Multi-objective Batch Scheduling in Collaborative Multi-product Flow Shop System by using Non-dominated Sorting Genetic Algorithm

To ensure the competitiveness of manufacturing companies in the market, batching and batch scheduling are among the most important tasks. This paper presents a simulation-optimization approach that combines the discrete event simulation (DES) and the genetic algorithm (GA) to solve the batching and batch scheduling problem in a hybrid flow shop (HFS). HFS is widely used for the production of medium and large quantities of different technologically complex products. Based on a real-world manufacturing company, the HFS simulation model was developed using the Tecnomatix Plant Simulation software package. By analysing the influencing factors that represent production costs, a new formulation of the total cost of production was proposed. The purpose of this case study was to ensure timely delivery and minimize production costs by integrating simulation and optimization tools. This research considers sequence-dependent setup times, and availability of manufacturing and transportation equipment. The results of this research showed that the proposed simulation-optimization approach can be applied to solve the problem in many industrial case studies.

Article history: Received 18 December 2010 Received in revised form 14 February 2011 Accepted 15 February 2011 Available online 15 February 2011 Group scheduling problem in cellular manufacturing systems consists of two major steps. Sequence of parts in each part-family and the sequence of part-family to enter the cell to be processed. This paper presents a new method for group scheduling problems in flow shop systems where it minimizes makespan (Cmax) and total tardiness. In this paper, a position-based learning model in cellular manufacturing system is utilized where processing time for each part-family depends on the entrance sequence of that part. The problem of group scheduling is modeled by minimizing two objectives of position-based learning effect as well as the assumption of setup time depending on the sequence of parts-family. Since the proposed problem is NP-hard, two meta heuristic algorithms are presented based on genetic algorithm, namely: Non-dominated sorting genetic algorithm (NSGA-II) and non-dominated rank genetic algorithm (NRGA). The algorithms are tested using randomly generated problems. The results include a set of Pareto solutions and three different evaluation criteria are used to compare the results. The results indicate that the proposed algorithms are quite efficient to solve the problem in a short computational time. © 2011 Growing Science Ltd. All rights reserved

The research aimed to study the productive conditions of the olive crop in North Sinai Governorate by achieving several sub-goals such as: an analysis of the structure of production costs and revenues and the net yield of the olive crop in the farms of the study sample, statistical measurement of the most important factors affecting the productivity of the olive crop by estimating the production function, the statistical estimate of the functions of production costs, Analyzing the productive problems facing olive producers with a sample study and proposals for solving them. The research reached these results, the most important of which are: 1- The total fixed costs amounted to 1650 pounds, representing about 44.46% of the total costs of olive acres. 2- The total variable costs amounted to 2061 pounds per acre, representing about 55.54% of the total costs, and the average share per kilogram reached 62 piasters from the total variable production costs. 3- The total costs amounted to 3711 pounds per acre. The average kilogram per share reached 1.12 pounds from the total production costs. 1- The average acre productivity was about 3.30 tons / acre, with an average revenue of 5775 pounds. 2- The presence of a positive (positive) and statistically significant relationship between the quantity of the resulting olive yield in the study sample and the number of trees (acre density) used by municipal fertilizer, the amount of human work, the number of irrigation hours, and this means that the increase in the user from these variables or one of them leads to an increase in productivity Acres of olives according to their ability to influence the amount of production. While there is a negative (negative) and statistically significant relationship between the quantity of the resulting olive harvest and the area of ​​the farm, and the one used from pesticides. 6- By increasing each of the number of trees (acre density), used by human labor, the amount of municipal fertilizer, the number of irrigation hours by about 1% (with other factors constant), the output of olives among the sample farmers increases by 0.364% 0.301%, 0.214%, 0.124 %, respectively. The research ended with some recommendations that would advance the promotion of olive cultivation and production in North Sinai.

