International Journal of Industrial Engineering & Production Research

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     Fully fuzzy linear programming is applied to water resources management due to its close connection with human life, which is considered to be of great importance. This paper investigates the decision-making concerning water resources management under uncertainty based on two-stage stochastic fuzzy linear programming. A solution method for solving the problem with fuzziness in relations is suggested to prove its applicability. The purpose of the method is to generate a set of solutions for water resources planning that helps the decision-maker make a tradeoff between economic efficiency and risk violation of the constraints. Finally, a numerical example is given and is approached by the proposed method.
 

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The number of natural disasters and people affected by them has been increasing in recent years. The field of optimization is a significant element of a relief operation and has been extensively studied so far, especially during the last two decades. The design of a relief logistic network as a strategic decision and the relief distribution as an operational decision are the most important activities for disaster operation management before and after a disaster occurs. In the proposed mathematical model, pre-disaster decisions are determined according to the post-disaster decisions in a multi-stage stochastic problem. Then a well-known approach called branch and fixed coordination are applied to optimize the proposed model. The computational results confirm that the proposed approach has proper performance for disaster management in a multi-stage stochastic problem.

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Sustainable supply chain networks have attracted considerable attention in recent years as a means of dealing with a broad range of environmental and social issues. This paper reports a multi-objective mixed-integer linear programming (MILP) model for use in the design of a sustainable closed loop supply chain network under uncertain conditions. The proposed model aims to minimize total cost, optimize environmental impacts of establishment of facilities, processing and transportation between each level as well as social impacts including customer satisfaction. Due to changes in business environment the uncertainty existed in the research problem, in this paper the chance constrained fuzzy programming approach applied to cope with uncertainties in parameter of the proposed model. Then the proposed multi-objective model solves as single-objective model using LP-metric method.

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Todays, the variety of new products will raise the competition between manufacturers. Product portfolio management (PPM) as a suitable tool can influence the customer’s taste and increase the profit of firms. In this paper, the factors of PPM, production planning and a two-player continuous game theory are considered simultaneously. Some constraints are also assumed such as the availability of raw materials and the demand of each product based on some criteria. Two firms have same offered products and compete with each other. The relationships between two producers will be modeled by a non-zero two- player game. A numerical example is presented too. The proposed model is single period that the inventory is equal to zero in the start and finish of period. The objective functions show the profit of products and the constraints are included the utility of products for each customer, the market's share as a function of the probability of customer selection for each section, the type of distribution function for sale quantity, the accessible quantity of the sum of used materials by two producers and etc.
The results shows that demand changing effects on the profit of two players, but effects more on the second player. Also the sale price changing effects on the profit of two players, but effects more on the first player. The obtained data shows that if extra sale price increase the profit of first player will increase while the profit of second player is constant approximately.

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In this paper, a novel data driven approach for improving the performance of wastewater management and pumping system is proposed, which is getting knowledge from data mining methods as the input parameters of optimization problem to be solved in nonlinear programming environment. As the first step, we used CART classifier decision tree to classify the operation mode -number of active pumps- based on the historical data of the Austin-Texas infrastructure. Then SOM is applied for clustering customers and selecting the most important features that might have effect on consumption pattern. Furthermore, the extracted features will be fed to Levenberg-Marquardt (LM) neural network which will predict the required outflow rate of the period for each operation mode, classified by CART. The result show that F-measure of the prediction is 90%, 88%, 84% for each operation mode 1,2,3, respectively. Finally, the nonlinear optimization problem is developed based on the data and features extracted from previous steps, and it is solved by artificial immune algorithm. We have compared the result of the optimization model with observed data, and it shows that our model can save up to 2%-8% of outflow rate and wastewater, which is significant improvement in the performance of pumping system.

