International Journal of Applied Optimization Studies
General topics covered by the journal include, but not limited to, the following: Mathematical Modeling Supply Chain Management Routing Problems Production Planning and Inventory Control Human Resource Planning Project Scheduling and Management Manufacturing and Service Strategies Work Design, Measurement, and Improvement Reliability Engineering Quality Control: Statistical Process Control, Measurement System Analysis, Failure Mode and Effect Analysis, etc. Logistics, Transportation, Distribution, and Materials Handling Emergency Relief/Humanitarian Logistics Operations and Information Management Heuristic and Metaheuristic Algorithms Neural Networks Application
Copyright Policy: All Rights Reserved for International Journal of Applied Optimization Studies (IJAOS).
Cost and Profit Analyzing Mathematical Model: A Strategic Tool for Garment Manufacturers
Utilizing data-driven analysis leads to higher-quality decisions. Analytical approaches allow the manufacturers of ready-made garment (RMG) factories to examine various factors and consider multiple scenarios. Manufacturers often determine profit margins based on their personnel's experience rather than using data-driven analysis. This approach will help manufacturers determine the appropriate profit margin for pre-costing through cost minimization, which they can then incorporate into the total product cost for final pricing. The first step identifies and describes the variables required to develop a model. We will develop a manufacturing cost optimization model in the second stage using mixed-integer linear programming (MILP) techniques. By integrating various factors, such as production capacity, demand variations, raw material sourcing, and pricing strategies, the model aims to provide a strategic tool for garment manufacturers to enhance their profitability. We use a mathematical programming language (AMPL) to solve the formulated mixed integer linear program. This model can determine the profit by changing one parameter and keeping all other parameters unchanged. Using this concept, the manufacturer can assess the impact of a change in any parameter on profit. Manufacturers put their manufacturing costs in the data file of this model and can determine whether they generate profit or loss. Garment manufacturers can use this idea to calculate the percentage of profit margin they need to add to the total production cost to achieve profitability. We have framed the issue and presented some simulations to test our model. Based on collected data, the model estimates that increasing the cost value by 10% decreased the profit margin by 4% to maintain the same selling price.
Copyright (c) 2024 Munir Hossain, Mohammed Forhad Uddin https://creativecommons.org/licenses/by/4.0
A Multi-Objective Mathematical Model for Seating Plans in Public Places
Social distance-based allocation models have gained importance since the advent of COVID-19 to minimize infection risk while continuing daily life. A multi-objective mathematical model with three objectives has been developed in this context. The proposed model considers various factors such as immune system, mask or vaccination status or air purifiers in the place to maximize the number of people while minimizing the total and maximum individual infection risk. The AUGMECON2 method is applied to the model and the model is applied in a real place. The results are analyzed using different parameters according to the immune system, mask or vaccination status of the allocated people. It has been observed that changing the parameters provides improvements of up to 45% in total and maximum individual infection risk. Thanks to Pareto sets obtained by the model, the decision-makers can make quick decisions between improved results.
Copyright (c) 2024 ?eyda ?im?ek, Eren Özceylan https://creativecommons.org/licenses/by/4.0
Game Theory-Based Mathematical Planning Model for Integrated Production Planning in Supply Chains
In the face of intensifying global competition, the efficient management of supply chains has become paramount across practical industries. The intricate nature of supply chain processes, often compounded by extensive computational requirements, presents significant challenges in achieving effective Supply Chain Management (SCM). This article proposes an innovative approach to integrated production planning (IPP), aiming to address these challenges. Inspired by game theory, widely acknowledged for its applications in amalgamating heat and mass converter networks, the proposed approach seamlessly integrates demand and supply data. This integration offers supply chain planners deeper insights into the SCM process, facilitating streamlined reprogramming and decision-making. The framework is substantiated through the presentation of two illustrative case studies. The first involves a single-product scenario, while the second showcases the processing of multiple products on a single processor. Notably, the results obtained from these case studies align consistently with solutions derived from equivalent optimization problems solved using the GAMS. Furthermore, this article introduces a production sequencing algorithm tailored to the second case study. The novel algorithm significantly reduces computation time, achieving a remarkable one-sixth reduction compared to prior methodologies. This reduction underscores the potential for heightened computational efficiency within the proposed framework. In conclusion, the findings of this study demonstrate the efficacy of the game theory-based approach in yielding optimal comprehensive plans and serving as a promising foundation for enhancing computational efficiency through the adept application of mixed programming formulas.
