International Journal of Industrial Engineering and Productional Research
Volume:33 Issue: 2, Jun 2022

This paper considers a customer order scheduling (COS) problem in which each customer requests a variety of products processed in a two-machine flow shop. A sequence-independent attached setup for each machine is needed before processing each product lot. We assume that customer orders are satisfied by the job-based processing approach in which the same products from different customer orders form a product lot (job). Each customer order for a product is processed as a sublot (a batch of identical items) of the product lot by applying the lot streaming (LS) idea in scheduling. We assume that all sublots of the same product must be processed together by the same machine without intermingling the sublots of other products. The completion time of a customer order is the completion time of the product processed as the last product in that order. All products in a customer order are delivered in a single shipment to the customer when the processing of all the products in that customer order is completed. We aim to find an optimal schedule with a product lots sequence and the sequence of the sublots in each job to minimize the sum of completion times of the customer orders. We have developed a mixed-integer linear programming (MILP) model and a multi-phase heuristic algorithm for solving the problem. The results of our computational experiments show that our model can solve the small-sized problem instances optimally. However, our heuristic algorithm finds optimal or near-optimal solutions for the medium- and large-sized problem instances in a short time.

The Coronavirus (Covid-19) has become a threat to the world. The government has implemented various policies to prevent its spread, such as self-isolation, social distancing, etc. The regulation turned out to pose a big threat to many companies, especially in the retail sector. To survive in a pandemic, the company needs to ensure brand loyalty as an important factor in maintaining company stability. This study aims to determine the effect of Corporate Social Responsibility, Service Quality, Customer Satisfaction on Brand Loyalty, and the effect of Service Quality on Customer Satisfaction in coffee shop brands from the US. The method used is descriptive quantitative with 100 respondents from Greater Jakarta. The findings show that Corporate Social Responsibility and Service Quality do not directly influence Brand Loyalty, while Customer Satisfaction has a positive and significant relationship with Brand Loyalty. Meanwhile, Service Quality affects Customer Satisfaction positively and significantly.

The dynamic among farmer institutions has essential problems to be addressed, especially regarding the pattern and process of communication interactions developing farmer institutions. Therefore, an assembly of agribusiness information within the communication network of the farmer group is of primary interest for our study. This study aims to analyze the agribusiness network structure of beef cattle farmer groups in Subang Regency, West Java, Indonesia. The Social Network Analysis (SNA) used for discovering communication network structure. Data was collected through interviews using a questionnaire. The census method was used for the sampling technique and UCINET 6 used to analyze the data. The results of the study show: 1) The degree centrality and net draw illustrate the head of farmer groups still plays a role as a source of information for their members even if members can access 1-3 other sources, 2) The closeness centrality average is still high and approaching its maximum. The limitation of this study is that only in quantitative approach. Therefore, it is recommended to conduct further research in a qualitative approach to further analyze the roles play in the networks that can be considered in increasing group social capital.

Nowadays, supply chain management (SCM) is an interesting problem that has attracted the attention of many researchers. Transportation network design is one of the most important fields of SCM. In this paper, a logistics network design is considered to optimize the total cost and increase the network stability and resiliency. First, a mixed integer nonlinear programming model (MINLP) is formulated to minimize the transportation time and transportation cost of products. The proposed model consists of two main stages. One is a normal stage that minimizes the transportation and holding costs, all manufacturers are also assumed to be healthy and in service. In this stage, the quantity of customer demand met by each manufacturer is eventually determined. The second is the resilience stage. A method is presented by creating an information network in this supply chain for achieving the resilient and sustainable production and distribution chain that, if some manufacturers break down or stop production, Using the Restarting and load sharing scenarios in the reactive approach to increase resilience with accepting the costs associated with it in the supply network and return to the original state in the shortest possible time, the consequences of accidental failure and shutdown of production units are managed. Two capacities are also provided for each manufacturer Normal capacity to meet the producer's own demand Load sharing capacity, Determine the empty capacity and increase the capacity of alternative units to meet the out-of-service units demand In order to solve the model, we used GAMS & Matlab software to find the optimal solutions. A hybrid priority-based Non-dominated Sorting Genetic Algorithms (NSGA-II) and Sub-population Genetic Algorithm (SPGA- II) is provided in two phases to find the optimal solutions. The solutions are represented with a priority matrix and an Allocated vector. To compare the efficiency of two algorithms several criteria are used such as NPS, CS and HV. Several Sample problems are generated and solved that show the Sub-population Genetic Algorithm (SPGA- II) can find good solutions in a reasonable time limit.

