Neighborhood Search Framework Research Articles

Fleet repositioning in the tramp ship routing and scheduling problem with bunker optimization: A matheuristic solution approach

This paper investigates an important planning problem faced by dry bulk shipping operators, referred to as the Tramp Ship Routing and Scheduling Problem with Bunker Optimization (TSRSPBO). The problem is to maximize the overall profit of a fleet of vessels by selecting cargoes and determining ship routes and schedules. We consider this problem under a set of practically relevant features such as flexibility in cargo quantities, as well as bunkering decisions on where to procure fuel and how much. As a particularly novel feature, we address the regional allocation of vessels at the end of the planning period to be well prepared for meeting (uncertain) future demand. To incorporate this, we consider the TSRSPBO as a two-stage stochastic programming problem, where cargo selection, routing, and bunkering decisions are solved in the first-stage problem, and the recourse cost of fleet repositioning is considered in the second stage. We present arc flow and path flow formulations, where the latter employs a priori generation of feasible routes as input. For solving realistically sized instances, we propose a matheuristic based on an Adaptive Large Neighborhood Search (ALNS) framework that iteratively generates columns and solves the path flow model. Computational experiments based on real data show that this matheuristic finds high-quality solutions for large test instances with 120 cargoes, 30 vessels, and ten bunker ports in less than one hour. Also, considering the TSRSPBO as a two-stage stochastic problem achieves the highest profits and is solved almost as quickly as the deterministic problem variant.

Advancing multi-port container stowage efficiency: A novel DQN-LNS algorithmic solution

Maritime shipping handles approximately 90% of global cargo transport, with 60% utilizing steel containers, playing a pivotal role in the global supply chain. As the global economy recovers in the post-pandemic era, the demand for and efficiency requirements of container transportation are continually increasing. This study introduces a new method to enhance the efficiency and safety of inland river container transport, which has seen significant growth in feeder services. We propose a novel mathematical model specifically tailored for river-sea direct container ships. Unlike traditional staged stowage planning approaches, our model optimizes multi-port stowage planning within a single stage. Additionally, we introduce the Deep Q-Network-based Large Neighborhood Search (DQN-LNS) algorithm, a novel advancement rooted in the Adaptive Large Neighborhood Search (ALNS) framework. This algorithm harnesses the power of Deep Q-Networks (DQN) to dynamically select the optimal ruin and recreate strategies in each iteration, enhancing the stowage planning’s efficiency and accuracy. Extensive numerical experiments validate our contributions, showcasing the algorithm’s capacity to markedly improve operational efficiency and environmental performance in inland river logistics.

The rail-road Dial-a-Ride problem

This paper addresses an original static Dial-A-Ride service designed for sparsely populated areas. The service relies on vehicles capable of switching between the road and an existing vacant railway network, which induces railway scheduling constraints that must be integrated into the solution algorithm. In the context of the static Dial-A-Ride Problem (DARP), a set of known users must be picked up and dropped off to desired locations while adhering to time windows at nodes and maximal riding time constraints. In our problem, there are additional constraints related to the vehicles when they operate on the rail, creating interdependencies between trips.We develop a generic solution method based on the Adaptive Large Neighbourhood Search (ALNS) framework, combined with a set covering approach to target DARPs with shared resource constraints and synchronization. The method is benchmarked on instances created from a specific region in France. The numerical results show the effectiveness of our approach in integrating resource constraints and the utility of using road-rail vehicles to maximize the number of users served.

A variable neighborhood search framework combining improved simulated annealing search to solve the one-dimensional crating problem

The N-P problem, similar to bin packing, has a great impact on us in real life. Similar to the classical combinatorial problem, with the expansion of the problem size, usually there is no way to solve it by enumeration. The common solution is to solve by heuristic methods, in addition to the improved local search method by forbidden search or simulated annealing to solve. However, due to the multivariate nature of the bin packing problem, it is difficult to solve most of the bin packing problems by the above single method. In this paper, a variable neighborhood search combined with an improved local search is used to solve the one-dimensional bin packing problem. The method in this paper is generalizable and can easily improve the existing method to suit new problems, and the results are acceptable.

