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 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 A variable neighborhood search algorithm for solving the single machine scheduling problem with periodic maintenance

2019 ◽  
Vol 53 (1) ◽  
pp. 289-302 ◽  
Author(s):  
Hanane Krim ◽  
Rachid Benmansour ◽  
David Duvivier ◽  
Abdelhakim Artiba
Keyword(s):  
Single Machine ◽  
Variable Neighborhood Search ◽  
Search Algorithm ◽  
Machine Scheduling ◽  
Single Machine Scheduling ◽  
Computational Time ◽  
Neighborhood Search ◽  
Scheduling Problem ◽  
Periodic Maintenance ◽  
Neighborhood Search Algorithm

In this paper we propose to solve a single machine scheduling problem which has to undergo a periodic preventive maintenance. The objective is to minimize the weighted sum of the completion times. This criterion is defined as one of the most important objectives in practice but has not been studied so far for the considered problem. As the problem is proven to be NP-hard, and a mathematical model is proposed in the literature, we propose to use General Variable Neighborhood Search algorithm to solve this problem in order to obtain near optimal solutions for the large-sized instances in a small amount of computational time.

scholarly journals Multi-Objective Sustainable Truck Scheduling in a Rail–Road Physical Internet Cross-Docking Hub Considering Energy Consumption

2019 ◽  
Vol 11 (11) ◽  
pp. 3127 ◽  
Author(s):  
Tarik Chargui ◽  
Abdelghani Bekrar ◽  
Mohamed Reghioui ◽  
Damien Trentesaux
Keyword(s):  
Energy Consumption ◽  
Programming Model ◽  
Mixed Integer ◽  
Computational Time ◽  
Neighborhood Search ◽  
Optimal Solutions ◽  
Multi Objective ◽  
Sustainable Logistics ◽  
Cross Docking ◽  
Physical Internet

In the context of supply chain sustainability, Physical Internet (PI or π ) was presented as an innovative concept to create a global sustainable logistics system. One of the main components of the Physical Internet paradigm consists in encapsulating products in modular and standardized PI-containers able to move via PI-nodes (such as PI-hubs) using collaborative routing protocols. This study focuses on optimizing operations occurring in a Rail–Road PI-Hub cross-docking terminal. The problem consists of scheduling outbound trucks at the docks and the routing of PI-containers in the PI-sorter zone of the Rail–Road PI-Hub cross-docking terminal. The first objective is to minimize the energy consumption of the PI-conveyors used to transfer PI-containers from the train to the outbound trucks. The second objective is to minimize the cost of using outbound trucks for different destinations. The problem is formulated as a Multi-Objective Mixed-Integer Programming model (MO-MIP) and solved with CPLEX solver using Lexicographic Goal Programming. Then, two multi-objective hybrid meta-heuristics are proposed to enhance the computational time as CPLEX was time consuming, especially for large size instances: Multi-Objective Variable Neighborhood Search hybridized with Simulated Annealing (MO-VNSSA) and with a Tabu Search (MO-VNSTS). The two meta-heuristics are tested on 32 instances (27 small instances and 5 large instances). CPLEX found the optimal solutions for only 23 instances. Results show that the proposed MO-VNSSA and MO-VNSTS are able to find optimal and near optimal solutions within a reasonable computational time. The two meta-heuristics found optimal solutions for the first objective in all the instances. For the second objective, MO-VNSSA and MO-VNSTS found optimal solutions for 7 instances. In order to evaluate the results for the second objective, a one way analysis of variance ANOVA was performed.

scholarly journals Bi-local search based variable neighborhood search for job-shop scheduling problem with transport constraints

2020 ◽  
Author(s):  
Moussa Abderrahim ◽  
Abdelghani Bekrar ◽  
Damien Trentesaux ◽  
Nassima Aissani ◽  
Karim Bouamrane
Keyword(s):  
Manufacturing Systems ◽  
Variable Neighborhood Search ◽  
Job Shop ◽  
Job Shop Scheduling ◽  
Search Algorithm ◽  
Neighborhood Search ◽  
Efficient Production ◽  
Scheduling Problem ◽  
Job Shop Scheduling Problem ◽  
Shop Scheduling

AbstractIn job-shop manufacturing systems, an efficient production schedule acts to reduce unnecessary costs and better manage resources. For the same purposes, modern manufacturing cells, in compliance with industry 4.0 concepts, use material handling systems in order to allow more control on the transport tasks. In this paper, a job-shop scheduling problem in vehicle based manufacturing facility that is mainly related to job assignment to resources is addressed. The considered job-shop production cell has two types of resources: processing resources that accomplish fabrication tasks for specific products, and transporting resources that assure parts’ transport to the processing area. A Variable Neighborhood Search algorithm is used to schedule product manufacturing and handling tasks in the aim to minimize the maximum completion time of a job set and an improved lower bound with new calculation method is presented. Experimental tests are conducted to evaluate the efficiency of the proposed approach.

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.

scholarly journals Two-machine flow shop with synchronized periodic maintenance

2019 ◽  
Vol 53 (1) ◽  
pp. 351-365 ◽  
Author(s):  
Issam Krimi ◽  
Rachid Benmansour ◽  
Saïd Hanafi ◽  
Nizar Elhachemi
Keyword(s):  
Variable Neighborhood Search ◽  
Flow Shop ◽  
Programming Model ◽  
Flow Shop Scheduling ◽  
Mixed Integer ◽  
Neighborhood Search ◽  
Shop Scheduling ◽  
Availability Constraints ◽  
Periodic Maintenance ◽  
One Machine

In the literature, some works deal with the two-machine flow shop scheduling problem under availability constraints. Most of them consider those constraints only for one machine at a time and also with limited unavailability periods. In this work, we were interested by the unlimited periodic and synchronized maintenance applied on both machines. The problem is NP-hard. We proposed a mixed integer programming model and a variable neighborhood search for solving large instances in order to minimize the makespan. Computational experiments show the efficiency of the proposed methods.

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