Mathematical model for deadlock resolution in multiple AGV scheduling and routing network: a case study

2015 ◽  
Vol 42 (3) ◽  
pp. 252-263 ◽  
Author(s):  
Hamed Fazlollahtabar ◽  
Mohammad Saidi-Mehrabad ◽  
Ellips Masehian
Keyword(s):  
Mathematical Model ◽  
Decision Support ◽  
Material Handling ◽  
Mathematical Program ◽  
Solution Space ◽  
Automated Guided Vehicles ◽  
Content Type ◽  
Earliness And Tardiness ◽  
Deadlock Resolution

Purpose This paper aims to propose and formulate a complicated routing/scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system. Design/methodology/approach Considering the due date of AGVs requiring for material handling among shops in a jobshop layout, their earliness and tardiness are significant in satisfying the expected cycle time and from an economic view point. Therefore, the authors propose a mathematical program to minimize the penalized earliness and tardiness for a conflict-free and just-in-time production. Findings The model considers a new concept of turning point for deadlock resolution. As the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely, searching the solution space and finding optimal solutions are proposed. The performance of the proposed mathematical model is tested in a numerical example. Practical implications A case study in real industrial environment is conducted. The findings lead the decision-makers to develop a user interface decision support as a simulator to plan the AGVs’ movement through the manufacturing network and help AGVs to prevent deadlock trap or conflicts. The proposed decision support can easily be commercialized. Originality/value The benefits of such commercialization are increase in the quality of material handling, improve the delivery time and prevent delays, decrease the cost of traditional handling, capability of computerized planning and control, intelligent tracking and validation experiments in simulation environment.

A bi-level mathematical model for logistic management considering the evolutionary game with environmental feedbacks

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Peiman Ghasemi ◽  
Fariba Goodarzian ◽  
Angappa Gunasekaran ◽  
Ajith Abraham
Keyword(s):  
Mathematical Model ◽  
Lagrangian Relaxation ◽  
Evolutionary Games ◽  
Relaxation Method ◽  
Objective Functions ◽  
Content Type ◽  
Medical Centers ◽  
Location Routing ◽  
Proposed Model

PurposeThis paper proposed a bi-level mathematical model for location, routing and allocation of medical centers to distribution depots during the COVID-19 pandemic outbreak. The developed model has two players including interdictor (COVID-19) and fortifier (government). Accordingly, the aim of the first player (COVID-19) is to maximize system costs and causing further damage to the system. The goal of the second player (government) is to minimize the costs of location, routing and allocation due to budget limitations.Design/methodology/approachThe approach of evolutionary games with environmental feedbacks was used to develop the proposed model. Moreover, the game continues until the desired demand is satisfied. The Lagrangian relaxation method was applied to solve the proposed model.FindingsEmpirical results illustrate that with increasing demand, the values of the objective functions of the interdictor and fortifier models have increased. Also, with the raising fixed cost of the established depot, the values of the objective functions of the interdictor and fortifier models have raised. In this regard, the number of established depots in the second scenario (COVID-19 wave) is more than the first scenario (normal COVID-19 conditions).Research limitations/implicationsThe results of the current research can be useful for hospitals, governments, Disaster Relief Organization, Red Crescent, the Ministry of Health, etc. One of the limitations of the research is the lack of access to accurate information about transportation costs. Moreover, in this study, only the information of drivers and experts about transportation costs has been considered. In order to implement the presented solution approach for the real case study, high RAM and CPU hardware facilities and software facilities are required, which are the limitations of the proposed paper.Originality/valueThe main contributions of the current research are considering evolutionary games with environmental feedbacks during the COVID-19 pandemic outbreak and location, routing and allocation of the medical centers to the distribution depots during the COVID-19 outbreak. A real case study is illustrated, where the Lagrangian relaxation method is employed to solve the problem.

scholarly journals Value of project management in university–industry R&D collaborations

