Inventory, logistics, and transportation
Amin Farahbakhsh; Amir Saman Kheirkhah
Abstract
The inventory routing problem arises from the combination of the vehicle routing problem and the vendor-managed inventory problem. In this paper, we present a mathematical model and a novel genetic algorithm for solving the multi-period inventory routing problem. The objective is to supply products to ...
Read More
The inventory routing problem arises from the combination of the vehicle routing problem and the vendor-managed inventory problem. In this paper, we present a mathematical model and a novel genetic algorithm for solving the multi-period inventory routing problem. The objective is to supply products to scattered customers within a given time horizon while managing customer inventories to avoid shortages and minimize total inventory and transportation costs. To represent solutions for this problem, we introduce a new chromosomal structure. This structure offers simplicity in encoding and decoding solutions, maintains feasibility after crossover and mutation operations, addresses both routing and inventory management in a single step, and consolidates information about each solution method comprehensively. The algorithm parameters, including crossover and mutation rates, population size, number of iterations, and selection pressure, are fine-tuned using the Taguchi method. To assess algorithm efficiency, we utilize standard instances from the literature. Our results demonstrate that the proposed algorithm performs favorably compared to previous approaches.
Scheduling
Esmaeil Mehdizadeh; Fatemeh Soleimaninia
Abstract
The Flexible Job shop Scheduling Problem (FJSP), as a Production Scheduling Problem (PSP), is generally an extension of the Job shop Scheduling Problem (JSP). In this paper, the FJSP with reverse flow consisting of two flows of jobs (direct and reverse) at each stage is studied; the first flow initiates ...
Read More
The Flexible Job shop Scheduling Problem (FJSP), as a Production Scheduling Problem (PSP), is generally an extension of the Job shop Scheduling Problem (JSP). In this paper, the FJSP with reverse flow consisting of two flows of jobs (direct and reverse) at each stage is studied; the first flow initiates in Stage 1 and goes to Stage C (the last stage), and the second flow starts with Stage c and ends up in Stage 1. The aim is to minimize the makespan of the jobs (the maximum completion time). A Mixed Integer Programming (MIP) is presented to model the problem and the Branch and Bound (B&B) method is used to solve the problem. A numerical small-size problem is presented to demonstrate the applicability, for which the Lingo16 software is employed for a solution. Due to the NP-hardness of the problem, a meta-heuristic, namely the Vibration Damping Optimization (VDO) algorithm with tuned parameters using the Taguchi method, is utilized to solve large-scale problems. To validate the results obtained using the proposed solution algorithm in terms of the solution quality and the required computational time, they are compared with those obtained by the Lingo 16 software for small-size problems. Finally, the performance of the proposed algorithm is compared with a Genetic Algorithm (GA) by solving some randomly generated larger-size test problems, based on which the results are analyzed statistically. Computational results confirm the efficiency and effectiveness of the proposed algorithm and show that the VDO algorithm performs well.
Computational Intelligence
Milad Shahvaroughi Farahani; Hamed Farrokhi-Asl; Saeed Rahimian
Abstract
Investigating stock price trends and determining future stock prices have become focal points for researchers within the finance sector. However, predicting stock price trends is a complex task due to the multitude of influencing factors. Consequently, there has been a growing interest in developing ...
Read More
Investigating stock price trends and determining future stock prices have become focal points for researchers within the finance sector. However, predicting stock price trends is a complex task due to the multitude of influencing factors. Consequently, there has been a growing interest in developing more precise and heuristic models and methods for stock price prediction in recent years. This study aims to assess the effectiveness of technical indicators for stock price prediction, including closing price, lowest price, highest price, and the exponential moving average method. To thoroughly analyze the relationship between these technical indicators and stock prices over predefined time intervals, we employ an Artificial Neural Network (ANN). This ANN is optimized using a combination of Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Harmony Search (HS) algorithms as meta-heuristic techniques for enhancing stock price prediction. The GA is employed for selecting the most suitable optimization indicators. In addition to indicator selection, PSO and HS are utilized to fine-tune the Neural Network (NN), minimizing network errors and optimizing weights and the number of hidden layers simultaneously. We employ eight estimation criteria for error assessment to evaluate the proposed model's performance and select the best model based on error criteria. An innovative aspect of this research involves testing market efficiency and identifying the most significant companies in Iran as the statistical population. The experimental results clearly indicate that a hybrid ANN-HS algorithm outperforms other algorithms regarding stock price prediction accuracy. Finally, we conduct run tests, a non-parametric test, to evaluate the Efficient Market Hypothesis (EMH) in its weak form.
