مجله مدیریت تولید و عملیات
سال سیزدهم شماره 1 (پیاپی 28، بهار 1401)

Today, the manufacturing industry, with the expansion of the physical constraints of trade worldwide, has adopted modern information technologies to optimize the business process and achieve integration with geographically dispersed supply chain partners. Traditional supply chain models focus on optimizing physical flows. However, it is equally important to ensure that physical units can process appropriate information. This paper aims to propose a model for the optimization of information process performance in the IoT-based virtual supply chain. 

Design/methodology/approach:

 In this study, information processing performance in the closed-loop virtual supply chain has been optimized to maximize profit and information processing speed by considering costs of virtual, information security, and energy consumption. The final programming model has been optimized using meta-heuristic algorithms, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), and the Strength Pareto Evolutionary Algorithm (SPEA-II).

Findings:

 The results indicated that there is an inverse relationship between virtual supply chain profit and information processing speed (delay). The results of model solving using NSGA-II and SPEA-II algorithms underlined the virtual supply chain profit of 9.93×106 and 4.23×106, and the data processing speed of 337.48 and 94.07, respectively. Thus, the NSGA-II algorithm contributes more to the supply chain profitability.

Research limitations/implications :

The proposed model can be used in manufacturing industries equipped with IoT. Unavailability of practical examples and insufficient data are the two main limitations of the study.

 Practical implications:

 The proposed model improves the production process and helps managers to plan better for their supply chain management and make timely decisions by sharing information across the supply chain and being aware of the flows of products and associated parts.Social implications - The Internet of Things in the virtual supply chain provides an opportunity to manage logistics systems and results in efficient online delivery with minimal cost. The information flow integrates all links and participants in the virtual supply chain. It enables each member to obtain the accurate information needed for logistics capability, reduces resource wastage, and improves customer satisfaction. 

Originality/value: 

One of the innovative aspects of this research is the use of IoT in the virtual supply chain for the integration and transparency of information in the supply chain, considering the importance of information in the virtual supply chain and examining the impact of IoT usage on closed-loop virtual supply costs and target functions. In addition to considering the physical flow costs of the closed-loop, including production costs, separation costs, repair, disposal, recycling, etc., in the cost objective function, virtual flow costs included IoT usage costs and information security costs. Energy consumption was also included in the objective function. Also, due to the virtualization of the supply chain and the significant role of information, optimization of information processing speed was considered in modeling the supply chain performance, which is another innovative aspect of research.

The fundamental function of a cellular manufacturing system (CMS) is based on the definition and recognition of the type of similarity among the parts that should be produced in a planning period. Cell formation (CF) and cell/machine layouts are two major steps in implementing the CMS design. This paper aims to propose a new mathematical nonlinear programming model for cell formation that employs the rectilinear distance concept to determine layout in a continuous space. In the proposed model, the benefits of cellular layout consideration are used, and the objective function computes the cost of cell reconfiguration and the costs of intra-cell and inter-cell material handling movements. Due to its problem complexity, a genetic algorithm (GA) and a particle swarm optimization (PSO) algorithm are proposed to solve the problem. To address the efficiency of the linearized model and solution methods, the production information of a real case study is used and 30 test problems in different dimensions are presented.

 Design/methodology/approach:

 In this paper, a mathematical programming model of cell formation and cell layout has been proposed in a continuous space, using the concept of rectilinear distance. In the proposed model, production information similar to the production flow between machines, alternative process routing, cells capacity, and the inter-cell and intra-cell transportation costs has been considered. Due to the nature and complexity of the proposed model, two metaheuristic algorithms, i.e., GA and PSO have been also developed for larger problems. 

In this paper a real case study in the BATA company was studied and the result of configuration was illustrated. By computing the efficiency of the linearized model and solution methods, the production information of a real case study was used and 30 test problems in different dimensions were presented. Findings highlighted the high efficiency of the genetic algorithm in solving large-scale problems. 

Research limitations/implications:

 Issues such as assuming similar dimensions for machines or their constant availability are considered as the limitations of this study. 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:Considering unequal dimensions for machines; in the proposed model, machines were considered as squares of equal area with unit dimension. To obtain a more appropriate schema from the space of a job shop, machines’ dimensions can be assumed as the input parameters.Developing probabilistic models and fuzzy model factors (e.g., available machines, operation time, costs, transportation time, and demand for each part) can be considered fuzzy or probabilistic.To make the model more realistic, production data such as setup times and holding inventory between periods can be incorporated. Also, the proposed model can be integrated with the scheduling problem. 