2020 ◽  
Vol 13 (4) ◽  
pp. 819-843
Author(s):  
Gabriela Fernandes ◽  
David O' Sullivan ◽  
Eduardo B. Pinto ◽  
Madalena Araújo ◽  
Ricardo J. Machado
Keyword(s):  
Decision Support ◽  
Project Management ◽  
Empirical Research ◽  
Research Literature ◽  
Competing Values ◽  
Content Type ◽  
Key Stakeholders ◽  
Multi Stakeholder ◽  
University Industry

PurposeUniversity–industry projects provide special challenges in understanding and expressing the values required of project management (PM) in delivering stakeholder benefits. This paper presents a framework for understanding, identifying and managing the values of PM in major university–industry R&D projects.Design/methodology/approachThe value framework identifies for each of the key stakeholders, the key PM values that may require to be managed and are largely derived from research literature. Empirical research then explores, prioritises and selects key PM values that need to be managed for a specific project. A large case study is used involving one university and one industry collaborating on a multi-million Euro initiative over six years. Empirical research was conducted by researchers who observed at close quarters, the challenges and successes of managing the competing values of key stakeholders.FindingsThe value framework takes a stakeholders' perspective by identifying the respective PM values for each of six stakeholders: university–industry consortium, university, industry, R&D external entities, funding entity and society.Research limitations/implicationsThe research was performed using only one case study which limits the generalisability of its findings; however, the findings are presented as a decision support aid for project consortia in developing values for their own collaboration.Practical implicationsGuidance and decision support are provided to multi-stakeholder research consortia when selecting values that need to be managed for achieving tangible and intangible project benefits.Originality/valueThe paper demonstrates a proposed framework for designing and managing the value of PM in large multi-stakeholder university–industry R&D projects.

Factors influencing maintenance-related investments in industry: a multiple-case study

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Camilla Lundgren ◽  
Jon Bokrantz ◽  
Anders Skoogh
Keyword(s):  
Decision Support ◽  
Manufacturing Industry ◽  
Production Systems ◽  
Multiple Case Study ◽  
Sustainable Production ◽  
Content Type ◽  
Factors Influencing ◽  
Multiple Case ◽  
Investment Process

PurposeThe purpose of this study is to ensure productive, robust and sustainable production systems by enabling future investments in maintenance. This study aims to provide a deeper understanding of the investment process and thereby facilitate future maintenance-related investments. The objectives are to describe the investment process, map the decision support and roles involved and identify factors influencing the process.Design/methodology/approachThe study was designed as a multiple-case study, with three industrial cases of maintenance-related investments. A structured coding procedure was used to analyse the empirical data from the cases.FindingsThis paper provides a deeper understanding of the process of maintenance-related investments. Eleven factors influencing the investment process could be identified, three of which were seen in all three cases. These three factors are: fact-based decision-support, internal integration and foresight.Practical implicationsInvestments in modern maintenance are needed to ensure productive, robust and sustainable production in the future. However, it is a challenge in manufacturing industry to justify maintenance-related investments. This challenge may be solved by developing a decision-support system, or a structured work procedure, that considers the findings of this study.Originality/valueFrom this study, an extended view of the relation between quantifying effects of maintenance and maintenance-related investment is proposed, including surrounding factors influencing the investment process. The factors were identified using a structured and transparent coding procedure which is rarely used in maintenance research.

Sensor-based ambient intelligence for optimal energy efficiency

Sensor Review ◽  
2014 ◽  
Vol 34 (2) ◽  
pp. 170-181 ◽  
Author(s):  
David Robinson ◽  
David Adrian Sanders ◽  
Ebrahim Mazharsolook
Keyword(s):  
Energy Efficiency ◽  
Decision Support ◽  
Energy Consumption ◽  
Energy Management ◽  
Research Work ◽  
Management Systems ◽  
Manufacturing Companies ◽  
Content Type ◽  
Energy Management Systems