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The aim of the paper is to present a complex risk analysis of investing in agriculture Exchange Trade Funds (ETFs). The specific characteristics of agricultural investments should be taken into account as from the direct financial investments into agricultural ETFs, as for the general portfolio approach applying. To achieve the objectives of the work, the authors structured agriculture ETFs into 6 classes, which represent different types of ETFs. A special sample of 26 agricultural ETFs was formed. A complex risk analysis consisted of applying 5 different conceptual approaches to measuring investment risk. In particular, approaches based on measuring variability, applying the concept of Value-at-Risk are applied. The approach of estimating the shocks of changes in the profitability of the asset class in question is applied. The risk level in the aspect of sensitivity to changes in stock returns, bonds and the uncertainty index EPU is investigated. Built portfolios with minimal risk. Obtained results can be applied for investment decisions

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Supply chain management (SCM) is very well known efficient and effective managerial tool to check and analyze the performance of any enterprises. In the present work, efforts have taken to analyze and optimize the performance of small & medium enterprise (SME) in Pune region India. For this purpose a SCM based framework is prepared to get the realistic data from the industries through the questionnaire prepared on the basic of literature and the expert opinions. After finalizing the effective framework fitted to the various enterprises, a data in the pointer scale has been collected from the various stakeholders of the enterprises. The grey relational analysis (GRA), a multi-response optimization tool has been effectively used for getting the optimize result which will help the enterprises to plan the strategies for the betterment of the enterprises. Optimum results were implemented in the other enterprises. The responses were measured and compare with the optimum solution. From the responses, it has been observed that there is a significant enhancement in the response level of the other enterprises. Thus the SCM was effectively used for enhancing the performance of the SMEs in the region.      

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The logistical problems that companies must face tend to have conflicting interests between customers and service providers, which makes them difficult to solve. In turn, when the activities involve the transport of hazardous materials, the problem becomes critical in security terms, and makes logistics operations even more difficult. In the hazardous materials transportation literature, problems related to the routing of vehicles and the geographic location of supply or service centres are often addressed. However, there are not many studies related to the study of the loading, unloading and weighing operations of trucks that handle hazardous materials within industrial plants. That is why this work presents a case study of the installation of a new truck balance in an industrial plant in Argentina. To do this, the internal logistics operation and the current state of the plant's infrastructure are analyzed. A detailed study of the alternatives for the location of the balance was carried out following the criteria set by the company's management and the problem was solved using an empirical weighting method coordinated with the heads of the Supply Chain Department. A satisfactory solution was obtained.

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One of the most important fields of logistic network is transportation network design that has an important effect on strategic decisions in supply chain management. It has recently attracted the attention of many researchers. In this paper, a multi-stage and multi-product logistic network design is considered.
This paper presents a hybrid approach based on simulation and optimization (Simulation based optimization), the model is formulated and presented in three stages.  At first, the practical production capacity of each product is calculated using the Overall Equipment Effectiveness (OEE) index, in the second stage, the optimization of loading schedules is simulated. The layout of the loading equipment, the number of equipment per line, the time of each step of the loading process, the resources used by each equipment were simulated, and the output of the model determines the maximum number of loaded vehicles in each period. Finally, a multi-objective model is presented to optimize the transportation time and cost of products. A mixed integer nonlinear programming (MINLP) model is formulated in such a way as to minimize transportation costs and maximize the use of time on the planning horizon. We have used Arena simulation software to solve the second stage of the problem, the results of which will be explained. It is also used GAMS software to solve the final stage of the model and optimize the transporting cost and find the optimal solutions. Several test problems were generated and it showed that the proposed algorithm could find good solutions in reasonable time spans.

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In the last decade, customers’ active involvement during product development, commonly referred to as co-creation, has emerged as an effective tool to overcome barriers that keep firms from understanding customer needs. Still in its infancy, many co-creation aspects are under-researched; this may present difficulties in aligning firm goals with their co-creators, often leading to project failure. To make the co-creation process more systematic, a framework is presented in this paper that will allow firms to analyse product attributes before engaging in co-creation, concerning firm capabilities and interests and the capabilities and interests of their co-creators. The results of this analysis will help firms to align their goals with the goals of co-creators. Two exploratory case studies were conducted for illustration.

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One of the goals of the manufacturing industry in the globalisation era is to reduce defects. Due to a variety of factors, the products manufactured in the industry may not be defect-free. Six Sigma is one of the most effective methods for reducing defects. This paper focuses on implementing Six Sigma in the automobile industry's stator motor shaft assembly. The high decibel noise produced by the stator motor is regarded as a rejected piece. Six Sigma focuses on continuous improvement and aids in process optimization by identifying the source of the defect. In the Six Sigma process, the problem is measured and analysed using various tools and techniques. Before beginning this case study, its impact on the company in terms of internal and external customer cost savings is assessed. This case study was discovered to be in a high-impact area. The issue was discovered during the Core and Shaft pressing process. Further research leads to dimensional tolerance, which reduces the defect percentage from 16.5 percent to 0.5 percent.

