Journal of Optimization in Industrial Engineering
Volume:13 Issue: 27, Winter and Spring 2020

The multi-objective optimization for a multi-product multi-period four-echelon supply chain network consisting of manufacturing plants, distribution centers (DCs) and retailers each with uncertain services and uncertain customer nodes are aimed in this paper. The two objectives are minimization of the total supply chain cost and maximization of the average number of products dispatched to customers. The decision variables are the number and the locations of reliable DCs and retailers, the optimum number of items produced by plants, the optimum quantity of transported products, the optimum inventory of products at DCs, retailers and plants, and the optimum shortage quantity of the customer nodes. The problem is first formulated into the framework of a constrained multi-objective mixed integer linear programming model. After that, the problem is solved by using meta-heuristic algorithms that are Multi-objective Genetic Algorithm (MOGA), Fast Non-dominated Sorting Genetic Algorithms (NSGA-II) and Epsilon Constraint Methods via the MATLAB software to select the best in terms of the total supply chain cost and the total expected number of products dispatched to customers simultaneously. At the end, the performance of the proposed multi-objective optimization model of multi-product multi-period four-echelon supply chain network design is validated through three realizations and an innumerable of various analyses in a real world case study of Bangladesh. The obtained outcomes and their analyses recognize the efficiency and applicability of the proposed model under uncertainty.

A constraint is a limitation or a restriction that poses a threat to the performance and efficiency of a system. This paper presented a tactical review approach to production constraints modeling. It discussed the theory of constraints (TOC) as a thinking process and continuous improvement strategy to curtail constraints in other to constantly increase the performance and efficiency of a system. It also x-rayed the working process of implementing the TOC concept which consists of five steps called “Process of On-Going Improvement”. Furthermore, it talked about constraints programming and constraints-based models which were explained to some details. Finally, production constraints model formulation procedures for linear programming and non-linear programming scenarios were extensively discussed with reference to published literature as instances of production constraints modeling were also cited.

Supply chain network design in perishable product has become a challenging task due to its short life time, spoilage of product in degradation nature and stochastic market demand. This paper focused on designing and optimizing model for perishable product in stochastic demand, which comprises multiple levels from producer, local collector, wholesaler and retailers. The ultimate goal is to optimize availability and net profit of all members in supply chain network model for avocado fruit under stochastic demand. The network model has considered the quality deterioration rate of the product with increased order of transportation time. The validity of developed model was tested with data collected from avocado supply chain network in Ethiopian market.

In this paper, a new multi-objective integer non-linear programming model is developed for designing citrus three-echelon supply chain network. Short harvest period, product specifications, high perished rate, and special storing and distributing conditions make the modeling of citrus supply chain more complicated than other ones. The proposed model aims to minimize network costs including waste cost, transportation cost, and inventory holding cost, and to maximize network’s profits. To solve the model, firstly the model is converted to a linear programming model. Then three multi-objective meta-heuristic algorithms are used including MOPSO, MOICA, and NSGA-II for finding efficient solutions. The strengths and weaknesses of MOPSO, MOICA, and NSGA-II for solving the proposed model are discussed. The results of the algorithms have been compared by several criteria consisting of number of Pareto solution, maximum spread, mean ideal distance, and diversification metric.Computational results show that MOPSO algorithm finds competitive solutions in compare with NSGA-II and MOICA.

In this paper a model is proposed for distribution centers location and joint replenishment of a distribution system that is responsible for orders and product delivery to distribution centers. This distribution centers are under limitedwarehouse space and this can determine amount of requirement product by considering proposed discount.The proposed model is develop to minimize total costs consists of location, ordering, purchaseunder All-units quantity discount condition and items maintenance by adjustment Frequency of replenishment in each distribution center. To solve this model, first we solve the model with genetic algorithm by confining the time between too replenishments then by use of the Quantity Discount RAND algorithm method the upper and lower limits of the time between two replenishments will be determined. After obtaining the optimal upper and lower limits, the model will be resolved by harmony search and genetic algorithms. The results show that the presented chromosome structure is so efficient so that the statistical experiments result indicates there isn’t much difference between solution means after finding the optimal upper and lower limits. We used response surface methodology for tune proposed algorithms parameters. Efficiency of proposed algorithms is examined by diverse examples in different dimensions. Results of these experiments are compared by using of ANOVA and TOPSIS with indexes of objective function value and algorithms runtime. In both comparisons harmony search algorithm has more efficiency than genetic algorithm.

