مجله مدیریت تولید و عملیات
سال چهاردهم شماره 1 (پیاپی 32، بهار 1402)

Time and cost are significant factors in every project. By reducing the resources allocated to the project, project costs are reduced, while the reduction of available resources means the inability to simultaneously implement activities or activities in the shortest possible time, which in turn increases the duration of the project. This is although, in all projects, the completion of the projects in the earliest time is considered one of the important parameters of the project. Considering the highly practical application of the examined problem, project scheduling by investing multi-skill resources with the possibility of changing the skill level in fuzzy conditions can be considered a positive step towards creating project scheduling problems.                         
Design/methodology/approach: In this paper, the proposed mathematical model of meta-heuristic genetic algorithms to solve the proposed model is discussed and explained in detail. Several skills are needed to perform each activity. The goal is to optimally determine resource availability and find the best schedule by minimizing investment in resources.              
Considering the activities' need for different skills as well as the expertise of the project members in different skills, it seems obvious that each activity can be done with several different situations in terms of human resources allocation, which might be only for one activity, reaching more than 10 modes. As a result, compared to MRCPSP, this issue has a much higher complexity. The Resource Investment Problem (RIP) is a variant of RCPSP where renewable resource constraints are considered decision variables. In many projects, managers, in addition to making decisions about the time of implementation of activities, should determine the number of resources allocated to activities in each period of the implementation of activities according to the status of the project, which means ignoring the constant pattern of resource consumption for activities during their implementation.                          
Practical implications: By comparing the algorithms with the indicators of maximum extension, distance from the ideal solution, distance, and several Pareto solutions, it was found that the multi-objective genetic algorithm performs far better than the multi-objective Cuckoo algorithm regarding the criteria, distance from the ideal solution, and the largest expansion. However, in terms of the number of Pareto solutions, the algorithm is not superior to the other algorithms. Therefore, it can be concluded that the multi-objective genetic algorithm has relatively a better performance than the multi-objective Cuckoo algorithm.           
Social implications: In this research, each activity can be performed with several different situations in terms of human resource allocation, which may reach more than 10 situations just for one activity. As a result, compared to MRCPSP, this issue has a higher level of complexity. Literature review indicates that being multi-skilled increases the productivity, quality, and consistency of work and gives managers more flexibility in work allocation.              
Originality/value: One of the most important branches of project scheduling knowledge is the problem of project scheduling with limited resources. This new concept has led to the development of one of the most general modes of scheduling problems under the title of multi-mode project scheduling with limited resources, which solves many real problems and can be modeled for application.

This research aims to propose an integrative approach for selecting suppliers for Nasr Niroo Engineering Company in Yazd. The proposed model applies value engineering in a type-2 fuzzy environment.

Design/methodology/approach: 

First, the purchase value has been calculated for each supplier using type-2 fuzzy data and the experts' opinions. Then, by dividing the value by the cost coefficient, the purchase value function of each supplier has been calculated for each product. The purchase value coefficients have been determined according to the opinion of experts and the buyers of equipment. In this research, verbal data has been collected from the experts, and out of this data, type-2 fuzzy numerical data has been extracted, calculated, and applied. The influence of type-2 fuzzy numbers in reducing uncertainty in the collected expert opinions has been the cause of such a problem. Following the determination of the purchase value function for each supplier, it has been assumed that quantitative information such as order cost, purchase cost, as well as each supplier's return rate, are also effective in selecting each supplier.

In this study, the subject of supplier selection was examined and solved for four separate product categories - iron, fibre, insulators, and fittings - for which there were two, two. Four, and four different suppliers, respectively. After examining and solving the model considering the purchase value index as well as additional parameters including ordering and purchasing costs, each supplier's capacity, and the rate of return and failure of parts, five suppliers were selected. Two suppliers were selected for iron, and one supplier was selected for each of the other three products.  

Research limitations/implications: 

The application of the proposed model appears to be highly difficult due to the numerous fuzzy number calculations, and obtaining information in this area is one of the limitations of this model. Also, type-2 fuzzy numbers could not be used for mathematical modelling and solution, because no model has been presented yet for the optimal solution of linear programming problems using type-2 fuzzy numbers.

Practical implications: 

The simultaneous use of value engineering techniques or multi-criteria decision-making methods with mathematical models can improve qualitative factors along with improving quantitative factors. This method can be used in project portfolio selection problems and problems with multiple quantitative and qualitative factors as well as different prices and values.

