Journal of Industrial and Systems Engineering
Volume:14 Issue: 4, Autumn 2022

Nowadays, in the competitive global market, increasing market share is the main objective of the most manufacturers, however, customization, service speed, customer satisfaction, and environmental problems are vital factors that manufacturers ought to consider to expand their market share.so, the supply chain management can be applied as a proper approach to optimize these factors in whole supply chain to benefit the supply chain members. In this way, the current paper addresses an integrated production and distribution model with combination of Stackelberg competition and Make-to-order production system in different periods. In addition, this model wants to investigate how discounts impact the chain's profits with presence of competition and Make-to-Order production system. This study uses a modified Non-Dominated Sorting Genetic Algorithm II (NSGA-II) approach to solve the medium and large cases model because of the NP-hardness feature. Additionally, the model is applied to Furniture Company to demonstrate its efficacy and validity and results are provided. According to the obtained results, the modified algorithm has better performance in solving model in medium and large-scale cases. The proposed model would be beneficial to increase network efficiency by integrating production-distribution planning.

Recent advances in manufacturing systems and multifunction machines have caused products to be produced through several alternative process plans. Therefore, the integration of process planning and scheduling, as two of the most critical functions, becomes essential to enhance manufacturing systems’ productivity. Several different algorithms have solved the integrated process planning and scheduling (IPPS) problem in the literature. All proposed algorithms require a list of available process plans in advance (type-1). In this paper, an efficient mixed-integer linear programming (MILP) model is presented based on the term "combination." Besides, a type-2 priority-based heuristic algorithm (PBHA II) is proposed using dispatching rules with prioritizing jobs, combinations, and operations to solve the IPPS problems expressed by AND/OR graphs and with a makespan criterion. The MILP model and proposed heuristic algorithm are tested on the most challenging benchmark problems. Experimental results show the superiority of the MILP model over the best one in the literature, as well as the effectiveness and high performance of PBHA II. New upper bounds have been obtained in a short computational time for 7 of 24 most complex problems, which have been used by many researchers over the last two decades.

Today, the uncertainty in the estimated time and cost of industrial projects is considered as an important challenge in the science of project management. If risk management is done regularly to identify potential problems and find their solution, it will easily complement other processes such as organizing, planning, budgeting, and cost control. In this regard, one of the most important and effective solutions to this problem is risk analysis (primary, secondary, and residual). In this research, an optimization model has been proposed to select actions to respond to risk for all three primary, secondary and residual risks. This research is quantitative. In building the model, the objective function is to minimize the total risk costs and the costs of reducing the time constraints applied to the relationship between two activities. Then, by determining a suitable reasonable time for the whole project and solving the model, an optimal set of actions to respond to the risks is determined. The basic innovation of this research, which does not cause the selection of a predetermined strategy, is the two limitations that examine the two dimensions of time and cost in response to primary and secondary risk. The results indicate that the initial risk costs have decreased. Also, by responding to the primary risk, secondary risks were created, which imposed a cost on the system, but this cost was reduced by assigning secondary strategies, as well as the optimal cost of activity failure with the sensitivity analysis that was done, the maximum amount of time that the project can end It was equal to 78 days and more than that makes the cost of failure of activities to be zero. Also, in this research, the genetic meta-heuristic algorithm and the Particle swarm algorithm were used to solve the problem in high dimensions, and the results showed that there is no difference in the results of these two algorithms.

In some statistical process monitoring applications, the quality of a product or process can be determined by a linear or nonlinear regression relationship between a response variable and one or more explanatory variables called "profile". Sometimes, it is possible to describe the quality of a process or product by several simultaneous profiles in which the response variables are interdependent and are modeled as a set of linear functions of one explanatory variable, that is referred to as multivariate simple linear profile structure. In this paper, we propose a new method for monitoring phase II of multivariate simple linear profile. In this method, namely MHWMA/EWMA, a MHWMA control chart and an EWMA control chart based on generalized variance are used for monitoring the differences between the reference profile and the sample profile. Using the Mont-Carlo simulation, the statistical performance of the proposed method is evaluated in terms of the average run length criterion. The obtained results showed that the method effectively detects shifts in the profile parameters. The proposed MHWMA/EWMA method is compared with an existing method, and the results showed that this method has a good performance in detecting different types of shifts in profiles' parameters. In addition, the applicability of the proposed methods is illustrated using a real case of calibration application.

