Neighborhood Search Research Articles

An online surrogate-assisted neighborhood search algorithm based on deep neural network for thermal layout optimization

Thermal layout optimization problems are common in integrated circuit design, where a large number of electronic components are placed on the layout, and a low temperature (i.e., high efficiency) is achieved by optimizing the positions of the electronic components. The operating temperature value of the layout is obtained by measuring the temperature field from the expensive simulation. Based on this, the thermal layout optimization problem can be viewed as an expensive combinatorial optimization problem. In order to reduce the evaluation cost, surrogate models have been widely used to replace the expensive simulations in the optimization process. However, facing the discrete decision space in thermal layout problems, generic surrogate models have large prediction errors, leading to a wrong guidance of the optimization direction. In this work, the layout scheme and its temperature field are represented by images whose relation can be well approximated by a deep neural network. Therefore, we propose an online deep surrogate-assisted optimization algorithm for thermal layout optimization. First, the iterative local search is developed to explore the discrete decision space to generate new layout schemes. Then, we design a deep neural network to build an image-to-image mapping model between the layout and the temperature field as the approximated evaluation. The operating temperature of the layout can be measured by the temperature field predicted by the mapping model. Finally, a segmented fusion model management strategy is proposed to online updates the parameters of the network. The experimental results on three kinds of layout datasets demonstrate the effectiveness of our proposed algorithm, especially when the required computational budget is limited.

Selective disassembly sequence planning under uncertainty using trapezoidal fuzzy numbers: A novel hybrid metaheuristic algorithm

Nowadays, recycling end-of-life (EoL) products has emerged as a vital approach to address resource scarcity. Within the recycling process, disassembly plays a pivotal role and has garnered substantial attention from researchers. Disassembly sequence planning (DSP) is a crucial method to enhance disassembly efficiency. Among the various DSP models, selective disassembly sequence planning (SDSP) has gained prominence as a means to save time and reduce costs. It empowers operators to locate specific components or materials in real time, thereby boosting efficiency and minimising resource wastage. However, a notable research gap exists in the domain of SDSP, particularly in uncertain environments. To bridge this gap and render SDSP solutions more practical for real-world disassembly operations, this study adopts trapezoidal fuzzy numbers to represent uncertain information within the disassembly process and formulates a comprehensive SDSP model. In response to the intricate challenges posed by this problem, we propose a hybrid approach termed nondominated sorting genetic algorithm-II with simulated large neighborhood search (NSGA–II–SLNS). This innovative algorithm leverages the strengths of the nondominated sorting genetic algorithm-II (NSGA-II), simulated annealing algorithm (SA), and large neighborhood search (LNS). Additionally, we introduce several novel search operators into NSGA–II–SLNS, including a crossover and mutation strategy based on chaotic mapping, as well as a local search operator founded on the SA criterion and LNS. To assess the effectiveness of the proposed algorithm and model, extensive numerical case studies are conducted in this research. The outcomes contribute to the advancement of rapid, nearly optimal SDSP strategies in the face of uncertainty and ambiguity in problem settings.

A neighborhood comprehensive learning particle swarm optimization for the vehicle routing problem with time windows

Vehicle routing problem with time windows (VRPTW), which is a typical NP-hard combinatorial optimization problem, plays an important role in modern logistics and transportation systems. Recent years, heuristic and meta-heuristic algorithms have attracted many researchers’ attentions to solve the VRPTW problems. As an outstanding meta-heuristic algorithm, particle swarm optimization (PSO) algorithm exhibits very promising performance on continuous problems. However, how to adapt PSO to efficiently deal with VRPTW is still challenging work. In this paper, we propose a neighborhood comprehensive learning particle swarm optimization (N-CLPSO) to solve VRPTW. To improve the exploitation capability of N-CLPSO, we introduce a new remove-reinsert neighborhood search mechanism, which consists of the removed operator and the reinsert operator. When performing the removed operator, the probability of adjacency between two customers is calculated by an information matrix (IM), which is constructed based on the customers’ time-space information and elite individuals’ local information. When executing the reinsert operator, the IM and a cost matrix (CM), which is introduced to record the cost of customer insertion, are used to find an optimal insert position. Moreover, to enhance the exploration of N-CLPSO, a semi-random disturbance strategy is proposed, in which elites’ longest common sequences (LCS) are saved, aiming to prevent population degradation. The N-CLPSO algorithm is tested on 56 Solomon benchmark instances, and it attains the optimal solutions on 29 instances. The simulation results and comparison results illustrate that the proposed algorithm outperforms or can compete with the majority of other 3 PSO variants as well as other 12 state-of-the-art algorithms.

