مجله مدیریت تولید و عملیات
سال دوازدهم شماره 3 (پیاپی 26، پاییز 1400)

In recent years, significant energy consumption and facing global warming have led to concern worldwide. Therefore, governments have turned to deterrent actions such as imposing daily tariffs in different intervals to tackle energy consumption. This article addresses unrelated parallel machine energy-efficient scheduling problems by considering sequence-independent setup times and energy consumption tariffs. The objective function is that jobs should be assigned to machines and processed in different intervals so that the cost of consumed energy becomes as less as possible. It should be noted that the assumed sequence-independent setup times are addressed in two different modes, setup times jointed to processing time and setup times disjointed from processing time. Design/methodology/approach: To optimize the total energy consumption cost in unrelated parallel machine scheduling problems with sequence-independent setup times jointed to processing time and disjointed from processing time, mixed-integer linear programming (MILP) models have been proposed from two different points of view. The first model has been formulated according to the predecessor jobs of a special job, while the second model has been conducted based on the immediate predecessor job. Also, a fix and relax heuristic (FRH) algorithm has been conducted to solve large- scale instances. All mathematical models and the heuristic algorithm have been coded in the Visual C# 2017 environment and implemented using the CPLEX 12.8 Concert Technology on a PC with 32GB RAM and Intel Corei7 4.0 GHz CPU (4 cores). Also, a sizeable number of instances have been solved to evaluate the efficiency of mathematical models and the heuristic algorithm and to ensure their accuracy.

According to numerical analysis, both mathematical models solved the instances of up to 20 jobs and 80 machines optimally for sequence-independent setup-times jointed to processing time, and sequence-independent disjointed from processing time problems. However, generally speaking, the mathematical model based on predecessor jobs was more efficient than another mathematical model, especially in terms of run time. Moreover, the proposed fix and relax-based heuristic algorithm solved instances of up to 20 machines and 190 jobs for the disjointed setup times problem, and up to 20 and 220 instances for the jointed setup times problem. It should be noted that all instances were generated analogously to the literature.

A vast number of exogenous factors contributed to the scheduling problems in the real world, which can disturb the scheduling process easily, frequent power outages, machine breakdown, and operator absence. Besides, considering all the real world's possibilities raises extreme complexity in problems. Therefore, similar to other studies, some assumptions were considered as follows: - machines are always available at all times; - idle is allowable for machines; - the energy consumption rate of various machines is different for each job; - each machine's energy consumption rate during processing and setups is different for each job, it is assumed as constant; - preemption is not allowed in the job's processing and setups; - all jobs are available at the beginning of the planning horizon; and - each machine can process or do the setup for only one job at a time.

Given that unrelated parallel machines are one of the most practical scheduling environments, this article can be effective in production sites and operation lines that contain such a kind of machine. Besides, unrelated parallel machines cover identical and related parallel machines. Consequently, this paper is the building blocks of cost-effective and environmentally friendly scheduling programs. Also, the application of unrelated parallel machines is not merely restricted to production problems. In other words, unrelated parallel machine scheduling problems can be used in other real-world cases, such as airplane scheduling and elevator scheduling. Originality/value - In this paper, unrelated parallel machine energy-efficient scheduling has been addressed considering sequence-independent setup times. Since it was a common belief that sequence-independent setup times could be included in processing times, sequence-independent setup times have been neglected so far. However, in this innovative study for the first time, an unrelated parallel machine energy efficient problem was investigated with sequence-independent setup times. Mathematical programming models and a heuristic algorithm were proposed for such a practical problem.

Today, productivity is one of the most important drivers of economic growth. At the organizational level, a high level of productivity has indicated optimal performance to gain a competitive advantage. Despite the significant role of the productivity of service organizations in economic growth, few studies have been conducted in this context. Therefore, this study aims to develop a model for a comprehensive analysis of productivity in the service industry. 

In this research, the Multi-Grounded Theory (MGT) approach has been used. First, the Meta-synthesis approach (theoretical grounding) has been used to accurate an in-depth review of a wide range of separated quantitative and qualitative research results; then, in empirical grounding, 13 experts were interviewed (the integrated methods of purposive sampling and snowball was selected). To identify the factors affecting the evaluation of service productivity data has been collected from the industry experts. The findings were then combined to achieve a comprehensive view of the various dimensions of service productivity assessment. 

