scholarly journals A new mathematical model to cover crew pairing and rostering problems simultaneously

2021 ◽  
Vol 9 (2) ◽  
Author(s):  
Saeed Saemi ◽  
◽  
Alireza Rashidi Komijan ◽  
Reza Tavakkoli-Moghaddam ◽  
Mohammad Fallah ◽  
...  
Keyword(s):  
Optimal Solution ◽  
Computational Time ◽  
Scheduling Problem ◽  
Single Model ◽  
Sequential Order ◽  
Swarm Optimization ◽  
Crew Rostering ◽  
Proposed Model ◽  
Crew Pairing ◽  
Required Courses

Crew scheduling problem includes two separate subproblems, namely, crew pairing and crew rostering problems. Solving these two subproblems in a sequential order may not lead to an optimal solution. This study includes two main novelties. It combines these two subproblems and presents them in a single model. On the other hand, despite previous researches that considered a pairing continuously, the proposed model benefits from the capability of considering one or more days off in a pairing assigned to a crew member. This is extremely useful as it enables the crew to participate in required courses, doing medical checks, etc. Two solution approaches, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), are used to solve the model. Eventually, the performance of the proposed algorithms is evaluated. Both ended to satisfactory results; however, PSO relatively outperformed GA in terms of solution optimality and computational time.

scholarly journals The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem

2019 ◽  
Vol 20 (2) ◽  
pp. 105
Author(s):  
Ikhlasul Amallynda
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms 

scholarly journals The Discrete Particle Swarm Optimization Algorithms For Permutation Flowshop Scheduling Problem

2019 ◽  
Vol 20 (2) ◽  
pp. 1
Author(s):  
Ikhlasul Amallynda
Keyword(s):  
Particle Swarm Optimization ◽  
Flow Shop ◽  
Particle Swarm ◽  
Optimal Solution ◽  
Flow Shop Scheduling ◽  
Computational Time ◽  
Scheduling Problem ◽  
Discrete Particle Swarm Optimization ◽  
Swarm Optimization ◽  
Discrete Particle

In this paper, two types of discrete particle swarm optimization (DPSO) algorithms are presented to solve the Permutation Flow Shop Scheduling Problem (PFSP). We used criteria to minimize total earliness and total tardiness. The main contribution of this study is a new position update method is developed based on the discrete domain because PFSP is represented as discrete job permutations. In addition, this article also comes with a simple case study to ensure that both proposed algorithm can solve the problem well in the short computational time. The result of Hybrid Discrete Particle Swarm Optimization (HDPSO) has a better performance than the Modified Particle Swarm Optimization (MPSO). The HDPSO produced the optimal solution. However, it has a slightly longer computation time. Besides the population size and maximum iteration have any impact on the quality of solutions produced by HDPSO and MPSO algorithms 

A Non-Permutation Flow Shop Manufacturing Cell Scheduling Problem with Part's Sequence Dependent Family Setup Times

2014 ◽  
Vol 5 (4) ◽  
pp. 70-86 ◽  
Author(s):  
Behzad Nikjo ◽  
Yaser Zarook
Keyword(s):  
Flow Shop ◽  
Optimal Solution ◽  
Setup Times ◽  
Computational Time ◽  
Scheduling Problem ◽  
Dynamic Flow ◽  
Exact Methods ◽  
Release Date ◽  
Manufacturing Cell ◽  
Cell Scheduling

This article presents a new mathematical model for a dynamic flow shop manufacturing cell scheduling problem (DFMCSP) with agreeable job release date for each part family where family setup times are dependent on sequence of parts within families. It means this article considers non-permutation schedules for both sequence of families and sequence of parts within families. The objective is minimizing the Makespan (Cmax). Since, this problem belongs to NP-Hard class. Therefore, reaching an optimal solution in a reasonable computational time by using exact methods is extremely difficult. Thus, this article proposes meta-heuristic methods such as Genetic Algorithms (GA) and Tabu Search (TS). Finally, the computational results compare efficiency of the proposed algorithms under the performance measures.

scholarly journals Improved Combinatorial Benders Decomposition for a Scheduling Problem with Unrelated Parallel Machines

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Francisco Regis Abreu Gomes ◽  
Geraldo Robson Mateus
Keyword(s):  
Parallel Machines ◽  
Benders Decomposition ◽  
Mathematical Formulation ◽  
Optimal Solution ◽  
Master Problem ◽  
Computational Time ◽  
Scheduling Problem ◽  
Unrelated Parallel Machines ◽  
Warm Start ◽  
Dependent Setup Times