Machine configuration is a crucial strategic decision in designing a flow shop system (FSS) and directly affects its performance. This involves selecting device suppliers and determining the number of machines to be configured. This study addresses a bi-objective optimization problem for an FSS that considers repair actions and aims to determine the most suitable machine configuration that balances the production reliability and purchase cost. A nondominated sorting genetic algorithm II (NSGA-II) is used to determine all the Pareto solutions. The technique for order preference by similarity to an ideal solution is then used to identify a compromise alternative. It is necessary to assess the production reliability of any machine configuration identified by the NSGA-II. The FSS under the machine configuration is modeled as a multistate flow shop network, and Absorbing Markov Chain and Recursive Sum of Disjoint Products are integrated into the NSGA-II for reliability evaluation. The experimental results of solar cell manufacturing demonstrate the applicability of the proposed hybrid method and validate the efficiency of the NSGA-II compared with an improved strength Pareto evolutionary algorithm.

The highly competitive and volatile market puts companies in a tough position. While cost and time efficiency are important to stay competitive, environmental awareness is more and more critical. The reconfigurable manufacturing system (RMS) paradigm is suggested to cope with these new challenges. In addition to its six fundamental characteristics, it is seen as an enabler for Industry 4.0. This article investigates the multi-objective process planning problem in an environmentally conscious manner in a reconfigurable manufacturing environment. Four criteria are minimised: total production cost, total production time, total amount of greenhouse gas produced by machines, and total quantity of hazardous liquid wastes. To address the problem, modified versions of the non-dominated sorting genetic algorithm (NSGA) method, namely new dynamic NSGA-II (NewD-NSGA-II) and New NSGA-III, are developed and evaluated. Rich experimental results are presented and analysed using three metrics to demonstrate the efficacy of the proposed approaches: inverted generational distance (IGD), diversity measure (DM), and cardinality of the mixed Pareto fronts (CMPF). The effects of the similarity coefficient on the convergence of the NewD-NSGA-II and New NSGA-III are investigated, and the TOPSIS technique is used to assist the decision-maker in evaluating and selecting the best process plans.

In a construction project, the cost and duration of activities could change due to different uncertain variables such as weather, resource availability, etc. Resource leveling and allocation strategies also influence total time and costs of projects. In this paper, two concepts of time-cost trade-off and resource leveling and allocation have been embedded in a stochastic multiobjective optimization model which minimizes the total project time, cost, and resource moments. In the proposed time-cost-resource utilization optimization (TCRO) model, time and cost variables are considered to be fuzzy, to increase the flexibility for decision makers when using the model outputs. Application of fuzzy set theory in this study helps managers/planners to take these uncertainties into account and provide an optimal balance of time, cost, and resource utilization during the project execution. The fuzzy variables are discretized to represent different options for each activity. Nondominated sorting genetic algorithm (NSGA-II) has been used to solve the optimization problem. Results of the TCRO model for two different case studies of construction projects are presented in the paper. Total time and costs of the two case studies in the Pareto front solutions of the TCRO model cover more than 85% of the ranges of total time and costs of solutions of the biobjective time-cost optimization (TCO) model. The results show that adding the resource leveling capability to the previously developed TCO models provides more practical solutions in terms of resource allocation and utilization, which makes this research relevant to both industry practitioners and researchers.

abstract: The purpose of the research was to determine the sequencing of the production of n jobs in m operations in a small footwear company in an environment of flow shop machine characteristics, which optimizes the total time of completion of the job in the production system (Makespan). We used heuristic algorithms that were applied through Lekin and WinQSB softwares, and for the optimization algorithm we designed a mathematical model that was solved by Juliabox software. Results show that the integer linear programming and local search minimize the makespan with 3807 minutes, and different production sequences for each algorithm, which consider permutation, which improves the traditional way of programming the production in 97 minutes, however, the optimization presents better results in the performance measures of average waiting time, average time of flow, and average job in process. Application of heuristic algorithms proves to be simple and fast, but the mathematical model of optimization designed and encoded in the software is a flexible and valuable tool for decision making in production programming, which could be applied in other footwear companies, and in other productive sectors whose companies have the same characteristics of the case study, reducing costs and improving delivery times.

