Prasad Bari, Prasad Karande,
Volume 34, Issue 2 (6-2023)
Abstract
This paper presents a model for minimizing the makespan in the flow shop scheduling problem. Due to the impact of increased workloads, flow shops are becoming more popular and widely used in industries. To solve the challenge of minimizing makespan, a Hybrid-Heuristic-Metaheuristic-Genetic-Algorithm (HHMGA) is proposed. The proposed HHMGA algorithm is tested using the simulation software and demonstrated with steel industry data. The results are compared with those of the best available flow shop problem algorithms such as Palmer’s slope index, Campbell-Dudek-Smith (CDS), Nawaz-Enscore-Ham (NEH), genetic algorithm (GA) and particle swarm optimization (PSO). According to empirical results and relative differences from the lower bound, the proposed technique outperforms the three heuristics and two metaheuristics algorithms in three of six cases, while the remaining three produce the same results as the NEH heuristic. In comparison to the steel industry's regular job scheduling technique, the simulation model based on HHMGA can save 4642 hours. It was discovered that the suggested model enhanced the job sequence based on the makespan requirements.
Mehdi Seifbarghy, Mehri Nasrabadi,
Volume 34, Issue 3 (9-2023)
Abstract
One of the most key parts of a health system is the blood supply chain whose design is challenging due to the perishability of blood. In this research, an optimization model for multi-product blood supply chain network design is presented by considering blood deterioration. We consider a four-echelon blood supply chain that consists of blood donation centers, blood processing centers, blood products storage centers and hospitals as the user of the blood products. The locations of blood processing centers and blood products storage centers should be determined. Furthermore, considering different levels of technologies for blood processing, the suitable level for each opened center should be determined. In addition, different types of vehicle are also considered for blood transfer between different levels of the network. The objective is minimizing the total logistical costs including the costs of opening and running the blood processing centers and blood product storage centers and blood products transfer costs between different levels of the supply chain. Finally, we apply the given model to a real case study in Iranian blood supply chain, and sensitivity analysis is performed on some parameters. In the end, some managerial insights are given
Shahla Zandi, Reza Samizadeh, Maryam Esmaeili,
Volume 34, Issue 3 (9-2023)
Abstract
A coalition loyalty program (CLP) is a business strategy adopted by companies to increase and retain their customers. An operational challenge in this regard is to determine the coordination mechanism with business partners. This study investigated the role of revenue-sharing contracts (RSCs) considering customer satisfaction in coalition loyalty reward supply chain planning. A two-stage stochastic programming approach was considered for the solution considering the demand uncertainty. We aimed to investigate the impact of RSCs on the decision-making and profitability of the host firm of this supply chain taking into account the maximization of the profit coming from the CLP compared to the more common wholesale price contract (WPC). After the model was solved, computational experiments were performed to evaluate and compare the effects of RSCs and WPCs on the performance of the loyalty program (LP). The results revealed that RSC is an effective incentive to increase the host’s profit and reduce its cost. These findings add new insights to the management literature, which can be used by business decision makers.
Ammar Fadhil Al-Maliki, Moharam Habibnejad Korayem,
Volume 34, Issue 3 (9-2023)
Abstract
A computational approach is presented to obtain the optimal path of the end-effector for the 10 DOF bipedal robot to increase its load carrying capacity for a given task from point to point. The synthesizing optimal trajectories problem of a robot is formulated as a problem of trajectory optimization. An Iterative Linear Programming method (ILP) is developed for finding a numerical solution for this nonlinear trajectory. This method is used for determining the maximum dynamic load carrying capacity of bipedal robot walking subjected to torque actuators, stability and jerk limits constraints. First, the Lagrangian dynamic equation should be written to be suitable for the load dynamics which together with kinematic equations are substantial for determining the optimal trajectory. After that, a representation of the state space of the dynamic equations is introduced also the linearized dynamic equations are needed to obtain the numerical solution of the trajectory optimization followed by formulation for the optimal trajectory problem with a maximum load. Finally, the method of ILP and the computational aspect is applied to solve the problem of trajectory synthesis and determine the dynamic load carrying capacity (DLCC) to the bipedal robot for each of the linear and circular path. By implementing on an experimental biped robot, the simulation results were validated.
