International Journal of Industrial Engineering & Production Research

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We model a real-world case problem as a price competition model between two leader-follower supply chains that each of them consists of one manufacturer and one retailer. T he manufacturer produces partially differentiated products and sells to market through his retailer. The retailer sells the products of manufacturer to market by adding some values to the product and gains margin as a fraction of the all income of selling products. We use a two-stage Stackelberg game model to investigate the dynamics between these supply chains and obtain the optimal prices of products. We explore the effect of varying the level of substitutability coefficient of two products on the profits of the leader and follower supply chains and derive some managerial implications. We find that the follower supply chain has an advantage when the products are highly substitutable. Also, we study the sensitivity analysis of the fraction of requested margin by retailer on the profit of supply chains.

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Today’s world economy situation forces enterprise organizations toward more soft and flexible organization, management, and production processes. They need to explore the most suitable Knowledge Management (KM) tool not only to identify gaps and overlaps but also to maintain and support innovation cross organizations. In this study, a multiple-experts-multiple-criteria decision making model is developed to select the most appropriate set of KM tools to support the innovation processes in organizations. Also, the model aims to suggest conditions for the improvement of KM in different types of knowledge-based inter-organizational collaborations. The application of the model is demonstrated by an illustrative example.

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Nowadays, in turbulent and violate global markets an Agile Supply Chain (ASC) network is frequently considered as a dominant competitive advantage for survival. To achieve the competitive advantage, companies must align with suppliers and customers to streamline operations, as well as agility beyond individual companies. There are many definitions and models about agile supply chain and most of them have emphasized on capabilities and enablers, and their sub attributes, as two critical factors, but regardless of time and its effect on the main attributes. This paper tries to present the role of time on predicting the agility of supply chain, by studying effect of intervention time on enablers and eventually predict the progress trend of agility in supply chain. To gain this end we use ANFIS output to assess agility and compare the effects of agile enablers in period of time on capabilities in Iran Khodro manufacture. This recognition helps managers to consider time as leverage factor and focus on this factor to enhance existent agility level and achievement the desired one.

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The manufacturing organizations today are having a competition of supply chain versus supply chain. Existing research work fails to relate human metrics with supply chain performance. The authors intend to empirically assess the effects of human metrics on supply chain performance in the context of Indian manufacturing organizations. A rigorous literature review has identified 12 variables. The variables are individually measured and later on reduced in number by factor analysis. As a pilot study, primary data is collected from 100 manufacturing organizations by means of a questionnaire, both offline and online, which is administered across India and a scale is developed. t-test and factor analysis resulted in 3 factors related to human metrics. The outcomes of the research work provide valuable implications for the Indian manufacturing organizations to understand the factors affecting supply chain performance.

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Abstract Purpose of this paper: This paper presents an analysis toward understanding the business value components that a health care organization can drive by adopting RFID technology into its system. This researcher proposes a framework for evaluating the business value of RFID technology. To do so, emphasis is put on delivering business value through refining business processes and expanding the business model. Author illustrates the concepts drawing on the experience of nine case studies already presented on the health care topics and service industries. Thereafter, a framework, as a set of propositions based on relevant literature, case studies from the field, and author's intuition, is formulated. The proposed propositions need to be validated through empirical evidence. To fully understand this topic some applications of radio frequency identification in health care management and other industries are briefly discussed and nine cases from health care industry are studied. Design/methodology/approach: Identifies key elements of radio frequency identification through the review of healthcare management literature and case studies. For this purpose, nine cases are reviewed from the health care industry and then key features of those cases are employed to determine four proposals for further studies. Findings: To make healthcare management systems functional and successfully operational we can use radio frequency identification solutions to reduce operating costs through decreasing the labor costs, including automation, improving tracking and tracing, and preventing the lost of materials under any circumstances. Practical implications (if applicable): What is original/value of paper: The emphasis is put on delivering business value through refining business processes and expanding the business model. A framework, as a set of propositions based on relevant literature, case studies from the field, and author's intuition is formulated and presented for further studies using true case studies.