Copyright (c) 2023 Sourena Rahmani, Faranak Sadeghitabar, Mohammad Safari, Hamed Aghalar https://creativecommons.org/licenses/by/4.0
An Innovative Two-Phase Local Search Approach for Efficient University Course Scheduling under Academic Constraints
Scheduling university courses represents a formidable challenge, given the multifaceted academic constraints inherent to higher education institutions. This paper addresses the intricate task of optimizing university course scheduling, which involves accommodating numerous constraints, such as classroom and faculty availability. While numerous methodologies have been proposed to tackle this problem, our work introduces a novel approach employing a two-phase local search method. In the initial phase of our method, a viable solution is generated, laying the foundation for a well-structured course schedule. Subsequently, in the second phase, we refine the generated schedule using carefully crafted techniques to enhance its overall quality. Our approach has been meticulously implemented and rigorously evaluated across various datasets, yielding promising results. The outcomes of our study underscore the efficacy of our two-step methodology in resolving the complex challenge of university course scheduling. This research contributes a robust solution to aid academic institutions in streamlining their course scheduling processes while adhering to academic constraints.
Copyright (c) 2023 Kourosh Mokhtari, Seyydeh Atefeh Mousavi Abandansar, Fariba Goodarzian https://creativecommons.org/licenses/by/4.0
Multi-Objective Optimization of Supplier and Distribution Center Location Selection with Inventory Allocation in an Uncertain Environment Considering CO2 Emissions
This research focuses on efficiently selecting supplier and distribution center locations in a stochastic supply chain environment. It formulates the problem as a multi-objective optimization model aiming to minimize establishment costs, inventory expenses, and transportation costs while considering capacity limitations. To solve this complex problem, the study uses a single-objective mixed integer programming model along with LP-metrics and the T-H method. The research conducts a thorough comparison of two different methods in terms of solution quality and computational efficiency, supported by statistical hypothesis testing. Additionally, multi-criteria decision-making techniques like VIKOR and PROMETHEE II are applied to rank the effectiveness of these methods. The proposed model is validated through thirty sample problems, demonstrating its reliability and suitability for addressing the challenges of supplier and distribution center location selection in an uncertain supply chain environment.
Copyright (c) 2023 Peiman Ghasemi, Umar Muhammad Modibbo, Irfan Ali https://creativecommons.org/licenses/by/4.0
Optimizing Mazut Distribution Networks for Minimizing Transportation Costs: A Scenario-Based Approach
Efficient transportation infrastructure plays a pivotal role in any nation's development, and the petroleum industry holds particular significance in Iran's economic landscape. The transportation of petroleum products stands as a critical component of this industry, directly impacting various facets of the economy. Reducing traffic congestion not only alleviates strain on infrastructure but also mitigates traffic-related risks, air pollution, and transport expenditures. This, in turn, empowers the government to allocate savings to other sectors and bolster exports to neighboring countries. This research employs a linear optimization model to analyze the transportation of petroleum products, specifically Mazut, within Iran. By employing a scenario-based approach, we compare transportation costs in two distinct scenarios: one involving the utilization of Swap trades and the other focusing on increasing internal refinery production to supply Northern provinces without Swap transactions. We meticulously calculate transportation costs, leveraging TORA software and considering supply-demand dynamics. Our findings reveal that the implementation of Swap transactions yields an annual 2% reduction in transportation costs for Mazut products. This substantial cost-saving underscores the imperative nature of embracing Swap strategies within the country's petroleum distribution network.
