scholarly journals Production Scheduling and Customer Orders Assignment in a Three-Level Supply Chain

2014 ◽  
Vol 2014 ◽  
pp. 1-12
Author(s):  
F. Sadeghi Naieni Fard ◽  
B. Naderi ◽  
A. A. Akbari
Keyword(s):  
Production Scheduling ◽  
Variable Neighborhood Search ◽  
Programming Model ◽  
Search Algorithm ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Distribution Centers ◽  
Proposed Model ◽  
Production Distribution ◽  
Neighborhood Search Algorithm

In the classical production-distribution centers problem, only assignment of customers, distribution centers, and suppliers is determined. This paper extends the problem of production-distribution centers assignment by considering sequencing decisions in the supply network. Nowadays, meeting delivery time of products is a competitive benefit; therefore, the objective is to minimize total tardiness. This problem is mathematically formulated by a mixed integer programming model. Then, using the proposed model, small instances of the problem can be optimally solved by GAMS software. Moreover, two metaheuristics based on variable neighborhood search and simulated annealing are proposed to solve large instances of the problem. Finally, performance of the proposed metaheuristics is evaluated by two sets of balanced and unbalanced instances. The computational results show the superiority of the variable neighborhood search algorithm.

scholarly journals Variable Neighborhood Search for Parallel Machines Scheduling Problem with Step Deteriorating Jobs

2012 ◽  
Vol 2012 ◽  
pp. 1-20 ◽  
Author(s):  
Wenming Cheng ◽  
Peng Guo ◽  
Zeqiang Zhang ◽  
Ming Zeng ◽  
Jian Liang
Keyword(s):  
Variable Neighborhood Search ◽  
Parallel Machines ◽  
Programming Model ◽  
Search Algorithm ◽  
Optimal Schedule ◽  
Deteriorating Jobs ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Scheduling Problem

In many real scheduling environments, a job processed later needs longer time than the same job when it starts earlier. This phenomenon is known as scheduling with deteriorating jobs to many industrial applications. In this paper, we study a scheduling problem of minimizing the total completion time on identical parallel machines where the processing time of a job is a step function of its starting time and a deteriorating date that is individual to all jobs. Firstly, a mixed integer programming model is presented for the problem. And then, a modified weight-combination search algorithm and a variable neighborhood search are employed to yield optimal or near-optimal schedule. To evaluate the performance of the proposed algorithms, computational experiments are performed on randomly generated test instances. Finally, computational results show that the proposed approaches obtain near-optimal solutions in a reasonable computational time even for large-sized problems.

scholarly journals Variable Neighborhood Search Algorithms for an Integrated Manufacturing and Batch Delivery Scheduling Minimizing Total Tardiness

2019 ◽  
Vol 9 (21) ◽  
pp. 4702
Author(s):  
Cheol Min Joo ◽  
Byung Soo Kim
Keyword(s):  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Search Algorithms ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Manufacturing Plant ◽  
Selection Probability ◽  
Case Selection ◽  
Neighborhood Search Algorithms ◽  
Neighborhood Search Algorithm

This article addresses an integrated problem of one batching and two scheduling decisions between a manufacturing plant and multi-delivery sites. In this problem, two scheduling problems and one batching problem must be simultaneously determined. In the manufacturing plant, jobs ordered by multiple customers are first manufactured by one of the machines in the plant. They are grouped to the same delivery place and delivered to the corresponding customers using a set of delivery trucks within a limited capacity. For the optimal solution, a mixed integer linear programming model is developed and two variable neighborhood search algorithms employing different probabilistic schemes. We tested the proposed algorithms to compare the performance and conclude that the variable neighborhood search algorithm with dynamic case selection probability finds better solutions in reasonable computing times compared with the variable neighborhood search algorithm with static case selection probability and genetic algorithms based on the test results.

A Variable Neighborhood Search Algorithm for Network Expansion Deferment in a Hub Network

2015 ◽  
Vol 6 (1) ◽  
pp. 17-32 ◽  
Author(s):  
Masoud Rabbani ◽  
Amir Farshbaf-Geranmayeh ◽  
Mohsen Hasani ◽  
Mahyar Rezaei
Keyword(s):  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Planning Horizon ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Extensive Literature ◽  
Network Expansion ◽  
Current Period ◽  
Proposed Model ◽  
Hub Network

The hub network expansion problem over planning horizon is addressed in this paper. In the extensive literature on hub network problem, it has widely been assumed that all P hub facilities must be located in current period and they do not take into account variation of demands, investment opportunities and net present cost. In this study, it is supposed that P hub facilities should have been located over planning horizon under variation of demands of every pair of nodes over time periods and also considering congestion effects at hub nodes. In this study, a mixed integer nonlinear programming formulation which minimizing the net present cost of planning horizon is presented. The Variable Neighborhood Search (VNS) algorithm is developed and successfully solved many instances of standard Childhood and Beyond (CAB) dataset and the results verify applicability of the proposed model and algorithm.

A variable neighborhood search algorithm for optimal protection coordination of power systems

2021 ◽  
Author(s):  
H. R. E. H. Bouchekara ◽  
M. S. Shahriar ◽  
M. S. Javaid ◽  
Y. A. Sha’aban ◽  
M. Zellagui ◽  
...  
Keyword(s):  
Power Systems ◽  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Neighborhood Search Algorithm ◽  
Optimal Protection

scholarly journals Two meta-heuristics for solving the multi-vehicle multi-covering tour problem with a constraint on the number of vertices

2020 ◽  
pp. 14-14
Author(s):  
Manel Kammoun ◽  
Houda Derbel ◽  
Bassem Jarboui
Keyword(s):  
Genetic Algorithm ◽  
Local Search ◽  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Descent Method ◽  
Computational Experiments ◽  
Neighborhood Search ◽  
Variable Neighborhood Descent ◽  
Covering Tour Problem ◽  
Neighborhood Search Algorithm

In this work we deal with a generalized variant of the multi-vehicle covering tour problem (m-CTP). The m-CTP consists of minimizing the total routing cost and satisfying the entire demand of all customers, without the restriction of visiting them all, so that each customer not included in any route is covered. In the m-CTP, only a subset of customers is visited to fulfill the total demand, but a restriction is put on the length of each route and the number of vertices that it contains. This paper tackles a generalized variant of the m-CTP, called the multi-vehicle multi-covering Tour Problem (mm-CTP), where a vertex must be covered several times instead of once. We study a particular case of the mm-CTP considering only the restriction on the number of vertices in each route and relaxing the constraint on the length (mm-CTP-p). A hybrid metaheuristic is developet by combining Genetic Algorithm (GA), Variable Neighborhood Descent method (VND), and a General Variable Neighborhood Search algorithm (GVNS) to solve the problem. Computational experiments show that our approaches are competitive with the Evolutionary Local Search (ELS) and Genetic Algorithm (GA), the methods proposed in the literature.

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