Improving Additive Manufacturing production planning: A sub-second pixel-based packing algorithm

Additive Manufacturing (AM), the technology of rapid prototyping directly from 3D digital models, has made a significant impact on both academia and industry. When facing the growing demand of AM services, AM production planning (AMPP) plays a vital role in reducing makespan and costs for AM service companies. This research focuses on the AMPP problem under the unrelated machine environment and two-dimensional irregular packing constraints such that the makespan can be minimized. Since there is no promising sub-second algorithm for solving the 2D irregular packing sub-problems in current literature, the efficiency of checking the packing feasibility of batches is the bottleneck of algorithms of AMPP. Therefore, we propose an efficient pixel-based AM packing algorithm (PAMPA) which can tackle irregular packing sub-problems that allow hole filling and free rotation. In PAMPA, parts are placed one by one in a Bottom-Left (BL) way and the rotation angle is determined by a novel angle selection heuristic. A new codification method and contour-guided method are proposed to accelerate separating overlap. The placement sequence is optimized by a biased random-key genetic algorithm (BRKGA). Furthermore, by adopting PAMPA, we introduce a variable neighbourhood search (VNS) framework to solve the AMPP. Finally, new instances are generated to conduct experiments. Based on the new instances and instances from (ESICUP), the efficiency of PAMPA is analysed and verified. The proposed VNS framework can reduce the makespan by 8 h on average compared with the initial solution, which illustrates that our PAMPA is beneficial for AMPP. Some interesting insights are also revealed and discussed based on the case study.

Variable neighborhood search approach with intensified shake for monitor placement

AbstractSeveral problems are emerging in the context of communication networks and most of them must be solved in reduced computing time since they affect to critical tasks. In this research, the monitor placement problem is tackled. This problem tries to cover the communications of an entire network by locating a monitor in specific nodes of the network, in such a way that every link remains surveyed. In case that a solution cannot be generated in the allowed computing time, a penalty will be assumed for each link uncovered. The problem is addressed by considering the variable neighborhood search framework, proposing a novel constructive method, an intelligent local search to optimize the improvement phase, and an intensified shake to guide the search to more promising solutions. The proposed algorithm is compared with a hybrid search evolutionary algorithm over a set of instances derived from real‐life networks to prove its performance.

Neural large neighborhood search for routing problems

Learning how to automatically solve optimization problems has the potential to provide the next big leap in optimization technology. The performance of automatically learned heuristics on routing problems has been steadily improving in recent years, but approaches based purely on machine learning are still outperformed by state-of-the-art optimization methods. To close this performance gap, we propose a novel large neighborhood search (LNS) framework for vehicle routing that integrates learned heuristics for generating new solutions. The learning mechanism is based on a deep neural network with an attention mechanism and has been especially designed to be integrated into an LNS search setting. We evaluate our approach on the capacitated vehicle routing problem (CVRP), the split delivery vehicle routing problem (SDVRP), and the capacitated team orienteering problem (CTOP). We show that the NLNS approach is able to outperform a handcrafted LNS on the CVRP and SDVRP and match the performance of a standard LNS on the CTOP. NLNS is thus able to quickly and effectively learn high performance heuristics to maneuver through the search space of difficult routing problems, coming close to the performance of state-of-the-art optimization approaches.

Modeling and solving a multi-trip multi-distribution center vehicle routing problem with lower-bound capacity constraints

Vehicle routing problem (VRP) for delivering goods from distribution centers to customers is one of the main operations in logistics. Optimizing route plans for vehicles allows companies to save a huge amount of operational costs. The VRP problem is also one of the most studied problems in the domain of operations research. There are several variants of the VRP problem that have been considered in the literature. This paper proposes a new variant of the vehicle routing problem taking into account most of the well-studied features, especially with a new constraint on the lower bound of the capacity of vehicles which has not been investigated in the literature. The problem requirements come from one of the biggest dairy distribution companies in Vietnam. With over 1000 customer points included in a plan on average, the company takes at least one working day to make a route plan. We formulate the considered problem as a mixed-integer linear programming problem, analyze the challenges of the lower-bound capacity constraints and propose an adaptive large neighborhood search framework for solving it. Experimental results on large realistic and randomly generated instances show the efficiency and the applicability of the proposed method. The application of the proposed method led to the rapidity in generating the solution; this task that used to take one day is decreased to just two hours.