Reducing cost of material handling has been a big challenge for companies. Flexible manufacturing system employed automated guided vehicles (AGV) to maintain efficiency and flexibility. This paper presents a new non-linear mathematical programming model to group n machines into N loops, to make an efficient configuration for AGV system in Tandem layout. The model minimizes both inter-loop, intra-loop flow and use balanced-loops strategy to balance workload in system simultaneously. This paper significantly considers multiple-load AGVs, which has capability of reducing fleet size and waiting time of works. A modified variable neighborhood search method is applied for large size problems, which has good accuracy for small and medium size problems. The results indicate that using multiple load AGV instead of single load AGV will reduce system penalty cost up to 44%.

In this research, a bi-objective model is developed to deal with a supply chain including multiple suppliers, multiple manufacturers, and multiple customers, addressing a multi-site, multi-period, multi-product aggregate production planning (APP) problem. This bi-objective model aims to minimize the total cost of supply chain including inventory costs, manufacturing costs, work force costs, hiring, and firing costs, and maximize the minimum of suppliers' and producers' reliability by the considering probabilistic lead times, to improve the performance of the system and achieve a more reliable production plan. To solve the model in small sizes, a ε-constraint method is used. A numerical example utilizing the real data from a paper and wood industry is designed and the model performance is assessed. With regard to the fact that the proposed bi-objective model is NP-Hard, for large-scale problems one multi-objective harmony search algorithm is used and its results are compared with the NSGA-II algorithm. The results demonstrate the capability and efficiency of the proposed algorithm in finding Pareto solutions.

Statistically constrained economic design for profiles usually refers to the selection of some parameters such as the sample size, sampling interval, smoothing constant, and control limit for minimizing the total implementation cost while the designed profiles demonstrate a proper statistical performance. In this paper, the Lorenzen-Vance function is first used to model the implementation costs. Then, this function is extended by the Taguchi loss function to involve intangible costs. Next, a multi-objective particle swarm optimization (MOPSO) method is employed to optimize the extended model. The parameters of the MOPSO are tuned using response surface methodology (RSM). In addition, data envelopment analysis (DEA) is employed to find efficient solutions among all near-optimum solutions found by MOPSO. Finally, a sensitivity analysis based on the principal parameters of the cost function is applied to evaluate the impacts of changes on the main parameters. The results show that the proposed model is robust on some parameters such as the cost of detecting and repairing an assignable cause, variable cost of sampling, and fixed cost of sampling.

Crowdfunding, a new financing method for funding ideas or ventures through a large number of relatively small contributions from many individuals has witnessed the phenomenal development over the past decade. It involves bypassing traditional financial intermediaries and using online web-based platforms to connect users of funds with retail funders. This research first explores in greater detail the crowdfunding phenomenon, discussing its main aspects, as well as the role of the involved Stakeholders, and then introduce the A system dynamics approach to designing a crowdfunding model in technological entrepreneurship ecosystem with a focus on technology incubator centers. The present study is based on the system dynamics method and this research, in terms of the purpose is applied and in terms of the survey method. So for analysis of data, Vensim software has been used. The simulation results show that technological entrepreneurship ecosystem policy combinations can effectively promote crowdfunding, which attracts more entrepreneurs to provide their ideas. So crowdfunding could promote entrepreneurial to give a greater impact on economics, and contribute to building a more sustainable society.

Computer-aided process planning (CAPP) is an essential component in linking computer-aided design (CAD) and computer-aided manufacturing (CAM). Operation sequencing in CAPP is an essential activity. Each sequence of production operations which is produced in a process plan cannot be the best possible sequence every time in a changing production environment. As the complexity of the product increases, the number  of feasible sequences increase exponentially, consequently the best sequence is to be chosen. This paper aims at  presenting the application of a newly developed meta-heuristic called the hybrid teaching–learning-based optimization (HTLBO) as a global search technique for the quick identification of the optimal sequence of operations with consideration of various feasibility constraints. To do so, three case studies have been conducted to evaluate the performance of the proposed algorithm and a comparison between the proposed algorithm and the previous searches from the literature has been made. The results show that HTLBO performs well in operation sequencing problem.

The statistical modeling of social network data needs much effort  because of the complex dependence structure of the tie variables. In order to formulate such dependences, the statistical exponential families of distributions can provide a flexible structure. In this regard, the statistical characteristics of the network is provided to be encapsulated within an Exponential Random Graph Model (ERGM). Applying the ERGM, in this paper, we follow to design a statistical process control through network behavior. The results demonstrated the superiority of the designed chart over the existing change detection methods in controlling the states. Additionally, the detection process is formulated for the social networks and the results are statistically analyzed.