Social implications: 

For reasons such as the utilization of experts' perspectives on the quality factors affecting the selection of suppliers, the model described in this study can help ensure proper supplier selection; transforming the opinions of experts into type-2 fuzzy numbers, which is a practical method for reducing errors and uncertainty in judgments; employing value engineering to analyze viewpoints while keeping in mind the value of purchasing from each provider; using a mathematical model that accounts for costs, the percentage of orders that fail, and defects of order, which may be based on prior experiences or the supplier's announcement. Also, the use of type-2 fuzzy numbers is one of the solutions to face uncertainty in a system.

Originality/value: 

The topic of measuring the value of purchasing from suppliers is concentrated in this study instead of using other indicators such as purchasing risk, as a new indicator. Also, the simultaneous review of quantitative and qualitative data for the selection of suppliers can be considered one of the strengths of the reviewed model. Utilizing indications such as purchase value will be far more effective and better than using multi-criteria decision-making procedures, as seen when comparing this model with other applied models. The use of type-2 fuzzy numbers to lessen the uncertainty associated with using expert opinions and the development of the value calculation method in the area of supplier selection are the two major innovations of this study.

The supply chain management of an organization has a critical role in its success. In the past decades, competition between companies has transformed into the competition between their supply chains. Due to food safety and health concerns in today's society, livestock and poultry medicines are produced and distributed for prevention and treatment. The use of vaccines and timely access to appropriate drugs can reduce disease outbreaks and increase productivity in livestock and poultry industries. The key to success in this matter lies in having an effective and efficient pharmaceutical supply chain. A two-level supply chain of livestock and poultry medicine has been examined in this study. Also, three levels of decisions in the supply chain problem have been considered in this study. The strategic level involves location decisions; the tactical level involves inventory management; and the operational level involves routing. The possibility of drug expiration has been taken into account and sustainability has been also considered due to the drug disposal effects and transportation on the environment. The demand in this network has been assumed to be non-deterministic. Furthermore, quantitative and time-dependent discounts have been considered simultaneously to control the inventory by changing the customers' behaviour.Design/methodology/approach: Three dimensions of sustainability have been optimized by utilizing three objective functions, including minimization and maximization. The economic aspect of a sustainable supply chain is taken into account by minimizing costs. Holding cost, shortage cost, expiration cost, transportation cost, and fixed opening cost have been considered in this study. The focus of the social dimension has been on creating job opportunities and reducing life risks during transportation. Air pollution and greenhouse gas emissions have been considered as the third objective. Robust optimization has been applied to cope with uncertainty. Two meta-heuristic algorithms have been applied to solve the model which has been explained in detail in this paper. A robust counterpart of the multi-objective model presented in this paper was solved by epsilon constraint in CPLEX and by two metaheuristic algorithms, NSGA-II and PBMOSA in the larger size model. The Taguchi settings were applied to both algorithms, and each algorithm was run 20 times with its parameters adjusted to compare efficiency. After comparing 20 executions of the two algorithms using four criteria, it was found that the NSGA-II algorithm performed better. However, there was no significant difference in such an advantage. In addition, the results of the algorithms were compared under deterministic and non-deterministic conditions. In deterministic conditions, objective functions were better, as expected.Practical implications: In this research, a method was proposed to manufacturing companies to plan and make decisions such as distribution locations, appropriate discounts and determining the optimal route. Before this study, in the distribution chain company, only costs and profitability were considered, while according to the concerns of this company, sustainability issues were considered for the first time. Because in this research, location and routing were both considered, social and environmental issues were examined, which are directly related to the issues. Also, before this research, the uncertainty in demand was not considered, which imposed costs on the company. However, by considering the uncertainty, the profit and cost got closer to reality and made the research more practical.Social implications: Since in this paper, the social dimension of the sustainable supply chain was considered as a job opportunity and life risk issue, it seems that this research has a significant social impact.Originality/value: According to the literature, this research ensured the three levels of decision-making in the supply chain, i.e. strategic, tactical, and operational, in terms of location, inventory, and routing. Also, the uncertainty of the supply chain was taken into account, and the expiration of the medicine was considered. Both time-dependent and quantity discounts were considered in the model and all three dimensions of a sustainable supply chain were taken into account.