The multi-manned joint assembly line balancing and feeding problem

The Joint Assembly Line Balancing and Feeding Problem (JALBFP) assigns a line feeding mode to each component (Assembly Line Feeding Problem) and each task to a workplace of a station (Assembly Line Balancing Problem). Current literature offers numerous optimisation models that solve these problems sequentially. However, only few optimisation models, provide a joint solution. To solve the JALBFP for a multi-manned assembly line, we propose a Mixed Integer Linear Programming (MILP) model and a heuristic that relies on the Adaptive Large Neighborhood Search (ALNS) framework by considering multiple workplaces per station and three different feeding policies: line stocking, travelling kitting and sequencing. The objective function minimises the cost of the whole assembly system which considers supermarket, transportation, assembly operations, and investment costs. Although the JALBFP requires higher computation times, it leads to a higher total cost reduction compared to the sequential approach. Through a numerical study, we validate the heuristic approach and find that the average deviation to the MILP model is around 1%. We also compare the solution of the JALBFP with that of the sequential approach and find an average total cost reduction of 10.1% and a maximum total cost reduction of 43.8%.

A Coordinated Vehicle-Drone Arc Routing Approach Based on Improved Adaptive Large Neighborhood Search.

Through urban traffic patrols, problems such as traffic congestion and accidents can be found and dealt with in time to maintain the stability of the urban traffic system. The most common way to patrol is using ground vehicles, which may be inflexible and inefficient. The vehicle–drone coordination maximizes utilizing the flexibility of drones and addresses their limited battery capacity issue. This paper studied a vehicle–drone arc routing problem (VD-ARP), consisting of one vehicle and multiple drones. Considering the coordination mode and constraints of the vehicle–drone system, a mathematical model of VD-ARP that minimized the total patrol time was constructed. To solve this problem, an improved, adaptive, large neighborhood search algorithm (IALNS) was proposed. First, the initial route planning scheme was generated by the heuristic rule of “Drone-First, Vehicle-Then”. Then, several problem-based neighborhood search strategies were embedded into the improved, adaptive, large neighborhood search framework to improve the quality of the solution. The superiority of IALNS is verified by numerical experiments on instances with different scales. Several critical factors were tested to determine the effects of coordinated traffic patrol; an example based on a real road network verifies the feasibility and applicability of the algorithm.

A Physical Internet (PI) based inland container transportation problem with selective non-containerized shipping requests

The imbalance of inbound and outbound containers generates large numbers of idle runs of container trucks in inland container transportation. In tradition, the trucking company acquires shipping requests from the spot market to increase profitability, with the precondition that both origin and destination locations of the acquired request are the same as the idle travel. Such precondition undoubtably leads to limited benefits and poor stability, given that shipping requests from the spot market are of high randomness. This paper proposes to adopt the idea of Physical Internet (PI) and assign an acquired request to several trucks, allowing transfers from one truck to another at logistics hubs. In this way, temporally and spatially dispersed idle resources could be consolidated to achieve the shipments of selective shipping requests, in a relay manner. To our knowledge, this paper is among the first to study the new paradigm of inland container transportation and formally model it as a PI-based selective vehicle routing problem (PI-SVRP). A mixed integer program (MIP) is presented and validated with CPLEX on small instances. In order to tackle real world instances, several novel heuristics have been designed particularly according to the special problem structure and embedded into an adaptive large neighborhood search (ALNS) framework. Finally, substantial instances are generated based on real data and a series of numerical studies are performed to derive managerial implications for practitioners.

Robust eBuses Charging Location Problem

The implementation of a sustainable and efficient electric bus (eBus) transportation network requires addressing multiple concerns, such as limited driving range and battery charging/discharging time. Currently, eBuses can travel between 200 to 300 km on a single charge, and fast charging stations can fully recharge a battery in a matter of minutes. However, a failure in a charging station might negatively impact the operation of the system with unnecessary delays for the users. Taking this into account, we propose and implement a model for the Robust eBuses Charging Location problem that takes into account potential vulnerabilities of the transportation system. Our model incorporates a protection mechanism that allows eBuses to reach a backup charging station in case the regular one is down. We propose a MIP model to tackle this problem with minimal disruptions in the regular operation of the eBuses. Furthermore, we also present a Large Neighbourhood Search framework to efficiently tackle the problem. Our empirical evaluation suggests that our framework can operate a robust service with a small number of charging stations for three Irish cities and our Large Neighbourhood Search approach largely outperforms a popular commercial MIP solver.