Companies and firms, nowadays, due to mounting competition and product diversity, seek to apply virtual cellular manufacturing systems to reduce production costs and improve quality of the products. In addition, as a result of rapid advancement of technology and the reduction of product life cycle, production systems have turned towards dynamic production environments. Dynamic cellular manufacturing environments examine multi-period planning horizon, with changing demands for the periods. A dynamic virtual cellular manufacturing system is a new production approach to help manufacturers for decision making. Here, due to variability of demand rates in different periods, which turns to flow variability, a mathematical model is presented for dynamic production planning. In this model, we consider virtual cell production conditions and worker flexibility, so that a proper relationship between capital and production parameters (part-machine-worker) is determined by the minimum lost sales of products to customers, a minimal inventory cost, along with a minimal material handling cost. The problems based on the proposed model are solved using LINGO, as well as an epsilon constraint algorithm.

To respond to the urgent call for preventive action against COVID-19 pandemic implications for societies, this research is carried out. The main aim of our research is providing a new insight for the effects of the newly emerged restrictions by COVID-19 on the SD Goals (SDGs). This research applied a qualitative approach for supporting the SDGs achievement post-COVID in Iran, as a developing country in the Middle East, in two phases. In the first phase, using a fuzzy Delphi method, the SDGs affected by COVID-19 were identified. In the next phase, a fuzzy cognitive map, as a qualitative system dynamics modeling, was conducted to specify the key interconnections among the SDGs post COVID-19. Finally, three strategies including focus on people in vulnerable situation, support for industrial units and small and medium-sized enterprises, and national aggregation to Fight COVID-19 were examined. As a result, different scenarios associated with the three proposed strategies were tested based on the identified interconnections among the SDGs to reduce the potential negative effects of COVID-19 crisis on the achievement of the SDGs. The results provide a decision support for stakeholders and policy makers involved in SD action plan.

Integrated treatment planning for cancer patients has high importance in intensity modulated radiation therapy (IMRT). Direct aperture optimization (DAO) is one of the prominent approaches used in recent years to attain this goal. Considering a set of beam directions, DAO is an integrated approach to optimize the intensity and leaf position of apertures in each direction. In this paper, first, a mixed integer-nonlinear mathematical formulation for the DAO problem in IMRT treatment planning is presented. Regarding the complexity of the problem, two well-known metaheuristic algorithms, particle swarm optimization (PSO) and differential evolution (DE), are utilized to solve the model. The parameters of both algorithms are calibrated using the Taguchi method. The performance of two proposed algorithms is evaluated by 10 real patients with liver cancer disease. The statistical analysis of results using paired samples t-test demonstrates the outperformance of the PSO algorithm compared to differential evolution, in terms of both the treatment plan quality and the computational time. Finally, a sensitivity analysis is performed to provide more insights about the performance of algorithms and the results revealed that increasing the number of beam angles and allowable apertures improve the treatment quality with a computational cost.

Nowadays, major challenges in the cold chain of perishable products, such as dairy products, are that these products do not reach customers on time. Answering the question of how to make the cold supply chain of perishable products more agile, the possibility of more control over this issue can be increased. This study tries to investigate the factors affecting the agility of the cold supply chain and after identifying the effective factors, rank them using the GRAY-DEMATEL-AHP. To data gathering, the literature of the subject and the opinions of experts and stakeholders who have sufficient experience in the cold chain have been used and the identified factors have been confirmed after several revisions by the Delphi through snowball sampling. Also, in order to take advantage of both the GRAY and DEMATEL approaches, this paper uses a combination of these two methods to examine causal relationships among the factors affecting the agility of the cold supply chain. The results show that Among the sourcing sub-factor, government decision-making and policies with a weight of 0.212 has gained the first rank and in the sub-factor of distribution, loading time and speed of action in distribution, with a weight of 0.188, has gained the first rank. Also, among the sub-factor of production, accurate planning and speed of action in order production, with a weight of 0.342, has gained the first rank. This paper adds valuable knowledge to the study of the dairy industry cold supply chain agility.

Flood is a serious problem that can occur in many countries in the world. For tropical countries such as Indonesia, flooding is generally caused by rainfall that is high above normal. Almost all cities in Indonesia experience flooding every year, including DKI Jakarta, the capital city of Indonesia. Based on data from the National Disaster Management Agency (BNPB) in 2020, East Jakarta is a city that is prone to flooding. Considering that there are so many losses caused by flooding, it is necessary to have a disaster mitigation effort to minimize the possible risk of flooding. One of the risk mitigations due to natural disasters is to buy insurance products. However, not all people buy flood-impacted insurance products because of their economic and social factors. This research aims to create a model with Probit Regression Model to determine the factors that influence Indonesian's interest to buy flood-impacted insurance products. Furthermore, this study conducts a test. The results show that from the 19 factors used, eight factors significantly affect Indonesia's interest in purchasing flood-impacted insurance products. In the end, this research calculates the level of model accuracy and obtained 84.3%.