The large-scale high-intensity homogeneous magnetic field is an important resource to perform magnetic immunity tests for large scientific facilities. Generally, a multi-coil system with large cross-sectional area is used to generate the required test field, which has high power loss and conductor mass. In this article, we propose a novel method that introduces magnetically shielded room (MSR) to enhance the coil magnetic field and reduce power loss. The analytical model of the coil system based on MSR is established through the multi-reflection image method. In addition, the multi-objective optimization model is brought forward based on the non-dominated sorting genetic algorithm (NSGA-II). Theoretical results indicate that the magnetic field can be significantly enhanced under the presence of MSR. Consequently, prominent reductions in total current by about 61.4% and in total power loss by 75.5%–81.7% are achieved. Finally, the effectiveness of the theoretical result is numerically verified by the finite-element analysis (FEA). The proposed method provides a novel and promising way to reduce the power loss and conductor mass of such high-intensity magnet systems and other similar applications.

A project scheduling problem investigates a set of activities that have to be scheduled due to precedence priority and resource constraints in order to optimize project-related objective functions. This paper focuses on the multi-mode project scheduling problem concerning resource constraints (MRCPSP). Resource allocation and leveling, renewable and non-renewable resources, and time-cost trade-off are some essential characteristics which are considered in the proposed multi-objective scheduling problem. In this paper, a novel hybrid algorithm is proposed based on non-dominated sorting ant colony optimization and genetic algorithm (NSACO-GA). It uses the genetic algorithm as a local search strategy in order to improve the efficiency of the ant colony algorithm. The test problems are generated based on the project scheduling problem library (PSPLIB) to compare the efficiency of the proposed algorithm with the non-dominated sorting genetic algorithm (NSGA-II). The numerical result verifies the efficiency of the proposed hybrid algorithm in comparison to the NSGA-II algorithm.

Batch scheduling and dispatching in the hybrid flow shop production mode is an inevitable development trend, as it effectively improves low resource utilization and reduces excessive work-in-process inventory. This study proposes a multi-objective batch scheduling technique for the hybrid flow shop based on the concept of batch flow. It establishes a multi-objective optimization model with the sub-batch as the smallest processing unit and utilizes an improved genetic algorithm to enhance search capability, resolving the optimization issues of work-in-process inventory and delivery time in the hybrid flow shop. The approach is further developed and analyzed using actual data from a specific workshop, demonstrating its effectiveness in enhancing production efficiency and reducing work-in-process inventory. The results validate the feasibility and efficacy of the proposed algorithm.

A solar-powered combined cooling, heating, and power (CCHP) plant integrated with a water electrolysis unit is investigated in terms of energy, exergy, and exergo-economic (3E) assessments. A comprehensive parametric study and optimization is conducted following the thermodynamic and exergo-economic assessment of the proposed system to evaluate the key performance parameters of the system for efficiency and economic factors. This system employs a heliostat field and a receiver tower by taking advantage of thermal energy from the sun and produces a continuous energy supply with an integrated phase-change material (PCM) tank to store the heat. In addition, a supercritical CO2 Rankine cycle (RC), an ejector refrigeration cooling (ERC) system, and a PEM water electrolyzer are coupled to produce cooling, heating, power, and hydrogen. Thermodynamic analysis indicates that the system exergy efficiency and energy efficiency are improved to 33.50% and 40.61%, respectively, while the total cost rate is 2875.74 USD/h and the total product cost per exergy unit is 25.65 USD/GJ. Additionally, the system produces a net generated power, heating load, and cooling load of 11.70, 13.92, and 2.60 MW, respectively, and a hydrogen production rate of 12.95 g/s. A two-objective optimization approach utilizing a non-dominated sorting genetic algorithm (NSGA) was performed, demonstrating that the system’s ideal design point offers a cost rate of 1263.35 USD/h and an exergetic efficiency of 34.17%.