Constraint Logic Programming for Real-World Test Laboratory Scheduling

The Test Laboratory Scheduling Problem (TLSP) and its subproblem TLSP-S are real-world industrial scheduling problems that are extensions of the Resource-Constrained Project Scheduling Problem (RCPSP). Besides several additional constraints, TLSP includes a grouping phase where the jobs to be scheduled have to be assembled from smaller tasks and derive their properties from this grouping. For TLSP-S such a grouping is already part of the input. In this work, we show how TLSP-S can be solved by Answer-set Programming extended with ideas from other constraint solving paradigms. We propose a novel and efficient encoding and apply an answer-set solver for constraint logic programs called clingcon. Additionally, we utilize our encoding in a Very Large Neighborhood Search framework and compare our methods with the state of the art approaches. Our approach provides new upper bounds and optimality proofs for several existing benchmark instances in the literature.

An adaptive large neighborhood search for the multiple-day music rehearsal problems

This paper presents an Adaptive Large Neighborhood Search (ALNS) framework to solve the Multiple-Day Music Rehearsal Problem (MMRP), where music pieces with different player sets and durations are arranged in a predefined number of rehearsal days so that the total days of attendance and waiting times experienced by all players are minimized. Two variants of the MMRP, namely the MMRP without setup times (MMRP-0) and the MMRP with setup times (MMRP-1), are herein explored based on mathematical formulations of the Capacitated Vehicle Routing Problem (CVRP) and the Music Rehearsal Problem (MRP). Extensive computational results on 120 generated instances and 78 benchmark instances indicate that the ALNS is greatly efficient as it can provide equivalent or better solutions than the exact method and a benchmark heuristic from the literature, with much less computational time. We also find that the ALNS tends to perform better in large and complicated MMRP settings, considering that it outperforms the time-restricted CPLEX in 34 out of 120 generated instances and successfully finds 4 new best-known solutions to 8 large benchmark instances.

An efficient heuristic for a hub location routing problem

This paper examines a new model for hub location known as the hub location routing problem. The problem shares similarities with the well studied uncapacitated single allocation p-hub median problem except that the hubs are now connected to each other by a cyclical path (or tour) known as the global route, each cluster of non-hub nodes and assigned hub is also connected by a single tour known as a local route, and the length of the local routes is constrained to a maximum number of nodes. Thus, aside from the normal tasks of hub selection and allocation of non-hub nodes, up to p + 1 travelling salesman problems need to be solved. A heuristic based on the general variable neighborhood search framework is proposed here to solve this very complicated problem. The improvement phase of the algorithm uses a sequential variable neighborhood descent with multiple neighborhoods required to suit the complex nature of the problem. A best sequencing of the neighborhoods is established through empirical testing. The perturbation phase known as the shaking procedure also uses a well-structured selection of neighborhoods in order to effectively diversify the search to different regions of the solution space. Extensive computational testing shows that the new heuristic significantly outperforms the state-of-the-art. Out of 912 test instances from the literature, we are able to obtain 691 new best known solutions. Not only are the improvements in objective values quite impressive, but also these new solutions are obtained in a small fraction of the time required by the competing algorithms.

Hybrid VNS–LP algorithm for online optimal coordination of directional overcurrent relays

Optimal coordination of directional overcurrent relays (DOCRs) in interconnected power systems is a highly constrained, non-convex and non-linear optimisation problem. In this study, use of a series of linear programming (LP) problems in a variable neighbourhood search (VNS) framework is proposed, in which pickup current settings can be considered either as continuous or discrete variables. Therefore, the proposed hybrid algorithm (VNS–LP) is suitable for coordination problem-solving in a network with different types of DOCRs. Furthermore, two versions of the VNS algorithm are proposed, the first of which is very easy to implement, while, the other is somewhat more sophisticated, and at the same time much faster and possibly suitable for online coordination in smart grids applications. One advantage of the second algorithm is that it relies only on elementary arithmetic and requires low RAM, which makes it implementable in any low-level programming environment. This, in turn, can increase the interoperability, which is of crucial importance when automating tasks. The efficiency of the proposed algorithm was investigated in several test systems. Numerical results indicate that the two proposed approaches outperform the previous methods. Moreover, the coordination problem is solved in a reasonably short time using a faster approach.