This paper addresses the unrelated parallel machines scheduling problem with sequence and machine dependent setup times. Its goal is to minimize the makespan. The problem is solved by a combinatorial Benders decomposition. This method can be slow to converge. Therefore, three procedures are introduced to accelerate its convergence. The first procedure is a new method that consists of terminating the execution of the master problem when a repeated optimal solution is found. The second procedure is based on the multicut technique. The third procedure is based on the warm-start. The improved Benders decomposition scheme is compared to a mathematical formulation and a standard implementation of Benders decomposition algorithm. In the experiments, two test sets from the literature are used, with 240 and 600 instances with up to 60 jobs and 5 machines. For the first set the proposed method performs 21.85% on average faster than the standard implementation of the Benders algorithm. For the second set the proposed method failed to find an optimal solution in only 31 in 600 instances, obtained an average gap of 0.07%, and took an average computational time of 377.86 s, while the best results of the other methods were 57, 0.17%, and 573.89 s, respectively.

An Improved Boltzmann Machine for Strategic Investment Planning in Power System Environment

2017 ◽  
Vol 6 (2) ◽  
pp. 259 ◽  
Author(s):  
S. H. M. Tahar ◽  
S. B. Yaakob ◽  
A. Ahmed
Keyword(s):  
Power System ◽  
Optimal Solution ◽  
Mixed Integer ◽  
Computational Time ◽  
Second Phase ◽  
Boltzmann Machine ◽  
Investment Planning ◽  
Strategic Investment ◽  
System Environment ◽  
Proposed Model

The objective of this research is to propose an effective method to determine an optimal solution for strategic investment planning in power system environment. The proposed method will be formulated by using mean-variance analysis approach in the form of mixed-integer quadratic programming problem. Its target is to minimize the risk and maximize the expected return. The proposed method consists of two phase neural networks combining Hopfield network at the first phase and Boltzmann machine in the second phase resulting the fast computational time. The originality of the proposed model is it will delete the unit of the second phase, which is not selected in first phase in its execution. Then, the second phase is restructured using the selected units. Due to this feature, the proposed model will improve times and the accuracy of obtained solution. The significance of output from this project is the improvement of computational time and the accurate solution will be obtained. This model might help the decision makers to choose the optimal solution with variety options provided from this proposed method. Therefore, the performance of strategic investment planning in power system engineering certainly enhanced.

scholarly journals Meeting Scheduling Application using Availability and Priority Attendance Heuristics

2019 ◽  
Vol 9 (2) ◽  
pp. 4302-4306
Keyword(s):  
Constraint Satisfaction ◽  
Time Slot ◽  
Dynamic Environment ◽  
Optimal Solution ◽  
Optimal Time ◽  
Scheduling Problem ◽  
Applied Model ◽  
Meeting Schedule ◽  
Proposed Model ◽  
Meeting Scheduling

Meeting scheduling is a repetitive and time consuming task for many organizations. Emails and electronic calendars has been used to help a meeting host in this process. However, it does not automate the process of searching the optimal time slot. Manual scheduling may result in suboptimal schedule. Therefore, automation is needed for meeting scheduling problem. The purpose of this research is to propose an applied model consisting of both acquiring participants’ existing schedule, and searching for an optimal time slot. Previous studies groups the solution of meeting scheduling into either constraint satisfaction or heuristics approach. Heuristics is more appropriate for a dynamic environment. The heuristics-based model is designed to consider participant availability and participant prioritization. The more participants are available, the better the time is as a candidate for optimum schedule. In the proposed model, the availability of certain key person, experts, or host may carry more weight than normal participant. An Android based application is developed as a prove of concept of the proposed model. Google Calendar API is used in this model to acquire the existing schedule, then each time slot is assigned a score based on availability weighting. The time slot with the highest score is considered the optimal solution. Evaluation is done by simulating the scheduling part for various numbers of meetings and time slots. The result shows that the model is capable of searching the optimal meeting schedule in less than one second for each of the experiment.

scholarly journals A HYBRID ALGORITHM FOR THE UNCERTAIN INVERSE p-MEDIAN LOCATION PROBLEM

2021 ◽  
pp. 1399
Author(s):  
Akram Soltanpour ◽  
Fahimeh Baroughi ◽  
Behrooz Alizadeh
Keyword(s):  
At Risk ◽  
Value At Risk ◽  
Location Problem ◽  
Particle Swarm Optimization Algorithm ◽  
Optimal Solution ◽  
Swarm Optimization ◽  
Uncertain Variables ◽  
Hybrid Particle Swarm Optimization ◽  
Proposed Model ◽  
Risk Simulation

