Selective Vehicle Routing Problem Research Articles

Logistics of temporary testing centers for coronavirus disease

The ongoing COVID-19 pandemic has caused the death of millions of people, and PCR testing is widely used as the gold standard method to detect the infections to restrict the outbreak. Through the interviews conducted with people from the field in South Korea, the UK, and Turkey, we have found that there are numerous testing strategies worldwide. Those testing strategies include drive-through and home delivery testing capabilities, local test sites, and mobile test centers. Our primary motivation is to propose a generic model based on the best practices in the UK and South Korea. Also, we aim to present a case study on Turkey for the implementation of vital procedures and increase their availability.This paper represents a study on how to construct a temporary testing logistics system during the initial phases of pandemics to increase the availability of PCR testing with the primary objective of maximizing total sample collection. The design also considers minimizing the maximum walking distance to increase the convenience of sample collection for the people living in the neighborhoods. The proposed system consists of temporary testing centers and a central laboratory. Temporary testing centers perform direct tours to the potential areas to collect samples and bring the collected sample to the designated central laboratories located at central hospitals. Moreover, to represent the non-linear inheritance of the pandemic progress within a population, we consider diminishing sample potentials over time and coverage. This new problem is defined as an extension of the Selective Vehicle Routing Problem and Covering Tour Problem.We propose a mathematical model and four two-stage math-heuristic algorithms to determine the location and routing of the temporary testing centers and their lengths of stay at each visited location. The performances of the proposed solution methodologies are tested on two data sets. The first set is constructed by the confirmed cases of the districts of Seoul, Korea, and by the interview of health personnel of H+ Yangji Hospital COVID-19 semi-mobile booth application, and the second set is constructed by 99 hospital/health centers from distinct neighborhoods of 22 districts of Istanbul, Turkey. The Pareto set of optimum solutions is generated based on total sample collection and maximum walking distance. Finally, sensitivity analyses on some design parameters are conducted.

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.

Carbon-efficient scheduling problem of electric rubber-tyred gantry cranes in a container terminal

Green container terminals have gained much attention to satisfy the demand for sustainable and environmentally friendly transport. As one of the major polluters in container terminals, rubber-tyred gantry cranes have already been widely replaced by electric rubber-tyred gantry cranes (ERTGs). This article exploits the features of ERTGs and studies the ERTG scheduling problem in container terminal yards considering the carbon dioxide emissions and task delays simultaneously. The problem is converted into a selective vehicle routing problem with soft time windows (SVRPSTW) and formulated as a mixed integer linear programming model. A column generation algorithm embedded in a branch-and-bound framework is employed to solve the problem. A hybrid acceleration strategy combining the label-setting algorithm with a tabu search algorithm is proposed. Finally, the proposed method is validated via computational experiments. The proposed method provides an efficient tool for carbon-efficient scheduling of ERTGs and thus contributes to the development of green ports.

On the Selective Vehicle Routing Problem

The Generalized Vehicle Routing Problem (GVRP) is an extension of the classical Vehicle Routing Problem (VRP), in which we are looking for an optimal set of delivery or collection routes from a given depot to a number of customers divided into predefined, mutually exclusive, and exhaustive clusters, visiting exactly one customer from each cluster and fulfilling the capacity restrictions. This paper deals with a more generic version of the GVRP, introduced recently and called Selective Vehicle Routing Problem (SVRP). This problem generalizes the GVRP in the sense that the customers are divided into clusters, but they may belong to one or more clusters. The aim of this work is to describe a novel mixed integer programming based mathematical model of the SVRP. To validate the consistency of the novel mathematical model, a comparison between the proposed model and the existing models from literature is performed, on the existing benchmark instances for SVRP and on a set of additional benchmark instances used in the case of GVRP and adapted for SVRP. The proposed model showed better results against the existing models.

