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A Case Study of Route Optimisation for Phuket Healthy Drink Delivery System
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The product transportation is an important procedure in businesses, which consumes costs and times. Effective transportation management can reduce expenses significantly. New technologies such as Global Position System (GPS), maps, notification and network connection can be integrated all together in one small smart phone that provides accessing from anytime and anywhere. In this paper, we propose a smart logistics framework that combines different technologies in order to solve management and tracking issues in a real transportation environment.
Swallow Swarm Optimization is a new metaheuristic of swarm intelligence based algorithm appeared by Neshat in 2013 in the continuous case. This optimization algorithm inspired by the intelligent behaviors of swallows. In This paper, we provide an adaptation of the swallow swarm optimization (SSO) to solve the famous traveling salesman problem (TSP), as one of the known combinatorial optimization problems. In order to test the performance of the algorithm described herein, we resolve a set of benchmark instances from TSPLIB library. The results obtained demonstrate that DSSO is performant than other metaheuristics methods.
The purpose of this paper is to review the literature on Milk Run Logistics and to present an overview of its implementation practices adopted by the manufacturing organizations. The paper also discusses milk run logistics in the procurement system with a special emphasis on the automobile industry. Milk run system is all about logistics support for the supply chain. Milk run system results in reduction in cost of transportation, travelling path and fuel consumption. The effects of the direct shipment on the traffic conditions and on the environment have also been studied. By introducing the milk run logistics under heavily congested traffic conditions, the supplier can have full control on the procurement process. Also, the number of trucks on road can be reduced thus resulting in improvement in traffic conditions. The effect of the milk run logistics on the reduction of CO2 is also discussed. The promotion of Milk-Run logistics can be highly evaluated from the viewpoint of environmental policy.
This paper explores a dynamic programming approach to the solution of three sequencing problems: a scheduling problem involving arbitrary cost functions, the traveling-salesman problem, and an assembly line balancing problem. Each of the problems is shown to admit of numerical solution through the use of a simple recursion scheme; these recursion schemes also exhibit similarities and contrasts in the structures of the three problems. For large problems, direct solution by means of dynamic programming is not practical, but procedures are given for obtaining good approximate results by solving sequences of smaller derived problems. Experience with a computer program for the solution of traveling-salesman problems is presented.
In this paper, we apply the particle swarm optimization (PSO) to the traveling salesman problem. A mapping from a real-valued vector to the length of a tour is defined, and PSO will find a real-valued vector which can be mapped to a tour with minimum length. The experimental results are also shown to demonstrate the effectiveness of the proposed method.
A novel particle swarm optimization (PSO)-based algorithm for the traveling salesman problem (TSP) is presented. An uncertain searching strategy and a crossover eliminated technique are used to accelerate the convergence speed. Compared with the existing algorithms for solving TSP using swarm intelligence, it has been shown that the size of the solved problems could be increased by using the proposed algorithm.Another PSO-based algorithm is proposed and applied to solve the generalized traveling salesman problem by employing the generalized chromosome. Two local search techniques are used to speed up the convergence. Numerical results show the effectiveness of the proposed algorithms.
The Traveling Salesman Problem (TSP) is often cited as the prototypical hard combinatorial optimization problem. As such, it would seem to be an ideal candidate for nonstandard algorithmic approaches, such as simulated annealing, and, more recently, genetic algorithms. Both of these approaches can be viewed as variants on the traditional technique called local optimization. This paper surveys the state of the art with respect to the TSP, with emphasis on the performance of traditional local optimization algorithms and their new competitors, and on what insights complexity theory does, or does not, provide.
It is shown that a certain tour of 49 cities, one in each of the 48 states and Washington, D.C., has the shortest road distance. Operations Research, ISSN 0030-364X, was published as Journal of the Operations Research Society of America from 1952 to 1955 under ISSN 0096-3984.
The relationship between the symmetric traveling-salesman problem and the minimum spanning tree problem yields a sharp lower bound on the cost of an optimum tour. An efficient iterative method for approximating this bound closely from below is presented. A branch-and-bound procedure based upon these considerations has easily produced proven optimum solutions to all traveling-salesman problems presented to it, ranging in size up to sixty-four cities. The bounds used are so sharp that the search trees are minuscule compared to those normally encountered in combinatorial problems of this type.
Smart Logistics Framework: A Case Study of Phuket RO Water System
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