Today, due to the increase and complexity of customers' needs, the products available in the market do not satisfy the needs of the customers. Hence, the most effective way for an organization to achieve its objectives is to create distinction through both developing new products and bringing about variations in those products. This study, while pursuing the objective of evaluating product development design, aims to propose a multi-objective design. This model analyzes the developed design from two perspectives of general criteria and variation capacity norms.
Design/methodology/approach: The current research embarks on identifying general criteria using Balanced Scorecard (BSC). The proposed model offers criteria according to which the required standard measures for the evaluation of the product development design are customized and extracted. After determining the evaluation criteria related to each perspective – general and variation capacity- the mentioned criteria have been validated. Then, by using the existing methods, these criteria have been quantified. Finally, the identified standard measures have been employed in the proposed multi-purpose optimized model. 
In this paper, a real case was studied in the Iran Electronics Industries, and the result of the configuration was illustrated. Quantitative values ​​of validated criteria were determined with the help of academic and industry experts and existing methods. After calculating the quantitative values ​​of the criteria for the evaluation of phased array radar development designs, these values were used in the multi-objective optimization model and measured in terms of the amount of risk, revenue, organizational strategy and the amount of effort towards redesign. Finally, by solving the optimization model using GAMZ win 64 25.1.12 software and the weighted sum method, the best development plan was selected.
Research limitations/implications: The main limitation of this study is the lack of accurate information in the early stages of product development. In this research, it was assumed that the information provided by the experts and designers of the relevant industry is correct and real. It is suggested to use fuzzy data in future studies so that the result has more reliability. For future research, the following subjects can be attractive and the present study can provide the necessary background for researchers who seek to work on such subjects: i) In the current research, the time parameter was not considered. It is suggested to consider time in future research; ii) to identify non-common components between the two current and future generations, the method of calculating the priority of standardization of components and considering the technical and financial ability of the producer was used. It is suggested to decide according to possible technologies in the future; iii) another perspective can be used to categorize and identify criteria; iv) to calculate reliability and maintainability, the lifetime of systems was assumed to be exponential and with a constant failure rate. It is suggested to use Weibull distribution with variable failure rate for a more realistic calculation, and v) it is suggested to compare the results of this study with other studies.
Practical implications: Finding comprehensive criteria to evaluate product development designs and providing an effective mathematical model for evaluation can lead to selecting the best product development designs. Selecting an optimal design can prevent the increase in product modification costs and the effort to redesign and reduce the time to supply new products to the market.
Originality/value: Based on the literature review, particularly the internal research, a method that evaluates product development designs with comprehensive criteria including finance, customer, business and internal work, growth and learning, and variation capacity has not been found. Also, the Design for Variety approach has not been used in the initial phases of research and it is applied when the product is made and released to the market.

This study aims to investigate a supply chain problem of operating room consumable items that are not reused after consumption. In this supply chain, maximizing the satisfaction of the surgeons and minimizing the total costs are considered. Also, due to the importance of choosing suppliers from the surgeons' point of view, it is possible to prioritize suppliers based on criteria such as quality and cost. Furthermore, to get closer to real-world situations, uncertain demands of patients due to their physical conditions and various diseases, the capacities of the pharmacy, operating rooms, and the sterile core used for sterilizing the non-sterile items have been considered. The scope of this research includes different operating rooms, and the initially required number of consumable items according to the opinion of the surgeon. If an emergency occurs during the operation (such as sudden bleeding, item failure, or operating room personnel error) and the patient needs more items, the nurse goes to the hospital pharmacy to get the necessary items and brings them to the operating room, during the operation.
Design/methodology/approach: In this research, due to the uncertain demand for consumable items in the operating room, three pessimistic, probable, and optimistic scenarios have been used; and due to the discreteness and uncertainty of the data distribution, Mulvey's robust method has been applied. The problem has been solved in two phases. In the first phase, the additive ratio assessment (ARAS) multi-criteria decision-making method has been used to prioritize suppliers, and in the second phase, according to the size of the problem, the epsilon-constraint method, for the small-sized problem, and Non-dominated Sorting Genetic Algorithms (NSGA-II) and Multi-Objective Harmony Search (MOHS) for large-sized problems have been used to minimize the total costs of the supply chain, and maximize surgeons’ satisfaction. In addition, to set the parameters of both meta-heuristic algorithms, the Taguchi method, which is one of the most well-known parameter-setting methods, has been used.
To compare exact and metaheuristic algorithms, 10 examples were designed randomly. The comparisons showed that the results of the epsilon-constraint method were better than the meta-heuristic algorithms but it could only solve small-sized problems, and it required more time as a sensitive influencing factor in operating room planning. Also, to analyze the NSGA-II and MOHS algorithms, the obtained results were examined from the perspective of solution time, Number of Pareto Solutions (NPS), Mean Ideal Distance (MID), Diversification Metric (DM), and Spacing Measure (SM) indicators. They were also compared with each other using statistical hypothesis tests. The results showed that such algorithms had a significant difference from the point of view of the NPS and DM indicators at the significance level of 0.05, but they did not differ much in terms of the other two indicators. However, in terms of solution time, the MOHS was more suitable than the NSGA-II algorithm.
Research limitations/implications: One of the limitations of this research is the collection of real-world data, especially in estimating the demand for each item according to different conditions.
Practical implications: Comparing the NSGA-II and MOHS algorithms using different indicators, especially solution time which is significant for operating room planning, MOHS algorithms were better than the NSGA-II.
Social implications: Using the proposed algorithms, hospital managers can reduce total costs, guarantee the quality of consumable operating room items, and increase the satisfaction of the surgeon, who is in charge of providing better services to the patients.
Originality/value: In this paper, two meta-heuristic algorithms were proposed for non-deterministic supply chain planning for consumable operating room items, considering surgeon satisfaction and cost, and their efficiencies were compared with each other. The two-mentioned algorithms have not been used in previous studies. Both academic researchers and hospital managers can benefit from applying the findings of this study.