The results of this study have led to the development of a model for analyzing and evaluating service productivity. In the developed model with a comprehensive view of internal and external causal factors, the quantitative and qualitative indicators were determined for service productivity evaluation from the perspective of both service provider and customer. Also, internal and external organizational measures and strategies, intervening conditions, and internal and external organizational contexts affecting internal and external organizational strategies and consequences resulting from the correct evaluation of productivity in the service industry have been analyzed. Research limitations/implications: One of the limitations of most qualitative studies is the generalizability of the findings. Since the findings of this study included the result of the analysis of the views and experiences of selected experts and previous theories, the generalizability of the findings should be performed with caution. To prevent the limitations related to the analysis and the analyst, and to ensure more certainty in the findings, experts participated in the study in two stages, and a valid questionnaire was developed. The results were evaluated from the perspective of "comprehensibility", "comprehensiveness", "applicability" and "innovation". The results of the surveys also indicated the validity of the final model. Due to the time of the research in the period of the outbreak of COVID-19 disease, the researchers faced many limitations for interviewing experts, and therefore most of the interviews were conducted by calling the respondents via telephone. Practical implications: This study helps to use an integrated approach in evaluating organization productivity, despite the quantitative and qualitative indicators in both the customer and service provider dimensions. It leads to increased productivity with full knowledge of this indicator. Also, considering that the proposed model can show a picture of the company's productivity status, the results of the implementation of the introduced model are helpful in carrying out strategic planning and adopting improvement approaches to increase the organization's productivity. Social implications: Due to the significant growth of services in GDP and considering the influencing contribution of productivity in the economy and improving living standards, this study recommends a comprehensive view of service productivity. The proposed model is recommended for implementation in the service organizations depending on their type (i.e., mass, educational, professional) to adapt the indicators and productivity evaluation criteria. 

In this study, to develop a model for productivity analysis in the service industry, an attempt was made to identify the quantitative and qualitative indicators affecting productivity evaluation, comprehensively. Also, in this study, productivity evaluation was performed from both service provider and customer perspectives, because the customer is a part of the service process. Given the important role of productivity in the growth of organizations in any operating industry, and the small number of studies conducted in this field in Iran, the main contribution of this research is to create a deeper understanding of the field of service productivity.

To address problems in product development, decisions about product architecture and supply chain, and specially supplier network, are usually sequenced. This study aims to propose a model considering the product architecture and the supplier network at the same time in the early stages of product design for two optimization goals: i) to increase the weight of the supplier cooperation network; and to minimize the total cost of transactions and the proposed modules’ cost. The relationship between suppliers and within and between modules has been also considered in the early stages of design. The cost of interactions has been estimated, using clustering to obtain the Design Structure Matrix (DSM). The proposed solution was examined in an artificial network and on the electro-optical camera. This research aims to minimize interaction costs and maximize suppliers’ collaboration networks.

In the first step, a DSM was developed for Product Architecture. Then the mathematical optimization MO_MINLP with a two objective model was developed. The proposed model was solved by GAMS software using Weighted Sum, LP metric, and augmented epsilon constraint methods. Consequently, the results were compared and a sensitivity analysis was performed.

The model aimed to minimize costs, including the total cost of internal and external transactions. The module proposed cost and maximized the weight of the supplier cooperation network. To evaluate the model, an artificial numerical problem and a real problem on the electrooptical camera were studied. The experimental results confirmed that the proposed model can effectively design supplier networks and assign modules to the best suppliers. The analysis of the results indicated that considering the transactions cost and supplier cooperation network simultaneously in a bi-objective model more effectively optimizes and designs the supplier network. The model’s solution results implied reduced redesign and rework with minimum cost. The results also highlighted: i) Simultaneous consideration of product architecture and supplier network in the form of a team and network of suppliers. ii) Nonlinear bi-objective model: to increase the weight of the supplier cooperation network; and to minimize the total cost of internal and external interactions for the realization of product modules for the main manufacturer and the module proposed cost by the supplier, taking into account the skills and capacity and supporting suppliers. iii) Considering the relationship between suppliers and within and between modules iv) Assisting in the selection of the right supplier in the early stages of design v) Estimation of the cost of interactions, using DSM clustering. Research limitations/implications: In the case study, the most critical limitation in collecting necessary information and developing a computational model was the lack of historical data for product architecture and parameters. Practical implications: Applying the proposed approach reduced the cost of product design and its associated operations. Practitioners can use this approach in selecting the best supplier and modules. The proposed approach reduced rework and redesign and supplier network costs in early product design processes. Social implications: Implementation of the proposed approach decreased social costs by reduced rework and redesign and supplier network costs in early product design processes. Such an achievement was also related to the social network of suppliers.