Copyright (c) 2023 Karthikeyan Kaliyaperumal https://creativecommons.org/licenses/by/4.0
An Enhanced Metaheuristic Approach for Optimizing Graph-Based K-Means Clustering through Collective Intelligence
Data clustering is a fundamental aspect of data mining, serving as a crucial tool for data description and organization. The objective of clustering is to group data points into distinct or overlapping clusters, where data within each cluster exhibit high similarity while being dissimilar to data in other clusters. The emerging clustering technique, known as clustering based on graph K nearest reciprocal neighborhood, has demonstrated notable advancements by addressing issues associated with conventional clustering algorithms, such as the reliance on predefined cluster counts and initial center selection. However, a notable challenge remains in determining the optimal neighborhood parameter 'K' that governs the selection of nearest neighbors. In this paper, we introduce a solution leveraging the power of collective intelligence and metaheuristic algorithms to effectively address the sensitivity issue surrounding the 'K' parameter. Specifically, we employ the Jaya algorithm, distinguished by its minimal control parameters, which not only facilitates population convergence towards optimal solutions but also efficiently distances itself from suboptimal ones. This unique characteristic contributes to a heightened convergence rate when compared to similar metaheuristic methods. We conduct rigorous experiments utilizing datasets from the UCI repository, demonstrating the superiority of our proposed approach over existing methods in terms of clustering accuracy. Our research offers a robust and innovative solution for refining graph-based K-means clustering, further enhancing its applicability and effectiveness in real-world data analysis scenarios.
Copyright (c) 2023 Cheng-Jui Tseng, Tzu-Chia Chen https://creativecommons.org/licenses/by/4.0
Closed-Loop Supply Chain Network Design Optimization with a Multi-Objective Approach: Leveraging the Red Deer Metaheuristic Algorithm
In an era marked by increasing global complexity, the strategic design of a sustainable supply chain has become imperative. Decision-makers are confronted with the challenge of reconciling economic objectives with environmental and social impacts. In this context, gaining a competitive edge hinge on two critical factors: reducing operational costs and elevating service levels. Achieving economic efficiency while adopting a sustainability-oriented approach is the central focus of this research within the closed-loop supply chain domain. This study introduces a comprehensive multi-objective model for supply chain optimization. Distinguishing itself from prior models, our mathematical framework incorporates the social dimension of sustainability into the total cost considerations and accounts for environmental factors. Furthermore, it comprehensively models both forward and reverse flows inherent to closed-loop supply chains. The complexities and diverse objectives within sustainable closed-loop supply chain network design necessitate innovative methodologies. To address this challenge, we introduce the novel Red Deer metaheuristic algorithm, alongside two other cutting-edge algorithms and two conventional ones. Comparative analysis of their performance across problems of varying sizes underscores the remarkable strength and efficacy of the Red Deer algorithm, making it a potent tool for solving complex closed-loop supply chain optimization challenges.
Copyright (c) 2024 Bhishma Karki https://creativecommons.org/licenses/by/4.0
A Two-Phase Local Search Approach for Optimizing University Course Scheduling under Academic Constraints
Efficiently scheduling university courses while accommodating a plethora of academic constraints poses a substantial challenge for educational institutions. This paper addresses the intricacies of course scheduling, acknowledging the presence of multiple restrictions, encompassing class and faculty constraints. Existing methodologies have proposed solutions to this complex optimization problem. In this research, we introduce a novel two-phase local search approach designed to tackle the university course scheduling problem. Our approach unfolds in two distinct phases. Initially, we generate a feasible solution to the scheduling problem. Subsequently, in the second phase, we enhance the solution's quality through the application of refined optimization techniques. We have implemented this method across diverse datasets and meticulously scrutinized the outcomes. Our empirical results underscore the efficacy of the proposed two-step approach in delivering high-quality solutions to the intricate problem of university course scheduling.
Copyright (c) 2024 Kourosh Mokhtari, Fariba Goodarzian, Seyydeh Atefeh Mousavi Abandansar https://creativecommons.org/licenses/by/4.0
Optimizing Integrated Production-Distribution in a Sustainable Closed-Loop Supply Chain: A Bi-Objective Approach
Efficient integrated production-distribution planning has emerged as a focal point in contemporary forward supply chain management, celebrated for its substantial economic advantages. This research delves into the intricacies of integrated production-distribution planning within the context of a multi-tiered green closed-loop supply chain, encompassing recycling centers, manufacturing/remanufacturing facilities, and decentralized distribution centers. We introduce a comprehensive three-tiered bi-objective model aimed at enhancing profitability while concurrently mitigating adverse environmental impacts. To address the inherent complexity of this problem, we employ a refined multi-choice goal programming methodology and leverage a hierarchical iteration algorithm for resolution. Our research contributes to the evolving field of sustainable supply chain management by offering insights into the optimization of integrated production and distribution within a closed-loop framework, aligning economic interests with environmental stewardship.
