A genetic algorithm for a bi-objective mathematical model for dynamic virtual cell formation problem

Nowadays, with the increasing pressure of the competitive business environment and demand for diverse products, manufacturers are force to seek for solutions that reduce production costs and rise product quality. Cellular manufacturing system (CMS), as a means to this end, has been a point of attraction to both researchers and practitioners. Limitations of cell formation problem (CFP), as one of important topics in CMS, have led to the introduction of virtual CMS (VCMS). This research addresses a bi-objective dynamic virtual cell formation problem (DVCFP) with the objective of finding the optimal formation of cells, considering the material handling costs, fixed machine installation costs and variable production costs of machines and workforce. Furthermore, we consider different skills on different machines in workforce assignment in a multi-period planning horizon. The biobjective model is transformed to a single-objective fuzzy goal programming model and to show its performance; numerical examples are solved using the LINGO software. In addition, genetic algorithm (GA) is customized to tackle large-scale instances of the problems to show the performance of the solution method

An integrated model of cellular manufacturing and supplier selection considering product quality

Today’s business environment has forced manufacturers and plants to produce high-quality products at low cost and the shortest possible delivery time. To cope with this challenge, manufacturing organizations need to optimize the manufacturing and other functions that are in logical association with each other. Therefore, manufacturing system design and supplier selection process are linked together as two major and interrelated decisions involved in viability of production firm. As a matter of fact, production and purchasing functions interact in the form of an organization’s overall operation and jointly determine corporate success. In this research, we tried to show the relationship between designing cellular manufacturing system (CMS) and supplier selection process by providing product quality considerations as well as the imprecise nature of some input parameters including parts demands and defects rates. A unified fuzzy mixed integer linear programming model is developed to make the interrelated cell formation and supplier selection decisions simultaneously and to obtain the advantages of this integrated approach with product quality and consequently reduction of total cost. Computational results also display the efficiency of proposed mathematical model for simultaneous consideration of cellular manufacturing design and supplier selection as compared to when these two decisions separately taken into account

Cell forming and cell balancing of virtual cellular manufacturing systems with alternative processing routes using genetic algorithm

Cellular manufacturing (CM) is one of the most important subfields in the design of manufacturing systems and as a recently emerged field of study and practice, virtual cellular manufacturing (VCM) inherits the importance from CM. One type of VCM problems is VCM with alternative processing routes from which the route for processing each part should be selected. In this research, a bi-objective mathematical programming model is designed in order to obtain optimal routing of parts, the layout of machines and the assignment of cells to locations and to minimize the production costs and to balance the cell loads. The proposed mathematical model is solved by multi-choice goal programming (MCGP). Since CM models are NP-Hard, a genetic algorithm (GA) is utilized to solve the model for large-sized problem instances and the results obtained from both methods are compared. Finally, a conclusion is made and some visions for future works are offered.Cellular manufacturing (CM) is one of the most important subfields in the design of manufacturing systems and as a recently emerged field of study and practice, virtual cellular manufacturing (VCM) inherits the importance from CM. One type of VCM problems is VCM with alternative processing routes from which the route for processing each part should be selected. In this research, a bi-objective mathematical programming model is designed in order to obtain optimal routing of parts, the layout of machines and the assignment of cells to locations and to minimize the production costs and to balance the cell loads. The proposed mathematical model is solved by multi-choice goal programming (MCGP). Since CM models are NP-Hard, a genetic algorithm (GA) is utilized to solve the model for large-sized problem instances and the results obtained from both methods are compared. Finally, a conclusion is made and some visions for future works are offered

Ecotourism supply chain during the COVID-19 pandemic: A real case study

The coronavirus (COVID-19) disease has caused serious and irreversible damage to the ecotourism industry, posing serious challenges to all parts of the ecotourism supply chain. The ecotourism supply chain is made up of various components, the most important of which are ecotourism centers. During these pandemic times, the primary concerns of these centers are to improve their deplorable economic conditions and retain customers for the post-coronavirus era. As a result, an investigation should be conducted to address these concerns and provide appropriate solutions to help them overcome the challenges that have emerged. To achieve the research goal, a bi-objective mathematical model for the ecotourism supply chain in an uncertain environment is developed, accounting for the effects of COVID-19. The first objective function minimizes the total cost of the supply chain, while the second maximizes customer satisfaction. The proposed mathematical model is solved using a fuzzy goal programming (FGP) method. A sensitivity analysis study is also carried out to examine the performance of some basic parameters. Furthermore, the model is tested in a real case study to determine its efficacy. Finally, some effective managerial insights are proposed to improve the situation of the centers during the pandemic. © 2021 The Author

The Minimum Dataset and Inclusion Criteria for the National Trauma Registry of Iran: A Qualitative Study

