An adaptive large neighborhood search heuristic for the share-a-ride problem

The Share-a-Ride Problem (SARP) aims at maximizing the profit of serving a set of passengers and parcels using a set of homogeneous vehicles. We propose an adaptive large neighborhood search (ALNS) heuristic to address the SARP. Furthermore, we study the problem of determining the time slack in a SARP schedule. Our proposed solution approach is tested on three sets of realistic instances. The performance of our heuristic is benchmarked against a mixed integer programming (MIP) solver and the Dial-a-Ride Problem (DARP) test instances. Compared to the MIP solver, our heuristic is superior in both the solution times and the quality of the obtained solutions if the CPU time is limited. We also report new best results for two out of twenty benchmark DARP instances.     

An adaptive large neighborhood search heuristic for the cumulative capacitated vehicle routing problem

The cumulative capacitated vehicle routing problem (CCVRP) is a variation of the classical capacitated vehicle routing problem in which the objective is the minimization of the sum of arrival times at customers, instead of the total routing cost. This paper presents an adaptive large neighborhood search heuristic for the CCVRP. This algorithm is applied to a set of benchmark instances and compared with two recently published memetic algorithms.     

A GRASP with adaptive large neighborhood search for pickup and delivery problems with transshipment

The pickup and delivery problem (PDP) has been studied extensively for applications ranging from courier, cargo and postal services, to public transportation. The work presented here was inspired by a daily route planning problem at a regional air carrier who was trying to determine the benefits of transshipment. Accordingly, a primary goal of this paper is identify the circumstances under which measurable cost saving can be achieved when one aircraft transports a request from its origin to an intermediate point and a second aircraft picks it up and delivers it to its final destination. In structuring the analysis, we describe a unique way to model this transshipment option on a directed graph and introduce a specialized two-route insertion heuristic that considers when to exploit this option. Based on the new representation, most existing heuristics for the PDP can be readily extended to handle transshipments.To find solutions, we developed a greedy randomized adaptive search procedure (GRASP) with several novel features. In the construction phase, shipment requests are inserted into routes until all demand is satisfied or no feasible insertion exists. In the improvement phase, an adaptive large neighborhood search algorithm is used to modify portions of the feasible routes. Specialized removal and insertion heuristics were designed for this purpose. In the absence of test cases in the literature, we also developed a procedure for randomly generating problem instances. Testing was done on 56 existing PDP instances which have 50 requests each, and on 50 new data sets with 25 requests each and one transshipment location. For the former, the performance and solution quality of the GRASP were comparable to the best known heuristics. For the latter, GRASP found the solutions within 1% of optimality on 88% of the instances.     

A variable neighborhood search algorithm for the optimization of a dial-a-ride problem in a large city

On-demand transportation is becoming a new necessary service for modern (public and private) mobility and logistics providers. Large cities are demanding more and more share transportation services with flexible routes, resulting from user dynamic demands. In this study a new algorithm is proposed for solving the problem of computing the best routes that a public transportation company could offer to satisfy a number of customer requests. In this problem, known in the literature as the dial-a-ride problem, a number of passengers has to be transported between pickup and delivery locations trying to minimize the routing costs while respecting a set of pre-specified constraints (maximum pickup time, maximum ride duration and maximum load per vehicle). For optimizing this problem, a new variable neighborhood search has been developed and tested on a set of 24 different scenarios of a large-scale dial-a-ride problem in the city of San Francisco. The results have been compared against two state-of-the-art algorithms of the literature and validated by means of statistical procedures proving that the new algorithm has obtained the best overall results.     

An adaptive neighborhood search metaheuristic for the integrated railway rapid transit network design and line planning problem

We model and solve the Railway Rapid Transit Network Design and Line Planning (RRTNDLP) problem, which integrates the two first stages in the Railway Planning Process. The model incorporates costs relative to the network construction, fleet acquisition, train operation, rolling stock and personnel management. This implies decisions on line frequencies and train capacities since some costs depend on line operation. We assume the existence of an alternative transportation system (e.g. private car, bus, bicycle) competing with the railway system for each origin–destination pair. Passengers choose their transportation mode according to the best travel times. Since the problem is computationally intractable for realistic size instances, we develop an Adaptive Large Neighborhood Search (ALNS) algorithm, which can simultaneously handle the network design and line planning problems considering also rolling stock and personnel planning aspects. The ALNS performance is compared with state-of-the-art commercial solvers on a small-size artificial instance. In a second stream of experiments, the ALNS is used to design a railway rapid transit network in the city of Seville.     