The proposed mathematical model innovatively integrated product architecture and supplier network using MO_MINLP and used the results to optimize a typical industrial product.

Nowadays, due to highly competitive markets, meeting customer demand, quickly with the best quality and lowest cost, has become inevitable and achievable with the optimal utilization of resources and facilities. Just in Time (JIT) production system is an effective strategy to that end. Various measures and factors are involved in implementing JIT in the supply chain. The identification and analysis can give managers a clear vision of the supply chain and JIT so that this system can be implemented with the best performance in organizations. In this study, the most significant and effective measures involved in JIT were identified. Then the importance and priority of these measures were determined in terms of impact on JIT's performance. Moreover, the relationship between measures and their effectiveness was determined. The research results can be applied for the efficient implementation of JIT.

Following the literature review and receiving expert opinions by statistical analysis (Friedman test), the factors affecting JIT were classified into six key measures: i) information–feedback; ii) manufacturing unit; iii) purchase unit; iv) sales unit; v) customers; and vi) suppliers, which were the foundation for designing the questionnaire as the primary research tool in the field study. A questionnaire was designed based on the DEMATEL-Dalalah technique. It was used for quantitative analysis of relationships and interactions between factors and Interpretive Structural Modeling (ISM) to prioritize the measures. In a case study using a questionnaire, the opinions of 25 experts from the production line planning unit of the Iran Khodro Binalood Car Assembly Plant were collected and analyzed.

The information-feedback and purchasing unit factors were correlated to all other factors, indicating that such factors require special attention in organizations. It was also found that customers had the most negligible impact on JIT. In general, three factors of purchasing unit, information-feedback, and sales unit were considered as independent factors, and the other three factors – including manufacturing unit, suppliers, and customers – were dependent. Also, the ISM approach categorized the factors into three levels. The purchasing unit directly affected information feedback and sales unit factors. Then these two factors affected the factors at level 1, i.e., the manufacturing unit, customers, and suppliers; hence, these three factors had the most impressibility. Research limitations/implications: There are different methods for data collection and analysis and accordingly, through various data collection and analyses, the assessment can be performed from different aspects. It is suggested to researchers evaluate various factors affecting JIT in different industries. If more statistical data are available from experts in various industries, this subject can be explored from different aspects. The concept of JIT implementation in the country was not concentrated in this study. The involvement of different companies in such a system could be a great help in collecting more data for further research. Practical implications: The improvement of the purchasing unit positively affects the entire system due to its high importance and impact on JIT. Moreover, the information-feedback factor is always part of the infrastructure of any organization that other units can use to interact with each other; thus, facilitating communication is very significant. Also, the sales unit can predict the level of demand for the future due to being in touch directly with customers and receiving the demand, which, if correctly predicted, will put JIT in the right direction. Another factor is the manufacturing unit, which is the primary basis of any manufacturing firm. Its interruption, as it is affected by many factors, can challenge the performance of JIT. Its constant improvement should not be neglected. Since customers and suppliers on both sides of the supply chain are typically dependent factors, it is crucial to consider other measures for ensuring the satisfaction of such factors, which in turn leads to optimal circulation and performance of the whole system.

The main originality/value of this study is the analysis and identification of critical measures affecting JIT in the supply chain. Considering crucial quality factors to enhance the organization's productivity in using JIT, and the application of the DEMATEL-Dalalah technique as well as ISM to analyze the collected data are other aspects of the research innovation. The identification and evaluation of the positive and significant relationships among influential and impressible factors on JIT, and a real case study in a car manufacturing company distinguish this study from previous studies.

This paper aims to design and regulate the Adaptive Neural Fuzzy Inference Systems (ANFIS) of supplier development, determine the importance of different categories of supplier development (SD), and suggest appropriate activities based on the results for the development of suppliers in the automotive industry.