Practical implications:

 CMS, which is the most important application of GT, overcomes the inefficiency of traditional approaches by reducing transportation time and distance. A flow shop layout has high efficiency in a mass production system, while a job shop is a very flexible system for producing various parts. Each of these systems does not have any other benefits. The CMS is an approach between the two manufacturing systems and aims to improve flexibility and efficiency to produce manufacturing groups in different sizes. In a CMS, machines and parts assignment to cells must impose a minimum cost on the system. Due to the practical nature of the proposed mathematical model, a real case was studied in which, the production information of the BATA company was used. 

Originality/value: 

In this study, a new non-linear mixed-integer programming model was proposed which considered the simultaneous cell formation and intra-cell and inter-cell layouts in a continuous space. The model aimed to determine the cell formation and the intra and inter-cell layout concurrently in a way that the total transportation cost of parts and the reconfiguration cost of cells were minimized. The proposed model attempted at calculating the material handling costs realistically. The material handling cost was computed based on the actual location of machines and cells on the shop floor regarding the dimensions of equal-sized machines. Handling both intra/intercellular materials using batch sizes for transferring parts was taken into account in calculating the transportation cost. The transportation cost was calculated based on the rectilinear travel distance, according to the center-to-center interval among machines.

The world's electricity industry has faced numerous challenges, such as rising electricity demand and greenhouse emissions, declining fossil fuel reserves, economic conditions, and rising costs. Such challenges have forced managers to supply energy by replacing fossil fuels with renewable energy sources. The technology advancements and electronic equipment in the consumption sector have increased the need for electricity. Hence, electricity generation has become more significant due to the type of power plant. This study aims to prioritize electricity generation by allocating it to the consumption sector, to reduce greenhouse gas emissions.

Design/methodology/approach:

 In this study, a mathematical has been proposed to determine the amount of electricity production from power plants with different economic, technical, and environmental conditions. Then, the optimal electricity allocated to various sectors (as consumers), such as household, commercial, transportation, industry, and agriculture has been examined. By simulating and proposing a linear programming model, regarding the energy data in 2017, 10 types of the power plant and energy balance have been taken into consideration. Based on a mathematical model and by considering three decision-making variables, while observing the limitations and requirements of the power plant, the amount of electricity generation has been determined. Then, the energy system in the consumption sector has been simulated by causal diagrams using the system dynamics approach.

The lack of fossil fuels and environmental pollution associated with energy development are challenging issues. Fossil fuel production and consumption contribute to global warming and acid rain. Therefore, one solution to protect the environment from the proliferation of energy waste and its consumption is the effective planning of energy systems. Findings indicated the role of proper planning and allocation for energy in the consumption sector, in reducing greenhouse gas emissions.

The lack of comprehensive and accurate data on the application of renewable technologies to generate electricity in Iran is one of the main limitations of the empirical study. One of the limitations of the proposed model is the consideration of renewable and non-renewable sources, simultaneously. Electricity from renewable fuel has also been less noticed.

Practical implications:

 Based on the findings it is concluded that the mathematical model with more comprehensive indicators and the system dynamics model can play a significant role in reducing greenhouse gas emissions. Therefore, the following strategies can be used to reduce greenhouse gas emissions nationwide:- The percentage of electricity generation from renewable energy sources should be increased and the use of fossil resources to generate electricity should be reduced, which is evident from the application of the mathematical function of the model.- Electricity generation from power plants based on the specified priority will significantly reduce greenhouse gas emissions.- According to the calculation of the environmental index, issuing construction permissions for coal and gas power plants should be prevented.- The needs of the domestic and commercial sectors must be met through renewable energy sources.

Originality/value:

 In this study, mathematical planning and dynamic system were used to study technical, economic, and environmental conditions in the reduction of greenhouse gas emissions. Considering the existence of objective functions in the mathematical model and the optimal results obtained from the production of 10 hypothetical power plants in this study, it is implied that the optimal production in solar, wind, combined cycle, heater, water, and natural gas centers has potentials for capacity expansion.

