Mathematics

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21 pages, 948 KiB

Open AccessArticle

Optimizing Maintenance of Energy Supply Systems in City Logistics with Heuristics and Reinforcement Learning

by Antoni Guerrero, Angel A. Juan, Alvaro Garcia-Sanchez and Luis Pita-Romero

Mathematics 2024, 12(19), 3140; https://doi.org/10.3390/math12193140 - 7 Oct 2024

Viewed by 796

Abstract

In urban logistics, effective maintenance is crucial for maintaining the reliability and efficiency of energy supply systems, impacting both asset performance and operational stability. This paper addresses the scheduling and routing plans for maintenance of power generation assets over a multi-period horizon. We [...] Read more.

In urban logistics, effective maintenance is crucial for maintaining the reliability and efficiency of energy supply systems, impacting both asset performance and operational stability. This paper addresses the scheduling and routing plans for maintenance of power generation assets over a multi-period horizon. We model this problem as a multi-period team orienteering problem. To address this multi-period challenge, we propose a dual approach: a novel reinforcement learning (RL) framework and a biased-randomized heuristic algorithm. The RL-based method dynamically learns from real-time operational data and evolving asset conditions, adapting to changes in asset health and failure probabilities to optimize decision making. In addition, we develop and apply a biased-randomized heuristic algorithm designed to provide effective solutions within practical computational limits. Our approach is validated through a series of computational experiments comparing the RL model and the heuristic algorithm. The results demonstrate that, when properly trained, the RL-based model is able to offer equivalent or even superior performance compared to the heuristic algorithm. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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15 pages, 1630 KiB

Open AccessArticle

Mathematical Model for Optimal Agri-Food Industry Residual Streams Flow Management: A Valorization Decision Support Tool

by Íñigo Barasoain-Echepare, Marta Zárraga-Rodríguez, Adam Podhorski, Fernando M. Villar-Rosety, Leire Besga-Oyanarte, Sofía Jaray-Valdehierro, Tamara Fernández-Arévalo, Luis Sancho, Eduardo Ayesa, Jesús Gutiérrez-Gutiérrez and Xabier Insausti

Mathematics 2024, 12(17), 2753; https://doi.org/10.3390/math12172753 - 5 Sep 2024

Viewed by 501

Abstract

We present a mathematical model for agri-food industry residual streams flow management, which serves as a decision support tool for optimizing their valorization. The aim is to determine, under a cost-benefit analysis approach, the best strategy at a global level. The proposed mathematical [...] Read more.

We present a mathematical model for agri-food industry residual streams flow management, which serves as a decision support tool for optimizing their valorization. The aim is to determine, under a cost-benefit analysis approach, the best strategy at a global level. The proposed mathematical model provides the optimal valorization scenario, namely the set of routes followed by agri-food industry residual streams that maximizes the total profit obtained. The model takes into account the complete stoichiometry of the residual stream at each step of the valorization route. Furthermore, the model allows for the calculations of different scenarios to support decision-making. The proposed approach is illustrated through a case study using a real-case network of a region. The case study bears evidence that the use of the model can lead to significant profit increases compared to those obtained with current practices. Moreover, notable profit improvements are obtained in the case study if the selling price of all the value-added products considered increases or if the processing cost of the animal feed producer decreases. Therefore, our model enables the detection of key factors that influence the optimal strategy, making it a powerful decision-support tool for optimizing the valorization of agri-food industry residual streams. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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35 pages, 4711 KiB

Open AccessArticle

Multi-Objective Optimization of Resilient, Sustainable, and Safe Urban Bus Routes for Tourism Promotion Using a Hybrid Reinforcement Learning Algorithm

by Keartisak Sriprateep, Rapeepan Pitakaso, Surajet Khonjun, Thanatkij Srichok, Peerawat Luesak, Sarayut Gonwirat, Chutchai Kaewta, Monika Kosacka-Olejnik and Prem Enkvetchakul

Mathematics 2024, 12(14), 2283; https://doi.org/10.3390/math12142283 - 22 Jul 2024

Cited by 2 | Viewed by 1410

Abstract

Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, and sustainable bus route optimization is essential to ensure reliable service, minimize environmental impact, and maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood [...] Read more.