Incremental density clustering framework based on dynamic microlocal clusters

With the prevailing development of the internet and sensors, various streaming raw data are generated continually. However, traditional clustering algorithms are unfavorable for discovering the underlying patterns of incremental data in time; clustering accuracy cannot be assured if fixed parameters clustering algorithms are used to handle incremental data. In this paper, an Incremental-Density-Micro-Clustering (IDMC) framework is proposed to address this concern. To reduce the succeeding clustering computation, we design the Dynamic-microlocal-clustering method to merge samples from streaming data into dynamic microlocal clusters. Beyond that, the Density-center-based neighborhood search method is proposed for periodically merging microlocal clusters to global clusters automatically; at the same time, these global clusters are updated by the Dynamic-cluster-increasing method with data streaming in each period. In this way, IDMC processes sensor data with less computational time and memory, improves the clustering performance, and simplifies the parameter choosing in conventional and stream data clustering. Finally, experiments are conducted to validate the proposed clustering framework on UCI datasets and streaming data generated by IoT sensors. As a result, this work advances the state-of-the-art of incremental clustering algorithms in the field of sensors’ streaming data analysis.

Multi-Depot Vehicle Routing Problem with Drones: Mathematical formulation, solution algorithm and experiments

The use of Unmanned Aerial Vehicles (UAVs) is expected to grow rapidly in the coming years, driven by technological advancements, cost-effectiveness, and the increasing demand for faster and more efficient delivery solutions. This article deals with the mathematical formulation of the Multi-Depot Vehicle Routing Problem with Drones (MDVRP-D), whereby a set of heterogeneous trucks, each paired with a UAV, are located in different depots. Both types of vehicles deliver goods to customers; UAVs are dispatched from trucks while en route to make the last-mile delivery. A metaheuristic algorithm based on the Ant Colony Optimization (ACO) principle is proposed as the solution. This algorithm has been adapted for this newly proposed problem; the novel mechanics include the probabilistic decision to dispatch an UAV, the selection of a customer to be served, and local search optimization. Extensive computational experiments are performed to verify the proposed algorithm. First, its performance is compared with Adaptive Large Neighborhood Search (ALNS) metaheuristics on a set of Vehicle Routing Problem with Drones (VRP-D) benchmarks. A set of various benchmark instances are subsequently proposed for the newly formulated MDVRP-D (differing in complexity and graph topology). Finally, the behavior of the proposed algorithm is thoroughly analyzed, especially in respect of features connected with UAVs. The findings presented in this article provide valuable contributions to the NP-hard models related to the Travelling Salesman Problem (TSP) and to the very popular ACO-based algorithms.

A slack speed-up based discrete artificial bee colony algorithm for resource-constrained operating room scheduling problem

In today’s world, with the rapidly increasing demand for health care and the scarcity of health care resources, it is very important to provide more services under limited conditions. This paper focuses on a resource-constrained operating room scheduling (RCORS) problem with the characteristic of multi-stage. First, a mixed integrated programming model is presented to uncover the impact of no-wait and resources on the problem. Second, a slack speed-up based discrete artificial bee colony (SDABC) algorithm is devised to tackle varying configurations effectively. The solution is represented as a one-dimensional list, from which a complete schedule is derived by a decoding heuristic. Additionally, a speed-up evaluation procedure based on a slack-resource model is developed. In SDABC, an initial population with high quality and diversity is generated based on a problem-specific heuristic. Different strategies are employed in the three stages of SDABC to balance the local optimal, population optimal, and global optimal. A variable neighborhood search is introduced to enhance local exploration capabilities. Finally, we conduct a large number of experiments to verify the performance of the proposed algorithm. Numerical analysis proves that SDABC has significant advantages for solving the RCORS problem.

Self-adaptive General Variable Neighborhood Search algorithm for parallel machine scheduling with unrelated servers

This paper introduces a machine learning-augmented General Variable Neighborhood Search algorithm, termed Self-adaptive General Variable Neighborhood Search (SGVNS), for solving the two-identical parallel machine scheduling problem with unrelated servers. The studied problem is of high practical relevance in various industries and presents a significant gap in the existing literature. The motivation for this study stems from the healthcare industry, specifically surgery scheduling with limited anesthesiologists’ availability. Two mathematical formulations are presented to solve this scheduling problem. However, these formulations are limited to small-scale problems, prompting the need for an efficient metaheuristic. Therefore, a novel variant of the General Variable Neighborhood Search metaheuristic is developed, incorporating a self-adaption mechanism based on an Extreme Learning Machine (ELM) algorithm. Extensive experiments are conducted to assess the performance of the SGVNS metaheuristic on various classes of instances. The results demonstrate the effectiveness and efficiency of SGVNS in solving previously unsolved small-sized instances, outperforming both formulations while maintaining reasonable computation times.