Human performance and reliability monitoring have become the main issue for many industries since human error ratios cannot be mitigated to the zero level and many accidents, malfunctions, and quality defects are happening due to the human in production systems. Since the human resources implement a different range of tasks, the calculation of human error probability (HEP) is complicated, and several methods have been proposed to identify and quantify the HEP. This fact expresses the necessity of a Decision Support System (DSS) to calculate the HEP and propose optimal scenarios to increase human reliability and decrease its related cost such as quality defect and rework cost. This study develops a DSS that calculates the HEP based work specifications and proposes optimal scenarios to deal with error occurrence probability. The scenarios are provided using an AHP according to experts' opinions about the cost and time of corrective actions. The proposed DSS has been applied to a real case, and the provided results show that the proposed DSS can provide effective scenarios to deal with human error in production systems.

The typical problems facing garment manufacturers are long production lead time, bottlenecking, and low productivity. The most critical phase of garment manufacturing is the sewing phase, as it generally involves a number of operations or for the simple reason that it’s labor intensive. In assembly line balancing, allocation of jobs to machines is based on the objective of minimizing the workflow among the operators, reducing the throughput time as well as the work in progress and thus increasing the productivity. Sharing a job of work between several people is called division of labor. Division of labor should be balanced equally by ensuring the time spent at each station approximately the same. Each individual step in the assembly of product has to be analyzed carefully, and allocated to stations in a balanced way over the available workstations. Each operator then carries out operations properly and the work flow is synchronized. In a detailed work flow, synchronized line includes short distances between stations, low volume of work in process, precise of planning of production times, and predictable production quantity. This study deals with modeling of assembly line balancing by combining both manual line balancing techniques with computer simulation to find the optimal solution in the sewing line of Almeda textile plc so as to improve productivity. In this research arena software, is employed to model and measure the performance of the existing and proposed sewing line of the federal police trousers sewing line model. For each operation, the researchers have taken 15 sampling observations using stopwatch and recorded the result. All the collected data are statistically analyzed with arena input analyzer for statistical significance and determination of expressions to be used to the simulation modeling; SAM is also calculated for these operations to be used to the manual line balancing. An existing systems simulation model is developed and run for 160 replications by the researchers to measure the current performance of the system in terms of resource utilization, WIP, and waiting time. The existing systems average utilization is 0.53 with a line efficiency of 42%. This study has developed a new Sewing assembly line model which has increased the system utilization to 0.69 at a line efficiency of 58.42% without incurring additional cost.

Efficiency and effectiveness is of importance for selection and localization. There should be regular methodology for targeting in the market by several methods. There is a necessity to have clear study for selection. In the current research, it has been studied the optimal localization at shopping centers. If there is not accuracy and validity, there will be achieved negative results for these centers such as high costs. Nowadays, these centers have turned into a part of consumer life. Today, they have penetrated consumers behavior and impacted on marketing mix. We can understand the importance of them from real shopping to window -shopping. As a meta-heuristic algorithm that inspired by natural systems, genetic algorithm has been used for problem salving as a mathematic model. The nature of genetic algorithm, which has created a relationship between humanity science and mathematics, is the reason for using it in the research. Given developed indices, Selected Iranian cities were selected for this study. Findings of the research showed that we can achieve accurate results with metaheuristic methods. The research is an applied research in terms of purpose, which is to develop applied knowledge in a certain field.

The Teaching-Learning-Based Optimization (TLBO) algorithm is a new meta-heuristic algorithm which recently received more attention in various fields of science. The TLBO algorithm divided into two phases: Teacher phase and student phase; In the first phase a teacher tries to teach the student to improve the class level, then in the second phase, students increase their level by interacting among themselves. But, due to the lack of additional parameter to calculate the distance between the teacher and the mean of students, it is easily trapped at the local optimum and make it unable to reach the best global for some difficult problems. Since the Harmony Search (HS) algorithm has a strong exploration and it can explore all unknown places in the search space, it is an appropriate complement to improve the optimization process. Thus, based on these algorithms, they are merged to improve TLBO disadvantages for solving the structural problems. The objective function of the problems is the total weight of whole members which depends on the strength and displacement limits. Indeed, to avoid violating the limits, the penalty function applied in the form of stress and displacement limits. To show the superiority of the new hybrid algorithm to previous well-known methods, several benchmark truss structures are presented. The results of the hybrid algorithm indicate that the new algorithm has shown good performance.