In a system, prediction of remaining useful lifetime (RUL) of servicing before reaching to a specified breakdown threshold is a very important practical issue, and research in this field is still regarded as an appreciated research gap. Operational environment of an equipment is not constant and changes regarding to stresses and shocks. These random environmental factors accelerate system deterioration by affecting on the level or rate of degradation path. The present study focuses on providing a practical operational guideline to estimate the RUL of a system with general degradation path after receiving a shock which only affects on the degradation level. Due to exact estimation of the shock arrival times and measuring the magnitudes of future shocks to investigate shock effects on RUL is almost impossible in the real world and laborious in practice, in this research a new procedure based on total defect size monitored in the constant inspection periods and Accelerated Factor (AF) is proposed to analyze RUL of the system. A Micro-Electro-Mechanical system (MEMS) is used as an example and the results show the applicability of the proposed approach.

In today's world, COVID-19 pandemic has affected many organizations. Pandemic issues have created financial and social problems for businesses. Crisis and risk management have a significant impact on reducing consequences of pandemics. Rapid response to risk enhances the performance of organizations in times of crisis. Therefore, a framework to provide risk treatment in a pandemic crisis seems essential. To do this, this paper presents a framework to identify risk factors posed by pandemics. In this regard comprehensive risk factors by considering sustainability concept are illustrated for university. Then, identified risk factors are evaluated by best–worst methodology (BWM) and then the important risks are recognized. Using the importance of risk and the strengths and weaknesses of the business, solutions to reduce the impact of risk are suggested to managers. The results of this paper can be used in order to enhance resiliency of the organization in front of the pandemics from social and financial viewpoints.

In large-scale emergency, the vehicle routing problem focuses on finding the best routes for vehicles. The equitable distribution has a vital role in this problem to decrease the number of death and save people's lives. In addition to this, air pollution is a threat to people’s life and it can be considered to omit other kinds of disasters happens because of it. So, a new MINLP model presented is going to face a real situation by considering real world assumptions such as fuzzy demands and travel time, multi depots and items, vehicle capacity and split delivery. The first objective function is to minimize the sum of unsatisfied demand which follows a piecewise function and the second one is to minimize the cost which depends on the fuel consumption. In order to solve the multi-objective problem with fuzzy parameters, nonlinear function has been linearized by convex combination and a new crisp model is presented by defusing fuzzy parameters. Finally, NSGA-П algorithm is applied to solve this problem and the numerical results gained by this procedure demonstrate its convergence and its efficiency in this problem.

In current study, a particle swarm clustering method is suggested for clustering triangular fuzzy data. This clustering method can find fuzzy cluster centers in the proposed method, where fuzzy cluster centers contain more points from the corresponding cluster, the higher clustering accuracy. Also, triangular fuzzy numbers are utilized to demonstrate uncertain data. To compare triangular fuzzy numbers, a similarity criterion based on the intersection region of the fuzzy numbers is used.  The performance of the suggested clustering method has been experimented on both benchmark and artificial datasets. These datasets are used in the fuzzy form. The experiential results represent that the suggested clustering method with fuzzy cluster centers can cluster triangular fuzzy datasets like other standard uncertain data clustering methods. Experimental results demonstrate that, in almost all datasets, the proposed clustering method provides better results in accuracy when compared to Uncertain K-Means and Uncertain K-medoids algorithms.

Operating rooms have become the most important areas in hospitals because of the scarcity and cost of resources. The present study investigates operating room scheduling and rescheduling considering the priority of surgical patients in a specialized hospital. The ultimate purpose of scheduling is to minimize patient waiting time, surgeon idle time between surgeries, and penalties for deviations from operating room preferences. A mathematical programming model is presented to solve the problem. Because the problem is strongly NP-hard, two heuristic algorithms are presented. A heuristic algorithm based on a mathematical programming model with local search obtains near-optimal solutions for all the samples. The average relative deviation of this algorithm is 0.02%. In continuous, heuristic algorithms performance have been investigated by increasing the number of patients and reduce the number of recovery beds. Next, a rescheduling heuristic algorithm is presented to deal with real-time situations. This algorithm presents fewer changes resulting from rescheduling in comparison with the scheduling problem.