To design ANFIS, Grid Partitioning, Subtractive Clustering, and FCM have been used. Then, they have been regulated using Back Propagation (BP), Hybrid, Ant Colony Optimization (ACO), Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) methods. A five-step procedure has been used for sensitivity analysis of the supplier development categories. By comparing the results of the regulated systems, the most appropriate ones were selected. Also, based on the supplier development score in different categories separately, the supplier development score was predicted for 53 strategic suppliers in the automotive industry. By sensitivity analysis, SD-related categories were prioritized to guide automotive industry manufacturers to use SD-related activities.

Findings indicated that the FCM compared to the other two methods; meta-heuristic regulation methods compared to BP and Hybrid, and ACO, DE, and GA compared to PSO led to better results. The ACO in all systems, the DE in four systems, and the GA in two categories were identified as the dominant methods, while the PSO was not dominant in any of the categories. This finding implies the priority of meta-heuristic algorithms as ACO> DE> GA> PSO, based on the data of this study. The results of the correlation between the scores of the ANFIS and the average scores of the experts show that the designed ANFIS has high accuracy.Practical implications: The results of this study will direct manufacturers' investments and direct involvement in SD. The findings encourage manufacturers and suppliers of the Iranian automotive industry first to activities related to the development of environmental capabilities and then activities related to the development of three other categories. The managers of Iran's automotive industry are suggested to apply the activities related to the development of suppliers' environmental capabilities. These activities include evaluating the supplier's environmental performance and feedback, sharing environmental information, ethics and social responsibility, obtaining environmental and social certifications, developing programs to improve the quality of life of target communities, green procurement, and environmental awareness, logistics activities Inversion, and joint efforts to improve performance are sustainable.

This study was one of the first in-house studies to compare the results of meta-heuristic algorithms compatible with ANFIS in the field of SD. In addition, in terms of implementation, it offered suitable SD activities to car manufacturers.

This study aims to investigate the effect of incentive alignment and information sharing on the operational performance of the supply chain with emphasis on the mediating role of technology absorption capacity in small and medium enterprises (SMEs).

This research is qualitative in terms of data and descriptive-applied in terms of research orientation. The required data were collected by distributing a questionnaire to 100 participants. The four main research constructs included incentive alignment, information sharing, operational performance, and absorption capacity, which together constituted the conceptual model of the research. Three criteria of reliability, convergent validity, and divergent validity were used to evaluate the fit of measurement models. To test the research hypotheses, structural equation modeling (SEM) and Smart PLS v.3 software were used. In this study, Smart PLS 3 software was used to avoid the limitations of the covariance-based approach, including sample size, distribution properties, and the type of variables studied from the component-based approach (PLS). Also, the mean replacement approach in Smart PLS 3 software was used to deal with the lost data. 

The results indicated that incentive alignment has a significant positive effect on absorption capacity, information sharing on supply chain operational performance, information sharing on absorption capacity, and absorption capacity on supply chain operational performance. Also, the research findings indicated that absorption capacity has a significant positive effect on the relationship between incentive alignment and supply chain operational performance, and information sharing and supply chain operational performance.

Based on the results of this study, small and medium-sized companies are suggested to establish basic principles and values among their working groups, to build the personality of employees, and to promote information sharing within the organization. Employees' opinions should be used in the company's decisions and delegating authority to the lower levels of the organization. Also, by promoting the spirit of trust in their employees, advertise the culture of absorption capacity among the employees of the organization, and establish incentive policies for this purpose in the organization. Because information sharing is the result of everyday relationships between employees, these relationships store trust, mutual understanding, shared values, and behavioral values in a resource called information sharing. To create and promote incentive alignment, a culture should prevail in the organization that encourages and educates these factors to pave the way for the expansion of operational performance within the organization.

In the 21st century, the importance of coordination between members of a supply chain has become essential for survival. Jobs compete as a supply chain, not as independent companies. Also, the absorption capacity is useful for companies because it allows them to identify, and absorb the value of new information gathered from internal and external sources and use it to support their business decisions. At the core of collaborative activities is information sharing, which is a participatory mechanism in which the supplier may acquire and use demand and retailer inventory status. As supply chains compete with each other, alignment encourages different companies to maximize rewards throughout the chain by creating value for consumers. Investigating the abovementioned subjects together distinguishes this study from the previous study.