Variable neighborhood search for multi-objective resource allocation problems

The Resource Allocation Problem (RAP) is a classical problem in the field of operations management that has been broadly applied to real problems such as product allocation, project budgeting, resource distribution, and weapon-target assignment. In addition to focusing on a single objective, the RAP may seek to simultaneously optimize several expected but conflicting goals under conditions of resources scarcity. Thus, the single-objective RAP can be intuitively extended to become a Multi-Objective Resource Allocation Problem (MORAP) that also falls in the category of NP-Hard. Due to the complexity of the problem, metaheuristics have been proposed as a practical alternative in the selection of techniques for finding a solution. This study uses Variable Neighborhood Search (VNS) algorithms, one of the extensively used metaheuristic approaches, to solve the MORAP with two important but conflicting objectives—minimization of cost and maximization of efficiency. VNS searches the solution space by systematically changing the neighborhoods. Therefore, proper design of neighborhood structures, base solution selection strategy, and perturbation operators are used to help build a well-balanced set of non-dominated solutions. Two test instances from the literature are used to compare the performance of the competing algorithms including a hybrid genetic algorithm and an ant colony optimization algorithm. Moreover, two large instances are generated to further verify the performance of the proposed VNS algorithms. The approximated Pareto front obtained from the competing algorithms is compared with a reference Pareto front by the exhaustive search method. Three measures are considered to evaluate algorithm performance: D1_R, the Accuracy Ratio, and the number of non-dominated solutions. The results demonstrate the practicability and promise of VNS for solving multi-objective resource allocation problems.     

Hybrid column generation and large neighborhood search for the dial-a-ride problem

Demographic change towards an ever aging population entails an increasing demand for specialized transportation systems to complement the traditional public means of transportation. Typically, users place transportation requests, specifying a pickup and a drop off location and a fleet of minibuses or taxis is used to serve these requests. The underlying optimization problem can be modeled as a dial-a-ride problem. In the dial-a-ride problem considered in this paper, total routing costs are minimized while respecting time window, maximum user ride time, maximum route duration, and vehicle capacity restrictions. We propose a hybrid column generation and large neighborhood search algorithm and compare different hybridization strategies on a set of benchmark instances from the literature.     

Multi-objective adaptive large neighborhood search for distributed reentrant permutation flow shop scheduling

Factory management plays an important role in improving the productivity and quality of service in the production process. In particular, the distributed permutation flow shop scheduling problem with multiple factories is considered a priority factor in the factory automation. This study proposes a novel model of the developed distributed scheduling by supplementing the reentrant characteristic into the model of distributed reentrant permutation flow shop (DRPFS) scheduling. This problem is described as a given set of jobs with a number of reentrant layers is processed in the factories, which compromises a set of machines, with the same properties. The aim of the study is to determine the number of factory needs to be used, jobs assignment to certain factory and sequence of job assigned to the factory in order to simultaneously satisfy three objectives of minimizing makespan, total cost and average tardiness. To do this, a novel multi-objective adaptive large neighborhood search (MOALNS) algorithm is developed for finding the near optimal solutions based on the Pareto front. Various destroy and repair operators are presented to balance between intensification and diversification of searching process. The numerical examples of computational experiments are carried out to validate the proposed model. The analytical results on the performance of proposed algorithm are checked and compared with the existing methods to validate the effectiveness and robustness of the proposed potential algorithm in handling the DRPFS problem.     

An adaptive large neighborhood search heuristic for Two-Echelon Vehicle Routing Problems arising in city logistics

In this paper, we propose an adaptive large neighborhood search heuristic for the Two-Echelon Vehicle Routing Problem (2E-VRP) and the Location Routing Problem (LRP). The 2E-VRP arises in two-level transportation systems such as those encountered in the context of city logistics. In such systems, freight arrives at a major terminal and is shipped through intermediate satellite facilities to the final customers. The LRP can be seen as a special case of the 2E-VRP in which vehicle routing is performed only at the second level. We have developed new neighborhood search operators by exploiting the structure of the two problem classes considered and have also adapted existing operators from the literature. The operators are used in a hierarchical scheme reflecting the multi-level nature of the problem. Computational experiments conducted on several sets of instances from the literature show that our algorithm outperforms existing solution methods for the 2E-VRP and achieves excellent results on the LRP.     

A two phase variable neighborhood search for the multi-product inventory routing problem

We address a multi-product inventory routing problem and propose a two-phase Variable Neighborhood Search (VNS) metaheuristic to solve it. In the first phase, VNS is used to solve a capacitated vehicle routing problem at each period to find an initial solution without taking into account the inventory. In the second phase, we iteratively improve the initial solution while minimizing both the transportation and inventory costs. For this, we propose two different algorithms, a Variable Neighborhood Descent and a Variable Neighborhood Search. We present an heuristic and a Linear Programming formulation, which are applied after each local search move, to determine the amount of products to collect from each supplier at each period. During the exploration, we use priority rules for suppliers and vehicles, based on the current delivery schedule over the planning horizon. Computational results show the efficiency of the proposed two-phase approach.     