Copyright (c) 2023 Ssu-Han Chen https://creativecommons.org/licenses/by/4.0
Crude Oil Distribution Optimization: A Multi-Objective Mathematical Model Aligned with Sustainable Development Goals
Crude oil, serving as the world's primary energy source, plays a pivotal role in shaping a nation's economic standing and global reputation. The intricate orchestration of its supply chain stands as a paramount global concern. In this research, we present a multi-objective, multi-period mathematical programming model tailored for the design of an upstream oil supply chain network. Our model is intricately aligned with the principles of sustainable development, simultaneously optimizing economic, environmental, and social objectives. Our approach to sustainability encompasses economic and environmental dimensions. We minimize total costs while addressing the pressing issue of greenhouse gas emissions. Furthermore, we uphold the social aspect of sustainability by maximizing job creation opportunities. Our comprehensive model encompasses various components, including crude oil production and gas extraction centers, oil storage facilities, processing centers, and oil demand terminals. To tackle the multi-objective nature of the model, we employ a solution approach rooted in fuzzy concepts. Numerical results substantiate the model's robustness and underscore the paramount importance of integrating sustainable development goals into the design of crude oil supply chains. Moreover, this model offers a strategic framework for upstream oil industry companies seeking to develop sustainable crude oil supply chain management strategies.
Copyright (c) 2023 Peiman Ghasemi, Seyedeh Asra Ahmadi, Adel Pourghader Chobar https://creativecommons.org/licenses/by/4.0
A Survey of the Integration of the Coverage Concept in Routing Problems
Many academics and business professionals have developed vehicle routing models to solve multiple logistical challenges. Due to their significance in resolving several real-world problems, these problems have been given a lot of attention in the study. The most frequent problems in operations research are vehicle routing problems (VRP), traveling salesman problems (TSP), and location routing Problems (LRP). Several constraints and variants were treated and introduced to the routing problems, such as time window, capacity constraints, and a heterogeneous fleet of vehicles. These variants have multiple impacts that can affect, for example, the environment. For this, the researchers treat the problems according to their needs, and in this case, they introduce the green VRP or any other problem according to the situation. Scientists were interested in routing problems because of their capacity to solve the majority of real cases, which aims to minimize routing costs and maximize profit, and this the objective of all the routing problems. Sometimes it's hard or impossible to visit every node due to limitations on some resources, such as time, budget, and energy. This idea allows us to indirectly satisfy the demand of unvisited customers by visiting them.
Copyright (c) 2024 Fatma Ben Amor, Manel Kammoun, Taicir Loukil https://creativecommons.org/licenses/by/4.0
Evaluation of Request Order Sequencing Methods in the Selection Process of Substitute Employees
This paper presents a simulation-based evaluation of various reactive scheduling methods designed to address workforce uncertainty in personnel scheduling, with a focus on substitution requests. We model employee availability and the demand for workers in a call center environment, using a mixed integer programming solver for shift scheduling and a probabilistic model for absenteeism. Our study compares different sequencing methods for substitution requests, assessing their effectiveness across a range of parameters, such as absenteeism probability and employee acceptance rates. The findings highlight the impact of sequencing choices on schedule fulfillment and substitution request efficiency, offering insights for optimizing personnel scheduling in uncertain environments. This research contributes to improving reactive scheduling strategies, essential for efficient workforce management.
Copyright (c) 2024 Soichiro Yokoyama, Tomoya Chisaka, Tomohisa Yamashita, Hidenori Kawamura https://creativecommons.org/licenses/by/4.0
The Moderating Role of Project Complexity and Strategic Agility Between Absorptive Capacity, Project Success, And Business Success
Organizations must harness external sources to thrive amidst complexity and change in a rapidly evolving business landscape. Drawing upon Complexity Theory, this research delves into the impact of absorptive capacity on project and business success within the realm of IT projects in Financial Services. Moreover, it investigates the moderating effects of project complexity and strategic agility on these relationships. A survey of 135 small and medium enterprises revealed that project complexity and strategic agility play pivotal roles in shaping the outcomes of absorptive capacity on project and business success. This study contributes to project management literature by shedding light on the nuanced interplay between absorptive capacity, project complexity, and strategic agility. It underscores the importance of organizations evaluating these factors to safeguard and expand their market presence amidst fierce competition and evolving stakeholder expectations. Organizations can enhance their resilience and sustain long-term success by recognizing the significance of absorptive capacity and adaptability in navigating dynamic environments.