The efficiency of the processes of performing tasks of various types in Flow Shop systems is ensured by the formation of packages and optimization of their orders for performing operations with them on the devices of these systems. The process of package execution is affected by instrument failures and downtime associated with recovery. The impact of instrument failures and repairs can be reduced by pre-maintenance. During the maintenance time intervals, the devices are unavailable for the implementation of their assigned functions. By processing statistical data, the time intervals between the pre-maintenance of the devices can be determined (the periods of their availability are determined). For this reason, it is important to solve the problems of optimizing package compositions, their inclusion in the time intervals for the availability of devices, and schedules for their execution in these intervals. With small problem sizes, their solutions can be determined by using mathematical models of mixed integer linear programming. The purpose of the work is to build a new mathematical model of mixed integer linear programming, the use of which allows us to determine the optimal solutions of the type under consideration. To achieve this goal, the methods of constructing mathematical programming models are used in the work. At the first stage, the formation of a nonlinear mathematical model of integer programming was implemented. At the second stage, in order to reduce the time required to obtain solutions, the model was linearized. To verify the model, an application was developed in the IBM ILOG CPLEX program. In the course of the research, results were obtained that showed the effectiveness of the model in solving the tasks of planning the execution of task packages in conveyor systems with limited availability of devices. The scientific novelty of the results suggests that the model uses a method for transferring tasks between two devices directly at the end of their execution on the previous device. Their theoretical significance consists in obtaining new expressions that make it possible to determine the values of indicator variables when the function values exceed the specified boundaries. Based on these expressions, constraints are formed that require the inclusion of all tasks of different types in packages in the time intervals of instrument availability.

In the present work, a thermal modeling is conducted for optimal design of compact heat exchangers in order to minimize cost and entropy generation. In this regard, an ε−NTU method is applied for estimation of the heat exchanger pressure drop, as well as effectiveness. Fin pitch, fin height, fin offset length, cold stream flow length, no-flow length, and hot stream flow length are considered as six decision variables. Fast and elitist nondominated sorting genetic algorithm (i.e., nondominated sorting genetic algorithm II) is applied to minimize the entropy generation units and the total annual cost (sum of initial investment and operating and maintenance costs) simultaneously. The results for Pareto-optimal front clearly reveal the conflict between two objective functions, the number of entropy generation units and the total annual cost. It reveals that any geometrical changes, which decrease the number of entropy generation units, lead to an increase in the total annual cost and vice versa. Moreover, for prediction of the optimal design of the plate fin heat exchanger, an equation for the number of entropy generation units versus the total annual cost is derived for the Pareto curve. In addition, optimization of heat exchangers based on considering exergy destruction revealed that irreversibilities, such as pressure drop and high temperature difference between cold and hot streams, play a key issue in exergy destruction. Thus, more efficient heat exchanger leads to have a heat exchanger with higher total cost rate. Finally, the sensitivity analysis of change in the optimum number of entropy generation units and the total annual cost with change in the decision variables of the plate fin heat exchanger is also performed, and the results are reported.

Under intense industry competition, decision makers must ensure that products demanded by consumers can be quickly produced with minimum production cost. However, because uncertainties are unavoidable and inevitably affect decision makers, numerous studies have discussed how to control uncertainties or minimize their effects. Multiple uncertainties that interact simultaneously may cause a combined effect in actual systems. Therefore, this study presents a robust optimization model with two uncertainties, extending the method of robust optimization with one uncertainty. To demonstrate the applicability of the proposed model with two uncertainties, this study uses the supplier selection problem with component purchase quantity allocation in supply chain management as an analysis case. This considers the reliability of production and transportation and develops a multi-objective robust optimization model with two uncertainties. In addition, a nondominated sorting genetic algorithm is proposed for solving the proposed multi-objective robust optimization model. The relationship between price of robustness and budget parameters is explored by considering the robust optimization model with production and transportation uncertainties proposed in this study. Finally, there is a comparative analysis between the results for price of robustness in the proposed two-uncertainty model and in the one-uncertainty model.

The purpose of this paper is to solve the problem of low conveying efficiency and serious blade wear during vertical screw conveying of cohesive particles. Firstly, the reliability of DEM simulation was verified by comparing the simulated and theoretical values and the influence regularity of different design parameters (rotational speed, pitch, and clearance) on screw conveying characteristics were analyzed based on DEM. In addition, the effect of design parameters on the screw conveying characteristics is identified by ANOVA. Then, the multiobjective optimization model with the both of maximizing the average mass flow rate and minimizing the maximum wear depth of the blade was established using the polynomial fitting regression, which was solved by the non-dominated sorting genetic algorithm (NSGA-II). Finally, the comprehensive evaluation was used to determine the best design parameters. The above research results provide a certain reference for the study of cohesive particle’s vertical screw conveying characteristics and equipment optimization design.