Large neighborhood search for the bike request scheduling problem

This paper introduces an efficient algorithm for the bike request scheduling problem (BRSP). The BRSP is built around the concept of request, defined as the pickup or dropoff of a number of identical items (bikes) at a specific station, within a certain time window, and with a certain priority. The aim of the BRSP is to sequence requests on (and hence determine the routes of) a set of vehicles, in such a way that the sum of the priorities of the executed requests is maximized, all time windows are respected, and the capacity of the vehicles is not exceeded. The generation of the set of requests is explicitly not a part of the problem definition of the BRSP. The primary application of the BRSP, from which it derives its name, is to determine the routes of a set of repositioning vehicles in a bike sharing system, although other applications exist. The algorithm introduced in this paper is based on a set of related greedy randomized adaptive search procedure followed by variable neighborhood descent (GRASP + VND) operators embedded in a large neighborhood search (LNS) framework. Since this paper presents the first heuristic for the BRSP, a computational comparison to existing approaches is not possible. We therefore compare the solutions found by our LNS heuristic to those found by an exact solver (Gurobi). These experiments confirm that the proposed algorithm scales to realistic dimensions and is able to find near‐optimal solutions in seconds.     

An adaptive large neighborhood search heuristic for the Electric Vehicle Scheduling Problem

This paper addresses the Electric Vehicle Scheduling Problem (E-VSP), in which a set of timetabled bus trips, each starting from and ending at specific locations and at specific times, should be carried out by a set of electric buses or vehicles based at a number of depots with limited driving ranges. The electric vehicles are allowed to be recharged fully or partially at any of the given recharging stations. The objective is to firstly minimize the number of vehicles needed to cover all the timetabled trips, and secondly to minimize the total traveling distance, which is equivalent to minimizing the total deadheading distance. A mixed integer programming formulation as well as an Adaptive Large Neighborhood Search (ALNS) heuristic for the E-VSP are presented. ALNS is tested on newly generated E-VSP benchmark instances. Result shows that the proposed heuristic can provide good solutions to large E-VSP instances and optimal or near-optimal solutions to small E-VSP instances.     

An adaptive tabu search approach for buffer allocation problem in unreliable non-homogenous production lines

The buffer allocation problem, i.e. how much buffer storage to allow and where to place it within the line, is an important research issue in designing production lines. In this study, a novel adaptive tabu search approach is proposed for solving buffer allocation problem in unreliable and non-homogeneous production lines. The objective is to maximize the throughput of the line, which is constrained by the capacity of each buffer space and also the total buffer capacity to allocate to these spaces. Besides proposing a new strategy to tune the parameters of tabu search adaptively during the search, an experimental study is carried out to select an intelligent initial solution scheme among three alternatives so as to decrease the search effort to obtain the best solutions. The performance of the proposed approach is evaluated by computational tests and very promising results are obtained.     

Variable neighborhood search for the inventory routing and scheduling problem in a supply chain

The inventory, routing and scheduling decisions are three major driving factors for supply chain performance. Since they are related to one another in a supply chain, they should be determined simultaneously to improve the decision quality. In the past, the inventory policy, vehicle routing and vehicle scheduling are determined sequentially and separately. Hence, the total cost (inventory, routing and vehicle costs) would increase. In this paper, an integrated model for the inventory routing and scheduling problem (IRSP) is proposed. Since searching for the optimal solution for this model is a non-polynomial (NP) problem, a metaheuristic, variable neighborhood search (VNS), is proposed. The proposed method was compared with other existing methods. The experimental results indicate that the proposed method is better than other methods in terms of average cost per day.     

Two level General variable neighborhood search for Attractive traveling salesman problem

Attractive traveling salesman problem (AtTSP) consists of finding maximal profit tour starting and ending at a given depot after visiting some of the facilities. Total length of the tour must not exceed the given maximum distance. Each facility achieves profit from the customers, based on the distance between the facility and customers as well as on the attractiveness of that facility. Total profit of a tour is equal to a sum of profits of all visited facilities. In this paper, we develop a new variant of Variable neighborhood search, called 2-level General variable neighborhood search (2-GVNS) for solving AtTSP. At the second level, we use General variable neighborhood search in the local search lor building neighboring solution and checking its feasibility. Our 2-GVNS heuristic outperforms tabu search heuristic, the only one proposed in the literature so far, in terms of precision and running times. In addition, 2-GVNS finds all optimal known solutions obtained by Branch and cut algorithm and offers several new best known solutions.     