Copyright (c) 2023 Pankaj Tiwari https://creativecommons.org/licenses/by/4.0
A Bio-Inspired Approach Enabled Group Decision Meetings Facilitation
Group decision meetings consume a great deal of time and effort in organizations. Group Decision Support Systems (GDSS) provide computational support to group decision-makers to support these processes. However, most of them are perceived to be extremely unproductive in terms of efficiently utilizing the participants’ time and effectively achieving the group decision-meeting objectives. These shortcomings occur frequently because effective guidelines or procedures are not used. To overcome these problems, GDSS would benefit from embedding some facilitation techniques and integrating a human facilitator who guides the group members through the decision-making process. This article proposes a framework for group facilitation that supports facilitators based on a model of the decision-making process where group facilitation tasks are automated, at least partially, increasing the ability of a facilitator to monitor and control the group decision-meeting process. We consider decision support approaches such as bio-inspired optimization methods that potentially offer these capabilities and assist the facilitator and decision-makers. We use the Elephant Herding Optimization (EHO) method to develop the system, and its evaluation is mainly based on the Analytic Hierarchy Process. We experiment with our approach with the breakdown diagnosis application in a complex industrial system. The results demonstrate the feasibility and effectiveness of our proposal.
Copyright (c) 2024 Abdelkader Ould Mahraz, Bakhta Nachet, Abdelkader Adla https://creativecommons.org/licenses/by/4.0
Heuristic Solution Approaches for Due Diligence in Disaster Management
The number of studies in the field of disasters is rising as a result of the rising number of disasters worldwide. Planning, carrying out, supervising, and coordinating disaster management operations are crucial. Special infrastructures are needed for emergencies such as search and rescue, humanitarian aid, evacuation operations, etc. Establishing due diligence as soon as a crisis strikes is one of the most crucial aspects of disaster management. In order to minimize the amount of victims, debris must be found quickly, especially during earthquakes. Due diligence becomes challenging when severe disasters like earthquakes disrupt facilities like the phone, internet, and electricity lines. Finding the locations of the wreckage is crucial to assisting with search and rescue operations. This study looks at an arc routing problem to assess the state of buildings in an earthquake-affected area. The goal is to locate the locations as soon as possible by at least once inspecting every path and road within the disaster area. Getting a quick fix for arc routing problem is more important for disaster management than getting the best outcomes. Two distinct heuristic approaches have been put forth in this regard, and the outcomes have been contrasted.
Copyright (c) 2024 Ferhat Yuna, Burak Erkayman https://creativecommons.org/licenses/by/4.0
Performance of Geopolymer Concrete Against Acid Attacks Using Taguchi Method
The acid and sulfate resistance of geopolymer concrete generated from treated fly ash is the primary subject of this investigation. The major goal is to find out if geopolymer concrete can be used for structural requests in harsh climates. The study includes the replacement of 90% of regular Portland cement with fly ash. The effect of numerous issues on the performance of geopolymer concrete is studied, including molarities of alkali activator, sodium silicate/sodium hydroxide ratio, alkali activator/binder ratio, and binder content. All specimens are treated at 65o C. After 28 days, specimens from each mix are put into a 10% concentration sulfuric acid bath for 8 weeks. The performance of the mixes was investigated using some mechanical properties. The outcomes show that geopolymer concrete has exceptional resistance to acid and sulfate attacks at all molarities tested compared to traditional concrete, with the 8-molarity mix suffering the least strength loss and the 12-molarity mix suffering the most. Overall, the results show that it is possible to make geopolymer concrete from treated fly ash that is strong and long-lasting enough for structural purposes, even in hostile chemical environments.
