A Parallel Genetic Algorithm Framework for Transportation Planning and Logistics Management

Abstract

Small to medium sized transportation and logistics companies are usually constrained by limited computing and IT professional resources on implementing an efficient parallel metaheuristic algorithm for planning or management solutions. In this paper we extend the standard meta-description for genetic algorithms (GA) with a simple non-trivial parallel implementation. Our parallel GA framework is chiefly concerned with the development of a straightforward way for engineers to modify existing genetic algorithm implementations for real transportation and logistics problems to make use of commonly available hardware resources without completely reworking complex, useful and usable codes. The framework presented at its parallel base is a modification of the primitive parallelization concept, but if implemented as described it may be gradually extended to fit the qualities of any underlying problem better (via the adaptation of the merging and communications functions).We present our framework and computational results for a classical transportation related combinatorial optimization problem-the traveling salesman problem with a standard sequential genetic algorithm implementation. Our empirical analysis shows that this simple extension can lead to considerable solution improvements. We also tested our assumptions that the framework is easily implemented by an engineer not initially familiar with genetic algorithms to implement the framework for another minimum multiprocessor scheduling problem. These case studies verify that our framework is better than primitive parallelization because it gives empirically better results under equitable conditions. It also outperforms fine grained parallelization as it is easier and faster to implement. INDEX TERMS Parallel metaheuristics, genetic algorithm, transportation planning, logistics management. I. INTRODUCTION Genetic algorithms (GA) are an iterative search method in which new answers are produced by combining two predecessor answers and mimicking the process of natural selection [1]. Its metaheuristic can routinely generate useful solutions to optimization and search problems, therefore GA has been widely used in transportation planning [2]-[5] and logistics operations management applications and software systems [6]-[8]. Some current relevant applications include The associate editor coordinating the review of this manuscript and approving it for publication was Guangdong Tian. pavement lifecycle analysis [9], additive manufacturing [10], accident emergency response [11] and land use planning [12]. Other frameworks for transportation analysis might benefit from the addition of parallel migration implementation in scenarios such as traffic congestion [13]-[15], vehicular sensing [16]-[18], activity planning [19]-[21], mobility management [22]-[24], spatial-temporal modeling [25]-[27] and others [28]-[31]. A. MOTIVATIONS Genetic algorithm is a common tool that has been employed in many transportation and logistics settings. However,

Artificial Neural Networks to Solve the Singular Model with Neumann–Robin, Dirichlet and Neumann Boundary Conditions

“…GA is applied in various fields of technologies, applied sciences, and engineering that work through its reproduction operators. In recent years, GA and PSO based heuristic optimization solvers have been applied in transportation planning and logistics management [ 34 ], microgrid energy management systems [ 35 ], optimization of multimodal functions [ 36 ], mobile position estimation problem [ 37 ], the vehicle routing problem in cloud implementation [ 38 ], satellite formation reconfiguration [ 39 ], and the optimization of multi-objective energy models [ 40 ].…”

Energy Aware Parallel Scheduling Techniques for Network-on-Chip Based Systems

“…[24] Focuses on integrating task mapping, task ordering and voltage scaling. To take advantage of the different computational resources available on a given system, researchers in [17,25,26] propose parallel genetic algorithms (PGA). In [17] PGA measures the fitness value of each person in a single population leveraging highly parallel processors, such as the master-slave-based GA platform.…”

Loop Selection for Multilevel Nested Loops Using a Genetic Algorithm

“…break; (21) end if (22) end for (23) end while (24) Generate Chromosome X q ′ that corresponds to L q ′ (25) end function ALGORITHM 2: Chromosome repair. 8 Mathematical Problems in Engineering (6) Fitness evaluation.…”

“…erefore, to solve the loop selection problem of multilevel nested loops, some heuristic methods can only be used to obtain approximate solutions, from which a satisfactory solution is then chosen. Compared with traditional heuristic methods, genetic algorithms (GAs) have a very strong search ability; they can find the global optimal solution of a problem with a high probability, and their inherent parallelism is more suitable for processing optimization problems [16][17][18][19][20][21][22][23]. erefore, a GA was adopted in this study to solve the loop selection problem.…”