Intelligent-guided adaptive search for the maximum covering location problem

Computational intelligence techniques are part of the search process in several recent heuristics. One of their main benefits is the use of an adaptive memory to guide the search towards regions with promising solutions. This paper follows this approach proposing a variation of a well-known iteration independent metaheuristic. This variation adds a learning stage to the search process, which can improve the quality of the solutions found. The proposed metaheuristic, named Intelligent-Guided Adaptive Search (IGAS), provides an efficient solution to the maximum covering facility location problem. Computational experiments conducted by the authors showed that the solutions found by IGAS were better than the solutions obtained by popular methods found in the literature.     

Heuristics for solving continuous berth allocation problem considering periodic balancing utilization of cranes

This study investigates a berth allocation problem considering the periodic balancing utilization of quay cranes in container terminals. The proposed model considers that the quay cranes allocated to a work shift should be fully used and other real-world considerations, such as the continuous quay line, the penalties for early arrivals and departure delays. To solve the model, several heuristics are developed: the model for large problems is decomposed into sub-models that are solved by rolling-horizon heuristics; neighborhood search heuristics are used for optimizing a berthing order of vessels; parallel computing is used to improve the algorithmic performance. The method performs well when applied to real-world large-scale instances with promising computation time that is linearly related to the number of vessels.     

Hybrid rolling-horizon optimization for berth allocation and quay crane assignment with unscheduled vessels

Port operations usually suffer from uncertainties, such as vessels’ arrival time and handling time and unscheduled vessels. To address this, this study presents a dynamic berth allocation and crane assignment specific problem (BACASP) when unscheduled vessels arrive at the port, which is branded the berth allocation and quay crane assignment specific problem with unscheduled vessels (UBACASP). A rolling-horizon based method is proposed to decompose the UBACASP into a multi-stage static decision BACASP, wherein a rescheduling margin-based hybrid rolling-horizon optimization method is developed by incorporating the event-driven and periodical rolling-horizon strategies as the urgency of dynamic events is evaluated. In each rolling horizon, a mixed integer linear programming model (MILP) is presented for the BACASP to minimize the total port stay time of vessels and the penalties of delays associated with the spatial and temporal constraints, such as the length of continuous berth, number of quay cranes (QCs) and non-crossing of QCs. A discretization strategy is designed to divide the continuous berth into discrete segments, and convert the BACASP to a discrete combinatorial optimization problem, which is efficiently solved by the proposed adaptive large neighborhood search algorithm (ALNS). Case studies with different problem characteristics are conducted to prove the effectiveness of the solution methods proposed in this study. Moreover, the performances of the ALNS and the existing methods for solving the BACASP are compared, and the advantages and disadvantages of different rolling strategies under different degrees of uncertainties are deeply analyzed.     

Variable neighborhood search to solve the generalized orienteering problem

This paper presents a variable neighborhood search (VNS) algorithm to solve the extension of the orienteering problem known as the generalized orienteering problem (GOP). Our algorithm aims to use a reduced number of neighborhoods without compromising the quality of the results. This reduced number of neighborhoods, together with the precalculation of scores associated with points of interest, allows us, in most cases, to outperform all previous metaheuristics proposed for this problem. This is the first time a VNS is being applied to the GOP, and it provides promising computational results. In particular, in the case studies considered in the paper, we were able to find 35 new best solutions, all of which were found using a shorter computational time. Furthermore, the information regarding other best‐known solutions provided in the literature has also been improved, with corrections to some previously published errors regarding scores and distances. In addition, the benchmark has been extended with the incorporation of new case studies based on real data from three of the most popular tourist cities in Spain.     

Variable neighborhood search for the pharmacy duty scheduling problem

In this paper, we study on the Pharmacy Duty Scheduling (PDS) problem, where a subset of pharmacies should be on duty on national holidays, at weekends and at nights in order to be able to satisfy the emergency drug needs of the society. PDS problem is a multi-period p-median problem with special side constraints and it is an NP-Hard problem. We propose four Variable Neighborhood Search (VNS) heuristics. The first one is the basic version, BVNS. The latter two, Variable Neighborhood Decomposition Search (VNDS) and Variable Neighborhood Restricted Search (VNRS), aim to obtain better results in less computing time by decomposing or restricting the search space. The last one, Reduced VNS (RVNS), is for obtaining good initial solutions rapidly for BVNS, VNDS and VNRS. We test BVNS, VNRS and VNDS heuristics on randomly generated instances and report the computational test results. We also use VNS heuristics on real data for the pharmacies in central İzmir and obtain significant improvements.