Identifying the constraining factors of production and yield gap is very important. Therefore; this research was performed to identify the production constraining factors of local rice cultivars. All management practices from nursery preparation to harvesting stages for 100 paddy fields of local rice cultivars were recorded through field studies, in Sari, from 2015-2016. In the CPA, the actual and calculated potential yield were 4495 and 5703 kg/ha, respectively and the gap was 1221 kg/ha. The yield gap caused by number of top-dressing variables was 324 kg/ha, equal to 27% of the total yield gap. The yield gap related to previous year of legumes cultivation was 218 kg ha-1, equal to 18% of the total yield variation. Among the 10 variables entered in the CPA model, the effects of top-dress fertilizer application and its application frequency and foliar application were remarkable, which could compensate a significant part of the yield gap (444 kg/ha, 37% of total) in the farmers’ fields by managing these variables. According to boundary line analysis (BLA) finding, actual yield mean on the basis of optimal limit related to 12 variables under study was 5369 kg/ha, with 881 kg/ha yield gap . Mean relative yield and relative yield gap for 12 variables (transplanting date, seedling age, number of seedlings per hill, planting density, nitrogen and phosphorous per hectare, nitrogen before transplanting, harvesting date, lodging problem, pest problem, diseases problem and weeds problem) were 83.64 and 16.35 kg/ha, respectively. Based on the finding, it can be stated that the model precision is appropriate and can be applied for both estimation of the quantity of yield gap and determining the portion of each restricting yield variables.

Many supply chains lack flexibility and adaptability in today's competitive market, resulting in customer dissatisfaction, backorders, and several extra costs for the business. Additionally, the inability to quickly meet the customer's demands andthe unnecessary transportation costs is also one of the significant challenges facedby the fixed facilities' supply chain. To address these challenges, this study analyzedthe mobile facilities supply chain and the production, distribution, and delivery ofgoods conducted by trucks based on customer preferences. This study proposes abi-objective mixed-integer linear programming model to ensure the mobilefacilities' routing and manufacturing schedules are optimized to meet the customer's needs. Furthermore, this model minimizes production and distribution costs in the shortest amount of time. An exact decomposition algorithm based on Bendersdecomposition is used to find high-quality solutions in a reasonable amount of timeto tackle the problem efficiently. We present several acceleration strategies forincreasing the convergence rate of Benders' decomposition algorithm, includingPareto optimality cut and warm-up start. The warm-up start acceleration strategyitself is a meta-heuristic based on particle swarm optimization (PSO). Using theBenders decomposition, we demonstrate the superior accuracy of our solutionmethodology for large-scale cases with 10 kinds of products ordered by 30customers using 10 mobile facilities.

An Open-Pit production scheduling (OPPS) problem focuses on specifying block production scheduling to achieve the highest possible Net Present Value (NPV). This paper presents a new mathematical model for OPPS under uncertainty. To this end, a robust box and ellipsoidal counterpart approach was used. The proposed method was implemented in a hypothetical model. A Genetic Algorithm (GA) and an exact mathematical modeling approach were used to solve the model. It was shown that the scheduling of deterministic and robust models in various conditions is different. Considering the type of robust counterparts, different production plans under various conditions were scheduled. Furthermore, the price of robustness was determined for various levels of conservation.

With increasing competition in the business world and the emergence and development of new technologies, many companies have turned to integrated production and distribution for timely production and delivery at the lowest cost of production and distribution and with the least delay in delivery. By increasing human population and the increase in greenhouse gas emissions and industrial waste, in recent years the pressures of global environmental organizations have prompted private and public organizations to take action to reduce environmental pollutants. This paper presents a nonlinear mixed integer model for the production and distribution of goods with specified shipping capacity and specific delivery time for customers. The proposed model is applicable to flexible production systems; it also provides routing for the means of transportation of products, as well as the reduction of emissions from production and distribution. The model is presented, and then by mathematical linearization is transformed into a mixed integer linear model. The data of a furniture company is used to solve the linear model, and then the linear model with the company data is solved by CPLEX software. The numerical results show that as costs increase, delays are reduced and consequently, customer satisfaction increases, and as costs increase the air pollution decreases.