The increasing complexity of omnichannel retailing has necessitated retailers to redesign processes and forecasting methods and accept new approaches based on machine learning and artificial intelligence. Improving the accuracy of demand forecasting and managing customer needs from different channels due to reducing demand uncertainty are the most important challenges in omnichannel retailing that retailers should deal with. A better understanding of consumer behaviour patterns leads to more accurate demand forecasting, which in turn helps gain insight into transportation flows, improves distribution management, and enables better planning and execution of supply chain operations. This study aims to reduce the uncertainty of demand in omnichannel retailing by improving the accuracy of demand forecasting by considering customers buying behaviour through using machine learning methods.
Design/methodology/approach: In this study to forecast future sales based on customers buying behaviour, a cosmetics retailer’s historical data on the monthly sales from February 2020 to June 2022 is used. The ID of eight products has been selected to analyze the performance of proposed methods and the method that the company applied to forecast demand. Clustering has been implemented using the dynamic time-warping algorithm due to the unequal length of the products’ time series. Initially, the nonlinear autoregressive neural network (NAR) has been applied to the time series in each cluster and later, the nonlinear autoregressive neural network with exogenous input (NARX) has been applied to the time series. The performance of the methods has been evaluated by testing R-squared and all R-squared coefficients and root mean square error (RMSE) to analyze the accuracy measure.
The forecasting methods comparison, moving average (MA), the nonlinear autoregressive neural network (NAR), and the nonlinear autoregressive neural network with exogenous input (NARX) concerning testing R-squared coefficient, and also all R-squared and RMSE indicated that the nonlinear autoregressive neural network with exogenous input presented a good performance for all the products, so it confirmed that the application of the clustering to identification customers buying behaviour through the sales history of the products, integrated with artificial neural networks, to conduct demand forecasting, could be considered a good method for forecasting demand of omnichannel retailing supply chain products.
Practical implications: The proposed method of this study leads to uncertainty reduction in omnichannel retailing by understanding the buying behaviour of customers, identifying patterns and using its analysis in the processes and operations, and its integration with machine learning methods improves distribution management and provides better planning and implementation of supply chain operations. Managers can use the proposed method to accurately predict complex demand patterns in the retailing industry. Using business data in demand planning provides an extra advantage to managers to include important variables based on their judgments.
Social implications: Knowing the factors affecting the sale of a specific category of a product helps to effectively design promotions, advertising campaigns, the optimal combination of category displays and optimization of shelf space in retail stores. Also, accurate demand forecasts lead to better ordering policies, thus minimizing the cost of inventory management and optimal distribution and logistics planning to meet future demand.
Originality/value: The proposed method presents a predictive approach for an omnichannel retailing supply chain that leads to uncertainty reduction in omnichannel retailing by understanding the buying behaviour of customers, identifying patterns, and using its analysis in the processes and operations and its integration with machine learning methods to improve distribution management, and provides better planning and implementation of supply chain operations.