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Productions of the industries around the world depend on using equipment and machines. Therefore, it is vital to support the supply of equipment and spare parts for maintenance operations, especially in strategic industries that separate optimization of inventory management, supplier selection, network design, and planning decisions may lead to sub-optimal solutions. The integration of forward and reverse spare part logistics network can help optimize total costs. In this paper, a  mathematical model is presented for designing and planning an integrated forward-reverse repairable spare parts supply chain to make optimal decisions. The model considers the uncertainty in demand during the lead-time and the optimal assignment of repairable equipment to inspection, disassembly, and repair centers. A METRIC (Multi-Echelon Technique for recoverable Item Control) model is integrated into the forward-reverse supply chain to handle inventory management. A case study of National Iranian Oil Company (NIOC) is presented to validate the model. The non-linear constraints are linearized by using a linearization technique; then the model is solved by an iterative procedure in GAMS. A prominent outcome of the analyses shows that the same policies for repair and purchase of all the equipment and spare parts do not result in optimal solutions. Also, considering supply, repair, and inventory management decisions of spare parts simultaneously helps decision-makers enhance the supply chain's performance by applying a well-balanced repairing and purchasing policy.

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Tourists are getting more aware of the environment. To determine the effectiveness of eco-tourism infrastructure management, the motivation and segmentation of demand for eco-tourism have been analysed using functional theory as a guide. The empirical analysis was conducted in the Dnipro, Zaporizhzhia, and Kherson regions. 382 surveys were obtained by random sampling. To make the data analysis, factor analysis and non-hierarchical segmentation were performed. The results indicate that there are several eco-tourism motivational aspects including self-development, interpersonal relationships, defence functions, building personal relationships, reward, and appreciation from nature. Three different segments of eco-tourists were also identified based on their motives related to nature, reward, and escape. Characteristics of different segments were also specified. This study will help government agencies and private companies improve their travel content and develop more effective marketing plans.
The research has shown that in most cases, the success of any project is in cooperation between NGOs, locals, authorities, and the private sector. The optimal level of local participation is determined by the specifics and scale of each project, which may focus on individual villages or several communities that experience any impact of tourism.
The economic essence of the concept of tourism motivation has been improved, which is defined as a set of needs that affect a person in the process of participation in tourism activities and are a central factor in the decision-making process. Studying the most important motivations of eco-tourists in the region, three groups of motives have been identified: cultural and educational activities, proximity to nature, health and rehabilitation measures.

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The Project Management Body of Knowledge (PMBOK) is a widely used model of project management based on prior experience. This standard does not distinguish between small and large projects, but small projects, with their limited schedules and budgets, face challenges using the extensive structure proposed by this standard. It has been suggested that the standard can be adapted to each project within its specifications; however, the tailoring procedures are complex, time-consuming, and at times impossible to apply to small projects. The present study examined whether or not the PMBOK is an appropriate model for small projects. To address this issue, a questionnaire was prepared and sent to 134 professional project managers. Analysis of the data confirmed that the assumption that PMBOK is a challenge to small projects was not contradicted. Most participants agreed that the procedure should be tailored to prioritize the standard tools and guiding techniques, in addition to the knowledge areas, for small projects.

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The essence of the economic security of the Ukraine's agricultural sector, its role in further globalization, as well as the  trends of changes in key indicators of economic security till 2024 are considered in the article. The economic security of the agricultural sector is determined by the authors as an important component of the economic security of a country, it provides the state of a system (as a whole or individual entities), which in conditions of permanent influence of external and internal factors ensures the stability and progressive development of the agricultural sector  providing conditions for preservation and further reproduction of resource potential, guarantees food security, promotes the development of rural areas.
The emphasis is focused on the special role of food security in the context of achieving the Global Sustainable Development Goals.
The key indicators of achieving the Sustainable Development Goal 2 "Overcoming hunger, achieving food security, improving nutrition and promoting sustainable agricultural development" are analyzed. The positive dynamics is established for the following indicators: Productivity labour in agriculture; Index of agricultural products; The share of agricultural lands under organic production in the total area of this year lands; The consumer price index for food. Other indicators show that Ukraine lags far behind the targets.
The need to form a unified approach to the management of economic security of the agricultural sector has become the basis for the development of conceptual foundations for the strategy of economic security of the Ukraine's agricultural sector in the context of globalization. The implementation of this strategy will strengthen further the food security.