Urban transportation systems in tourism-centric cities face challenges from rapid urbanization and population growth. Efficient, resilient, and sustainable bus route optimization is essential to ensure reliable service, minimize environmental impact, and maintain safety standards. This study presents a novel Hybrid Reinforcement Learning-Variable Neighborhood Strategy Adaptive Search (H-RL-VaNSAS) algorithm for multi-objective urban bus route optimization. Our mathematical model maximizes resilience, sustainability, tourist satisfaction, and accessibility while minimizing total travel distance. H-RL-VaNSAS is evaluated against leading optimization methods, including the Crested Porcupine Optimizer (CPO), Krill Herd Algorithm (KHA), and Salp Swarm Algorithm (SSA). Using metrics such as Hypervolume and the Average Ratio of Pareto Optimal Solutions, H-RL-VaNSAS demonstrates superior performance. Specifically, H-RL-VaNSAS achieved the highest resilience index (550), sustainability index (370), safety score (480), tourist preferences score (300), and accessibility score (2300), while minimizing total travel distance to 950 km. Compared to other methods, H-RL-VaNSAS improved resilience by 12.24–17.02%, sustainability by 5.71–12.12%, safety by 4.35–9.09%, tourist preferences by 7.14–13.21%, accessibility by 4.55–9.52%, and reduced travel distance by 9.52–17.39%. This research offers a framework for designing efficient, resilient, and sustainable public transit systems that align with urban planning and transportation goals. The integration of reinforcement learning with VaNSAS significantly enhances optimization capabilities, providing a valuable tool for mathematical and urban transportation research communities. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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21 pages, 4085 KiB

Open AccessArticle

A Two-Phase Approach to Routing a Mixed Fleet with Intermediate Depots

by Nan Ding, Manman Li and Jianming Hao

Mathematics 2023, 11(8), 1924; https://doi.org/10.3390/math11081924 - 19 Apr 2023

Cited by 2 | Viewed by 1333

Abstract

The adoption of all-electric vehicles (EVs) has grown rapidly in the transportation industry, particularly for urban parcel deliveries. However, the limited driving range of EVs and the high investment cost of establishing charging infrastructures are still the holdbacks to routing these EVs. In [...] Read more.

The adoption of all-electric vehicles (EVs) has grown rapidly in the transportation industry, particularly for urban parcel deliveries. However, the limited driving range of EVs and the high investment cost of establishing charging infrastructures are still the holdbacks to routing these EVs. In this paper, we present the vehicle routing problem of a mixed fleet of EVs and conventional vehicles (CVs) via intermediate depots as an alternative strategy to address the challenges, with CVs delivering parcels from the central depot to intermediate depots and EVs delivering parcels from intermediate depots to customers. In addition, we propose an intelligent dispatching scheme to allow EVs to be used for multiple routes. A two-phase approach is developed to first cluster the customers to the intermediate depots and then route the mixed fleet. The strategy is implemented for both small- and large-sized instances, and the results show that using an intelligent dispatching scheme can significantly reduce the number of EVs used. Furthermore, the use of smaller-range EVs is also investigated., and a discussion of potential implementation issues is provided. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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14 pages, 803 KiB

Open AccessArticle

Stylized Model of Lévy Process in Risk Estimation

by Xin Yun, Yanyi Ye, Hao Liu, Yi Li and Kin-Keung Lai

Mathematics 2023, 11(6), 1414; https://doi.org/10.3390/math11061414 - 15 Mar 2023

Viewed by 1191

Abstract

Risk management is a popular and important problem in academia and industry. From a small-scale system, such as city logistics, to a large-scale system, such as the supply chain of a global industrial or financial system, efficient risk management is required to prevent [...] Read more.