Copyright (c) 2024 Ahmed Radhi, Alyaa A Al-Attar, Hasan Mohammed Ahmed https://creativecommons.org/licenses/by/4.0
Development of a Multi-Skill Job Rotation Model by Minimizing the Workload Variance
Performing risky or repetitive activities by the operator in each system causes some ergonomic dangers. To reduce these risks, employees must participate in such activities in equal amounts as much as possible. Evaluating this job and finding out how to allocate employees to various tasks during planning time is known as job rotation. In this paper, employees' shift work schedule and job rotation are investigated to reduce costs and consider This study aims to provide an optimal approach to the issue of shift work schedule and job rotation in which the main model is a nonlinear integer programming, and a genetic algorithm is used to solve the mathematical model. To examine the proposed model, the performance of the genetic algorithm is compared with the exact method (GAMS software). The results show that the performance of the genetic algorithm is better than the exact method in significant problems and requires lower computational time.
Copyright (c) 2023 Seyed Emadedin Hashemi, Maryam Bakhshi https://creativecommons.org/licenses/by/4.0
Impact of Fuel Prices on Transport Behavior: A Case Study in Istanbul
Traffic flow is an inescapable problem in almost all large cities around the world. Obviously, some social and economic factors make traffic congestion a serious and hard-solvable problem in reality. A key factor in recent years is a sharp increase in fuel prices in the international markets. However, how this price fluctuation affects traffic flows and transport behavior, mainly in large cities like Istanbul, is an important question to study. This study addresses the effect of fuel price changes on Istanbul's traffic flows. The primary purpose of this study is to understand the relation between fuel price variations and traffic flows in Istanbul, as well as other important factors that contribute to the congestion problem investigation. To do this, traffic data from the most crowded regions of Istanbul was first collected, and first the It was mainly observed that the gasoline prices in Istanbul have a significant impact on the traffic flows, and a unit increase in gasoline price reduces the flow rate between 3000 and 7000 units. In addition to the empirical results, a survey was conducted to identify additional factors that affect traffic flows. The survey aimed to investigate the transport behavior of drivers as fuel prices increase in Istanbul. According to the survey results, drivers tend to prefer using the public transportation network more as the gasoline price increases.
Copyright (c) 2023 Sonya Javadi, Bü?ra Tombul, Zeyenep Y?lmaz https://creativecommons.org/licenses/by/4.0
Production Inventory Model of Constant Production and Linear Demand Rate
Stock is one of the most important components of a fulfillment e-business corporation's operation and manipulation. An inventory model for devices that deteriorate at an average rate is the source of the examination. Preservation technology (PT) can significantly lower the cost of deterioration. The paper's freshness is diminishing, the price is typical, demand is contingent on availability, and shortages are not permitted. The version is solved analytically by reducing the entire price of stock, and a numerical assessment is provided to show the solution and model application. These are the study's standout features. For commercial initiative corporations where the call for the price is time-mounted, this model might be utilized to optimize the full price of an inventory. This model assists companies in determining the best raw material replenishment plan and finished product production strategy. The primary goal of this research is to implement a production model in the inventory to determine request dimensions under conditions of linear demand and constant production. The support and control of creating inventories of disintegrating objects has received much attention lately.
Copyright (c) 2023 Hemant Kumar, Kailash Chandra Sharma, Dr. Dharamender Singh https://creativecommons.org/licenses/by/4.0
Dimensions of the entrepreneurial orientation of business organizations and its impact on achieving competitive advantage
Abstract The current research seeks to determine the nature of the relationship between the dimensions of entrepreneurial orientation in business organizations and their impact on achieving competitive advantage. The research started from a basic problem, which is the extent to which the dimensions of the entrepreneurial orientation are applied in the surveyed sample and its impact on achieving competitive advantage. The research community is the private hospitals in Baghdad (Al-Qimma National Hospital, Al-Masara National Hospital, Al-Jarrah National Hospital, and International National Hospital), and the main tool for data collection is the (resolution) that was distributed to the sample of (80) doctors. Statistician (SPSS), and the research reached a set of conclusions and recommendations.