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This research aimed 1) to study the effects of the factors influencing the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand; 2) to study the development of a model for factors affecting the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand; and 3) to study the guideline and development of the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand. The study employed the quantitative research methodology and the statistical devices of percentage and Structural Equation Modeling (SEM). The population and sample group comprised executives in the ceramic industry in Thailand.
The findings revealed the following: 1) The factors concerning knowledge of information technology, warehouse management and digital system positively affected the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand at the statistically significant levels of β = 0.324, 0.163 and 0.271 respectively. The antecedent variables which had Direct Effect (DE) and Total Effect (TE) on the latent variable of the performance of warehouse management to increase the competitiveness were 1. knowledge of information technology (DE = 0.324, and TE = 0.324), 2. warehouse management (DE = 0.271, and TE = 0.271), and 3. digital system (DE = 0.163, and TE = 0.163) respectively; 2) The results of the study of the guideline and development of a model for factors affecting the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand revealed that the factors concerning knowledge of information technology, warehouse management and digital system were well-fitted with the empirical data at statistically significant levels; and 3) The factors concerning knowledge of information technology, warehouse management and digital system contributed to the performance of warehouse management to increase the competitiveness of the ceramic industry in Thailand in terms of speed, time and customer service.

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The purpose behind this research work is to develop a GREEN performance metrics for wider firms. The metrics for wider firms is developed by using the nine independent and one dependent variable. The five independent variables include job position, recruitment, selection, training and development, performance assessment, rewards, team formation, organizational culture management and organizational learning management. The dependent variable is perceived performance. The instruments that are used for data collection include questionnaires and survey forms. The sample size is 200 out of which actual respondents are 150. The SPSS is used for analysis. Regression analysis, descriptive analysis and correlation are run to find the relation and impact of one variable over other and with perceived performance. Findings include the development of GREEN performance metrics for wider firms. The future studies may include the formation of green teams, employee motivation to be green, GHRM in services sector, GHRM and organizational culture management etc.

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Supply chain management is one of the requirements for achieving economic growth in any supply chain. If managers' decisions are optimally allocated, it will be possible for companies and industries with a competitive and profitable advantage to grow and develop. The main desire of any company for survival is to minimize costs and maximize profitability. Due to the increasing complexity and dynamics of the situation, decision-making in this area requires more advanced analytical methods. Accordingly, the Real options theory has emerged, which introduces a new way of thinking about investing, especially in conditions of uncertainty. In this paper, a multi-period model is considered that examines the demand uncertainty in each period and also uses the Real options theory to seek the optimal strategy for investors in conditions of uncertainty and the effect of investors’ discretion on it. Using a decision tree to estimate the probable demand in each period and using Monte Carlo simulations to identify the lowest cost scenario in each period, the model has been solved in this research. In the case of the uncertainty parameter, sensitivity analysis is performed, and under different values ​​of this parameter, the obtained result is evaluated and validated. And the extension of outsourcing will increase the company’s profitability and meet higher demand and lower costs.

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A coalition loyalty program (CLP) is a business strategy employed by for-profit companies to increase or retain their customers. One of the operational challenges of these programs is how to choose the mechanism of coordination between business partners. This paper examines the role of revenue sharing contracts in the loyalty points supply chain of a CLP with stochastic advertising-dependent demand where the program operator (called the host) sells loyalty points to the partners of the program. The purpose of the study is to examine the effect of this coordination mechanism on the decisions and profits of the members of the chain using the Stackelberg game method and determine whether the presence of revenue sharing contracts benefits the chain members when the advertising is done by the host and when the advertising cost is shared between the host and its partners. The results show that when the host gives bonus points to end customers (advertising), revenue sharing contracts become a powerful incentive for the profitability of the host and its partners. The findings provide new insights into the management of CLPs, which can benefit business decision-makers.