Abstract
The impacts of the e-commerce growth have increased the urgency in designing and adopting new alternative delivery strategies. In this context, it is important to consider the particularities of each city like its terrain conditions. This article aims at exploring the impact of road slopes on parcel delivery operations, and how they condition the adoption and implementation of alternative, more sustainable delivery strategies. To this end, a microscopic traffic simulator was used to evaluate different delivery strategies including ICE vans, electric vans, and cargo bikes in three different slope scenarios. This evaluation was based on a medium-sized European city and conducted by comparing the same parcel delivery route at three levels: operational (route length, duration, and waiting time), energy consumption, and emissions. The results revealed that as the road slopes increased, more time was needed to deliver all packages, waiting times grew longer, and vehicles' energy consumption and emissions levels intensified. From the flat terrain to the most sloped terrain, there was an increase in duration of around 5% for traditional and electric vans, 35% for large cargo bikes, and 14% for small cargo bikes. The ICE van suffers a 105% increase in waiting time; the electric van 71%; the large cargo bike 68% and the small cargo bike 52%. Energy consumption also varied, with ICE vans and small cargo bikes consuming nearly 30% more energy, while electric vans and large cargo bikes consumed 4% and 60% more energy, respectively. The ICE van's emissions of CO, HC, PMx, NOx, and CO2 are 13%, 10%, 1%, 20%, and 29% higher, respectively. Moreover, in flatter terrains, the better strategies are the electric van or a large cargo bike, while in more sloped terrains, the most adequate one is the electric van. These findings suggest that the electric van is the best overall strategy for different terrains and different decision-making profiles, ranking first in more than 70% of the profiles across all three terrains.

Abstract
The efficient management of assets delivers value and is essential for achieving service objectives, managing risks, and reducing costs. This paper proposes decision-support methods to help capital-intensive industries manage their assets and optimise their life cycle. Optimisation approaches were developed to support longterm investment planning by maximising the value created and minimising the budget used. Also, the trade-off for both objectives was analysed. Using the proposed models will lead to efficient management of available capital and excellent service delivery. Thus, water companies will fulfil the regulator's requirements and present well-founded decision-making. This study was applied to a Portuguese water utility.

Abstract
The expansion of ethanol production in Brazil sparks several sustainability concerns, including debates on “food versus fuel”, the environmental impacts of monocultures, and indirect land-use change. Since livestock farming occupies a significantly greater area than sugarcane for ethanol production in Brazil and has a large yield gap, sugarcane-livestock integration can be a promising alternative. This integrated system considers crop production systems, biorefinery processing and meat production in both intensive and extensive livestock farming. Optimizing this system for both economic and environmental aspects can be challenging to implement and computationally expensive as this system's complexity arises from nonlinear subsystems and their intertwining input-output flows. For these reasons, this paper develops metamodels from detailed models to: (i) Optimize the extensive livestock farming, (ii) Optimize the confined animal feeding, and (iii) Optimize the integrated system. The main objective is to maximize the Net Present Value relative to investment. This study contributes to the literature by developing innovative models for ethanol-beef integrated production systems and methods for optimizing such systems to avoid negative externalities on food security and environmental impacts. © 2024 Elsevier B.V.

Abstract
Renewable energy and electric mobility are crucial in addressing current environmental and energy challenges. As the number of electric vehicles increases, more charging infrastructure connected to the electricity distribution network is required. This paper proposes an approach to sizing a fast charging station for electric vehicles. This challenge is addressed by including a battery energy storage system (BESS) and considering the self-production from a renewable energy source (solar energy) in the system. The aim is to minimise the total energy costs, avoid future infrastructure upgrades, and take advantage of the integration of renewable energy resources. The methodology used is a Biased Random Key Genetic Algorithm (BRKGA) based meta-heuristic. Computational experiments were conducted for the sizing of a charging station under four different scenarios that minimise energy costs. The results show that incorporating BESS can lead to a significant reduction in the costs related to the purchase of energy from the grid. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

Abstract
This work extends the energy-efficient job shop scheduling problem with transport resources by considering speed adjustable resources of two types, namely: the machines where the jobs are processed on and the vehicles that transport the jobs around the shop-floor. Therefore, the problem being considered involves determining, simultaneously, the processing speed of each production operation, the sequence of the production operations for each machine, the allocation of the transport tasks to vehicles, the travelling speed of each task for the empty and for the loaded legs, and the sequence of the transport tasks for each vehicle. Among the possible solutions, we are interested in those providing trade-offs between makespan and total energy consumption (Pareto solutions). To that end, we develop and solve a bi-objective mixed-integer linear programming model. In addition, due to problem complexity we also propose a multi-objective biased random key genetic algorithm that simultaneously evolves several populations. The computational experiments performed have show it to be effective and efficient, even in the presence of larger problem instances. Finally, we provide extensive time and energy trade-off analysis (Pareto front) to infer the advantages of considering speed adjustable machines and speed adjustable vehicles and provide general insights for the managers dealing with such a complex problem.

Abstract
PurposeThe service environment is becoming increasingly turbulent, leading to calls for a systemic understanding of it as a set of dynamic service ecosystems. This paper advances this understanding by developing a typology of service ecosystem dynamics that explains the varying interplay between change and stability within the service environment through distinct behavioral patterns exhibited by service ecosystems over time. Design/methodology/approachThis study builds upon a systematic literature review of service ecosystems literature and uses system dynamics as a method theory to abductively analyze extant literature and develop a typology of service ecosystem dynamics. FindingsThe paper identifies three types of service ecosystem dynamics-behavioral patterns of service ecosystems-and explains how they unfold through self-adjustment processes and changes within different systemic leverage points. The typology of service ecosystem dynamics consists of (1) reproduction (i.e. stable behavioral pattern), (2) reconfiguration (i.e. unstable behavioral pattern) and (3) transition (i.e. disrupting, shifting behavioral pattern). Practical implicationsThe typology enables practitioners to gain a deeper understanding of their service environment by discerning the behavioral patterns exhibited by the constituent service ecosystems. This, in turn, supports them in devising more effective strategies for navigating through it. Originality/valueThe paper provides a precise definition of service ecosystem dynamics and shows how the identified three types of dynamics can be used as a lens to empirically examine change and stability in the service environment. It also offers a set of research directions for tackling service research challenges.