Copyright (c) 2024 Omar F. Hassan Al.obaidy, Hameed Salim AL-kaabi, Muhammad Ibrahim Jawad Al-Dulaimi https://creativecommons.org/licenses/by/4.0
Cut Order Planning Under Cost, Time and Waste Constraints in Furniture Industry
The study addresses the optimization of fabric cutting of production in the furniture industry, aiming to minimize costs and improve production efficiency. The problem is defined as the need to optimize variables such as marker type, quantity, and the number of plies to reduce wastage. A marker is a fabric layer placed in a single or multiple plies on which the cutting layout for the pattern is placed. The methodology involves analyzing the current system, identifying problem symptoms, and developing models for cut order planning. The research focuses on minimizing fabric-cutting costs by making optimal marker types and plies decisions. A mixed integer linear programming (MILP) model is formulated to optimize fabric-cutting decisions, leading to cost savings in production processes. The results indicate the potential for improved cut-order planning, increasing potential savings, reducing production timelines, and enhancing overall operational performance by considering cost, time, and waste constraints.
Copyright (c) 2023 Betul Coban, Merve Safa Alpergün https://creativecommons.org/licenses/by/4.0
Efficiently Addressing the Flow Shop Scheduling Problem with Hybrid Obstructions using the Ant Colony Algorithm
The flow shop scheduling problem (FSSP), characterized by the continuous flow of production work across multiple machines, presents formidable computational complexity, necessitating innovative and meta-heuristic approaches for effective solutions. In this research, we harness the potential of the ant colony algorithm (ACA), a promising meta-heuristic technique, to tackle the FSSP incorporating combined obstructions. Our primary objective is to minimize overall production time. This paper introduces various types of obstructions inherent to the FSSP, elucidating the intricacies of the contemporary workshop scheduling challenge. We then present a comprehensive outline of the steps comprising our proposed ACA. To assess the algorithm's effectiveness and practical applicability, we rigorously implement it on a selection of benchmark problems sourced from the existing literature in the field. The results obtained are meticulously compared with optimal solutions, highlighting the algorithm's robust performance. Our findings reveal a promising approach to solving complex FSSP, with notable improvements in efficiency and scheduling optimization. This research represents a significant contribution to the field of flow shop scheduling, offering a viable and innovative solution for addressing intricate production scheduling challenges. The successful application of the ACA in this context underscores its potential for resolving real-world manufacturing dilemmas efficiently.
Copyright (c) 2023 Mohammad Amin Khedri, Nima Saleh, Zahra Sadat Zamani https://creativecommons.org/licenses/by/4.0
Coordinating Supply Chains in Competitive Environments through Wholesale Price Contracts under Uncertainty
The coordination of supply chains is a critical challenge faced by managers in the competitive market. Addressing this concern becomes paramount in the context of rivalry between production and distribution chains. The paper delves into the coordination analysis of a dual monopoly, where two manufacturers and two retailers collaborate to distribute goods, with the two products substituting each other. The chains engage in pricing competition to determine the selling price, and a wholesale price contract is employed for coordination analysis. The research demonstrates that supply chain coordination is achieved when the producer's profit margin is zero, implying that the product's price offered to the retailer equals the producer's production costs. However, achieving this balance might be challenging since manufacturers typically aim for a profit. Consequently, fluctuations in wholesale prices become instrumental in evaluating the distribution channel's efficiency. The study further explores the impact of factors such as product substitution and chain competitiveness on the efficiency of the supply chains.
Copyright (c) 2023 Amin Hosseini, Maryam Ghalkhanbaz https://creativecommons.org/licenses/by/4.0
Inventory Model for Retailer-Supplier’s trade credit policy
The concept of progressive trade credit optimises financial transactions among the supply chain (SC) partners and provides more control to utilise cash and credit flow. This trade credit effectively manages downstream financing activities such as account receivables and purchase discount schemes. This study attempts to obtain the retailer’s robust replenishment policy to optimise the maximum profit. In this work, a progressive trade credit is considered, which suggests that if the vendor settles the financial credit till Mduration, the vendor is not supposed to pay Interest, but if the seller decides the account subsequently M duration however before N (M<N), the seller (retailer)has to bear interest amount by the rate N(M<N). Further, suppose after N duration later account is clear, the seller will bear Interest at the rate 1c2 (1c2>1c1). This work tries to develop an optimal inventory policy by considering two scenarios T<N and T>=N. A sensitivity analysis was also conducted to evaluate the strength of the proposed model.