A heuristic and a benchmark for the stowage planning problem

The stowage planning problem has recently gained the attention of a number of academic researchers. Unfortunately, many of the published works are either based on oversimplified assumptions or on confidential data. This practice hinders the research field from growing. In this paper, we present a novel set of realistic vessel data along with a set of benchmark instances. Moreover, a formal definition of a single-port stowage planning problem, based on the current state of the art, is presented. The proposed optimisation problem is solved using a variant of the adaptive large neighbourhood search framework, where novel repair and destroy methods are presented. Computational results show that the solution approach is able to find high-quality seaworthy stowage plans within 60 s.

A metaheuristic for evaluation of an integrated special transport service

ABSTRACT This work concerns evaluation of integrated demand responsive services, in which certain parts of the passengers’ door-to-door journeys are served by fixed route public transport. The purpose of combining a special transport service with a fixed route service is to reduce the high, publicly subsidized, operational costs of the special transport service. To be able to recommend in which situations, or areas, it is beneficial to use an integrated service we present a metaheuristic for solving the routing problem in such a service. The metaheuristic can be implemented as part of an evaluation tool for policy makers and officials, providing insights into the effects of an integrated demand responsive service compared to a non-integrated. The metaheuristic is based on the adaptive large neighbourhood search (ALNS) framework. It is applied to a data set from a real-world, rural, demand responsive special transport service and the fixed route service available in the area. The objective used in our heuristic is to minimize the distance driven by the demand responsive vehicles. The distance driven is strongly related to the operational cost of the service. Our tests show that the distance driven by the demand responsive vehicles is reduced by 16%, giving a substantial cost reduction for the special transport service in the given area. Highlights Evaluation of integrated demand responsive service Combining special transport with a fixed route service to reduce operational costs Metaheuristic, based on the adaptive large neighbourhood search (ALNS) framework Comparing integrated service to non-integrated

Construction-Based Optimization Approaches to Airline Crew Rostering Problem

An airline crew rostering problem (ACRP) is one of the most important problems in an airline planning process. It aims at determining an optimal assignment of pairings, which refer to sequences of flights starting from and ending at the same crew base, to aircrew to form roster lines. In practice, ACRP is subject to various types of constraints. We present a constraint-implicit mathematical model taking into account the basic, horizontal, and vertical constraints. In order to solve a kind of ACRP, we propose a construction-based variable neighborhood search (VNS) framework that can build rosters effectively. Three construction methods, i.e., crew-by-crew, pairing-by-pairing, and orthogonal constructions, are introduced. To evaluate our approaches, we conduct extensive experiments on two scenarios (intense and light workload) of instances originated from a Chinese airline company and make comparisons among different VNS approaches. The computational results show that the proposed approaches are capable of producing high-quality solutions in both scenarios.

The location routing problem using electric vehicles with constrained distance

The introduction of Electric Vehicles (EVs) in modern fleets facilitates a shift towards greener road transportation practices. However, the driving ranges of EVs are limited by the duration of their batteries, which raises some operational challenges. This paper discusses the Location Routing Problem with a Constrained Distance (LRPCD), which is a natural extension of the Location Routing Problem when EVs are utilized. A fast multi-start heuristic and a metaheuristic are proposed to solve the LRPCD. The former combines biased-randomization techniques with the well-known Tillman’s heuristic for the Multi-Depot Vehicle Routing Problem. The latter incorporates the biased-randomized approach into the Variable Neighborhood Search (VNS) framework. A series of computational experiments show that the multi-start heuristic is able to generate good-quality solutions in just a few seconds, while the biased-rendomized VNS metaheuristic provides higher-quality solutions by employing more computational time.