Large-scale Neighborhood Search Algorithm Research Articles

A large-scale neighborhood search algorithm for multi-activity tour scheduling problems

A large-scale neighborhood search algorithm for multi-activity tour scheduling problems

Research on a Joint Distribution Vehicle Routing Problem Considering Simultaneous Pick-Up and Delivery under the Background of Carbon Trading

In order to explore the positive impact of the joint distribution model on the reduction in logistics costs in small-scale logistics enterprises, considering the demand on enterprises for simultaneous pick-up and delivery, as well as the cost of carbon emissions, this study considers the vehicle routing problem of simultaneous pick-up and delivery under a joint distribution model. First of all, an independent distribution model and a joint distribution model including fixed transportation, variable transportation, time penalty, and carbon emissions costs are established; second, by adding the self-adaption cross-mutation probability and the destruction and repair mechanism in the large-scale neighborhood search algorithm, the genetic algorithm is improved to adapt to the solution of the model in this paper, and the effectiveness of the improved algorithm is verified and analyzed. It is found that the improved genetic algorithm is more advantageous than the original algorithm for solving the problems of both models designed in this paper. Finally, the improved genetic algorithm is used to solve the two models, and the results are compared and analyzed. It is found that the joint distribution model can reduce the total cost by 6.61% and the carbon emissions cost by 5.73%. Additionally, the impact of the carbon trading mechanism on the simultaneous pick-up and delivery vehicle routing problem under the joint distribution model is further explored. The results of this study prove that enterprises can effectively reduce costs, improve profits, reduce carbon emissions, and promote the sustainable development of logistics enterprises under the condition of joint distribution.

Research on delivery problem based on two-stage multi-objective optimization for takeout riders

From the perspective of takeout riders, in order to ensure the safety of riders, reasonably arrange the distribution tasks of riders and improve the distribution efficiency of the platform, this paper proposes a new distribution mode. In the future, efficiency is the core competitiveness of the takeout platform, and humanization is an important condition for the long-term development of the takeout platform. Both are indispensable. This paper simplifies the traditional many to many distribution mode into one to many distribution mode, and realizes the new distribution mode through two-stage multi-objective optimization algorithm. In the first stage, genetic algorithm is applied to analyze and optimize the new distribution mode for the first time; In the second stage, the large-scale neighborhood search algorithm is applied to destroy and reorganize the distribution route obtained in the first stage according to the objective function to obtain the optimal distribution route. Aiming at the three scenarios of consumer group distribution generated by random simulation, high-density and low-density consumer group distribution in Shenyang, this paper uses MATLAB tools to simulate. Through the analysis of simulation results, the working hours and delivery distances of takeout riders are reduced, and the maximum delivery volume is limited. So the method proposed in this paper can not only improve the intelligent level of distribution, but also propose a more humanized distribution mode from the perspective of takeout riders, which greatly supports the sustainable development of the delivery system of network connected takeout platform.

Two-Stage Stochastic Programming Model for Improving Transportation Network Resilience of Relief Supplies in Sequential Hazards Scenario

Cascading failure of road transport networks caused by complex sequential natural hazards adversely affects the use of pre-positioning relief supplies. Therefore, it is vital to improve the transportation network resilience of relief supplies by using an optimization model. The purpose of this paper is to pre-position and distribute relief supplies in uncertain scenarios of sequential hazards. A two-stage stochastic programming model to maximize the total resilience is proposed to provide an optimal plan against the uncertain impact of sequential natural hazards. The combined impact of the Jiuzhaigou 7.0 magnitude earthquake and its associated landslide is a prototype disaster scenario for the implementation of the method proposed in this paper. The model is solved by a neighborhood search-based genetic algorithm (NS-based GA), which has both the global search capability of a genetic algorithm and the local search capability of a large-scale neighborhood search algorithm, can improve the solution finding capability. A case study focusing on finding the optimal solution for the pre-position and distribution of relief supplies in the sequential hazard of Jiuzhaigou earthquake is conducted to illustrate the validity of the proposed model.