Today, the ever-expanding supply chain of the food industry has made controlling and turning the slogan "Food Security: From Farm to Table" a difficult task in reality. Blockchain technology is a new and emerging technology that ensures transparency, traceability, and data security in the movement of food products from the farm to the table, promises to improve food security in supply chains, where fraud and counterfeiting have become integrated. Therefore, the use of blockchain technology in the food supply chain, both now and in the future is an undeniable necessity. One of the requirements for using any technology is to identify barriers to its progress. The lack of accurate knowledge of such barriers will not only lead to failure to properly implement this technology in the food supply chain but will also impose considerable costs on it. Therefore, this study has been conducted to identify the key barriers to using blockchain technology, and develop a hierarchical model in the food supply chain.

To achieve the research objectives, first the most significant barriers were identified and then by Interpretive Structural Modeling (ISM), a model was proposed to illustrate the barriers' interrelationships.

The results indicated internal and legal barriers as the most significant barriers to the use of blockchain technology in the supply chain of the studied organization. Therefore, to use this technology, the main focus of managers should be on removing barriers to lower the levels of the hierarchical model.

Research limitations/implications:

 Given the novelty of blockchain technology, particularly in implementation, it should be mentioned that a relatively good understanding of this technology has not been formed in Iranian organizations yet, and the use of experts who have a deep knowledge of this technology has been one of the main limitations of the research. Another limitation was the lack of access to the supply chain suppliers of the Etka organization. This research was done only based on the opinion of experts in the central part of the supply chain, although the results can be used in the supply chain of Etka organization and other similar organizations.

Originality/value: 

The role and importance of using this technology in the food supply chain of Etka organization, as the largest organization in the food supply chain of the armed forces, and promoting food security of the armed forces are not deniable. Since no research has been conducted on this subject in the country, this study was compiled to identify the main barriers to using this technology in the food supply chain of the studied organization. This paper also attempted to prioritize the barriers for possible elimination in the form of a hierarchical model and facilitated the application of this technology in the food supply chain of the Etka organization.

The main aim of inventory control and production planning problems is to optimize the economic quantity of the order or determine the size of the production batch according to the capacities and limitations to minimize the total costs related to the order, purchase, maintenance, and delivery. The Economic Order Quantity (EOQ) model has been widely used to determine the order size or purchase of parts in production systems. Simultaneous consideration of the time and amount of ordering goods and minimizing system and customer costs is the main concern of inventory management. The assumptions of the classical EOQ model do not cover all inventory control systems in real terms. Leaving aside some of the assumptions, this paper aims to optimize and develop the EOQ model. The inventory system of this paper includes products that are capable of growing during the replenishment period, such as livestock. Also, it is assumed that the products of this system have a stochastic demand and a certain part of them has a lower desired quality. Newborn items are also ordered live and fed until slaughtered to the customer's desired weight and then slaughtered. Before all slaughtered items are sold, these products are screened to distinguish high-quality items from lower-quality items. To determine the optimal inventory policy, a model is proposed in this paper to maximize the expected total profit.

Design/methodology/approach: 

The studied inventory system examined the situation in which a company orders a certain number of items, e.g., chickens, that are in stochastic demand and are capable of growing over time. To maximize its total profit, the company should determine the number of goods that can be ordered at the beginning of a growth cycle. Total profit was defined as the difference between total revenue and total cost. Total revenue included revenue from the sale of items of good and lower quality, and the total cost included the total cost of purchasing, feeding, maintaining, setup and screening. The proposed model addressed two questions related to the order quantity and the order time. The objective function of the model was the expected total profit, while the decision variables were the batch size and cycle time, given the constraint that the total growth period and the facility setup time must be less than the consumption period.

In this study, a model of growing economic order quantity was proposed, which was expressed using a hypothetical numerical example. It was assumed that there is a company that buys day-old chicks, feeds and breeds them until they reach the desired weight, and sells them after checking the quality. Given sample quantities, the company should order 175 newborn items at the beginning of each cycle. Newborn items should grow in a period equivalent to 0.0941 years (34 days) and a period of consumption equivalent to 0.1928 years (70 days). The order must be registered every 0.1928 years (70 days) and the company expects to earn 42.2460 monetary units, annually. Quality screening should begin immediately upon consumption and occur over a period equivalent to 0.0499 years (18 days), after which imperfect quality items should be sold in a single batch.

