Travelling Salesman Problem using Prim Algorithm in High Performance Computing

Thanks to the advances in wide area network technology and the low cost of computing resources, High Performance Computing came into being and currently research area. One incentive of High Performance Computing is to summative the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal an efficient job scheduling algorithm system is an essential part of the High Performance Computing. This preliminary report emphasizes on the basic terms of the efficient job scheduling algorithm for traveling salesman problem in high performance computing. Job scheduling algorithm will reduce the traffic between the processors and can help improve resource utilization and quality of service. Traveling salesman problem is finding is the shortest path connecting number of locations such as cities, visited by a traveling salesman on his sales route. TSP has been used in The Two-Period Travelling Salesman Problem Applied to Milk Collection in Ireland and Usefulness of Solution Algorithms of the Travelling Salesman Problem in the typing of Biological Sequences in a Clinical Laboratory Setting

Travelling Salesman Problem using Prim Algorithm in High Performance Computing

Thanks to the advances in wide area network technology and the low cost of computing resources, High Performance Computing came into being and currently research area. One incentive of High Performance Computing is to summative the power of widely distributed resources, and provide non-trivial services to users. To achieve this goal an efficient job scheduling algorithm system is an essential part of the High Performance Computing. This preliminary report emphasizes on the basic terms of the efficient job scheduling algorithm for traveling salesman problem in high performance computing. Job scheduling algorithm will reduce the traffic between the processors and can help improve resource utilization and quality of service. Traveling salesman problem is finding is the shortest path connecting number of locations such as cities, visited by a traveling salesman on his sales route. TSP has been used in The Two-Period Travelling Salesman Problem Applied to Milk Collection in Ireland and Usefulness of Solution Algorithms of the Travelling Salesman Problem in the typing of Biological Sequences in a Clinical Laboratory Setting

The 2-period balanced traveling salesman problem

In the 2-period Balanced Traveling Salesman Problem (2B-TSP), the customers must be visited over a period of two days: some must be visited daily, and the others on alternate days (even or odd days); moreover, the number of customers visited in every tour must be balancedâ, i.e. it must be the same or, alternatively, the difference between the maximum and the minimum number of visited customers must be less than a given threshold. The salesman's objective is to minimize the total distance travelled over the two tours. Although this problem may be viewed as a particular case of the Period Traveling Salesman Problem, in the 2-period Balanced TSP the assumptions allow for emphasizing on routing aspects, more than on the assignment of the customers to the various days of the period. The paper proposes two heuristic algorithms particularly suited for the case of Euclidean distances between the customers. Computational experiences and a comparison between the two algorithms are also given.

A new algorithm for the 2-period Balanced Traveling Salesman Problem in Euclidean graphs

In a previous paper, we proposed two heuristic algorithms for the euclidean 2-period Balanced Travelling Salesman Problem (2B-TSP). In this problem, which arises from a similar one introduced by Butler et al., a certain number of customers must be visited at minimum total cost over a period of two days: some customers must be visited daily, and the others on alternate days (even or odd days). Moreover, the number of customers visited in every tour must be ‘balanced’, i.e. it must be the same or, alternatively, the difference between the maximum and the minimum number of visited customers must be less than a given threshold: this kind of constraint does not appear explicitly in the paper by Butler. In this paper a third algorithm is presented which overcomes some inadequacy of the algorithm A2 we proposed in the previous paper. The new algorithm’s performance is then analyzed, with respect particularly with the first proposed algorithm.period routing problem, period travelling salesman problem, logistic, heuristic algorithms