Passenger-Centric Slot Allocation at Schedule-Coordinated Airports

Schedule coordination is the primary form of demand management used at busy airports. At its core, slot allocation involves a highly complex combinatorial problem. In response, optimization models have been developed to minimize the displacement of flight schedules from airline requests, subject to physical and administrative constraints. Existing approaches, however, may not result in the best itineraries for passengers. This paper proposes an original passenger-centric approach to airport slot allocation to maximize available itineraries and minimize connecting times. Because of the uncertainty regarding passenger demand, the proposed approach combines predictive analytics to forecast passenger flows in flight networks from historical data and prescriptive analytics to optimize airport slot assignments in view of flight-centric and passenger-centric considerations. The problem is formulated as a mixed-integer nonconvex optimization model. To solve it, we propose an approximation scheme that alternates between flight-scheduling and passenger-accommodation modules and embed it into a large-scale neighborhood search algorithm. Using real-world data from the Singapore Changi and Lisbon Airports, we show that the proposed model and algorithm return solutions in acceptable computational times. Results suggest that slot-allocation outcomes can be made much more consistent with passenger flows at a relatively small cost in terms of flight displacement. Ultimately, this paper provides a new paradigm that can create more attractive flight schedules by bringing together airport-level considerations, airline-level considerations, and, for the first time, passenger-level considerations. Funding: Financial support from the Civil Aviation Authority of Singapore [Project on Airfield Management and Economics] is gratefully acknowledged. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1165 .

Multi-compartment vehicle routing optimization based on a hybrid Genetic Algorithm

To solve the problem of efficient distribution of convenience store chains, this paper creates a multi-compartment vehicle routing problem model with time- windows (MCVRP-TW) to meet the goal of minimizing the total distribution cost. The solution is based on a hybrid genetic algorithm, which using the “destruction” and “repair” operators of the large-scale neighborhood search algorithm and the Metropolis criterion of simulated annealing for local search operations. The results show that the proposed multi-compartment vehicle routing model with time windows and the hybrid algorithm can meet the requirement of practical route design and reduce the total distribution cost.

Research on LRP Integration of E-Commerce Logistics under the Background of Integration of Collection and Distribution

The distribution network composed of location and route is an important part of e-commerce logistics. With the continuous improvement of e-commerce requirements for logistics level, the practice of planning logistics network only from the perspective of the network location or the vehicle route can no longer meet the actual demand. In addition to the comprehensive consideration of the location-routing problem, the reverse logistics caused by customers’ returning goods should be taken into account. In this paper, the destruction and reorganization strategy of adaptive large-scale neighborhood search algorithm was introduced into the traditional genetic algorithm, so as to conduct research on the logistics location-routing problem under the background of integration of collection and distribution. Finally, the effectiveness of the optimized genetic algorithm was verified by Matlab tools and the existing bench-marking data set of the location-routing problem, which provided reference for the planning and decision-making of logistics enterprises.

Optimization of Marine Debris Collection Routing for Solar and Diesel Hybrid Power Vessels

With the increase of marine debris and the serious destruction of the ecological environment, marine debris collection has become an urgent task facing us. In this paper, the logistics network is used to optimize the routing of marine debris collection vessels. In order to reduce carbon emissions, we choose to use new energy waste collection vessels which have solar and diesel hybrid power. The objection of this paper is to minimize the total cost. The constraints include time window, vessel capacity and carbon emissions, which are solved by large-scale neighborhood search algorithm. Taking the East China Sea as an example, the results show that the total cost and total time can be saved by 20.58% and 17.43% respectively by choosing the best debris collection time. Under different environmental conditions, when the photovoltaic penetration of solar hybrid vessel reaches 75%, the cost can be saved by 22.14%, and the carbon emission can be reduced by 74.7%. Through the analysis of different types of vessels, it is found that 11.85% of the cost can be saved by selecting the best vessel type.

New energy vessel routing and optimization for marine debris collection under uncertain environment

In this paper, the method of logistics network is adopted to optimize the route of marine debris collection by liquefied natural gas hybrid vessels. We regarded the weight of marine debris as a random variable, took capacity constraint as chance constraint, considered tank capacity, time window and carbon emission constraints, and established a model with the objective of minimizing the total cost. After transforming the random chance constraint into a deterministic constraint, a large-scale neighborhood search algorithm was used to solve it. The model was verified by taking the Yellow Sea as an example, and the results showed that the best collection time can save 21.74% of total cost and 22.47% of travel time. Compared with pure diesel vessels, liquefied natural gas vessels can save costs by 32.98% and reduce carbon emission by 28.65%. Vessel types with the best capacity can save costs by 22.07% and reduce carbon emission by 25.53%.