Purpose – This paper aims to describe research work to create an innovative, and intelligent solution for energy efficiency optimisation. Design/methodology/approach – A novel approach is taken to energy consumption monitoring by using ambient intelligence (AmI), extended data sets and knowledge management (KM) technologies. These are combined to create a decision support system as an innovative add-on to currently used energy management systems. Standard energy consumption data are complemented by information from AmI systems from both environment-ambient and process ambient sources and processed within a service-oriented-architecture-based platform. The new platform allows for building of different energy efficiency software services using measured and processed data. Four were selected for the system prototypes: condition-based energy consumption warning, online diagnostics of energy-related problems, support to manufacturing process lines installation and ramp-up phase, and continuous improvement/optimisation of energy efficiency. Findings – An innovative and intelligent solution for energy efficiency optimisation is demonstrated in two typical manufacturing companies, within one case study. Energy efficiency is improved and the novel approach using AmI with KM technologies is shown to work well as an add-on to currently used energy management systems. Research limitations/implications – The decision support systems are only at the prototype stage. These systems improved on existing energy management systems. The system functionalities have only been trialled in two manufacturing companies (the one case study is described). Practical implications – A decision support system has been created as an innovative add-on to currently used energy management systems and energy efficiency software services are developed as the front end of the system. Energy efficiency is improved. Originality/value – For the first time, research work has moved into industry to optimise energy efficiency using AmI, extended data sets and KM technologies. An AmI monitoring system for energy consumption is presented that is intended for use in manufacturing companies to provide comprehensive information about energy use, and knowledge-based support for improvements in energy efficiency. The services interactively provide suggestions for appropriate actions for energy problem elimination and energy efficiency increase. The system functionalities were trialled in two typical manufacturing companies, within one case study described in the paper.

scholarly journals Adaptive decision support for suggesting a machine tool maintenance strategy

2018 ◽  
Vol 24 (3) ◽  
pp. 376-399 ◽  
Author(s):  
Abubaker Shagluf ◽  
Simon Parkinson ◽  
Andrew Peter Longstaff ◽  
Simon Fletcher
Keyword(s):  
Decision Support ◽  
Machine Tool ◽  
Cost Estimation ◽  
Expert Knowledge ◽  
Production Costs ◽  
Maintenance Cost ◽  
Maintenance Strategy ◽  
Content Type ◽  
Machine Tool Accuracy

Purpose The purpose of this paper is to produce a decision support aid for machine tool owners to utilise while deciding upon a maintenance strategy. Furthermore, the decision support tool is adaptive and capable of suggesting different strategies by monitoring for any change in machine tool manufacturing accuracy. Design/methodology/approach A maintenance cost estimation model is utilised within the research and development of this decision support system (DSS). An empirical-based methodology is pursued and validated through case study analysis. Findings A case study is provided where a schedule of preventative maintenance actions is produced to reduce the need for the future occurrences of reactive maintenance actions based on historical machine tool accuracy information. In the case study, a 28 per cent reduction in predicted accuracy-related expenditure is presented, equating to a saving of £14k per machine over a five year period. Research limitations/implications The emphasis on improving machine tool accuracy and reducing production costs is increasing. The presented research is pioneering in the development of a software-based tool to help reduce the requirement on domain-specific expert knowledge. Originality/value The paper presents an adaptive DSS to assist with maintenance strategy selection. This is the first of its kind and is able to suggest a preventative strategy for those undertaking only reactive maintenance. This is of value for both manufacturers and researchers alike. Manufacturers will benefit from reducing maintenance costs, and researchers will benefit from the development and application of a novel decision support technique.

scholarly journals Integrated analytical hierarchy process and grey relational analysis approach to measure supply chain complexity

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Sujan Piya ◽  
Ahm Shamsuzzoha ◽  
Mohammed Khadem ◽  
Mahmoud Al Kindi
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Analytical Hierarchy Process ◽  
Grey Relational Analysis ◽  
Extensive Literature ◽  
Content Type ◽  
Relational Analysis ◽  
Grey Relational ◽  
Hierarchy Process