A PSO based algorithm with an efficient optimal split procedure for the multiperiod vehicle routing problem with profit

The multiperiod vehicle routing problem with profit (mVRPP) is a selective vehicle routing problem where the planning horizon of each vehicle is divided into several periods. The aim of solving mVRPP is to design service itineraries so that the total amount of collected profit is maximized and the travel time limit of each period is respected. This problem arises in many real life applications, as the one encountered in cash-in-transit industry. In this paper, we present a metaheuristic approach based on the particle swarm optimization algorithm (PSO) to solve the mVRPP. Our approach incorporates an efficient optimal split procedure and dedicated local search operators proposed to guarantee high search intensification. Experiments conducted on an mVRPP benchmark show that our algorithm outperforms the state of the art metaheuristic approaches in terms of performance and robustness. Our PSO algorithm determines all the already known optimal solutions within a negligible computational time and finds $$88$$ strict improvements among the $$177$$ instances of the benchmark.

Dynamic UAV-based traffic monitoring under uncertainty as a stochastic arc-inventory routing policy

Given the rapid advances in unmanned aerial vehicles, or drones, and increasing need to monitor traffic at a city level, one of the current research gaps is how to systematically deploy drones over multiple periods. We propose a real-time data-driven approach: we formulate the first deterministic arc-inventory routing problem and derive its stochastic dynamic policy. The policy is expected to be of greatest value in scenarios where uncertainty is highest and costliest, such as city traffic monitoring during major events. The Bellman equation for an approximation of the proposed inventory routing policy is formulated as a selective vehicle routing problem. We propose an approximate dynamic programming algorithm based on Least Squares Monte Carlo simulation to find that policy. The algorithm has been modified so that the least squares dependent variable is defined to be the "expected stock out cost upon the next replenishment". The new algorithm is tested on 30 simulated instances of real time trajectories over 5 time periods of the selective VRP to evaluate the proposed policy and algorithm. Computational results on the selected instances show that the algorithm can outperform the myopic policy by 23% to 28% over those tests, depending on the parametric design. Further tests are conducted on classic benchmark arc routing problem instances. The 11-link instance gdb19 is expanded into a sequential 15-period stochastic dynamic example and used to demonstrate why a naive static multi-period deployment plan would not be effective in real networks.

Inverse vehicle routing for activity-based urban freight forecast modeling and city logistics

ABSTRACTGoods movement is one of the fastest growing transportation sectors, affecting both economic and environmental sustainability, particularly in dense urban areas with traffic congestion and air pollution. To meet this challenge, urban public agencies have paid attention to policies and systems to facilitate efficient and sustainable city logistics. This paper proposes a modeling framework to consider both spatial–temporal constraints and a means to calibrate the model from observable data, based on an adaptation of an activity-based passenger model called the household activity pattern problem. Conceptual comparisons with a state-of-the-art freight forecasting methodology are made using an example. Application of the model is illustrated through formulating and implementing a Sequential Selective Vehicle Routing Problem associated with drayage truck activities at the San Pedro Bay Ports in Southern California.

The selective vehicle routing problem in a collaborative environment

We consider a selective vehicle routing problem, in which customers belonging to different partners in a logistic coalition are served in a single logistic operation with multiple vehicles. Each partner determines a cost of non-delivery (CND) for each of its customers, and a central algorithm creates an operational plan, including the decision on which customers to serve and in which trip. The total transportation cost of the coalition is then divided back to the partners through a cost allocation mechanism.This paper investigates the effect on the cost allocation of a partner’s strategy on non-delivery penalties (high/low) and the properties of its customer locations (distance to the depot, degree of clustering). The effect of the cost allocation method used by the coalition is also investigated. We compare the well-known Shapley value cost allocation method to our novel problem-specific method: the CND-weighted cost allocation method.We prove that an adequate cost allocation method can provide an incentive for each partner to behave in a way that benefits the coalition. Further, we develop a transformation that is able to transform any cost allocation into an individually rational one without losing this incentive.