Digital transformation plays an increasingly important role in shaping the current business landscape and contributing to economic development. Using digital platforms and various technologies, companies can compete on a global scale with the help of digitization and industry 4.0 technologies. Global firms are benefiting from the rise of the digital economy. Facebook, Alibaba, and Uber compete in a new multi-sided platform world. Businesses such as media companies, banks, and software companies are among the many multi-sided platform firms that serve distinct groups of customers connected by interdependent demand. In this study, we analyze price-setting behavior in a multi-sided platform. As a well-known result of tax incidence by this article, consumers of more heavily taxed goods pay a higher price and buy fewer goods. A multi-sided market does not necessarily hold this result based on our findings. A higher ad valorem tax may actually lower end-user prices and boost sales. Due to this fact, multi-sided platforms may not engage in tax shifting by raising prices. The analysis is followed by a real-life case study of a diet application. The results and discussion are then presented for the proposed real-life case study.

The main purpose of this study is to identify and determine the most important sub-tasks of stockbroking that affect the reliability of human resources. The cognitive reliability and error analysis (CREAM) method has been used to calculate the human error. To consider the different effects of work condition factors for each condition performance common (CPC), they are weighted using the decision analytical network process (DANP) method. The highest amount of the detected errors related to execution error, interpretation error, and planning error are 67%, 25%, 8%, respectively and probability of total cognitive error in the task of "stockbroker" is 0.1414. Considering equal impact for all CPCs on performance reliability is the most important gap and limitation in most previous studies. In this study, the relationship between CPCs has been investigated using the DANP.  Moreover, the relationship between HEP and the work environment error are calculated by humans with the Napierian logarithm function.

In recent years, blockchain technology changed supply chain processes enormously. Moreover, transparency and traceability became necessary in supply chains due to customers’ need for more information on services or products. This paper attempts to ascertain transparency in a joint pricing and sustainable closed-loop supply chain network design problem using blockchain technology. To assure supply chain transparency, the pricing process is done using smart contracts. Smart contracts can modify malfunctions while purchasing returned products from customers. Then, using the derived prices of adopting smart contracts, the optimal design of the closed-loop supply chain network is obtained in an optimization process. Afterward, a fuzzy satisfying approach is used to find the optimal solution among economic, social, and environmental objective functions. Then, the model is evaluated using a numerical case problem. Sensitivity analyses are explicitly done to show the impacts of considering a blockchain-based method, production, and distribution capacity expansions, and sustainability concerns in the proposed problem. It is also shown that implementing a blockchain-based method delivers %5 more profit on average. It is also proved that expansions in production capacity are approximately %15 better than increasing distribution capacities. Finally, it is demonstrated that the fuzzy satisfying approach can deliver an optimal solution maximizing the minimum satisfaction of each objective function.

Data envelopment analysis (DEA) is a data-oriented approach to assess the performance of a set of entities known as decision-making units (DMUs), which transform multiple inputs into multiple outputs. On the other hand, the transplantation of organs is one of the most complex and challenging treatments in medicine, and organ allocation is the most important decision throughout the organ transplantation operation. Due to the enormous disparity between organ availability and demand, many individuals die while waiting for organ transplants despite major medical and technological improvements. Furthermore, kidney is the most commonly transplanted organ in the transplantation supply chain all over the world which is investigated in this paper. This research presents a two-stage network DEA model for assessing the efficiency of related DMUs. The main advantage of this study is considering network DEA with internal structures instead of black box DEA models in organ allocation problems. It should be noted that black box DEA models fail to present sufficient data for identifying the inefficiency of DMUs. In addition, it is unclear what occurs within the black box DEA models, and internal relations are not investigated. Finally, a real case study related to the organ allocation problem is presented, and the findings indicate that the proposed method in this study is strongly effective and outperforms the current kidney allocation system in Iran.