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Abstract Nowadays supply chain management has become one of the powerful business concepts for organizations to gain a competitive advantage in global market. This is the reason that now competition between the firms has been replaced by competitiveness among the supply chains. Moreover, the popular literature dealing with supply chain is replete with discussions of leanness and agility. Agile manufacturing is adopted where demand is volatile while lean manufacturing is used in stable demands. However, in some situations it is advisable to utilize a different paradigm, called leagility, to enable a total supply chain strategy. Although, various generic hybrids have been defined to clarify means of satisfying the conflicting requirements of low cost and fast response, little research is available to provide approaches to enhance supply chain leagility. By linking Leagile Attributes and Leagile Enablers (LAs and LEs), this paper, based upon Quality Function Deployment (QFD), strives to identify viable LEs to achieve a defined set of LAs. Due to its wide applicability, AHP is deployed to prioritize LAs. Also, fuzzy logic is used to deal with linguistics judgments expressing relationships and correlations required in QFD. To illustrate the usefulness and ease of application of the approach, the approach was exemplified with the help of a case study in chemical industry.

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Today’s competition is promoted from firm against firm to supply chain versus supply chain, globalization and competition is a common phenomenon so each organization should make its supply chain as a weapon against the others, in doing so this paper presented a conceptual, graphical, step by step methodology to construct supply chain strategies by integrating strategic management’s theories, application and analysis and supply chain point of view to help managers to compete in the market.

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Enterprise Resource Planning (ERP) has become the most strategic tool for an organization to employ. A leading ERP solution is SAP®. It has been employed by organizations to enable them to collaborate on different projects and to integrate all aspects of operations. Just as organizations have adopted ERP solutions, they employed quality initiatives that are designed to help organizations maximize efficiency. One of these quality initiatives organizations have turned to is Six Sigma. This paper explores the impact of SAP® solutions on the success of Six Sigma initiative implementation in an agile environment, where the needs of the customer changes rapidly. The literature shows that SAP® type ERP products play a critical role in the implementation process like Six Sigma, where the implementation process takes time. Also, the literature implies that in agile environments Six Sigma implementation can be ineffective.

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Abstract Project management in construction industry, in many cases, is imperfect with respect to the integration of Occupational Health and Safety (OHS) risks. This imperfection exhibits itself as complications affecting the riskiness of industrial procedures and is illustrated usually by poor awareness of OHS within project teams. Difficulties on OHS regularly came about in the construction industry. The integration of OHS risk is not systematic in construction areas in spite of progressing laws and management systems. As project safety and risk evaluation in construction industry is an important issue, thus, the way on doing evaluation and liability of estimation is necessary. In this paper, we propose a new systematic approach based on Latin Hypercube Sampling (LHS) for integrating occupational health and safety into project risk evaluation. This approach tries to identify and evaluate reinforcement effects in a systematic approach for integrating OHS risks into project risk assessment. Furthermore, the proposed method allows evaluating and comparing OHS risks before and after the mitigation plan. A case study is used to prove the workability, credibility of the risk evaluation approach and uncomplicated integration of OHS risks at a construction project. This approach enables continual revaluation of criteria over the direction of the project or when new information is obtained. This model enables the decision makers such as project managers to integrate OHS risks toward schedule plan and compare them before and after the mitigation plan. The mentioned model is found to be useful for predicting OHS risks in construction industries and thus avoiding accidents over the path of the project.

