Parallel genetic algorithm for periodic vehicle routing and scheduling problem

The model and algorithm for Vehicle Routing and Scheduling Problem (VRSP) in picking up and delivering customers to airport service were presented in this paper. VRSP is an extension of the class Vehicle Routing Problem (VRP) with more resource constraints. Mathematical models are described for the VRSP with minimizing the cost based on low carbon emissions. The Bi-directional Optimization Heuristic Algorithm (BOHA) is developed for this model. The results verified the effectiveness of the model and Algorithm. The proposed model and algorithm can provide quantitative support and help the air ticket company decision-maker develop economical, punctual shuttle service with high satisfaction.

Summary form only given. This article gives a brief overview of theoretical advances, computing trends, applications and future perspectives in parallel genetic algorithms. It explains basic terms and behavior of (parallel) genetic algorithms. Genetic algorithms are easily parallelized algorithms, therefore two kinds of possible parallelism, data parallelism and control parallelism, are mentioned and described towards them. Parallelism of genetic algorithms brings many advantages and gains. Classifications of these algorithms are often based on the type of computing model, a walk strategy and the used computing machinery. Afterwards significant milestones in the theory with latest advances are briefly mentioned. Then current trends in parallel computing with stress computer architectures of parallel systems, interconnection topologies, operating systems, parallel (genetic) libraries and programming paradigms are reviewed shortly. The sufficient space is devoted to the latest applications of parallel genetic algorithms. After the discussion section, perspectives of the algorithms are predicted till the year 2005. The information in the article is segregated into two periods before and after the year 2000 in all chapters. The second period is more interesting and of higher importance, because it highlights recent research efforts and gives some hints about possible future trends. That is why we devote much space to the second period. As there is no such an overview of the recent period of parallel genetic algorithms, our investigation could be appealing and useful in many aspects.

One of the important problems of sustainable logistics is routing vehicles in a sustainable manner, the green vehicle routing problem, or vehicle routing problems which aim to reduce CO2 emissions. In the literature research, it was seen that these problems were solved with heuristic, metaheuristic, or hyper-heuristic methods and hybrid approaches since they are in the NP-hard class. This work presents a parallel multi-process genetic algorithm that incorporates problem-specific genetic operators to minimize CO2 emissions in the capacity-constrained vehicle routing problem. Unlike previous research, the algorithm combines parallel computing with tailored genetic operators in order to enhance the diversity of solutions and speed up convergence. Genetic algorithm models were developed to minimize total distance, CO2 emissions, and both objectives simultaneously. Two genetic algorithm models were developed to minimize total distance and CO2 emissions. Experimental results using the reference CVRP examples such as A-n32-k5 and B-n44-k7 show that the proposed approach reduces CO2 emissions by 1.2% more than hybrid artificial bee colony optimization, 1.3% more than ant colony optimization, and 4% more than the traditional genetic algorithm. Experimental results using benchmark CVRP instances demonstrate that the proposed approach outperforms hybrid artificial bee colony optimization, ant colony optimization, and traditional genetic algorithms for most of the test cases. This is done by exploiting multi-core processors, and the parallel architecture has improved computational efficiency; the modules compare and update solutions against the global optimum. Results obtained show that prioritizing CO2 emissions as the only objective yields better results compared to multi-objective models. This study makes two significant contributions to the literature: (1) it introduces a novel parallel genetic algorithm framework optimized for CO2 emission reduction, and (2) it provides empirical evidence underscoring the advantages of emission-focused optimization in CVRP.

A scalable parallel genetic algorithm for the Generalized Assignment Problem

The vehicle routing problem (VRP) is a difficult and well-studied combinatorial optimization problem. We develop a parallel algorithm for the VRP that combines a heuristic local search improvement ...

The study aims to optimize the vehicle routing problem, considering infeasible routing, to minimize losses for the company. Firstly, a vehicle routing model with hard time windows and infeasible route constraints is established, considering both the minimization of total vehicle travel distance and the maximization of customer satisfaction. Subsequently, a Floyd-based improved genetic algorithm that incorporates local search is designed. Finally, the computational experiment demonstrates that compared with the classic genetic algorithm, the improved genetic algorithm reduced the average travel distance by 20.6% when focusing on travel distance and 18.4% when prioritizing customer satisfaction. In both scenarios, there was also a reduction of one in the average number of vehicles used. The proposed method effectively addresses the model introduced in this study, resulting in a reduction in total distance and an enhancement of customer satisfaction.

This paper investigates the application of a new class of neighborhood search algorithms—cyclic transfers—to multivehicle routing and scheduling problems. These algorithms exploit the two-faceted decision structure inherent to this problem class: First, assigning demands to vehicles and, second, routing each vehicle through its assigned demand stops. We describe the application of cyclic transfers to vehicle routing and scheduling problems. Then we determine the worst-case performance of these algorithms for several classes of vehicle routing and scheduling problems. Next, we develop computationally efficient methods for finding negative cost cyclic transfers. Finally, we present computational results for three diverse vehicle routing and scheduling problems, which collectively incorporate a variety of constraint and objective function structures. Our results show that cyclic transfer methods are either comparable to or better than the best published heuristic algorithms for several complex and important vehicle routing and scheduling problems. Most importantly, they represent a novel approach to solution improvement which holds promise in many vehicle routing and scheduling problem domains.

