scholarly journals Optimization for Service Routes of Pallet Service Center Based on the Pallet Pool Mode

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Kang Zhou ◽  
Shiwei He ◽  
Rui Song
Keyword(s):  
Shortest Path ◽  
Programming Model ◽  
Time Windows ◽  
Method Of Lines ◽  
Chance Constrained Programming ◽  
Service Center ◽  
Chinese Postman Problem ◽  
Routing Problem ◽  
Hybrid Intelligent Algorithm ◽  
Constrained Programming

Service routes optimization (SRO) of pallet service center should meet customers’ demand firstly and then, through the reasonable method of lines organization, realize the shortest path of vehicle driving. The routes optimization of pallet service center is similar to the distribution problems of vehicle routing problem (VRP) and Chinese postman problem (CPP), but it has its own characteristics. Based on the relevant research results, the conditions of determining the number of vehicles, the one way of the route, the constraints of loading, and time windows are fully considered, and a chance constrained programming model with stochastic constraints is constructed taking the shortest path of all vehicles for a delivering (recycling) operation as an objective. For the characteristics of the model, a hybrid intelligent algorithm including stochastic simulation, neural network, and immune clonal algorithm is designed to solve the model. Finally, the validity and rationality of the optimization model and algorithm are verified by the case.

A Fuzzy Programming Model of Supplier Selection

2012 ◽  
Vol 468-471 ◽  
pp. 668-673 ◽  
Author(s):  
Hua Jiang ◽  
Zhi Gang Lu
Keyword(s):  
Supplier Selection ◽  
Programming Model ◽  
Fuzzy Programming ◽  
Chance Constrained Programming ◽  
Hybrid Intelligent Algorithm ◽  
Fuzzy Variables ◽  
Fuzzy Expected Value ◽  
Order Quantities ◽  
Constrained Programming ◽  
Fuzzy Programming Model

An integrated supplier selection problem under fuzzy environment is studied in this paper. Firstly, the linear weight method is used to calculate the scores of suppliers according to their different attributes, such as: quality, service, warranty, delivery, reputation and position, which are assumed as fuzzy variables. Secondly, a fuzzy expected value programming model and a fuzzy chance-constrained programming model are proposed to select the best combination of the suppliers and determine the order quantities. A hybrid intelligent algorithm, based on fuzzy simulation, genetic algorithm and neural network, is used to solve the two models. Finally, a numerical example is given to illustrate the effectiveness of the proposed models.

Research on the Vehicle Routing Problem with Fuzzy Demands

2011 ◽  
Vol 186 ◽  
pp. 570-575
Author(s):  
Lian Xue ◽  
Xiao Xia Dai
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Differential Evolution Algorithm ◽  
Possibility Theory ◽  
Chance Constrained Programming ◽  
Routing Problem ◽  
Hybrid Intelligent Algorithm ◽  
Evolution Algorithm ◽  
Constrained Programming ◽  
Rational Range

In this paper, the vehicle routing problem with fuzzy demands is considered, and a fuzzy chance constrained programming mathematical model is established based on fuzzy possibility theory. Then fuzzy simulation and differential evolution algorithm are integrated to design a hybrid intelligent algorithm to solve the fuzzy vehicle routing model. Moreover, under the target that the total driving distance of vehicles is the shortest, the influence of the decision-maker’s preference on the final objective of the problem is discussed using the method of stochastic simulation, and the rational range of the preference number is obtained.

scholarly journals New general mixed-integer linear programming model for mobile workforce management

2021 ◽  
Author(s):  
András Éles ◽  
István Heckl ◽  
Heriberto Cabezas
Keyword(s):  
Programming Model ◽  
Time Windows ◽  
Mutual Exclusion ◽  
Parallel Execution ◽  
Mixed Integer ◽  
Management Problem ◽  
Routing Problem ◽  
Workforce Management ◽  
Computational Performance ◽  
Wide Range

AbstractA mathematical model is introduced to solve a mobile workforce management problem. In such a problem there are a number of tasks to be executed at different locations by various teams. For example, when an electricity utility company has to deal with planned system upgrades and damages caused by storms. The aim is to determine the schedule of the teams in such a way that the overall cost is minimal. The mobile workforce management problem involves scheduling. The following questions should be answered: when to perform a task, how to route vehicles—the vehicle routing problem—and the order the sites should be visited and by which teams. These problems are already complex in themselves. This paper proposes an integrated mathematical programming model formulation, which, by the assignment of its binary variables, can be easily included in heuristic algorithmic frameworks. In the problem specification, a wide range of parameters can be set. This includes absolute and expected time windows for tasks, packing and unpacking in case of team movement, resource utilization, relations between tasks such as precedence, mutual exclusion or parallel execution, and team-dependent travelling and execution times and costs. To make the model able to solve larger problems, an algorithmic framework is also implemented which can be used to find heuristic solutions in acceptable time. This latter solution method can be used as an alternative. Computational performance is examined through a series of test cases in which the most important factors are scaled.

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