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The main objective of the present survey is to conduct a comparative analysis of the challenges of the performance evaluation system in two private and public sectors and their impact on effectiveness of human resources management practices. This survey was conducted using the field-descriptive method. The historical study method was used to collect theoretical subjects and the field study method was applied to answer the research questions and confirm or reject the research hypotheses. Statistical population of the research included all experts and managers at various levels in two private and public sectors of the petrochemical industry. Cluster-stratified sampling method in two phases was utilized. According to the results of the present survey, there is a significant difference among identified challenges in the performance evaluation system in two private and public sectors of the petrochemical industry. Results demonstrate that the effectiveness level of human resource management practices in the performance evaluation system is higher than the average level in the public sector, effectiveness level of human resource management practices in the performance evaluation system is lower than the average level in the private sector of the petrochemical industry, and there is a significant difference between the effectiveness level of human resource management practices in the performance evaluation system in two private and public sectors of petrochemical industry. Studying the impact of existing challenges in the performance evaluation system on effectiveness of human resource management practices shows existence of a significant and reversed relation among the existing challenges in the performance evaluation system and effectiveness of human resource management practices in this regard.

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This paper addresses a production and outbound distribution scheduling problem in which a set of jobs have to be process on a single machine for delivery to customers or to other machines for further processing. We assume that there is a sufficient number of vehicles and the delivery costs is independent of batch size but it is dependent on each trip. In this paper, we present an Artificial Immune System (AIS) for this problem. The objective is to minimize the sum of the total weighted number of tardy jobs and the batch delivery costs. A batch setup time has to be added before processing the first job in each batch. Using computational test, we compare our method with an existing method for the mentioned problem in literature namely Simulated Annealing (SA). Computational tests show the significant improvement of AIS over the SA.

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This paper considers a bi-objective mathematical model for locations of landfills, transfer stations and material recovery facilities (MRFs) in order to serve the entire regions and simultaneously identify the capacities of landfills. This is a mixed-integer programming (MIP) model, whose objectives are to minimize the total cost and pollution simultaneously. To validate the model, a numerical example is solved an augmented ε-constraint method and the associated computational results are presented to show the number of solid waste facilities and location of sites for solid waste facilities.

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The locomotive assignment and the freight train scheduling are important problems in railway transportation. Freight cars are coupled to form a freight rake. The freight rake becomes a train when a locomotive is coupled to it. The locomotive assignment problem assigns locomotives to a set of freight rakes in a way that, with minimum locomotive deadheading time, rake coupling delay and locomotive coupling delay all freight rakes are hauled to their destinations. Scheduling freight trains consists of sequencing and ordering freight trains during the non-usage time between passenger trains but with no interference and with minimum delay times. Solving these two problems simultaneously is of high importance and can be highly effective in decreasing costs for rail transportation. In this paper, we aim to minimize the operational costs for the locomotive assignment and the freight train scheduling by solving these two problems concurrently. To meet this objective, an efficient and effective algorithm based on the ant colony system is proposed. To evaluate the performance of the proposed solution method, twenty-five test problems, which are based on the conditions of Iran Railways, are solved and the computational results are reported.

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In the current study, a dual-channel supply chain is considered containing one manufacturer and two retailers. It is assumed that the manufacturer and retailers have the same decision powers. A game-theoretic approach is developed to analyze pricing decisions under the centralized and decentralized scenarios. First, the Nash model is established to obtain the equilibrium decisions in the decentralized case. Then, the centralized model is developed to maximize the total profit of the whole system. Finally, the equilibrium decisions are discussed and some managerial insights are revealed. 

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Nowadays, with a growing body of features and technologies, supply chain management is being widely used to coordinate and optimize key processes such as increasing customer satisfaction, facilitating the processes, and enhancing product quality. In recent years, the emergence of IT and new business environments has led to the development of electronic supply chains. In order to use and benefit from the privileges of e-supply chains, organizations must identify the key factors in the implementation of e-supply chain management so that they can monitor the organization's current and future activities and take action to identify and modify and fix any bugs. The present study aimed at identifying these factors. Based on the available theoretical foundations and expert opinions, the factors affecting the implementation of electronic supply chain management were identified in seven factors with 31 indicators. To determine the weight of the identified factors considering the lack of independence between them, an integrated type-2 fuzzy AHP and type-2 fuzzy DEMATEL approach was used. Results showed that computer-based technology, infrastructure, inter-organizational relationships, and information are the most important factors.