Copyright (c) 2024 Archana Sharma, Chaman Singh, A. K. Malik, Umar Muhammad Modibbo https://creativecommons.org/licenses/by/4.0
The Relationship Between Brand Equity and Perceived Value on Purchase Decision among Shopee Consumers in KLang Valley, Malaysia
The purpose of this research is to test the influence of consumer’s behavior towards a business on how it affects the purchase intention in a business context by implementing Aaker’s model method and to test the relationship between brand equity and perceived value on purchase intention among Shopee consumers for Klang Valley Residence. In this research, a questionnaire was developed and distributed to 400 Klang Valley residents who understand Shopee as an online shopping platform. The data gathered from respondents was analyzed using IBM SPSS 28 Software. Multiple regression analysis was used to test the relationship between the four factors (brand association, brand awareness, perceived quality, and brand loyalty) and purchase intention. The test shows that all four variables, brand association, brand awareness, perceived quality, and brand loyalty, have a positive relationship with purchase intention. Meanwhile, brand associations were found to have the greatest positive relationship with purchase intention towards Klang Valley residence choosing Shopee as their preferred online shopping platform. Other than that, a few limitations are provided, which will send important suggestions for future development studies.
Copyright (c) 2023 Associate Professor Dr. Akram AP Akram, Lim Wan Yee https://creativecommons.org/licenses/by/4.0
Optimization of Bi-objective Un-equal Area Facility Layout Problem with Grid System Approach by Simulated Annealing
Facility layout is one of the classic and challenging problems in the literature of Operations Research. In this paper, a bi-objective, un-equal area, open-field FLP is investigated. Grid system FLP is chosen, because, there are problems in the real world, which have to be modeled discretely due to their high complexity. First of all, an integer non-linear mathematical model is proposed to address the grid system and un-equality of the size of the facilities. Then, in order to solve the model, a bi-objective simulated Annealing optimization algorithm is proposed. After that, by solving the two examples, the validation and efficiency of the proposed SA are shown by comparing with exact methods of small size instance and solving the large-sized problem respectively. Also, the non-dominated answers (Pareto optimal set) have been obtained, as a user can choose the desired layout based on his/her opinion. Finally, conclusions and suggestions are proposed.
Copyright (c) 2019 nima moradi, Shahram Shadrokh
Solving two- person zero- sum matrix games under fuzzy environment
This paper aims to study two-person zero- sum matrix games with payoff represented as interval- valued fuzzy numbers instead of the normal fuzzy numbers. Using the signed distance ranking, the fuzzy payoff matrix is converted into the corresponding deterministic. Then, a proposed method for solving the problem is presented. Finally, two examples are given to illustrate the practically and the efficiency of the method.
Copyright (c) 2019 Hamiden Abdelwahed Khalifa
Effective Factors in Network Marketing Success and Ranking Using Multi-criteria Decision Making Techniques
Network marketing is one of the most effective ways for people who want to be their own employer. But there were problems in this way, especially in our country. The most important problem was the creation of pyramid firms. But over time and gradually, network marketing managed to find its market in Iran and encourage a relatively large number of people to this area. There are many factors in the success or failure of effective network marketing, which we tried to identify the most important of these factors in the present study. In the next step, using the AHP technique, we will rank these factors, categorize and prioritize the main criteria. In addition to the AHP technique, Topsis technique was used to prioritize the same option. The results obtained from this study can serve as a source and reference for organizations and companies that want to pursue network marketing work.
Copyright (c) 2019 Mohammad Montazerolghaem
An Efficient Approach for Public Transportation Optimization Using Fuzzy Multiple Criteria Decision Making and Mathematical Modeling
One of the main challenges of transportation networks is to directing vehicles to their destination with the aim of reducing costs such as travel time, fuel consumption and more efficient use of existing network capacities. In fact, traffic guidance is sought after the appropriate distribution of traffic flows on all routes within the transportation network. In this paper, Hierarchical methods of the Fuzzy Analytical Hierarchy Process Model (FAHP) along with EXPERT CHOICE software are used to optimize public transport lines in which Arc GIS software is used to display transportation maps. An applied mathematical model is then developed to determine the optimal routing decisions. The study area in this study is zone 4 of Tehran. Finally, the case study for area 1 of zone 4 is described and explained by providing a mathematical model of public vehicles routing through entering the total weight of the paths obtained by the FAHP method.
Copyright (c) 2019 Mohammad Fayyazi, Parham Hayati