Flexible product quantization for fast approximate nearest neighbor search

Flexible product quantization for fast approximate nearest neighbor search

Research on the scheduling method of ground resource under uncertain arrival time

We present a two-stage scheduling approach including proactive and reactive scheduling to solve the ground resource scheduling problem with uncertain arrival time. In the first stage, an integer programming model is constructed to minimize the delay and transfer costs. After solving this model, we obtain a baseline scheduling plan that considers the service arrival time uncertainty. In the second stage, the feasibility of the subsequent benchmark plan is evaluated based on the current state of the services and resources. The reactive scheduling model is enabled when trigger conditions are met. Moreover, an improved adaptive large neighborhood search is designed to solve the proactive scheduling model effectively. Real data from an international airport in South China is used as a test case to compare different scheduling strategies. The results show that it is difficult to handle the uncertainty of the problem with the benchmark plan that simply considered buffer time. Compared with rolling time-domain scheduling, the average transfer cost of the scheduling strategy proposed in this paper increased slightly, but the average service delay cost can be reduced significantly. Algorithm-wise, instances of different scales are designed to verify the effectiveness of the improved adaptive large neighborhood search algorithm. The efficiency of the algorithm scheme is better than that of the Gurobi solver scheme in medium to large-scale problems. Therefore, the forward and reactive strategies can better handle the uncertainty of airport ground protection services as they can simultaneously guide the allocation and utilization of airport ground protection resources.

An efficient and eco-friendly operation mode for container transshipments through optimizing the inter-terminal truck routing problem

To enhance efficiency and reduce carbon emissions of waterway-waterway transshipments for a multi-terminal port group, a crucial strategic planning issue lies in determining whether a centralized or distributed mode of feeder vessel service is more efficient and eco-friendly. This paper proposes a two-stage integer-optimized model to optimize intra- and inter-terminal operations and determine the most effective mode of operation for feeder vessel services. Our model integrates the multi-terminal berth allocation problem and inter-terminal transportation (ITT), considering different cases of transshipment flows. To address this optimization problem, we develop a heuristic approach based on genetic and adaptive large neighborhood search algorithms, which is ideal method for large-scale scenarios. In our case study, the proposed optimization model and heuristic methods were adopted to determine the best operation mode for North Operation Area in Shanghai Yangshan Deep-Water Port (namely Yangshan Phase V). The results indicate that Yangshan Phase V should operate as a feeder vessel service terminal, providing primary feeder vessel services and complementary liner vessel services. This development plan not only minimizes container transshipment delays and overall ITT costs, but also helps reduce approximately 707.46 tons of annual carbon emissions caused by ITT at Yangshan Deep-Water Port.

A multi-day waste collection and transportation problem with selective collection and split delivery

This paper addresses a multi-day waste collection and transportation problem with selective collection and split delivery (MDWCTP-SCSD). Rather than emptying waste according to a fixed schedule, garbage trucks only visit community waste collection sites whose waste level reaches a predetermined threshold, reducing collection costs but increasing overflow risk. Collection sites likely to overflow the next day if unvisited today are also selected for preventive collection. Split delivery is permitted. The MDWCTP-SCSD is formulated as a mixed integer linear programming model. To solve this NP-hard problem, a heuristic combining variable neighborhood search and simulated annealing is proposed. In addition to classical operators, three new operators are developed to handle split delivery. Experiments demonstrate the proposed algorithm is competitive in solving two split delivery vehicle routing benchmark sets; outperforms CPLEX in solving small-scale instances within an hour; and quickly solves large-scale instances with high quality. Noteworthy findings indicate fleet costs and size can be reduced by approximately 50% if only visiting sites whose waste level reaches 80%.

Robot dispatching problem for compact storage and retrieval systems with dual-command transactions

A robotic compact storage and retrieval system (RCSRS) is a grid-based system that has been popularly implemented by online retailers around the world recently. This article addresses a robot dispatching problem for RCSRS, aiming to reduce robot empty travelling time and workstation idle time. The robot dispatching problem is to assign tasks to robots when the task sequences in workstations are given. Additionally, the robots follow a fast dual-command transaction process in this problem. Hence, a mixed integer programming model catering for the dual-command transaction process is formulated to optimize system performance. Furthermore, an adaptive neighbourhood search with an ε-greedy algorithm is proposed to solve the problem. The experimental results substantiate the superiority of the authors' proposed algorithm compared to various algorithms used in practice and discussed in the relevant literature. Moreover, optimal system configurations for the instances generated are suggested.