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Natural disasters, such as earthquakes, tsunamis, and hurricanes cause enormous harm during each year. To reduce casualties and economic losses in the response phase, rescue units must be allocated and scheduled efficiently, such that it is a key issues in emergency response. In this paper, a multi-objective mix integer nonlinear programming model (MOMINLP) is proposed to minimize sum of weighted completion times of relief operations as first objective function and makespan as second objective with considering time-window for incidents. The rescue units also have different capability and each incident just can be allocated to a rescue unit that has the ability to do it. By assuming the incidents and rescue units as jobs and machine, respectively, the research problem can be formulated as a parallel-machine scheduling problem with unrelated machines. Multi-Choice Goal programming (MCGP) is applied to solve research problem as single objective problem. The experimental results shows the superiority of the proposed approach to allocate and schedule the rescue units in the natural disasters.

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The aim of this study is to present a new method to predict project time and cost under uncertainty. Assuming that what happens in projects implementation which is expressed in the form of Earned Value Management (EVM) indicators is primarily related to the nature of randomness or unreliability, in this study, by using Monte Carlo simulation, and assuming a specific distribution for the time and cost of project activities, a significant number of predicting scenarios will be simulated. According to the data, an artificial neural network is used as efficient data mining methods to estimate the project time and cost at completion.

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Abstract
Production and distribution network (PDN) planning in multi-stage status is commonly complex. These conditions cause significant amount of uncertainty relating to demand and lead time. In this study, we introduce a PDN to deliver the products to customers in the least time and optimize the total cost of the network, simultaneously. The proposed network is four stage PDN including suppliers, producers, potential entrepots, retailers and customers with multi time period horizon with allowable shortage. A mixed integer programming model with minimizing total cost of the system and minimizing total delivery lead time is designed. We present a novel heuristic method called selective firefly algorithm (SFA) in order to solve several sized especially real world instances. In SFA, each firefly recognizes all better fireflies with more brightness and analyses its brightness change before moving, tacitly. Then, the firefly that makes best change is selected and initial firefly moves toward the selected firefly. Finally, the performance of the proposed algorithm is examined with solving several sized instances. The results indicate the adequate performance of the proposed algorithm.

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Nowadays, automakers have faced complexity of supply chain that have to improve procedures and processes in order to access high performance in both strategic and operational. The main purpose of this research is to evaluate model of strategic performance of supply chain (SPSC), which include information technology (IT), organizational learning (OL), and Product innovation (PRI) via qualitative research. In fact, this research follows how these factors effect on SPSC. IRANKHODRO Company (IKCO) as the biggest automaker in Middle East is our case study in order to research and evaluate mentioned model. A total number of 12 interviews were done based on a list of semi-structured open-ended questions in order to evaluate constructs and model. The development of constructs in IKCO according to respondents’ opinion was classified at three categories including high, medium and low levels. In high level, IT has been developed in IKCO more than another constructs, which consists of development of ORACLE system, SAP and KANBAN in order to cover information, procedures and processes across supply chain. While SPSC has been developed less than others.
 

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In today's world, natural disasters such as earthquakes, floods, crises such as terrorist attacks and protests threaten the lives of many people. Hence, in this research we present a mathematical modeling that provide efficient and effective model to locate temporary depot, equitable distribution of resources and movement of injured people to health centers, with the aim of developing the multi-objective model and considering multiple central depot, multiple temporary depot and several type of relief items in the model . This paper is considered certainty state and uncertainty of influencing parameters of the models in robust optimization for three different levels uncertainty and in different size with consideration of traditional goals function and humanitarian purposes functions simultaneously. The model has been solved with multi-objective Particle Swarm optimization algorithm (MOPSO) and GAMS software to validate the model. Some numerical examples are presented. In Addition, we present sensitivity analyzes of model and study the relationship of the number of temporary depot location and the number of injured people to move to health centers and the number of uncovered damaged points.