Selective vehicle routing for a mobile blood donation system

In this study, a mobile blood collection system is designed with the primary objective of increasing blood collection levels. This design also takes into account operational costs to aim for collection of large amounts of blood at reasonable cost. Bloodmobiles perform direct tours to certain activities to collect blood, but at the end of each day, they bring the collected blood to a designated depot to prevent its spoilage. The proposed system consists of the bloodmobiles and a new vehicle called the shuttle that visits the bloodmobiles in the field on each day and transfers the collected blood to the depot. Consequently, bloodmobiles can continue their tours without having to make daily returns to the depot.We propose a mathematical model and a 2-stage IP based heuristic algorithm to determine the tours of the bloodmobiles and the shuttle, and their lengths of stay at each stop. This new problem is defined as an extension of the Selective Vehicle Routing Problem and is referred to as the SVRP with Integrated Tours. The performances of the solution methodologies are tested first on a real data set obtained from past blood donation activities of Turkish Red Crescent in Ankara, and then on a constructed data set based on GIS data of the European part of Istanbul. The Pareto set of optimum solutions is generated based on blood amounts and logistics costs, and finally a sensitivity analysis on some important design parameters is conducted.

Selective vehicle routing problems under uncertainty without recourse

We argue that the selective vehicle routing problem is more appropriate than the conventional VRP in handling uncertainty with limited resources. However, previous formulations of selective VRPs have all been deterministic. Three new formulations are proposed to account for different optimization strategies under uncertain demand (or utility) level: reliable, robust, and fuzzy selective vehicle routing problems. Three parallel genetic algorithms (PGAs) and a classic genetic algorithm are developed and compared to the deterministic solution. PGAs differ based on their communication strategies and diversity in sub-populations. Results show that a PGA, wherein communication between demes, or subpopulations, occurs in every generation and does not eliminate repeated chromosomes, outperforms other algorithms at the cost of higher computation time. A faster variation of PGA is used to solve the non-convex reliable selective VRP, robust selective VRP and the large-scale fuzzy selective VRP, consisting of 200 nodes. Large scale application demonstrates the value of fuzzy selective vehicle routing problem FSVRP in humanitarian logistics.

The Multiconstraint Team Orienteering Problem with Multiple Time Windows

This paper introduces the multiconstraint team orienteering problem with multiple time windows (MC-TOP-MTW). In the MC-TOP-MTW, a set of vertices is given, each with a service time, one or more time windows, and a score. The goal is to maximize the sum of the collected scores, by a fixed number of tours. The tours are limited in length and restricted by the time windows and additional constraints. Next to a mathematical formulation of the MC-TOP-MTW, the main contribution of this paper is a fast and effective algorithm for tackling this problem, by hybridizing iterated local search with a greedy randomized adaptive search procedure. On a large test set, an average run has a score gap of only 5.19% with known high quality solutions, using 1.5 seconds of computational time. For 32% of the test instances, the known high quality solution was found or improved. This solution method also performs well on test instances of the TOPTW, the selective vehicle routing problem with time windows, and the MC-TOP-TW. A sensitivity analysis shows that the performance of the algorithm is insensitive to small changes in the parameter settings.

Heuristic and exact algorithms for a min–max selective vehicle routing problem

In this work, we investigate a vehicle routing problem where not all clients need to be visited and the goal is to minimize the longest vehicle route. We propose two exact solution approaches for solving the problem: a branch-and-cut (BC) algorithm and a local branching (LB) method that uses BC as its inner solver. Our computational experience indicates that, in practice, the problem is difficult to solve, mainly when the number of vehicles grows. In addition to the exact methods, we present a heuristic that relies on GRASP and on the resolution of a restricted integer program based on a set covering reformulation for the problem. The heuristic was capable of significantly improving the best solutions provided by BC and LB, in one tenth of the times taken by them to achieve their best upper bounds.

Exact algorithms for a selective Vehicle Routing Problem where the longest route is minimized

Abstract In this paper, we study a non-capacitated Vehicle Routing Problem (VRP) where not necessarily all clients need to be visited and the goal is to minimize the length of the longest vehicle route. An Integer Programming Formulation, a Branch-and-cut (BC) method and a Local Branching (LB) framework that uses BC as the inner solver are presented. Sharper upper bounds are obtained by LB, when the same time limit was imposed on the execution times of both approaches. Our results also suggest that the min-max nature of the objective function combined with the fact that not all vertices need to be visited make such problem very difficult to solve.