Process capability indices (PCIs) are developed to assess process performance based on the specification limits (SLs) provided by customer. Sometimes the quality of a process or product is characterized by a regression relationship between a response variable and one or more independent variables referred to as "profile". On the other hand, modern production systems often involve multistage manufacturing processes, in which the output of one stage is the input of the next stage. This property is known as the cascade property. Due to this property, the capability in each stage is dependent on the capability of the preceding stages. This study provides an approach to assess PCIs in a multistage process when the quality characteristics of interest are represented by multivariate linear profiles. Process performance is specified based on profile intercept and slope parameters. In other word, in addition to PCIs of the response variable in each stage, the PCIs of profile parameters are also investigated. By using the SLs of the response variable and considering in-control profile, the SLs for intercept and slope can be obtained. Therefore, PCIs for profile parameters can be computed. The results indicate that the proposed method eliminates the effect of the cascade property for different autocorrelation values. Simulation results reveal satisfactory performance of the proposed method for a two-stage process.

This paper presents an empirical investigation to study the effect of interest rate, inflation and gold price on stock price by considering the historical monthly prices from October, 2021, to October, 2022. The study also uses the prices of Exxon Mobil Corporation as the biggest oil producer in the United States for the same period. The study uses monthly 10-year Treasury-Yield, inflation and gold price for the same period. The study uses multiple linear regression and finds that two independent variables of inflation and interest rates influence oil price positively while gold price has no meaningful effect on oil price. In our survey, Interest rate is the most determinant of the price of Exxon Mobil share price (β = 11.66, t= 9.22) followed by Inflation (β = 6.506, t= 15.69) when the level of significance is one percent. The survey also investigates OPEC+ as well as the United States government actions during the study and finds minor influence on share price. The results indicate that any short-term interruption of oil price may lead to short to medium term on share price increase while the effect appears to have reverse effect on economy and slows down the world’s economy and in longer periods causes oil price reduction.

According to the conducted research, oil and gas industry projects have many complexities and uncertainties, and investment in these projects is associated with high risks. In this research, while identifying the most critical risks that have an impact on investing in oil and gas projects, they have been identified in the first place. Then, the importance of each of the specified criteria is determined. To achieve the aforementioned goals, modern computing methods have been used. In the phase of identifying factors from fuzzy Delphi; In the importance and prioritization stage, multi-criteria decision-making methods are used, and in the allocation stage, multi-objective mathematical modeling is used. Therefore, first, a list of 21 investment risks in industry and gas was collected by reviewing the literature and research backgrounds. The collected risks were refined and finalized using the fuzzy Delphi approach. Finally, the risks of sanctions by an institution or country, liquidity, health risks (such as the corona epidemic), financial potential, exchange rate fluctuations, and sudden changes in inflation as risks. considered in this research. Then, considering factors such as quality, cost, technology, time, and information preparation as indicators influencing the occurrence of considered risks, their importance has been determined using the best-worst method. According to the weight calculated for each of these factors, respectively equal to 0.23; 0.09; 0.52; 0.07, and 0.09 are estimated. Then, according to the importance obtained by using the GRA-VIKOR approach, the risk ranking was determined by considering the factors affecting them. Finally, by using the three-objective linear programming model with the objectives of maximizing the level of quality, minimizing cost and time, and solving it using the epsilon-constraint method, an appropriate response strategy is determined for each of the considered investment risks.