Rabie Mosaad Rabie, Hegazy Zaher, Naglaa Ragaa Saied, Heba Sayed,
Volume 35, Issue 1 (3-2024)
Abstract
Harris Hawks Optimization (HHO) algorithm, which is a new metaheuristic algorithm that has shown promising results in comparison to other optimization methods. The surprise pounce is a cooperative behavior and chasing style exhibited by Harris' Hawks in nature. To address the limitations of HHO, specifically its susceptibility to local optima and lack of population diversity, a modified version called Modified Harris Hawks Optimization (MHHO) is proposed for solving global optimization problems. A mutation-selection approach is utilized in the proposed Modified Harris Hawks Optimization (MHHO) algorithm. Through systematic experiments conducted on 23 benchmark functions, the results have demonstrated that the MHHO algorithm offers a more reliable solution compared to other established algorithms. The MHHO algorithm exhibits superior performance to the basic HHO, as evidenced by its superior average values and standard deviations. Additionally, it achieves the smallest average values among other algorithms while also improving the convergence speed. The experiments demonstrate competitive results compared to other meta-heuristic algorithms, which provide evidence that MHHO outperforms others in terms of optimization performance.
Welly Sugianto, Reazul Haq Abdul Haq, Mohd Nasrull Bin Abdol Rahman,
Volume 35, Issue 1 (3-2024)
Abstract
The automobile workshop queue system has been optimized using various approaches, such as queuing theory, simulation, and probability. The utilization of response surface methodology (RSM) for optimizing automobile workshop queue systems is not yet established. The utilization of RSM with direct observation enables the detection of patterns of correlations between variables and responses, which are then represented through mathematical equations. The optimization process involves numerous factors that impact queue performance, which can be categorized into two parts. The number of servers, number of phases, number of workers, worker experience, and layout are classified in inner design. This study examines the relationship between two components of the outer design, specifically the arrival rate and the interarrival time. The responses analyzed are queue cost, service time, average customer waiting time, and number of customers. The findings indicate that queue costs are not reliable for establishing the optimum value due to the significant impact of the cost structure on the structure of the optimal location. This study discovered that the number of leaving customers is related to queue costs and is relevant in selecting the optimal point. This study also formulates mathematical equations for predicting the optimal point. This study emphasizes the necessity for further investigation to uncover alternative mathematical equations that can precisely predict the optimal conditions for various types of services.
Melinska Ayu Febrianti, Qurtubi Qurtubi, Roaida Yanti, Hari Purnomo,
Volume 35, Issue 2 (6-2024)
Abstract
The retail industry is a vital sector of the world economy and is characterized by fierce competition, tight profit margins, and demanding consumers. Understanding customer buying behavior patterns is essential in devising the best retail strategy to enhance product sales. This research aims to comprehend customer shopping behaviors based on retail sales transactions and formulate the best strategies. By employing multi-level association rules, the dataset is arranged hierarchically into categories, sub-categories, and items. The sales transaction data used comprises 5830 transaction records over a month. The results of this study reveal 24 associations of categories, 49 associations of sub-categories, and 12 associations of product items. Moreover, the proposed marketing strategy offers recommendations including store layout improvement, planogram design, and bundled product offerings. This research addresses the gap in empirical evidence from a previous study and suggests further observation from diverse locations to authenticate the findings, which may yield various outcomes
Hamed Salehi Mourkani, Anwar Mahmoodi, Isa Nakhai Kamalabadi,
Volume 35, Issue 3 (9-2024)
Abstract
This research investigated the problem of joint inventory control and pricing for non-instantaneous deteriorating products; while, the quantity dependent trade credit is allowed. It was observed here that the buyer order amount is equal or more than the amount specified by the seller. The Shortage was not permitted in the system. It was aimed in present study to find a procedure for achieving the optimal selling price and replenish cycle and to be able to maximize the system's profit. To do so, first, the system's total profit function was derived. Then, the uniqueness of the optimal replenishment cycle for a given price was proved. Next, the concavity of the total profit function concerning the price was revealed, depending on the trade credit policy. Thereafter, an algorithm was provided to fulfill the optimal solution and eventually a dual-purpose numerical analysis was carried out both to show the model performance and to evaluate the sensitivity of the main parameters.