Heterogeneous fleet recyclables collection routing optimization in a two-echelon collaborative reverse logistics network from circular economic and environmental perspective

In recent years, production and consumption activities carried out in accordance with the “take-make-dispose” process have caused environmental damage and resource waste. Reverse logistics based on the concept of “sustainable development and circular economy” has gained public attention, including the recycling and reuse of recyclable wastes. In order to explore the impact of recyclable wastes transportation on cost, environment and resources, this paper focuses on a vehicle routing problem considering electric heterogeneous fleet for a two-echelon recycling network, recycling stations and recycling centers. With the goal of minimizing the total cost, a recycling heterogeneous fleet electric vehicle routing model with time windows is established under the consideration of vehicle load constraints, vehicle type constraints on the weight of loaded recyclable wastes and customer's allowable service time constraints. For carbon emissions reduction and environment protection, vehicles used in recycling stations are designated as electric vehicles. Then, a hybrid genetic algorithm with a large-scale neighborhood search algorithm is proposed. Finally, numerical experiments are conducted on vehicle types in recycling stations, and the computational results show the use of heterogeneous fleets could reduce costs. By analyzing the relationship between customer service time and total cost, the best service time rule could be obtained in the case of customer satisfaction. Meanwhile, by analyzing the relationship between the number and the loading capacity of electric vehicles in recycling stations and total cost, the best number and the loading capacity of electric vehicles could be obtained. This heterogeneous fleet recyclables collection routing model can greatly support the construction of recycling centers and the planning of recycling tasks, and provide a basis for recycling companies to deal with the relationship between economy, environment and resources.

A model and optimization-based heuristic for the operational aircraft maintenance routing problem

This paper investigates the Operational Aircraft Maintenance Routing Problem (OAMRP). Given a set of flights for a specific homogeneous fleet type, this short-term planning problem requires building feasible aircraft routes that cover each flight exactly once and that satisfy maintenance requirements. Basically, these requirements enforce an aircraft to undergo a planned maintenance at a specified station before accumulating a maximum number of flying hours. This stage is significant to airline companies as it directly impacts the fleet availability, safety, and profitability. The contribution of this paper is twofold. First, we elucidate the complexity status of the OAMRP and we propose an exact mixed-integer programming model that includes a polynomial number of variables and constraints. Furthermore, we propose a graph reduction procedure and valid inequalities that aim at improving the model solvability. Second, we propose a very large-scale neighborhood search algorithm along with a procedure for computing tight lower bounds. We present the results of extensive computational experiments that were carried out on real-world flight networks and attest to the efficacy of the proposed exact and heuristic approaches. In particular, we provide evidence that the exact model delivers optimal solutions for instances with up to 354 flights and 8 aircraft, and that the heuristic approach consistently delivers high-quality solutions while requiring short CPU times.

Very large-scale neighborhood search algorithms for the design of service overlay networks

Service Overlay Networks (SONs) allow virtual operators to create and deploy value-added Internet services with Quality of Service guarantees, while leaving the underlying network infrastructure unchanged. The deployment of a SON can be very expensive, and hence its planning requires careful decisions, including the overlay nodes’ placement and the capacity provisioning of the access links that connect the end-users to the SON infrastructure.In this work we first propose a novel Integer Linear Programming (ILP) model for the overlay network design problem which selects the optimal number and position of overlay nodes, the capacity reserved on each overlay link, as well as the optimal routing of the incoming traffic demands.Since such model can be solved to the optimum only for small network instances, we further propose an efficient and novel tabu search based heuristic for the planning of SONs that combines polynomial size and very large-scale neighborhoods. The very large-scale neighborhood of the solution given by tabu search is explored efficiently to obtain in a short time a new one that is both far from the current solution and cost-decreasing.We provide numerical results of the proposed heuristic on a set of realistic, large-size instances, including real ISP topologies, and discuss the effect of different parameters on the characteristics of the planned networks. Furthermore, we compare such results with the bound obtained solving our ILP model in small network scenarios. We show that in the considered network topologies the proposed heuristic performs very close to the optimum with a short computation time, thus providing a promising framework for the design of SONs.