Risk management is a popular and important problem in academia and industry. From a small-scale system, such as city logistics, to a large-scale system, such as the supply chain of a global industrial or financial system, efficient risk management is required to prevent loss from uncertainty. In this paper, we assume that risk factors follow the Lévy process, and propose a stylized model, based on regression, that can estimate the risk of a complicated system under the framework of nest simulation. Specifically, portfolio risk estimation using the Lévy process is discussed as an example. The stylized model simplifies the risk factors artificially, and provides useful basis functions to fit the portfolio loss with little computational effort. Numerical experiments showed the good performance of the stylized model in estimating risk for the Variance Gamma process and the Normal Inverse Gaussian process, which are two examples of the Lévy process. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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10 pages, 271 KiB

Open AccessArticle

The Vehicle Routing Problem with Simultaneous Pickup and Delivery Considering the Total Number of Collected Goods

by Qinge Guo and Nengmin Wang

Mathematics 2023, 11(2), 311; https://doi.org/10.3390/math11020311 - 6 Jan 2023

Cited by 3 | Viewed by 2984

Abstract

As a consequence of e-commerce development, large quantities of returned goods are shipped every day. The vehicle routing problem with simultaneous delivery and pickup (VRPSDP) has become one of the most important areas of logistics management. Most related studies are aimed at minimizing [...] Read more.

As a consequence of e-commerce development, large quantities of returned goods are shipped every day. The vehicle routing problem with simultaneous delivery and pickup (VRPSDP) has become one of the most important areas of logistics management. Most related studies are aimed at minimizing travel time. However, the total number of collected goods is also very important to logistics companies. Thus, only considering the traveling time cannot reflect actual practice. To effectively optimize these operations for logistics companies, this paper introduces the vehicle routing problem with simultaneous pickup and delivery considering the total number of collected goods. Based on the principles of considering the number of collected goods, a bi-objective vehicle routing model minimizing the total travel time and maximizing the total number of collected goods simultaneously is developed. A polynomial time approximation algorithm based on the

ε

-constraint method is designed to address this problem, and the approximation ratio of the algorithm is analyzed. Finally, the validity and feasibility of the proposed model and algorithm are verified by test examples, and several managerial insights are derived from the sensitivity analysis. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

21 pages, 3191 KiB

Open AccessArticle

Metaheuristics in Business Model Development for Local Tourism Sustainability Enhancement

by Pawnrat Thumrongvut, Kanchana Sethanan, Thitipong Jamrus, Chuleeporn Wongloucha, Rapeepan Pitakaso and Paulina Golinska-Dawson

Mathematics 2022, 10(24), 4750; https://doi.org/10.3390/math10244750 - 14 Dec 2022

Viewed by 1907

Abstract

This study focused on analyzing planning and scheduling services in the tourism industry. Because dealing with these issues necessitates consideration of several important factors and stakeholders in the tourism business, it is challenging to operate resources efficiently. The purpose of this research is [...] Read more.

This study focused on analyzing planning and scheduling services in the tourism industry. Because dealing with these issues necessitates consideration of several important factors and stakeholders in the tourism business, it is challenging to operate resources efficiently. The purpose of this research is to propose a novel approach that allows maximizing the profits of tourism-related service sectors while considering many real-life constraints, such as sequence-dependent travel time, tourist time windows, points of interest, and specific destination constraints. We test our mathematical model for solving first small-scale problems and then metaheuristics proposed for finding a solution for real-life size problems. Moreover, sensitivity analysis was used to analyze the case study’s worthiness when the total cost and the revenue factor were changed. A real case study from Thailand’s Khon Kaen and Kanchanaburi provinces were used to verify the proposed models. The results indicate that the proposed models can be applied to investment decisions and strategy development. Furthermore, the outputs of the proposed models (i.e., the mathematical and metaheuristics models) can be employed to enhance the sustainability of other supply chains. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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15 pages, 781 KiB

Open AccessArticle

Robust Appointment Scheduling in Healthcare

by Yuan Gao, Qian Zhang, Chun Kit Lau and Bhagwat Ram

Mathematics 2022, 10(22), 4317; https://doi.org/10.3390/math10224317 - 17 Nov 2022

Viewed by 2391

Abstract

The quality and experience of healthcare systems affect the economy and prosperity of cities all over the world. Governments of several countries are struggling to improve the efficiency of their healthcare systems and decrease healthcare spending costs. In this paper, we discuss one [...] Read more.