Background Burden of injuries is an important public health problem, especially in developing countries. However, a national standard tool for data collection of trauma registry has not been developed in Iran yet. Objectives The present study aimed to describe the steps undertaken in the development of the minimum dataset (MDS) and define the inclusion and exclusion criteria for a case of trauma registry by the national trauma registry of Iran (NTRI). Methods The working group consists of sixteen elected expert representatives from seven established countrywide active trauma research centers. Following a structured extensive review of the literature, the working party identified the data variables that included key registry goals for pre-hospital and hospital, outcome and quality assurance information. We used data variables from three trauma registry centers: National trauma data standard questionnaire, European trauma care (UT stein version), and Sina trauma and surgery research center. Then, we performed two email surveys and three focus group discussions and adapted, modified and finally developed the optimized MDS in order to prepare the quality care registry for injured patients. Results The finalized MDS consisted of 109 data variables including demographic information (n = 24), injury information (n = 19), prehospital information (n = 26), emergency department information (n = 25), hospital procedures (n = 2), diagnosis (n = 2), injury severity (n = 3), outcomes (n = 5), financial (n = 2), and quality assurance (n = 1). For a patient sustained one or more traumatic injury in a defined diagnostic ICD-10 codes, the inclusion criteria considered as one of the followings: If the patient stayed > 24 hours in the hospital, any death after hospital arrival, any transfer from another hospital during the first 24 hours from injury. Conclusions This study presents how we developed the MDS in order to uniform data reporting in the NTRI and define our inclusion and exclusion criteria for trauma registry. Applying the MDS and the case definition in pilot studies are needed in next steps

Technical Paper Production planning and worker training in dynamic manufacturing systems

a b s t r a c t Production planning is a vital activity in any manufacturing system, and naturally implies assigning the available resources to the required operations. This paper develops and analyzes a comprehensive mathematical model for dynamic manufacturing systems. The proposed model integrates production planning and worker training considering machine and worker time availability, operation sequence and multi-period planning horizon. The objective is to minimize machine maintenance and overhead, system reconfiguration, backorder and inventory holding, training and salary of worker costs. Computational results are presented to verify the proposed model

Developing a method for order allocation to suppliers in green supply chain

Currently, due to increased competition in the services and manufacturing, many companies are trying to lower price and good quality products offer to the market. In this paper, the multi-criteria decision-making techniques to evaluate and select the best supplier from among the existing suppliers. The first, hierarchical structure for selecting suppliers of raw materials used and the analytic hierarchy process to obtain the relative importance of quantitative and qualitative criteria related to green supply chain is applied.  Then, a fuzzy TOPSIS technique any raw material suppliers is ranked according to the relevant criteria. Finally, with regard to the weight of suppliers and demand of raw material and resource constraints by a multi-objective mathematical model, optimum order is determined. The objectives are to minimize the total cost, maximize amount of purchases of desirable suppliers and minimize of raw materials required are not provide. The proposed method in a case study used Food Company and the relevant results are expressed

A Bi-Objective Vehicle Routing Problem with Time Windows Considering Fuel Consumption and Co2 Emission

In this research, a new bi-objective routing problem is developed in which a conventional vehicle routing problem with time windows (VRPTW) is considered with environmental impacts and heterogeneous vehicles. In this problem, minimizing the fuel consumption (liter) as well as the length of the routes (meter) are the main objectives. Therefore, a mathematical bi-objective model is solved to create Pareto's solutions. The objectives of the proposed mathematical model are to minimize the sum of distance cost as well as fuel consumption and Co2 emission. Then, the proposed Mixed-Integer Linear Program (MILP) is solved using the ε-constraint approach Furthermore, numerical tests performed to quantify the benefits of using a comprehensive goal function with two different objectives. Managerial insights and sensitivity analysis are also performed to show how different parameters of the problem affect the computational speed and the solutions’ quality

Optimizing a multi-product closed-loop supply chain using NSGA-II, MOSA, and MOPSO meta-heuristic algorithms

Abstract This study aims to discuss the solution methodology for a closed-loop supply chain (CLSC) network that includes the collection of used products as well as distribution of the new products. This supply chain is presented on behalf of the problems that can be solved by the proposed meta-heuristic algorithms. A mathematical model is designed for a CLSC that involves three objective functions of maximizing the profit, minimizing the total risk and shortages of products. Since three objective functions are considered, a multi-objective solution methodology can be advantageous. Therefore, several approaches have been studied and an NSGA-II algorithm is first utilized, and then the results are validated using an MOSA and MOPSO algorithms. Priority-based encoding, which is used in all the algorithms, is the core of the solution computations. To compare the performance of the meta-heuristics, random numerical instances are evaluated by four criteria involving mean ideal distance, spread of non-dominance solution, the number of Pareto solutions, and CPU time. In order to enhance the performance of the algorithms, Taguchi method is used for parameter tuning. Finally, sensitivity analyses are performed and the computational results are presented based on the sensitivity analyses in parameter tuning

Analysis and evaluation of challenges in the integration of Industry 4.0 and sustainable steel reverse logistics network

Industry 4.0 (I4.0) is a comparatively new phenomenon, and it is most probable that developing countries would face challenges in adapting it for improving the processes of supply chains and moving toward sustainability. The steel industry is the core of industrial growth, and it has an indispensable role in the development of countries. Steel is a highly recyclable product, meaning that it can be reused infinitely, increasing the significance of its reverse logistics. Although many studies have been conducted in the area of I4.0 and supply chain management, less attention has been devoted to finding and analyzing potential challenges of I4.0 technologies integration in steel reverse logistics activities. Therefore, this study is conducted to identify and analyse the challenges to efficient integration of I4.0 and sustainable steel reverse logistics system. Data collection is conducted with the assistance of qualified experts familiar with the steel supply chain and I4.0 concept. The interrelations of challenges are specified by Interpretive Structural Modeling, and the final ranking of challenges is determined through the Fuzzy Analytical Network Process. After validating the completed questionnaires, the absence of experts in I4.0, lack of clear comprehension of I4.0 concepts, training programs, and governmental policies and support are determined as the most critical challenges. Finally, the results and discussion, which can help practitioners in the efficient adoption of I4.0 to have a sustainable reverse logistics system, are presented