Research limitations/implications: 

The proposed model can be extended by adding variables such as inflation, trade credit financing, allowable shortages, breakdowns, and quantitative discounts. Also, in the inventory system of this study, it was assumed that the screening process was 100% effective in separating items of good and lower quality. This issue, together with the learning effects on the screening process, suggest other potential areas for further model development.

Originality/value: 

In this paper, the assumptions of the classical model about the non-growth of items, good quality and equal to all products, and deterministic demand were discarded. Also, due to advances in technology and the existence of competitive markets, it was not possible to determine the exact amount of demand for firms. Considering stochastic demand as a way to deal with this uncertainty was inevitable. Therefore, it seems necessary to study this issue and propose a solution to remove the existing barriers in a way that in addition to providing the right amount of order, the profits of companies become maximized. The results of this study can be useful for all cases and units that have growing items and help to issue the right amount of orders, resulting in lower costs and higher profits.

One of the topics for manufacturers is to discuss the diversity of customer tastes. To manage this situation with the least change in products, multiple assembly lines make the necessary flexibility to produce the products. In multi-model assembly lines, different product types in different batches are produced and there is a setup time to prepare assembly lines between two types of products to produce another product type. This paper aims to investigate multi-model assembly lines and their sequencing, balancing, and worker assignment due to the existence of various tasks for workers according to learning and disremembering effects. Frequent changes in the product design of multi-model assembly lines according to customer demands can reduce the learning effect of workers and increase task times, while in another view, repeating tasks, particularly for products with more demands can increase the learning effect and reduce the task times. Therefore, in this study, the effects of workers' learning and disremembering multi-model assembly line balancing, sequencing, and worker assignment are investigated to minimize the number of workstations for a given cycle time not only to cover the different tastes of customers, but also indirectly minimize the costs of building stations, hiring, and employing manpower.

Design/methodology/approach:

 In this paper, as an innovation, a mixed-integer mathematical model for multi-model assembly line balancing, sequencing, and worker assignment with different workers' skill levels and learning and disremembering rates has been developed to minimize the number of stations. Based on the nature of the multi-model, random demand for each product has been considered. After mathematical modeling, different small-sized problems have been solved by the GAMS software. Results and sensitivity analysis underlined the validity of the proposed model. Since this problem is typically NP-hard, GAMS software cannot solve medium and large-sized problems in a reasonable time. Therefore, the Krill herd optimization and Particle Swarm Optimization (PSO) algorithms have been used for medium and large-sized problems, which have not been used earlier in similar cases. The Krill herd optimization algorithm has been used as the proposed algorithm and PSO has been used as a competing algorithm. The parameters of both algorithms have been adjusted by the Taguchi method, and the best level has been selected for each parameter.

12 test problems were solved with different sizes. Results indicated that only five GAMS problems could reach the optimal solution. For better comparison of the Krill herd optimization and the particle swarm optimization algorithm, each test problem was run 30 times and minimum, maximum, and average objective function and their running times were reported. The results indicated that the objective function of both metaheuristic algorithms was the same but the Krill herd optimization algorithm can achieve optimal or near-optimal answers in less time than GAMS and the PSO algorithm declared the efficiency of the proposed algorithm in solving these problems.

Research limitations/implications:

 One of the limitations in this research was the lack of cooperation of factories whose assembly lines were similar to the problem considered in this study, and in this regard, the real-world data was not accessible. Therefore, the standard test problems were used that existed in the famous database of assembly line balancing problems. Since the problem in this paper was new, some other required data, and different examples in different ways needed to be considered, randomly. Another limitation of using this research in a real-world situation was the challenge of exact determination of learning and disremembering rate of each worker which can be solved by using experts in the field of assessment and training.

Originality/value: 

In this paper, a mathematical model was developed for multi-model assembly line balancing, sequencing, and worker assignment according to the learning and disremembering effect. Since the problem was NP-Hard, as well as GAMS software, two metaheuristic algorithms were applied for a similar problem, and their efficiency was compared with each other. The two-mentioned algorithms have not been used in previous studies. Both academic researchers and production managers can benefit from applying the findings of this study.