Currently, the healthcare of elderly people arouses widespread concerns since the sharp increase of aging population puts severe stress on public medical resources. Home health care (HHC) is regarded as an alternative answer to hospitalization, while it plays an important role in reducing healthcare cost and improving service satisfaction. This work addresses a service resource routing and scheduling problem with sharing strategy among multiple HHC centers for given customers. Two objective functions are involved: minimizing the total operation cost including the fixed usage cost of centers, caregiver usage cost and service cost, and minimizing the total tardiness caused by delay service. Firstly, a mixed integer programming model is formulated to describe the concerned problem. Secondly, a multi-objective artificial bee colony algorithm with problem-specific knowledge (MABC-PK) is proposed. Three problem-specific knowledge-based heuristics are designed to initialize population. A crossover operation and a self-learning neighborhood selection method are developed to prompt collaborative search of population and external archive. Furthermore, two knowledge-based local search methods are proposed for refining solutions in the external archive via employing some observations and priority properties derived from the problem characteristics. Finally, extensive experiments are conducted by comparing the proposed approach with four widely-acknowledged multi-objective optimization methods and a mathematical programming solver CPLEX. The comparative results and statistical analysis confirm the strong competitiveness of MABC-PK for solving the concerned problem. 

Multi-Objective Home Health Care Routing and Scheduling With Sharing Service via a Problem-Specific Knowledge-Based Artificial Bee Colony Algorithm

Because of imbalanced spatial-temporal user demands, situations with no available bikes or docks often occur. In order to better response to user demands, many researchers are devoted to rebalancing bike sharing systems (BSS). Existing researches focus primarily on rebalancing BSS by trucks or by workers individually, resulting in two issues: (a) the high cost and massive emissions associated with trucks-based rebalancing; and (b) the low efficiency associated with workers-based rebalancing. This paper combines two rebalancing methods together and proposes a mixed rebalancing strategy to tackle the aforementioned issues. This strategy rebalances free-floating BSS (FBSS) by trucks on the eve of peak hours and by workers during peak hours, in which the worker-based rebalancing plan is given by a worker-and-system trade-off model which simultaneously considers the impact of workers' costs and real-time inventory of stations. A case study is conducted on two scenarios of the Beijing Mobike dataset: the Beigongdaximen subway station and the Jinyijiayuan residential area. The results show that our mixed rebalancing strategy can effectively rebalance FBSS by improving the following four indices: rebalancing cost, usage rate of shared bikes and docks, survival time of stations, and demand satisfaction. 

https://onlinelibrary.wiley.com/doi/pdfdirect/10.1049/itr2.12324

A worker‐and‐system trade‐off model for rebalancing free‐float bike sharing systems: A mixed rebalancing strategy

The research work on optimization has witnessed significant growth in the past few years, particularly within multi- and single-objective optimization algorithm areas. This study provides a comprehensive overview and critical evaluation of a wide range of optimization algorithms from conventional methods to innovative metaheuristic techniques. The methods used for analysis include bibliometric analysis, keyword analysis, and content analysis, focusing on studies from the period 2000–2023. Databases such as IEEE Xplore, SpringerLink, and ScienceDirect were extensively utilized. Our analysis reveals that while traditional algorithms like evolutionary optimization (EO) and particle swarm optimization (PSO) remain popular, newer methods like the fitness-dependent optimizer (FDO) and learner performance-based behavior (LPBB) are gaining attraction due to their adaptability and efficiency. The main conclusion emphasizes the importance of algorithmic diversity, benchmarking standards, and performance evaluation metrics, highlighting future research paths including the exploration of hybrid algorithms, use of domain-specific knowledge, and addressing scalability issues in multi-objective optimization. 

Advancements in Optimization: Critical Analysis of Evolutionary, Swarm, and Behavior-Based Algorithms

Task planning of space debris removal based on a hierarchical exploration artificial bee colony algorithm