Engineering Optimization
Parviz Tohidi Nasab; Mohsen Vaez Ghasemi; Ghasem Tohidi
Abstract
The Aircraft Scheduling Problem (ASP) refers to allocating each aircraft to the optimal take-off and landing time and the appropriate runway. This problem is the allocation of aircraft to the desired runway so that the total damage due to delays or haste in landing or take-off of all aircraft is minimized. ...
Read More
The Aircraft Scheduling Problem (ASP) refers to allocating each aircraft to the optimal take-off and landing time and the appropriate runway. This problem is the allocation of aircraft to the desired runway so that the total damage due to delays or haste in landing or take-off of all aircraft is minimized. Runway allocation, landing and take-off sequences, and scheduling for each aircraft must be done in a predetermined time window. Time should also be considered as the time of separation between landings and take-offs due to the wake vortex phenomenon. In general, the purpose of such problems is to make maximum use of the runway. Therefore, in this study, a mathematical model of robust landing and take-off scheduling at an airport is provided, assuming no access to the airport runway at certain times. Moreover, delays and haste in landing and take-off on the runway, limited access to aircraft, runway repair time, and the possibility of runway disturbances are investigated. Robust optimization is used to deal with uncertainty at take-off and landing times. Finally, Genetic and Imperialistic Competitive Algorithm are used to evaluate and analyze the problem because it is NP-HARD problem. The results indicate the ability of the proposed algorithms to find high-quality solutions in a short computation time for problems up to 7 runways and 60 aircraft.
Metaheuristics Algorithms
Fatemeh Sogandi
Abstract
Curve fitting is a computational problem in which we look for a base objective function with a set of data points. Recently, nonparametric regression has received a lot of attention from researchers. Usually, spline functions are used due to the difficulty of the curve fitting. In this regard, the choice ...
Read More
Curve fitting is a computational problem in which we look for a base objective function with a set of data points. Recently, nonparametric regression has received a lot of attention from researchers. Usually, spline functions are used due to the difficulty of the curve fitting. In this regard, the choice of the number and location of knots for regression is a major issue. Therefore, in this study, a Genetic algorithm simultaneously determines the number and location of the knots based on two criteria comprise of least square error and capability process index. The proposed algorithm performance has been evaluated by some numerical examples. Simulation results and comparisons reveal that the proposed approach in curve fitting has satisfactory performance. Also, a sensitivity analysis on the number of knots has been illustrated by an example. Finally, simulation results from a real case in statistical process control show that the proposed Genetic algorithm works well in practice.
S. M. Ahmed; T. K. Biswas; C. K. Nundy
Abstract
In this paper, an optimization model for aggregate planning of multi-product and multi-period production system has been formulated. Due to the involvement of too many stakeholders as well as uncertainties, the aggregate production planning sometimes becomes extremely complex in dealing with all relevant ...
Read More
In this paper, an optimization model for aggregate planning of multi-product and multi-period production system has been formulated. Due to the involvement of too many stakeholders as well as uncertainties, the aggregate production planning sometimes becomes extremely complex in dealing with all relevant cost criteria. Most of the existing approaches have focused on minimizing only production related costs, consequently ignored other cost factors, for instance, supply chain related costs. However, these types of other cost factors are greatly affected by aggregate production planning and its mismanagement often results in increased overall costs of the business enterprises. Therefore, the proposed model has attempted to incorporate all the relevant cost factors into the optimization model which are directly or indirectly affected by the aggregate production planning. In addition, the considered supply chain related costs have been segregated into two major categories. While the raw material purchasing, ordering, and inventory costs have been grouped into an upstream category, finished goods inventory, and delivery costs in the downstream category. The most notable differences with the other existing models of aggregate production planning are in the consideration of the cost factors and formulation process in the mathematical model. A real-life industrial case problem is formulated and solved by using a genetic algorithm to demonstrate the applicability and feasibility of the proposed model. The results indicate that the proposed model is capable of solving any type of aggregate production planning efficiently and effectively.