Cost optimization model design of fresh food cold chain system in the context of big data

The assessment of cold chain logistics for fresh products can be more precise with high-dimensional information data, providing valuable insights for the optimization of associated costs. Nonetheless, traditional data processing techniques fail to meet the processing efficiency required for such high-dimensional cold chain logistics data. Therefore, this paper proposes a spectral clustering algorithm based on the local standard deviation and optimized initial center, which comprehensively analyzes the fixed, transportation, refrigeration, and cargo damage costs of cold chain logistics. Additionally, this algorithm includes a variation operator based on clustering and introduces a large neighborhood search mechanism for optimizing the individual connectivity gene layer after selecting the gene layer site for variation. Simulation results demonstrate that the proposed algorithm exhibits better convergence in 15 iterations, reduces error rates, and significantly cuts down on the clustering process time. This ultimately leads to a reduction in the total cost of cold chain calculation.

A hybrid variable neighborhood search heuristic for the sustainable time-dependent truck-drone routing problem with rendezvous locations

A hybrid variable neighborhood search heuristic for the sustainable time-dependent truck-drone routing problem with rendezvous locations

Exploring fairness in food delivery routing and scheduling problems

Demand for delivery of take-away meals to customers has been growing worldwide, with deliveries often performed by non-specialised gig economy couriers working for online platform operators such as Deliveroo or Just Eat. This has led to the introduction of the ‘meal delivery problem’, characterised by a series of individual pickup and delivery tasks to be assigned to available couriers. While there is a vast set of algorithms proposed in the literature that aim to minimise total workload, very little attention has been given to equitably distributing work between couriers. We propose a new multi-objective problem that is aiming at distributing orders equitably between couriers as well as minimising total workload, where all information is known upfront. We propose an integer linear programming (ILP) model with a weighted objective function that is used to derive the Pareto front in small-scale problems by exploiting the ϵ−constraint approach. This formulation has been proven to solve in a reasonable time for problems with up to 60 orders, however, the optimal Pareto front can only be computed within a reasonable time for problems up to 30 orders. For problems with more orders, we propose a Variable Neighbourhood Search (VNS) algorithm, for which the fitness evaluation evolves in order to explore a wider set of the solution space. The VNS is compared against the ILP and also tested on more realistic size instances with up to 3123 orders, improving the performance over the business as usual and shows that equitable distribution of work can be achieved alongside reducing the total travelled distance.

Unrelated parallel batch processing machine scheduling with time requirements and two-dimensional packing constraints

In various complex manufacturing systems such as wafer fabrication, ceramic processes, and additive manufacturing, a single machine often performs multiple distinct jobs simultaneously, requiring non-overlapping positions in the 2D-plane. As the demand for manufacturing with two-dimensional (2D) packing constraints continues to rise, there is an urgent need to address the integrated scheduling of batch processing machines (BPMs) and 2D packing. However, only a limited number of studies have explored BPM scheduling problems with 2D packing constraints. In this study, we focus on the scheduling of unrelated parallel BPMs to process jobs with non-identical sizes, unequal release dates, and due dates. To model the machine’s capacity, we move beyond the traditional one-dimensional knapsack approach and consider it as a 2D rectangle, taking into account the 2D packing constraints. It is crucial to ensure that no overlapping or stacking occurs within the machine’s capacity under these constraints. To address this problem, we propose a mixed integer linear programming model as the initial approach. Additionally, we develop an adaptive large neighborhood search (ALNS) heuristic specifically designed for handling large instances. The ALNS incorporates eleven removal operators and seven insertion operators, offering a comprehensive exploration and improvement of solutions. To tackle the packing procedure, we adopt two distinct packing patterns, namely Skyline and Open Space. These patterns play a significant role in optimizing the packing of jobs within the machine’s capacity. Furthermore, we conduct extensive computational experiments, generating 48 categories of instances with varying job attributes. These experiments serve to compare the performance of the two packing patterns and validate the effectiveness of our ALNS approach. In conclusion, our research addresses the critical gap in BPM scheduling problems with 2D packing constraints. By introducing a mixed integer linear programming model and developing the ALNS heuristic with diverse operators, coupled with the adoption of different packing patterns, we present a comprehensive approach to tackle this complex problem. Computational experiments are conducted to compare packing patterns and verify the performance of our ALNS method.