Corrections to "Constrained weapon-target assignment: enhanced very large scale neighborhood search algorithm"

On page 201 of the above-named article, there are some errors in the statement of Problem I) that are corrected here.

Constrained Weapon–Target Assignment: Enhanced Very Large Scale Neighborhood Search Algorithm

Optimization problems solved using very large scale neighborhood (VLSN) search algorithms include scheduling problems, the capacitated minimum spanning tree problem, the traveling salesman problem, and weapon-target assignment (WTA). This correspondence paper presents an enhanced VLSN search algorithm for obtaining feasible solutions and constructing improvement graphs. This enhanced VLSN search algorithm solves the constrained WTA (CWTA) problem, in which the number of interceptors available to each weapon and the number of interceptors allowed to fire at each target have upper bounds. The proposed enhanced VLSN search algorithm can solve a CWTA problem with 100 targets and 100 weapons (where the upper bound on the number of interceptors for each weapon is one and both the lower and upper bounds on the number of interceptors for each target are equal to one) within an average of 3 s. This study demonstrates that the proposed Enhanced VLSN is superior to existing approaches.

A very large scale neighborhood search algorithm for the q-mode problem

The q-mode problem is a combinatorial optimization problem that arises in the context of partitioning a given collection of data vectors with categorical attributes. A neighborhood search algorithm is proposed for solving the q-mode problem. This algorithm is based on a very large scale neighborhood that is implicitly searched using network flow techniques. The algorithm is evaluated through a computational experiment using randomly generated instances. The results show that in general this algorithm obtains very-good-quality local optima, and that in instances with strong natural clusters the algorithm consistently finds optimal or near-optimal solutions.

A Very Large-Scale Neighborhood Search Algorithm for the Combined Through-Fleet-Assignment Model

The fleet-assignment model (FAM) for an airline assigns fleet types to the set of flight legs that satisfies a variety of constraints and minimizes the cost of the assignment. A through connection at a station is a connection between an arrival flight and a departure flight at the station, both of which have the same fleet type assigned to them, which ensures that the same plane flies both legs. Typically, passengers are willing to pay a premium for through connections. The through-assignment model (TAM) identifies a set of profitable throughs between arrival and departure flights flown by the same fleet type at each station to maximize the through benefits. TAM is usually solved after obtaining the solution from FAM. In this sequential approach, TAM cannot change the fleeting to get a better through assignment, and FAM does not take into account the through benefits. The goal of the combined through-fleet-assignment model (ctFAM) is to come up with a fleeting and through assignment that achieves the maximum combined benefit of the integrated model. We give a mixed integer-programming (MIP) formulation of ctFAM that is too large to be solved to even near optimality within allowable time for the data obtained by a major U.S. airline. We thus focus on neighborhood search algorithms for solving ctFAM, in which we start with the solution obtained by the previous sequential approach (that is, solving FAM first, followed by TAM) and improve it successively. Our approach is based on generalizing the swap-based neighborhood search approach of Talluri (1996) for FAM, which proceeds by swapping the fleet assignment of two flight paths flown by two different plane types that originate and terminate at the same stations and the same times. An important feature of our approach is that the size of our neighborhood is very large; hence the suggested algorithm is in the category of very large-scale neighborhood (VLSN) search algorithms. Another important feature of our approach is that we use integer programming to identify improved neighbors. We provide computational results that indicate that the neighborhood search approach for ctFAM provides substantial savings over the sequential approach of solving FAM and TAM.

A Very Large-Scale Neighborhood Search Algorithm for the Combined Through-Fleet-Assignment Model