The quality and experience of healthcare systems affect the economy and prosperity of cities all over the world. Governments of several countries are struggling to improve the efficiency of their healthcare systems and decrease healthcare spending costs. In this paper, we discuss one of the most critical and busy processes among healthcare topics: ambulatory care center (ACC) appointment scheduling systems. We investigate the appointment-scheduling issue using a robust optimization framework to minimize operational costs while ensuring the improvement of healthcare service quality. Our findings and insights provide healthcare practitioners with tools to improve operational efficiency and service quality. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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22 pages, 2703 KiB

Open AccessArticle

Dynamic Scheduling Strategy for Shared Agricultural Machinery for On-Demand Farming Services

by Li Ma, Minghan Xin, Yi-Jia Wang and Yanjiao Zhang

Mathematics 2022, 10(21), 3933; https://doi.org/10.3390/math10213933 - 23 Oct 2022

Cited by 8 | Viewed by 2247

Abstract

With the development of the “Internet +” model and the sharing economy model, the “online car-hailing” operation model has promoted the emergence of “online-hailing agricultural machinery”. This new supply and demand model of agricultural machinery has brought greater convenience to the marketization of [...] Read more.

With the development of the “Internet +” model and the sharing economy model, the “online car-hailing” operation model has promoted the emergence of “online-hailing agricultural machinery”. This new supply and demand model of agricultural machinery has brought greater convenience to the marketization of agricultural machinery services. However, although this approach has solved the use of some agricultural machinery resources, it has not yet formed a scientific and systematic scheduling model. Referring to the existing agricultural machinery scheduling modes and the actual demand of agricultural production, based on the idea of resource sharing, in this research, the soft and hard time windows were combined to carry out the research on the dynamic demand scheduling strategy of agricultural machinery. The main conclusions obtained include: (1) Based on the ideas of order resource sharing and agricultural machinery resource sharing, a general model of agricultural machinery scheduling that meet the dynamic needs was established, and a more scientific scheduling plan was proposed; (2) Based on the multi-population coevolutionary genetic algorithm, the dynamic scheduling scheme for shared agricultural machinery for on-demand farming services was obtained, which can reasonably insert the dynamic orders on the basis of the initial scheduling scheme, and realize the timely response to farmers’ operation demands; (3) By comparing with the actual production situation, the path cost and total operating cost were saved, thus the feasibility and effectiveness of the scheduling model were clarified. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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20 pages, 3099 KiB

Open AccessArticle

Hybrid Particle Swarm and Whale Optimization Algorithm for Multi-Visit and Multi-Period Dynamic Workforce Scheduling and Routing Problems

by Voravee Punyakum, Kanchana Sethanan, Krisanarach Nitisiri and Rapeepan Pitakaso

Mathematics 2022, 10(19), 3663; https://doi.org/10.3390/math10193663 - 6 Oct 2022

Cited by 7 | Viewed by 2490

Abstract

This paper focuses on the dynamic workforce scheduling and routing problem for the maintenance work of harvesters in a sugarcane harvesting operation. Technician teams categorized as mechanical, hydraulic, and electrical teams are assumed to have different skills at different levels to perform services. [...] Read more.

This paper focuses on the dynamic workforce scheduling and routing problem for the maintenance work of harvesters in a sugarcane harvesting operation. Technician teams categorized as mechanical, hydraulic, and electrical teams are assumed to have different skills at different levels to perform services. The jobs are skill-constrained and have time windows. During a working day, a repair request from a sugarcane harvester may arrive, and as time passes, the harvester’s position may shift to other sugarcane fields. We formulated this problem as a multi-visit and multi-period dynamic workforce scheduling and routing problem (MMDWSRP) and our study is the first to address the workforce scheduling and routing problem (WSRP). A mixed-integer programming formulation and a hybrid particle swarm and whale optimization algorithm (HPSWOA) were firstly developed to solve the problem, with the objective of minimizing the total cost, including technician labor cost, penalty for late service, overtime, travel, and subcontracting costs. The HPSWOA was developed for route planning and maintenance work for each mechanical harvester to be provided by technician teams. The proposed algorithm (HPSWOA) was validated against Lingo computational software using numerical experiments in respect of static problems. It was also tested against the current practice, the traditional whale optimization algorithm (WOA), and traditional particle swarm optimization (PSO) in respect of dynamic problems. The computational results show that the HPSWOA yielded a solution with significantly better quality. The HPSWO was also tested against the traditional genetic algorithm (GA), bat algorithm (BA), WOA, and PSO to solve the well-known CEC 2017 benchmark functions. The computational results show that the HPSWOA achieved more superior performance in most cases compared to the GA, BA, WOA, and PSO algorithms. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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19 pages, 502 KiB