The paired pickup and delivery problem with profit in a two-echelon delivery system with multiple trucks and drones

ABSTRACT Recently researchers proposed truck and drone coordination to increase delivery efficiency and suggested various truck-drone routing problems. In this paper, we also focused on truck and drone coordination and introduced the paired pickup and delivery problem with profit in a two-echelon delivery system. To solve the problem, we propose a hybrid variable neighborhood search algorithm. For this algorithm, we adapted existing neighborhood search operators from the literature and considering the structure of the proposed problem, developed new neighborhood search operators. Also, we have carried out numerous computational experiments to evaluate the proposed solution methods’ performance, where the results show the efficiency of the proposed algorithms. The results highlight that in the paired pickup and delivery problem, for small values of drone operational costs, employing the two-echelon truck and drone routing system increases the profit by up to 5.6 percent in comparison to the vehicle routing system with drones.

Adaptive Multi-Objective Algorithm for the Sustainable Electric Vehicle Routing Problem in Medical Waste Management

This paper addresses the complex issue of managing medical waste transportation using electric vehicles, with the goal of minimizing both energy consumption and the risks associated with hazardous waste. A multi-objective mixed-integer linear programming model is introduced, incorporating practical factors such as time windows, partial recharge policy, load-dependent discharge, infection risk, and trips to waste disposal facilities. Our proposed method, a combination of the multi-objective evolutionary algorithm using decomposition (MOEA/D) with adaptive large neighborhood search (ALNS) and local search (LS) techniques, is referred to as MOEA/D-ALNS. This method demonstrates superior performance compared with the non-dominated sorting genetic algorithm, NSGA-II, modified MOEA/D and MOEA/D-LNS in benchmark instances with realistic assumptions. Our experimental results revealed an inverse correlation between energy consumption and risk objectives. Sensitivity analyses showed that eliminating time-window constraints results in more energy-efficient and safer routes while maintaining a slightly lower battery energy level can strike an ideal balance between energy consumption, risk, and battery health. This research contributes to the understanding of infectious medical waste management with its consideration of electric vehicles and waste disposal. It lays a solid foundation for future studies aiming to improve the sustainability and efficiency of medical waste routing practices.

Optimization of Takeaway Delivery Based on Large Neighborhood Search Algorithm

<div>The drone logistics distribution method, with its small size, quick delivery, and zero-touch, has progressively entered the mainstream of development due to the global epidemic and the rapidly developing global emerging logistics business. In our investigation, a drone and a delivery man worked together to complete the delivery order to a customer’s home as quickly as possible. We realize the combined delivery network between drones and delivery men and focus on the connection and scheduling between drones and delivery men using existing facilities such as ground airports, unmanned stations, delivery men, and drones. Based on the dynamic-vehicle routing problem model, the establishment of a delivery man and drone with a hybrid model, in order to solve the tarmac unmanned aerial vehicle for take-out delivery scheduling difficulties, linking to the delivery man and an adaptive large neighborhood search algorithm solves the model. The objective function is to reduce customer waiting time. The average delivery time for orders was subsequently shortened thanks to the optimization of the cooperative delivery solution for drones and delivery personnel.</div>

Lower risks of new-onset hip fracture risks in users of sodium glucose cotransporter 2 (SGLT2) inhibitors versus users of dipeptidyl peptidase-4 (DPP4) inhibitors: a propensity score-matched analysis

Abstract Purpose This study aimed to compare the effects of sodium glucose cotransporter 2 inhibitors (SGLT2I) and dipeptidyl peptidase-4 inhibitors (DPP4I) on new-onset hip fractures. Methods This was a retrospective population-based cohort study including type-2 diabetes mellitus patients treated with either SGLT2I or DPP4I between January 1st 2015 and December 31st 2020 in Hong Kong. The primary outcome was new-onset hip fracture and the secondary outcome was all-cause mortality. Propensity score matching (1:1 ratio) using the nearest neighbour search was performed. Univariable and multivariable Cox regression were applied to identify significant predictors. Competing risks models and multiple approaches using the propensity score were performed. Results This cohort included 56393 patients with type-2 diabetes mellitus (median age: 62.1 years old [interquantile range, IQR]: 54.2-71.1; 57.45% males), of which 20432 patients ([incidence rate, IR]: 36.23%) used SGLT2I and 35961 patients (IR: 63.77%) used DPP4I. After the 1:1 propensity score matching, 449 (IR: 1.09%) patients had hip fractures, and 2012 patients (IR: 4.92%) died. SGLT2I was associated with significantly lower risks of hip fractures after adjusting for the demographics, past comorbidities, non-SGLT2I/DPP4I medications and laboratory results (hazard ratio: 0.55; 95% confidence interval: 0.42-0.89; P=0.0036). The results were consistent in the competing risk models and the different propensity matching approaches. Conclusions SGLT2I was associated with lower risks of new-onset hip fractures after propensity score matching and adjustments.Figure 1