PurposeThe purpose of this paper is to understand the drivers that create complexity in the supply chain and develop a mathematical model to measure the level of supply chain complexity (SCC).Design/methodology/approachThrough extensive literature review, the authors discussed various drivers of SCC. These drivers were classified into five dimensions based on expert opinion. Moreover, a novel hybrid mathematical model was developed by integrating analytical hierarchy process (AHP) and grey relational analysis (GRA) methods to measure the level of SCC. A case study was conducted to demonstrate the applicability of the developed model and analyze the SCC level of the company in the study.FindingsThe authors identified 22 drivers of SCC, which were further clustered into five complexity dimensions. The application of the developed model to the company in the case study showed that the SCC level of the company was 0.44, signifying that there was a considerable scope of improvement in terms of minimizing complexity. The company that serves as the focus of this case study mainly needs improvement in tackling issues concerning government regulation, internal communication and information sharing and company culture.Originality/valueIn this paper, the authors propose a model by integrating AHP and GRA methods that can measure the SCC level based on various complexity drivers. The combination of such methods, considering their ability to convert the inheritance and interdependence of drivers into a single mathematical model, is preferred over other techniques. To the best of the authors' knowledge, this is the first attempt at developing a hybrid multicriteria decision-based model to quantify SCC.

An integrated approach for line balancing and AGV scheduling towards smart assembly systems

2020 ◽  
Vol 40 (2) ◽  
pp. 219-234 ◽  
Author(s):  
Humyun Fuad Rahman ◽  
Mukund Nilakantan Janardhanan ◽  
Peter Nielsen
Keyword(s):  
Material Handling ◽  
Assembly Line ◽  
Computational Study ◽  
Automated Guided Vehicles ◽  
Robotic Assembly ◽  
Line Balancing ◽  
Assembly Systems ◽  
Scheduling Problems ◽  
Content Type ◽  
Smart Assembly

Purpose Optimizing material handling within the factory is one of the key problems of modern assembly line systems. The purpose of this paper is to focus on simultaneously balancing a robotic assembly line and the scheduling of material handling required for the operation of such a system, a topic that has received limited attention in academia. Manufacturing industries focus on full autonomy because of the rapid advancements in different elements of Industry 4.0 such as the internet of things, big data and cloud computing. In smart assembly systems, this autonomy aims at the integration of automated material handling equipment such as automated guided vehicles (AGVs) to robotic assembly line systems to ensure a reliable and flexible production system. Design/methodology/approach This paper tackles the problem of designing a balanced robotic assembly line and the scheduling of AGVs to feed materials to these lines such that the cycle time and total tardiness of the assembly system are minimized. Because of the combination of two well-known complex problems such as line balancing and material handling and a heuristic- and metaheuristic-based integrated decision approach is proposed. Findings A detailed computational study demonstrates how an integrated decision approach can serve as an efficient managerial tool in designing/redesigning assembly line systems and support automated transportation infrastructure. Originality/value This study is beneficial for production managers in understanding the main decisional steps involved in the designing/redesigning of smart assembly systems and providing guidelines in decision-making. Moreover, this study explores the material distribution scheduling problems in assembly systems, which is not yet comprehensively explored in the literature.

scholarly journals A semantic-based decision support platform to assist products’ eco-labeling process

2017 ◽  
Vol 117 (7) ◽  
pp. 1340-1361 ◽  
Author(s):  
Da Xu ◽  
Mohamed Hedi Karray ◽  
Bernard Archimède
Keyword(s):  
Decision Support ◽  
Domain Knowledge ◽  
Service Providers ◽  
Laundry Detergent ◽  
Content Type ◽  
Ontology Language ◽  
Complex Process ◽  
Comprehensive Knowledge ◽  
Description Framework

Purpose With the rising concern of safety, health and environmental performance, eco-labeled product and service are becoming more and more popular. However, the long and complex process of eco-labeling sometimes demotivates manufacturers and service providers to be certificated. The purpose of this paper is to propose a decision support platform aiming at further improvement and acceleration of the eco-labeling process in order to democratize a broader application and certification of eco-labels, also to consolidate the credibility and validity of eco-labels. Design/methodology/approach This decision support platform is based on a comprehensive knowledge base composed of various domain ontologies that are constructed according to an official eco-label criteria documentation. Findings Through standard Resource Description Framework and Web Ontology Language ontology query interface, the assets of the decision support platform will stimulate domain knowledge sharing and can be applied into other applications. A case study of laundry detergent eco-labeling process is also presented in this paper. Originality/value The authors present a reasoning methodology based on inference with Semantic Web Rule Language (SWRL) rules which allows decision making with explanation.