Iffan Maflahah, Dian Farida Asfan, Selamet Joko Utomo, Fathor As, Raden Arief Firmansyah,
Volume 35, Issue 4 (12-2024)
Abstract
Madura Island, comprising four regencies, exhibits a diverse array of agricultural resource potential, particularly in paddy, maize, cassava, and soybeans. Althought the Gross Regional Domestic Product assesses economic progress. it inadequately reflects the whole spectrum of potential within each region. A comprehensive observation of this diversity is required to facilitate a more focused development approach. This study aims to employ a hybrid hierarchical clustering method to delineate and classify the geographical regions of Madura Island according to their agricultural potential. K-means clustering, that part of hybrid hierarchical clustering approach was used to achieve aims of research. Number of farmers, land area, and commodities production were variable that used to classify regional based on its potentials. First, hierarchical method was performed to determine the appropriate number of clusters then K-means clustering was applied to classify the regions based on agricultural commodities. The results show effectively determined Madura Island's agricultural potential using the hybrid hierarchical clustering method, which categorizes locations based on characteristics of agricultural production. The research reveals six clusters, each characterized by a unique profile of primary commodity production, including paddy, corn, soybeans, and cassava. Implication of this result is offering insights into regional development of Madura based on agricultural potential.
Mohsen Nourizadeh, Moharram Habibnejad Korayem, Hami Tourajizadeh,
Volume 36, Issue 1 (3-2025)
Abstract
The purpose of this paper is to optimal control a dual-stage cable robot in a predefined path and to determine the maximum load-carrying capacity of this robot as a tower crane. Also, to expand the workspace of the robot two stages are employed. Today, cable robots are extensively used in load handling. Positive cable tension and collision-free cable control are the most important challenges of this type of robot. The high ratio of transposable loads to weight makes these robots very attractive for use as tower cranes. Dynamic Load Carrying Capacity (DLCC) is the maximum load that can be carried along a predefined path without violating the actuators and allowable accuracy constraints. State-Dependent Riccati Equation (SDRE) is employed to control the end-effector within the path to achieve the maximum DLCC. This approach is chosen since it can optimize the required motors' torque which consequently leads us to the maximum DLCC. In addition, the constraint of cables’ collision together is also checked along the predetermined path using the non-interference algorithm. The correctness of modeling is verified by comparing the results with previous research and the efficiency of the proposed optimal controlling strategy toward increasing the DLCC is investigated by conducting some comparative simulations. it is shown that the proposed cable robot by the aid of the designed optimal controller can increase the load carrying capacity successfully along any desired path using the allowable amount of motors' torque.
Ahmad Aliyari Boroujeni, Ameneh Khadivar,
Volume 36, Issue 2 (6-2025)
Abstract
The Traveling Salesman Problem (TSP) is a well-known problem in optimization and graph theory, where finding the optimal solution has always been of significant interest. Optimal solutions to TSP can help reduce costs and increase efficiency across various fields. Heuristic algorithms are often employed to solve TSP, as they are more efficient than exact methods due to the complexity and large search space of the problem. In this study, meta-heuristic algorithms such as the Genetic Algorithm and the Teaching-Learning Based Optimization (TLBO) algorithm are used to solve the TSP. Additionally, a discrete mutation phase is introduced to the TLBO algorithm to enhance its performance in solving the TSP. The results indicate that, in testing two specific models of the TSP, the modified TLBO algorithm outperforms both the Genetic Algorithm and the standard TLBO algorithm in terms of convergence to the optimal solution and response time.
Davood Nazari Maryam Abadi, Mohammad Bagheri,
Volume 36, Issue 2 (6-2025)
Abstract
In this paper, an optimal Electrical Cam (Ecam) profile is obtained by identifying the best breakpoint positions for piecewise polynomials using the cubic spline interpolation method. To achieve a curve that best tracks the reference Ecam curve, the breakpoint positions are determined using particle swarm optimization with random inertia weight (RNW-PSO). The previous programmable logic controller (PLC) used in the sanding mechanism was the DELTA DVP40ES2, utilizing the Ecam capability of DELTA ASD-A2 servo motors. To implement the Ecam function independently of the servo motor type, it has been integrated into a PLC, specifically the SIEMENS SIMATIC CPU 1215C. The optimized Ecam curve is then applied to a computer numerical control (CNC) sanding machine. Practical results demonstrate the effectiveness of the proposed method, showing improved sanding quality and better compliance with the reference curve.