The fleet-assignment model (FAM) for an airline assigns fleet types to the set of flight legs that satisfies a variety of constraints and minimizes the cost of the assignment. A through connection at a station is a connection between an arrival flight and a departure flight at the station, both of which have the same fleet type assigned to them that ensures that the same plane flies both legs. Typically, passengers are willing to pay a premium for through connections. The throughassignment model (TAM) identifies a set of profitable throughs between arrival and departure flights flown by the same fleet type at each station to maximize the through benefits. TAM is usually solved after obtaining the solution from FAM. In this sequential approach, TAM cannot change the fleeting in order to get a better through assignment, and FAM does not take into account the through benefits. The goal of the combined through-fleet-assignment model (ctFAM) is to come up with a fleeting and through assignment that achieves the maximum combined benefit of the integrated model. We give a mixed integer-programming formulation of ctFAM that is too large to be solved to even near-optimality within allowable time for the data obtained by a major US airline. We thus focus on neighborhood search algorithms for solving ctFAM, in which we start with the solution obtained by the previous sequential approach (that is, solving FAM first and followed by TAM) and improve it successively. Our approach is based on generalizing the swap-based neighborhood search approach of Talluri (1996) for FAM which proceeds by swapping the fleet assignment of two flight paths flown by two different plane types that originate and terminate at the same stations and the same times. An important feature of our approach is that the size of our neighborhood is very large; hence the suggested algorithm is in the category of very large-scale neighborhood (VLSN) search algorithms. Another important feature of our approach is that we use integer programming to identify improved neighbors. We provide computational results that indicate that the neighborhood search approach for ctFAM provides substantial savings over the sequential approach of solving FAM and TAM.

A very large-scale neighborhood search algorithm for the multi-resource generalized assignment problem

We propose a metaheuristic algorithm for the multi-resource generalized assignment problem (MRGAP). MRGAP is a generalization of the generalized assignment problem, which is one of the representative combinatorial optimization problems known to be NP-hard. The algorithm features a very large-scale neighborhood search, which is a mechanism of conducting the search with complex and powerful moves, where the resulting neighborhood is efficiently searched via the improvement graph. We also incorporate an adaptive mechanism for adjusting search parameters, to maintain a balance between visits to feasible and infeasible regions. Computational comparisons on benchmark instances show that the method is effective, especially for types D and E instances, which are known to be quite difficult.

A composite very large-scale neighborhood structure for the capacitated minimum spanning tree problem

The capacitated minimum spanning tree (CMST) problem is to find a minimum cost spanning tree in a network where nodes have specified demands, with an additional capacity constraints on the subtrees incident to a given source node s. The capacitated minimum spanning tree problem arises as an important subproblem in many telecommunication network design problems. In a recent paper, Ahuja et al. (Math. Program. 91 (2001) 71) proposed two very large-scale neighborhood search algorithms for the capacitated minimum spanning tree problem. Their first node-based neighborhood structure is obtained by performing multi-exchanges involving several trees where each tree contributes a single node. Their second tree-based neighborhood structure is obtained by performing multi-exchanges where each tree contributes a subtree. The computational investigations found that node-based multi-exchange neighborhood gives the best performance for the homogenous demand case (when all nodes have the same demand), and the tree-based multi-exchange neighborhood gives the best performance for the heterogeneous demand case (when nodes may have different demands). In this paper, we propose a composite neighborhood structure that subsumes both the node-based and tree-based neighborhoods, and outperforms both the previous neighborhood search algorithms for solving the capacitated minimum spanning tree problem on standard benchmark instances. We also develop improved dynamic programming based exact algorithms for searching the composite neighborhood.

Very Large-Scale Neighborhood Search for the Quadratic Assignment Problem

The Quadratic Assignment Problem (QAP) consists of assigning n facilities to n locations so as to minimize the total weighted cost of interactions between facilities. The QAP arises in many diverse settings, is known to be NP-hard, and can be solved to optimality only for fairly small size instances (typically, n is less than or equal to 25). Neighborhood search algorithms are the most popular heuristic algorithms to solve larger size instances of the QAP. The most extensively used neighborhood structure for the QAP is the 2-exchange neighborhood. This neighborhood is obtained by swapping the locations of two facilities and thus has size O(n^2). Previous efforts to explore larger size neighborhoods (such as 3-exchange or 4-exchange neighborhoods) were not very successful, as it took too long to evaluate the larger set of neighbors. In this paper, we propose very large-scale neighborhood (VLSN) search algorithms where the size of the neighborhood is very large and we propose a novel search procedure to heuristically enumerate good neighbors. Our search procedure relies on the concept of improvement graph which allows us to evaluate neighbors much faster than the existing methods. We present extensive computational results of our algorithms on standard benchmark instances. These investigations reveal that very large-scale neighborhood search algorithms give consistently better solutions compared the popular 2-exchange neighborhood algorithms considering both the solution time and solution accuracy.