Open AccessArticle

Antecedents in Determining Users’ Acceptance of Electric Shuttle Bus Services

by Naihui Wang, Yulong Pei and Yi-Jia Wang

Mathematics 2022, 10(16), 2896; https://doi.org/10.3390/math10162896 - 12 Aug 2022

Cited by 9 | Viewed by 2026

Abstract

The electric shuttle bus service is a pro-environmental transportation method with the advantages of conserving fossil fuel consumption and reducing greenhouse gas emissions. It could also provide flexible shuttle services and enhance travel convenience for residents. Although it has many advantages, users’ willingness [...] Read more.

The electric shuttle bus service is a pro-environmental transportation method with the advantages of conserving fossil fuel consumption and reducing greenhouse gas emissions. It could also provide flexible shuttle services and enhance travel convenience for residents. Although it has many advantages, users’ willingness to accept the electric shuttle bus service is crucial to its successful implementation. A theoretical research model that integrates UTAUT and NAM with an attitude construct is developed based on the data collected in China to explore antecedents of using electric shuttle bus services. The validity of the proposed model is examined by partial least squares structural equation modeling. According to analysis results, the proposed research model could explain 73.5% of the variance in adoption intention. Results demonstrate that attitude is the strongest antecedent of using electric shuttle bus services. Performance expectancy, personal norms, and social influence are the direct antecedents, and ascription of responsibility and effort expectancy is demonstrated as the indirect antecedents of using electric shuttle bus services. Results also offer valuable insights into how electric shuttle bus services can be accepted and implemented more readily. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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Review

Jump to: Research

34 pages, 4356 KiB

Open AccessReview

A Review of Emergency and Disaster Management in the Process of Healthcare Operation Management for Improving Hospital Surgical Intake Capacity

by Mohammad Heydari, Kin Keung Lai, Yanan Fan and Xiaoyang Li

Mathematics 2022, 10(15), 2784; https://doi.org/10.3390/math10152784 - 5 Aug 2022

Cited by 9 | Viewed by 6039

Abstract

To perform diagnosis and treatment, health systems, hospitals, and other patient care facilities require a wide range of supplies, from masks and gloves to catheters and implants. The “healthcare supply chain/healthcare operation management” refers to the stakeholders, systems, and processes required [...] Read more.

To perform diagnosis and treatment, health systems, hospitals, and other patient care facilities require a wide range of supplies, from masks and gloves to catheters and implants. The “healthcare supply chain/healthcare operation management” refers to the stakeholders, systems, and processes required to move products from the manufacturer to the patient’s bedside. The ultimate goal of the healthcare supply chain is to ensure that the right products, in the right quantities, are available in the right places at the right time to support patient care. Hospitals and the concept of a healthcare delivery system are practically synonymous. Surgical services, emergency and disaster services, and inpatient care are the three main types of services they offer. Outpatient clinics and facilities are also available at some hospitals, where patients can receive specialty consultations and surgical services. There will always be a need for inpatient care, regardless of how care models develop. The focus of this monograph was on recent OM work that models the dynamic, interrelated effects of demand-supply matching in the ED, OR, and inpatient units. Decisions about staffing and scheduling in these areas are frequently made independently by healthcare managers and clinicians. Then, as demand changes in real-time, clinicians and managers retaliate as best as they can to reallocate staffing to the areas that require it most at a particular moment in time in order to relieve patient flow bottlenecks. We, as OM researchers, must create models that help healthcare administrators enhance OR scheduling policies, ED demand forecasting, and medium- and short-term staffing plans that consider the interdependence of how demand develops. Full article

(This article belongs to the Special Issue Planning and Scheduling in City Logistics Optimization)

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