Arc routing under uncertainty: Introduction and literature review

Maio, A. De; Laganá, Demetrio; Musmanno, Roberto; Vocaturo, Francesca

doi:10.1016/j.cor.2021.105442

Cited by 16 publications

(4 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Uncertainty could also be addressed [see the review of De Maio et al. ( 2021 ) concerning other optimization problems in the transport area]. Fourth, as mentioned above, public transport strongly depends on the behavior of the passengers: the availability of a wider and more fine-grained database on the preferences of passengers could help to improve the overall model and, as a consequence, the decisions of the policymaker.…”

Section: Discussionmentioning

confidence: 99%

Optimization and public transport tendering: a case study in Southern Italy

Corvello

Musmanno

Pavone³

et al. 2023

Public Transp

Self Cite

View full text Add to dashboard Cite

This article presents a multi-start heuristic approach to a design problem motivated by a real-world application in the Italian transport system. Specifically, it focuses on the problem of designing optimal lots in the public transport organization. In defining lots (in terms of number, size, and boundaries) both cost and service level have to be considered. Under certain assumptions, we model the problem as a graph partitioning problem and consider the same performance measure indicated by the relevant decree-law enacted by the Italian Ministry of Transport. The multi-start algorithm proposed for individuating high-quality solutions for the problem uses adaptive large neighbourhood search. The results of a computational study based on real data from a region in Southern Italy are reported.

show abstract

Section: Discussionmentioning

confidence: 99%

Optimization and public transport tendering: a case study in Southern Italy

Corvello

Musmanno

Pavone³

et al. 2023

Public Transp

Self Cite

View full text Add to dashboard Cite

show abstract

“…TCARP can also be seen as a particular case of CARPDD in which the arc demands and the objective function have the same time units. For a comprehensive review of the literature De Maio et al [15] summarize the methodologies used in each particular case to model ARPs and the methods used to solve the problem either exactly or using heuristics.…”

Section: Related Workmentioning

confidence: 99%

“…This occurs when input data or graph properties cannot be adequately modeled in a static or deterministic environment. The CARP can then be formulated within the frameworks of robust optimization and stochastic programming De Maio et al [15].…”

Section: The Stochastic Capacitated Arc Routing Problemmentioning

confidence: 99%

Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times

et al. 2023

View full text Add to dashboard Cite

Stochastic, as well as fuzzy uncertainty, can be found in most real‐world systems. Considering both types of uncertainties simultaneously makes optimization problems incredibly challenging. In this paper we propose a fuzzy simheuristic to solve the Time Capacitated Arc Routing Problem (TCARP) when the nature of the travel time can either be deterministic, stochastic or fuzzy. The main goal is to find a solution (vehicle routes) that minimizes the total time spent in servicing the required arcs. However, due to uncertainty, other characteristics of the solution are also considered. In particular, we illustrate how reliability concepts can enrich the probabilistic information given to decision‐makers. In order to solve the aforementioned optimization problem, we extend the concept of simheuristic framework so it can also include fuzzy elements. Hence, both stochastic and fuzzy uncertainty are simultaneously incorporated into the CARP. In order to test our approach, classical CARP instances have been adapted and extended so that customers' demands become either stochastic or fuzzy. The experimental results show the effectiveness of the proposed approach when compared with more traditional ones. In particular, our fuzzy simheuristic is capable of generating new best‐known solutions for the stochastic versions of some instances belonging to the tegl, tcarp, val, and rural benchmarks.

show abstract

“…However, this was not the case in this study, since there is was specific set of smart bins with known locations that should have been serviced/emptied; hence, the VRP model was chosen. Moreover, in certain cases, the density of the points along a street is so large that the natural way to approach the corresponding routing problem is to adopt the ARP instead of the VRP [25]. Such cases did not apply in this study, where the locations of the bins were sparsely scattered around the city.…”

Section: Mathematical Modelingmentioning

confidence: 99%

Design of Cyber-Physical Waste Management Systems Focusing on Energy Efficiency and Sustainability

Akkad

Haidar

Bányai

2022

Designs

View full text Add to dashboard Cite

Industry 4.0 tools, such as the Internet of Things, artificial intelligence, digital twinning, and cloud computing, create a technological revolution that accelerates efforts to optimize the efficiency of cyber-physical operations and services. The waste management system requires a critical share of city logistics optimization, especially when using cyber-physical systems. Modern tools reduce the required municipal waste handling, such as loading and unloading, transportation, and warehousing, which leads to an increase in efficiency and flexibility, saving energy and time, and protecting the environment. In this paper, we present a cyber-physical waste management system solution by providing a cyber-physical model design and description, mathematical modeling, and two cases to investigate the impact on energy consumption and emissions. After an introduction and literature review, we describe the design of the cyber-physical model and tackle the first echelon. The designed system incorporates the IoT, smart bins with multi-percentage sensors, data and information analysis, vehicles’ actual routes, energy and emissions optimization, multi-echelon systems, time windows, and flexibility. Mathematical modeling equations for the optimized total energy consumption are presented. Thirty and twenty smart bins located in VIII District in Budapest are detailed as two case studies, where solutions for the optimized real routes and energy consumption are found using three metaheuristic algorithms: genetic, particle swarm, and simulated annealing optimization algorithms. The accrued emissions of CO, NMHC, CH4, NOx, and PM for the optimized solutions are calculated. Finally, the results are compared with a random traditional solution to measure the effectiveness.

show abstract

Arc routing under uncertainty: Introduction and literature review

Cited by 16 publications

References 99 publications

Optimization and public transport tendering: a case study in Southern Italy

Optimization and public transport tendering: a case study in Southern Italy

Solving the time capacitated arc routing problem under fuzzy and stochastic travel and service times

Design of Cyber-Physical Waste Management Systems Focusing on Energy Efficiency and Sustainability

Contact Info

Product

Resources

About