In recent years supply chain management becomes an important subject addressed by many researchers. A supply chain represents all stages that have added value to a product.Integration and synchronization of information and material flows of manufacturing sites belonging to a supply chain (SC) has become more practical and have attracted the attention by both industry practitioners and academic researchers.In this study after presenting previous works on scheduling in supply chain, the problem is described and mathematical model of the problem is presented. Then a genetic algorithm is proposed for solving the problem that has chromosomes with variable structures. Finally we provide concluding remarks and some scopes for future researches.This paper studies a 3-stage supply chain scheduling problem in which the first stage is composed of multiple suppliers with different production speeds that produce parts ordered by a manufacturing company at third stage. In the second stage vehicles with variable speeds and variable capacities convey the jobs from the suppliers to the manufacturing company at third stage. The main focus of this study is on the integration of the production and the transportation scheduling.For simplicity, it is assumed that all suppliers are in one geographical zone and the transportation times between them are negligible in comparison with the transportation time from the suppliers to the manufacturing company. However, in some realistic situations the suppliers may be located in multiple geographical zones, in sake of reducing complexity, it can be assumed that original problem can be divided in multiple sub-problems whereas each group of suppliers are located in one geographical zone and sharing the vehicles between the suppliers in different sub-problems are not allowed.Each vehicle after delivering a batch to the manufacturing company back haul empty to the suppliers’ zone for the next dispatching. The objective function of the problem is to minimize delivery time of a set of jobs to the manufacturing company that herein we address it as minimizing maximum completion time of all jobs, i.e, makespan.Since such problems have NP-hard structure, thus Genetic algorithm can be mentioned as an approach that frequently used for solving them. In this study a genetic algorithm named dynamic genetic algorithm (DGA) that has chromosomes with different structure is developed.DGA has six parameters as follows: 1) population size (popsize), 2) crossover rate (percross), 3) mutation rate (permut), 4) percentage of the best chromosomes are selected to the next population (best), 5) number of times with no improvement in fitness function for terminating the algorithm (termination) and 6) a parameter in crossover operation (r).After solving various test problems we empirically have found that values of 100 for popsize, 0.6 for percross, 0.8 for permute, 0.7 for best, 10 for termination and 0.7 for r may lead to better solutions.According to the authors' knowledge, this problem has not been studied previously. Since there is no algorithm to compare with dynamic genetic algorithm in the literature, we compared the results of DGA with those of two algorithms: a random search algorithm and an adapted algorithm based on the nearest problem in the literature to our problem (namely H1).In sake of comparisons, many test problems are produced randomly according to a defined structure and solved by DGA, the random search approach and H1.In order to compare DGA with the random search approach 4 critoria were used as follows: 1) Mean of solutions of DGA, 2) Mean of solutions of the random search, 3) Percentage of runs that DGA get better result than the random search and 4) Percentage of runs that DGA get equal result to the random search. Results show that DGA outperforms the random search approach in all cases. Also with increasing the number of jobs mean of solutions increased. Increasing the number of suppliers causes mean of solutions decreased but when there exist a bottleneck in this stage mean of solutions increased. Also by decreasing the process times of jobs in the first stage, the mean of solutions decreases. Increasing the vehicles' capacities causes the mean of solutions to decrease.DGA is also compared with an adapted algorithm based on the nearest problem in the literature to our problem, namely H1, proposed by Chang and Lee (2004).Their scheduling problem is the same as problem considered in this paper but they assumed that only one vehicle exists in the transportation stage while in our problem transportation fleet is composed of l vehicles with different speeds and capacities. Also they considered at most two suppliers with identical production speed but in our problem m suppliers exist with different production speeds. For the case of two suppliers, they proposed a heuristic algorithm and proved that it could cause at most 100% error under the worst-case with the bound being tight.In order to compare DGA with H1 four criteria for both algorithms are used as follows: 1) Mean of solutions, 2) Percentage of runs that an algorithm gets better result than the another one (PBR), 3) Percentage of runs that an algorithm get equal result to the another one (PER) and 4) Mean of solving time. Experimental results show DGA performance is much better than that of H1.