A deterministic contractor selection decision support system for competitive bidding

2017 ◽  
Vol 24 (1) ◽  
pp. 61-77 ◽  
Author(s):  
Nabil Semaan ◽  
Michael Salem
Keyword(s):  
Decision Support ◽  
Project Management ◽  
Decision Support System ◽  
Support System ◽  
Project Managers ◽  
Cost Overruns ◽  
Contractor Selection ◽  
Content Type ◽  
Selection Decision

Purpose The construction industry today is one of the biggest industries in the world. As projects continue to grow in complexity, project management continues to evolve. Contractor selection is a difficult task that owners and project managers face. Although previously researchers have worked on the subject of contractor selection, a comprehensive decision support system for contractor selection has not yet been developed. Recent reports of major delays and cost overruns in mega projects highlight the need for a model that is able to be flexible and comprehensive becomes evident. The paper aims to discuss these issues. Design/methodology/approach The research focuses on obtaining insights from field experts using both quantitative and qualitative methods. Then, a model was developed in the light of the data collected. Accordingly, the model was tested on a case study. Findings This paper presents a model for contractor selection that is wholesome in its take on the topic. The model incorporates both managerial and technical aspects of the problem. The model was tested on a case study and it was proven to be feasible in real world applications. The contractor selection decision support system serves the needs of both academics and industry managers, as an integral part of project management. Originality/value The model presented in this paper is innovative in its take on the problems. MCDA tools have been uniquely modified in this paper to cater to the needs of the selection problem while accounting for the criteria hierarchy that incorporates aspects that are instrumental for proper evaluation of a contractor’s likelihood of success.

A mathematical model formulation to design a traditional Indian agri-fresh food supply chain: a case study problem

2020 ◽  
Vol 27 (8) ◽  
pp. 2341-2363 ◽  
Author(s):  
Rakesh Patidar ◽  
Sunil Agrawal
Keyword(s):  
Mathematical Model ◽  
Supply Chain ◽  
Food Supply ◽  
Mixed Integer ◽  
Food Supply Chain ◽  
Content Type ◽  
Post Harvest ◽  
Fresh Food ◽  
Distribution Cost

PurposeThe purpose of this paper is to study and develop supply chain structure of traditional Indian agri-fresh food supply chain (AFSC). This paper proposes a mathematical model to design a traditional Indian AFSC to minimize total distribution cost and post-harvest losses in the chain.Design/methodology/approachThis paper formulates two mathematical models to structure and represent the flow of products in the existing chain. First, a three-echelon, multi-period, multi-product, mixed-integer linear programming (MILP) model is formulated to minimize the total distribution cost incurred in the chain. Further, the developed formulation is extended by considering the perishability of products in the second model.FindingsA real case study problem of Mandsaur district (India) is solved in LINGO 17.0 package to check the validity of the formulated models. The perishable (second) model of AFSC reports better results in terms of costs and post-harvest losses minimization. The results revealed that 92% of the total distribution cost incurred in the transportation of products from farmers to the hubs.Research limitations/implicationsThis paper includes implications for redesigning an existing supply chain network by incorporating an appropriate transportation strategy from farmers to hubs to minimize transportation inefficiency and enhance the profitability of farmers.Practical implicationsThe formulated AFSC model would help managers and policymakers to identify optimal locations for hubs where required infrastructure would be developed.Originality/valueAccording to the author's best knowledge, this paper is the first to design traditional Indian AFSC by considering the perishability of products.

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