Public bicycle systems (PBSs) serve as the ‘last mile’ of public transportation for urban residents, yet the problem of the difficulty in borrowing and returning bicycles during peak hours remains a major bottleneck restricting the intelligent and efficient operation of public bicycles. Previous studies have proposed reasonable models and efficient algorithms for optimizing public bicycle scheduling, but there is still a lack of consideration for actual road network distances between stations and the temporal characteristics of demand at rental points in the model construction process. Therefore, this paper aims to construct a public bicycle dispatch framework based on the spatiotemporal characteristics of borrowing and returning demands. Firstly, the spatiotemporal distribution characteristics of borrowing and returning demands for public bicycles are explored, the origin–destination (OD) correlation coefficients are defined, and the intensity of connections between rental point areas is analyzed. Secondly, based on the temporal characteristics of rental point demands, a random forest prediction model is constructed with weather factors, time characteristics, and rental point locations as feature variables, and station bicycle-borrowing and -returning demands as the target variable. Finally, bicycle dispatch regions are delineated based on actual path distances between stations and OD correlation coefficients, and a public bicycle regional dispatch optimization method is established. Taking the PBS in Ningbo City as an example, the balancing optimization framework proposed in this paper is validated. The results show that the regional dispatch optimization method proposed in this paper can achieve optimized dispatch of public bicycles during peak hours. Additionally, compared with the Taboo search algorithm (TSA), the genetic algorithm (GA) exhibits a 11.1% reduction in rebalancing time and a 40.4% reduction in trip cost.

Public Bicycle Dispatch Method Based on Spatiotemporal Characteristics of Borrowing and Returning Demands

Efficient solvers for traveling salesman problem (TSP) have great significance in the field of consumer electronic systems and devices. Existing studies require independent and repetitive runs for similar TSPs. To utilize useful knowledge buried in the twin TSP which is similar to the target TSP, a twin learning framework based on task matching and mapping strategy is proposed. We use an autoencoder to extract the feature vectors of the historical tasks and the target task to find the twin task. If not found, construct a twin task based on a graph-filter. Further, we get the solution of the target task with the optimized twin task by learning a mapping matrix from the twin task to the target task. Finally, we incorporate local search algorithms into the twin learning framework, which called strategy mapping solver (SMS) to further improve the quality of the solution. The efficacy of the SMS is evaluated through comprehensive empirical studies with commonly used TSP benchmarks, against meta-heuristic algorithms and a learning improvement heuristics algorithm, demonstrating its effectiveness for TSP. Moreover, a real-world combinatorial optimization application, laser engraving path planning, is presented to further confirm the efficacy of SMS. 

A twin learning framework for traveling salesman problem based on autoencoder, graph filter, and transfer learning

With the development of social urbanization, bike-sharing was born and developed rapidly. Many cities around the world believe that Shared bikes promote environmental protection and development towards sustainable society. In this paper, we consider two problems: bike-sharing system (BSS) redistribution efficiency maximization and system rebalance synchronize. Firstly, we describe BSS dynamic model with a linear form based on graph theory. Then, we propose a quantitative representation to measure the operational efficiency of BSS. We present a model predictive control (MPC) method to solve operational efficiency problem with system constraints. Both the dynamic state and the constraints in the redistribution are considered in MPC algorithm. Then, we verify the effectiveness of our proposed algorithm on different connection type BSS network. According to experimental results, the operational efficiency is maximized and BSS network can reach an equilibrium state during dynamic optimization. Compared to other methods, MPC approach is shown more effective.

Balancing of Bike-Sharing System via Constrained Model Predictive Control

Sharing transportation systems can significantly promote travelers convenience and efficiency. As a vital part, bike-sharing system (BSS) has effectively solved “the-last-mile” problem during transportation interchange, but bike imbalance between docks severely deteriorates operational efficiency of BSS. In this paper, we present multi-objective optimization and predictive control approaches to tackle the bike rebalancing problem, where optimal redistributing strategies can maximize the operational efficiency of BSS with respects to equilibrium state and redistribution cost. A dock-based BSS dynamic network is modeled based on a proximity graph, in which connection relation, bike usage, and redistribution flow are formulated. To measure the operational efficiency, a performance metric is presented to consider both benefits of user and operator. To satisfy bike renting, returning, and redistributing requirements, model predictive control (MPC) is then employed to compute feasible and optimal redistribution strategies based on the network model. The effectiveness of multi-objective optimization and MPC is verified on different topologies of BSS. Experimental results show that when the BSS reaches the equilibrium state, the operational efficiency will be maximized in the proposed approaches.

Network Rebalance and Operational Efficiency of Sharing Transportation System: Multi-Objective Optimization and Model Predictive Control Approaches

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A twin learning framework for traveling salesman problem based on autoencoder, graph filter, and transfer learning

Balancing of Bike-Sharing System via Constrained Model Predictive Control

Network Rebalance and Operational Efficiency of Sharing Transportation System: Multi-Objective Optimization and Model Predictive Control Approaches