (2) the greater equity of humanitarian response that the objective function enforces and (3) the greater robustness to variations in probability estimations of the resulting plans when ambiguity is considered.
This paper studies the multi-period home healthcare routing and scheduling problem with homogeneous electric vehicles and time windows. The problem aims to construct the weekly routes of healthcare nurses, which provide service to the patients located at a scattered geographic area. Some patients may require to be visited more than once in the same workday and/or in the same workweek. We consider three charging technologies; normal, fast, and super-fast. The vehicles might be charged during the working day at a charging station or at the end of the working day at the depot. Charging a vehicle at a depot at the end of a working day requires the transfer of the corresponding nurse from the depot to her/his home. The objective is to minimize the total cost that comprises the fixed cost of utilizing healthcare nurses, the energy charging costs, the costs associated with depot-to-nurse home transfer services, and the costs of a patient left unserved. We formulate a mathematical model and develop an adaptive large neighborhood search metaheuristic that has been efficiently crafted to handle specific problem features. We conduct extensive computational experiments on benchmark instances to assess the competitiveness of the heuristic and to deeply analyze the problem. Our analysis shows the importance of competency level matching as mismatching competency levels could increase the costs of home healthcare providers.
Home delivery services require the attendance of the customer during delivery. Hence, retailers and customers mutually agree on a delivery time window in the booking process. However, when a customer requests a time window, it is not clear how much accepting the ongoing request significantly reduces the availability of time windows for future customers. In this paper, we explore using historical order data to manage scarce delivery capacities efficiently. We propose a sampling-based customer acceptance approach that is fed with different combinations of these data to assess the impact of the current request on route efficiency and the ability to accept future requests. We propose a data-science process to investigate the best use of historical order data in terms of recency and amount of sampling data. We identify features that help to improve the acceptance decision as well as the retailer's revenue. We demonstrate our approach with large amounts of real historical order data from two cities served by an online grocery in Germany.
We study a stochastic multi-period two-echelon dual sourcing inventory system where the buyer can source a product from two different suppliers: a regular and an expedited supplier. The regular supplier is a low-cost offshore supplier, whereas the expedited supplier is a responsive nearshore supplier. Such dual sourcing inventory systems have been well studied in the literature, mostly being solely evaluated from the buyer's perspective. Since the buyer's decisions have an impact on the supply chain profit, we adopt the perspective of the entire supply chain, i.e., by taking the suppliers explicitly into consideration. In addition, we study this system for general (nonconsecutive) lead times for which the optimal policy is unknown or very complex. We numerically compare the performance of two different policies in a two-echelon setting: the Dual-Index Policy (DIP) and the Tailored Base-Surge Policy (TBS). From earlier studies we know that when the lead time difference is one period, DIP is optimal from the buyer's perspective, but not necessarily from the supply chain perspective. On the other hand, when the lead time difference grows to infinity, TBS becomes optimal for the buyer. In this paper, we evaluate the policies numerically (under various conditions) and we show that from a supply chain perspective, TBS typically outperforms DIP at a limited lead time difference of a few time periods. Based on data collected from 51 manufacturing firms, the results of our paper imply for many supply chains with a dual sourcing setting that TBS quickly becomes a beneficial policy alternative, especially given its simple and appealing structure.