In evaluating projects, there are many qualitative criteria, weighting, and quantifying, which have no definitive nature and are associated with various ambiguities. Also, because of the relationship between these conflicting criteria (goals), no single and multip optimal solutions (non-dominant set) should be sought. Because of the relationship between these inconsistent criteria (goals), no single and multiple optimal solutions (non-dominant set) should be sought. Accordingly, this study aims to provide an appropriate approach to develop a model for selecting construction projects in the public sector based on a mathematical multi-objective fuzzy model, which can cover the multi-objective nature of the problem and consider inherent inaccuracies and problem uncertainties. This paper first converts the model to a non-linear model by fractional planning concepts, defuzzification according to Jimenez and Yang approaches, then solves by a non-dominated sorting genetic algorithm (NSGA-II) to provide a more comprehensive model for governmental project selection public when allocating budget. This paper is attempted to develop a new model for selecting construction projects while considering the uncertainty of parameters using fuzzy theory in the public sector to show the performance of the developed model. The fuzzy model solution is compared with the deterministic model to analyze the results. The results show the improvements reflect the success rate of accomplishment for the corresponding goals in the fuzzy model compared to the exact one.

The competitive environment in the global market makes most countries look for better ways to solve problems in order to earn more money. One of the strategies proposed as a competitive one is to use a stable closed loop to improve performance. The present study, which has not reported any research in this field, proposes a multi-level sustainable chain-loop supply chain (SCLSC) network for pomegranate fruit. The mathematical model has been designed with the aim of offering the lowest price, the amount of response received and the reduction of costs. Our study distinguishes itself from other studies by considering the costs of using artificial intelligence in the production chain and in the reverse logistics sector, converting pomegranate waste into recycled products including ethanol for car fuel and organic fertilizer production. In order to examine the research gap and approach real-world applications, an applied example in Iran has been studied. Also, NSGA-II and MOPSO algorithms are used to solve the model, and in the new solution method, the HSA&TS multi-objective hybrid algorithm is proposed. In addition, in the comparison of algorithms, indicators in the one-way variance analysis table, the best time is . Therefore, the practical result show that the combined development algorithm of HSA&TS is a suitable technique and it is superior to other selected methods, it is also recommended, usable and implementable for the development of the logistics network.

Risk assessment is a part of risk management. There are different techniques for risk assessment. Monte Carlo simulation (MCS) is one of the quantitative methods for risk assessment. Extended MCS tried to solve the weakness of classic Monte Carlo simulation through using rotary algorithm. But in the real world, projects face with different risks simultaneously. Co-occurrence of risks cause to exacerbates or diminishes the effects of each other. In this paper, the effect of occurrence of other risks on one risk in each iteration is investigated and finally the utility function is calculated by considering co-occurrence of risks and rotary algorithm. The proposed model tested on petrochemical construction project data. In this project, six risks such as inflation rate, labour, temperature, rain, cost and time are identified through experts’ opinion. The results show that the utility function become closer to reality by considering the co-occurrence of risks.

The linear fractional bi-level problems are strongly NP-hard and non-convex, which results in high computational complexity to find the optimal solution. In this paper, we propose an efficient algorithm for solving a class of non-linear bi-level optimization problems, where the upper and lower objectives are linear fractional. The main idea behind the proposed algorithm is to obtain a single objective optimization problem via Taylor approximation. The proposed algorithm is composed of four steps. In the first, the lower level of the problem is converted into the convex optimization problem by using auxiliary variables and approximation techniques. Next, a single objective optimization problem is obtained by adopting the dual Lagrange method and Karush-Kuhn-Tucker (KKT) conditions. The obtained problem is non-convex with high computational complexity challenging to solve. Hence, the Fischer-Burmeister function is applied to smooth the problem. Finally, the first-order Taylor approximation is adopted to transform the non-linear problem into the linear one. Numerical results confirm the effectiveness of the proposed algorithm in comparison with Estimation of Distribution Algorithm (EDA) in terms of convergence performance.