A three-stage production system is considered in this paper. There are two stages to fabricate and provide the parts and an assembly stage to assemble the parts and complete the products in this system. Suppose that a number of products of different kinds are ordered. Each product is assembled with a set of several parts. At first the parts are produced in the first stage with parallel machines and then they are controlled and provided in the second stage and finally the parts are assembled in an assembly stage to complete the products. Two objective functions are considered: (1) minimizing the completion time of all products (makespan), and (2) minimizing the sum of earliness and tardiness of all products (Si(Ei/Ti). Since this type of problem is NP-hard, a new multi-objective algorithm is designed for searching local Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, various test problems are designed and the reliability of the proposed algorithm, based on some comparison metrics, is compared with two prominent multi-objective genetic algorithms, i.e. NSGA-II and SPEA-II. The computational results show that the performance of the proposed algorithms is good in both efficiency and effectiveness criteria.

In an Open-Pit production scheduling (OPPS) problem, the goal is to determine the mining sequence of an orebody as a block model. In this paper, linear programing formulation is used to aim this goal. OPPS problem is known as an NP-hard problem, so an exact mathematical model cannot be applied to solve in the real state. Genetic Algorithm (GA) is a well-known member of evolutionary algorithms that widely are utilized to solve NP-hard problems. Herein, GA is implemented in a hypothetical Two-Dimensional (2D) copper orebody model. The orebody is featured as two-dimensional (2D) array of blocks. Likewise, counterpart 2D GA array was used to represent the solution space of an OPPS problem. Thereupon, the fitness function is defined according to the OPPS problem objective function to assess the solution domain. Also, new normalization method was used to handle the block sequencing constraint. A numerical study is performed to compare the solutions of the exact and GA-based methods. It is shown that the gap between GA and the optimal solution by the exact method is less than % 5; hereupon GA is found to be efficient in solving OPPS problem.

In today's manufacturing processes, production optimization is very important to increase the competitive edge, so production managers are hardly trying to increase their production output (without increasing resources) by using different manufacturing processes and different fields of industrial engineering. These methods are used in productivity and decrease the cost of goods sold, which managers favor in all companies. This paper investigated the optimization problem in a flow shop production line with the probable time and the other constraints such as limited equipment, manufacturing process limit, by using scheduling techniques and creating a learning system by simulating. We use a simulation-based optimization approach that combines simulation and exact methods to solve the Flow Shop scheduling problem. simulation software used to reduce the constraints and exact model used for optimizing answers that can be efficient and effective. Implementing this model with all its probable components in a high-tech pharmaceutical company with many different products increases utilization and largely to those outputs have increased by 12%.

Machine maintenance is performed in production to prevent machine failure in order to maintain production efficiency and reduce failure costs. Due to the importance of maintenance in production, it is necessary to consider an integrated schedule for production and maintenance. Most of the literature on machine scheduling assumes that machines are always available. However, this assumption is unrealistic in many industrial applications. Preventive maintenance (PM) is often performed in a production system to prevent premature machine failure in order to maintain production efficiency. However, this assumption is inappropriate in real industrial cases. Machine maintenance plan is often performed in a production system to prevent premature machine failure in order to maintain production efficiency. Parallel machine layout is very common in modern production systems. Its performance sometime has a key impact on overall productivity. In this paper, a parallel machine scheduling problem with individual maintenance operations is considered. Then, a mathematical model is formulated including scheduling and maintenance operation optimization. The objective is to assign all jobs to machines so that the completion time and the average cost are minimized, jointly. Maintenance is considered in time intervals. To solve the proposed problem, a branch and bound (B&B) algorithm is adapted and proposed. The results show the applicability of the mathematical model in production systems and efficiency of the adapted B&B in comparison with Gams optimization software.