In this paper, we investigate the inverse p-median location  problem with variable edge lengths and variable vertex weights on networks in which the vertex weights and modification costs are the independent uncertain variables. We propose a  model for the uncertain inverse p-median location problem  with tail value at risk objective. Then, we show that  it  is NP-hard. Therefore,  a hybrid particle swarm optimization  algorithm is presented  to obtain   the approximate optimal solution of the proposed model. The algorithm contains expected value simulation and tail value at risk simulation.

scholarly journals A Unit Commitment Model with Implicit Reserve Constraint Based on an Improved Artificial Fish Swarm Algorithm

2013 ◽  
Vol 2013 ◽  
pp. 1-11
Author(s):  
Wei Han ◽  
Hong-hua Wang ◽  
Xin-song Zhang ◽  
Ling Chen
Keyword(s):  
Production Cost ◽  
Unit Commitment ◽  
Computational Time ◽  
Total Production ◽  
Spinning Reserve ◽  
Swarm Optimization ◽  
Artificial Fish Swarm Algorithm ◽  
Proposed Model ◽  
Artificial Fish Swarm ◽  
Swarm Algorithm

An implicit reserve constraint unit commitment (IRCUC) model is presented in this paper. Different from the traditional unit commitment (UC) model, the constraint of spinning reserve is not given explicitly but implicitly in a trade-off between the production cost and the outage loss. An analytical method is applied to evaluate the reliability of UC solutions and to estimate the outage loss. The stochastic failures of generating units and uncertainties of load demands are considered while assessing the reliability. The artificial fish swarm algorithm (AFSA) is employed to solve this proposed model. In addition to the regular operation, a mutation operator (MO) is designed to enhance the searching performance of the algorithm. The feasibility of the proposed method is demonstrated from 10 to 100 units system, and the testing results are compared with those obtained by genetic algorithm (GA), particle swarm optimization (PSO), and ant colony optimization (ACO) in terms of total production cost and computational time. The simulation results show that the proposed method is capable of obtaining higher quality solutions.

scholarly journals Impact of Trapezoidal Demand and Deteriorating Preventing Technology in an Inventory Model in Interval Uncertainty under Backlogging Situation

Mathematics ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 78
Author(s):  
Rajan Mondal ◽  
Ali Akbar Shaikh ◽  
Asoke Kumar Bhunia ◽  
Ibrahim M. Hezam ◽  
Ripon K. Chakrabortty
Keyword(s):  
Inventory Management ◽  
Optimization Problem ◽  
Mathematical Formulation ◽  
Optimal Solution ◽  
Inventory Model ◽  
Interval Uncertainty ◽  
Swarm Optimization ◽  
Preservation Technology ◽  
Proposed Model ◽  
The Impact

The demand for a product is one of the important components of inventory management. In most cases, it is not constant; it may vary from time to time depending upon several factors which cannot be ignored. For any seasonal product, it is observed that at the beginning of the season, demand escalates over time, then it is stable and after that, it decreases. This type of demand is known as the trapezoidal type. Also, due to the uncertainty of customers’ behavior, inventory parameters are not always fixed. Combining these two concepts together, an inventory model is formulated for decaying items in an interval environment. Preservative technology is incorporated to preserve the product from deterioration. The corresponding mathematical formulation is derived in such a way that the profit of the inventory system is maximized. Consequently, the corresponding optimization problem is converted into an interval optimization problem. To solve the same, different variants of quantum-behaved particle swarm optimization (QPSO) techniques are employed to determine the duration of stock-in time and preservation technology cost. To illustrate and also to validate the model, three numerical examples are considered and solved. Then the computational results are compared. Thereafter, to study the impact of different parameters of the proposed model on the best found (optimal or very close to optimal) solution, sensitivity analysis are performed graphically.

A Layer Hybrid Intelligent Algorithm for Solving Resources Scheduling Problem

2014 ◽  
Vol 644-650 ◽  
pp. 1506-1509
Author(s):  
Yun Jie Ma ◽  
Zi Hui Ren ◽  
Ping Zhu
Keyword(s):  
Machine Learning ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Optimal Solution ◽  
Ant Algorithm ◽  
Scheduling Problem ◽  
Intelligent Algorithm ◽  
Swarm Optimization ◽  
Hybrid Intelligent Algorithm ◽  
Modified Genetic Algorithm

A New hybrid intelligent algorithm is used to solve the resources scheduling problem. This new algorithm contains Adaptive Particle Swarm Optimization (APSO) algorithm and Modified Genetic Algorithm (MGA) and Machine Learning (ML) algorithm, MGA is used to realize global searching, APSO is used to get the local searching. The choose processing depend on the definite of information in ant algorithm. Machine learning principle was proposed, after some iteration, the part of the optimal solution was deserved. Then we search the optimal solution in each layer. Simulational results based on the well-known benchmark suites in the literature showed that the algorithm had better optimization performance.

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