This paper discusses the implementation of Dijkstra's (1959) classic double bucket algorithm for path finding in connected networks. The work reports on a modification of the algorithm embracing both static and dynamic heuristic components and multiple source nodes. The modified algorithm is applied in a 3D spatial information system (SIS) for routing emergency service vehicles. The algorithm has been implemented as a suite of modules and integrated into a commercial SIS software environment. Genuine 3D spatial data is used to test the algorithm on the problem of vehicle routing and rerouting under simulated earthquake conditions in the Japanese city of Okayama. Coverage graphs were also produced giving contour lines joining points with identical travel times.

Evolution in Time and Space – The Parallel Genetic Algorithm

Background: Logistics and transport, core of many business processes, are continuously optimized to improve efficiency and market competitiveness. The paper describes a modular coordination of vehicle routing and bin packing problems that enables independent instances of the problems to be joined together, with the aim that the vehicle routing solution satisfies all the constraints from real-world applications. Methods: The vehicle routing algorithm is based on an adaptive memory procedure that also incorporates a simple, one-dimensional bin packing problem. This preliminary packing solution is refined by a complex, three dimensional bin packing for each vehicle to identify the infeasible packages. The method iteratively adjusts virtual volumes until reaching near-optimal routes that respect bin-packing constraints. Results: The coordination enables independent applications of an adaptive memory procedure to vehicle routing and a genetic algorithm approach to bin packing while joining them in a computationally tractable way. Such a coordinated approach is applied to a frequently used public benchmark and proven to provide commensurate costs while significantly lowering algorithm complexity. Conclusions: The proposed method is further validated on a real industrial case study and provided additional savings of 14.48% in average daily distance traveled compared to the current industrial standard.

This paper presents an overview of recent advances in the field of the vehicle routing problem (VRP), based on papers published in high-quality journals during the period from January 2015 to July 2017. A distinctive feature of the presented survey is its focus on new versions of the VRP, which have recently emerged or gained momentum, and the corresponding new solution methods, with particular emphasis on computational intelligence (CI) approaches. The list of newly proposed or currently popular VRP formulations include last mile and same day delivery, crowdshipping, bike sharing systems, post-disaster response plans, local routing in large production or cargo plants, customer-centric VRP, autonomous delivery, unnamed aerial vehicle delivery, green VRP, waste collection VRP, rich VRP, or VRP with backhauls. Simultaneously, an adequate increase of interest in the application of traditional CI methods (e.g., genetic, memetic, ant colony or particle swarm optimization, simulated annealing, or their various hybrid versions) can be observed in the VRP domain. At the same time, approaches proven efficient in other optimization areas (e.g., hyperheuristics, methods based on Monte Carlo simulations, algorithms rooted in game theory and bi-level optimization-Stackelberg games, or cognitively motivated methods) have lately entered the VRP field and become a viable alternative to more traditional techniques. Since VRP is one of the fastest growing fields in the operations research area, we believe that an analysis of the recently published VRP papers from the perspective of their novelty in problem formulation and/or applied solution method can provide a true value for the CI community, especially young researchers entering the field and seeking challenges in this interesting and fast developing research area.

Genetic algorithms offer very good performances for solving large optimization problems, especially in the domain of error-correcting codes. However, they have a major drawback related to the time complexity and memory occupation when running on a uniprocessor computer. This paper proposes a parallel decoder for linear block codes, using parallel genetic algorithms (PGA). The good performance and time complexity are confirmed by theoretical study and by simulations on BCH(63,30,14) codes over both AWGN and flat Rayleigh fading channels. The simulation results show that the coding gain between parallel and single genetic algorithm is about 0.7 dB at BER = 10﹣5 with only 4 processors.

This paper proposes an efficient algorithm, with a reduced number of parameters, for solving the two‐dimensional loading‐capacitated vehicle routing problem (2L‐CVRP). This problem combines two of the most important issues in logistics, that is, vehicle routing and packing problems. Our approach contemplates unrestricted loading including the possibility of applying 90° rotations to each rectangular‐shaped item while loading it into the vehicle, which is a realistic assumption seldom considered in the existing literature. The algorithm uses a multistart approach that is designed to avoid local minima and also to make the algorithm an easily parallelizable one. At each restart, a biased randomization of a savings‐based routing algorithm is combined with an enhanced version of a classical packing heuristic to produce feasible good solutions for the 2L‐CVRP. The proposed algorithm has been compared with the classical benchmarks for two different 2L‐CVRP variants, that is, with and without item rotations. Experimental results show that our approach outperforms several best‐known solutions from previous work, both in terms of quality and the computational time needed to obtain them.

Reliability–redundancy allocation problem considering optimal redundancy strategy using parallel genetic algorithm

Modern group communication based applications require multiple parameters to be considered for routing in a Cellular network. Traditional algorithms fail in the situations where these parameters frequently change due to the dynamism prevailing in the network. A new technique for topology discovery in these types of networks using ant colony optimization (ACO) has been proposed based on the restricted flooding principle. To provide a better quality of service in routing with multiple constraints, a genetic algorithm based routing has been proposed to find optimal routes within a shorter span of time than the traditional deterministic routing algorithms. Moreover, with the exponential growth in the number of mobile users, to enable a large number of users to participate in a group communication, a parallel genetic algorithm (GA) is proposed in this paper. Our simulation results show that the topology discovery using ant colony optimization is faster. The Call service rate using parallel genetic algorithm is more than that of sequential genetic algorithm and the Call blocking rate of parallel genetic algorithm is less than that of sequential genetic algorithm, for large number of routers in the network.