The 2-period Balanced Traveling Salesman Problem

In the 2-period Balanced Traveling Salesman Problem (2B-TSP), the customers must be visited over a period of two days: some must be visited daily, and the others on alternate days (even or odd days); moreover, the number of customers visited in every tour must be ‘balanced’, i.e. it must be the same or, alternatively, the difference between the maximum and the minimum number of visited customers must be less than a given threshold. The salesman’s objective is to minimize the total distance travelled over the two tours. Although this problem may be viewed as a particular case of the Period Traveling Salesman Problem, in the 2-period Balanced TSP the assumptions allow for emphasizing on routing aspects, more than on the assignment of the customers to the various days of the period. The paper proposes two heuristic algorithms particularly suited for the case of Euclidean distances between the customers. Computational experiences and a comparison between the two algorithms are also given

A Hybrid approach for milk collection using trucks and trailers

acceptedVersio

Food choice and health across the life course: A qualitative study examining food choice in older Irish adults

Ireland has experienced much economic and social change in recent times along with rising levels of overweight. Two-thirds of adults are now overweight or obese. Excess weight is a major risk for chronic disease for all ages which heralds a great societal burden and presents challenges and opportunities for the food industry. Individual food choice is an important and complex factor to be understood in order for food manufacturers to enable healthy choices. It can be understood as a process in which patterns arise and change over time, influenced by a range of personal factors and environmental influences. This study aimed to gain insight into the key contextual influences on food choice patterns in older Irish adults of varied health status who have lived through much socioeconomic change. In-depth semi-structured interviews on food choice across the life course were conducted with 32 adults aged 61-79. Data was thematically analysed using content analysis. Patterns of eating within the changing food environment and dietary choices in the face of ageing and illness were influenced by accumulated life experiences. Findings can contribute to health and nutrition policies and to the design of tailored interventions and products to facilitate the adoption of healthful diets.food, health, food life experiences, Food Consumption/Nutrition/Food Safety,

A new algorithm for the 2-period Balanced Traveling Salesman Problem in Euclidean Graphs.

In a previous paper, we proposed two heuristic algorithms for the euclidean 2-period Balanced Travelling Salesman Problem (2B-TSP). In this problem, which arises from a similar one introduced by Butler et al., a certain number of customers must be visited at minimum total cost over a period of two days: some customers must be visited daily, and the others on alternate days (even or odd days). Moreover, the number of customers visited in every tour must be ‘balanced’, i.e. it must be the same or, alternatively, the difference between the maximum and the minimum number of visited customers must be less than a given threshold: this kind of constraint does not appear explicitly in the paper by Butler. In this paper a third algorithm is presented which overcomes some inadequacy of the algorithm A2 we proposed in the previous paper. The new algorithm’s performance is then analyzed, with respect particularly with the first proposed algorithm

A Decision Support System for Corporate Planning in the New Zealand Dairy Industry

Decisions; Support; Systems; Planning; Optimization; Heuristics; Network programming; Fixed Costs; Travelling salesman; Distances; Computer graphics. A Decision Support System (DSS) is described, the prime objective of which is to aid in the location of new investments in a multi-site, multi-product dairy processing company. A network program model is described which optimises the collection of milk from farm groups (netcells) and the allocation of the milk to a range of final products and byproducts through consideration of product prices, Process costs and transport costs. Constraints include process capacities, overtime capacities, and final product demands. Site dependant product yields are considered through use of an iteration procedure surrounding the network model. This procedure updates estimates of the mean company yield used to set upper and lower arc constraints in the product demand phase of the network model. Milk tanker collection distances are estimated by an expected travelling salesman distance method in conjunction with accurately measured netcell to factory 'bridging distances' and an inter-factory diversion network of road distances. To cope with daily fixed coat charges, a heuristic Procedure employing cost relaxations and a number of Pre-solution feasibility tests is used. Seasonally varying factors (milk supply, product yield and farms visited per tanker trip) are accommodated by solving the network model for the average day in each month for twelve months, then summing the results multiplied by the number of production days in each month. Implementation as a DSS was facilitated through use of an interactive computer system incorporating computer-generated graphic displays. Applications of the DSS to location planning, industry rationalization and other corporate planning activities are described. Recommendations on the use of the model to identify the feasible set of candidates for location studies are made, and methods for identifying the appropriate timing of investments are considered