mathematical programming

The importance of mass biology has increased due to pollution caused by biomass burial, the profitability of biomass energy, and the demand for energy in the supply chain network. The goal of this research is to design a model for the biomass supply chain network with an economic and ecological approach to reduce costs and carbon emissions. Research gaps have been addressed, which include determining desired and undesired process outputs, along with simultaneously examining material supply disruptions and final product demand. The mathematical model used is a mixed-integer linear programming model. The primary objective is to minimize costs, and the secondary objective is to minimize carbon emissions. To address this in a single-target function under uncertainty, the fuzzy TH mathematical model has been employed. Uncertainty and disruptions have been studied through scenario building. The model's validation includes a case study in Fars province, where the findings justify the construction of four power plants. The proposed model improved the accuracy of electricity production predictions by 2.1 percent. An analysis and sensitivity study was performed on the TH method's parameters and changes in customer demand values according to predictions. The results show that the proposed model performs well, achieving cost-effectiveness through the integration of economic and ecological approaches. It also successfully reduces greenhouse gas emissions, enhances energy security and stability, and demonstrates a positive impact.

Today, in order to maximize the productivity, sales and profits of factories, various factors must be considered. One of these factors is energy saving, which leads to success in any businesses. Another important factor is rework in the production process, which reduces waste and optimal use of resources. In this research, a linear mathematical programming model has been developed for a multi-stage production system considering energy consumption and the possibility of rework. The objective function of the model is calculated from a combination of energy costs and raw material costs, and the proposed model has three categories of balance constraints, demand constraints and time constraints. The balance constraints, the task of calculating the number of raw materials required and the amount of input materials to each part of the production stage, the demand constraints are the task of calculating the number of final products, and the inventory and time constraints are also the task of calculating the time available to the production of each product. A hypothetical production system is flow shop. To understand the proposed model better, a logical example is designed and solved and analyzed using GAMS software . In the current situation , energy consumption is one of the concerns of policy make rs in the fields of production and industry , and therefore this research with the propo sed model , helps decision makers in manufacturing industries to ensure optimal ene rgy consumption , optimal decisions in adopt multi - stage rework and production conditions .

This paper presents a global supplier selection model for the textile and clothing industry using a fuzzy multi-criteria group decision making approach. Then, the order quantity of each supplier is determined by a mathematical programming model. In the first step, a group fuzzy analysis hierarchical process approach is used to obtain the overall weight of the criteria and sub-criteria and then modified VIKOR is developed in order to calculate the vendor rating. In doing so, a modified VIKOR method with fuzzy-random data is extended due to the existence of both qualitative and quantitative criteria. The qualitative criteria are considered by fuzzy linguistic modeling and quantitative criteria from random data are formulated in a stochastic environment (based on historical data of suppliers). In the second step, a nonlinear programming model is developed to to determine the purchasing quantities from suppliers with multi-sourcing strategy. Finally, using a numerical study, the deployment of the above model is done in the clothing industry and crucial parameters are discovered by sensitivity analysis. Our findings indicate the critical role of customer’s demand and assigned capacity of suppliers in procurement plan.

This research studies cross-docking centers location and vehicles routing scheduling problems simultaneously in a three-level supply chain with discrete pick-up and delivery. The proposed problem is formulated as a mixed-integer nonlinear programming model with the aim of reducing total cost includes cross-docking centers construction cost, transportation fixed and variable costs, earliness and tardiness penalty costs. In this supply chain model, vehicles start from a cross-docking center and pick up different products from various suppliers and after classifying and preparing products at cross-docking centers, a different group of vehicles are sent to deliver products to customers. For delivering any kind of product to each customer, a soft time window is considered. Herein, three types of small, medium and large size instances have been generated randomly and solved by using the proposed simulated annealing algorithm. For small problems, the results from simulated annealing algorithm are compared with the solutions obtained by the exact methods.

Health care solid waste includes all types of waste that are produced as a result of medical and therapeutic activities in hospitals and health centers. About 15% to 20% of these waste materials are infectious waste, which falls within the category of hazardous materials. Infectious waste is the one that must be treated before disposal or recycling. Hence, this paper seeks to develop a bi-objective mixed integer programming model for the management of infectious waste. In the proposed model, in addition to minimizing the chain costs, the reduction of risks for the population exposed to the spread of contamination resulting from infectious waste is also desired. For this purpose, a multi-echelon chain is proposed by taking into account the green location-routing problem, which involves the location of recycling, disposal, and treatment centers through various treatment technologies and routing of vehicles between treatment levels and the hospital. The routing problem has been considered to be multi-depot wherein the criterion of reducing the cost of fuel consumption of heterogeneous cars is used for green routing. Finally, a hybrid meta-heuristic algorithm based on ICA and GA is developed and, following its validation, its function in solving large-scale problems has been investigated. The results indicate that the proposed algorithm is effective and efficient.

The turbulent and dynamic environment of today's business world has become increasingly challenging for organizations operating in different business areas. In such a situation, in order to get rid of these conditions, moving forward and towards the perspective of the new horizons of prosperity and survival is the dream of many of them. In the meantime, according to the specific circumstances and requirements that govern each institution or organization, and in order to achieve their desired productivity, they use certain strategies and programs that outsourcing is one of these strategies. Today, organizations are outsourcing to boost competitive ability and profit and focus on their competitive edge.In this research, a mathematical programming approach is proposed to optimize the issue of outsourcing in the supply chain. In this approach, at first the mathematical model of the problem showed and then in order to solve the problem of the theory of Markov chains described. The objective function of the problem involves minimizing the cost of purchasing, outsourcing and lost demand. In order to solve the problem, three genetic metamorphic algorithms, gray wolves and antlion have been used. After examining the numerical expressions, Gray wolf's algorithm has the highest level of performance. In order to expand the applied dimensions of research in real-world conditions, a company (MPEICO) that manufactures insulators is considered as the case study..

Effective design of supply chain networks is necessary for micro-economic development. The aim of this study is to design green closed-loop supply chain network with financial decisions considering economic and environmental dimensions of development. Such decisions consist of non-supply chain investments and available loans. Uncertainty of demand and investments related to other investments (ROI) are taken into account, too. Proposed model is multi-product, multi-objective, multi-period, stochastic and closed-loop which is modeled as a mixed integer linear programming problem. A scenario path model is applied in order to deal with the uncertainties. The results approved the effectiveness of considering financial decisions. By increasing the number of available loans, the level of the service delivered to the whole system will increase accompanied by a decreasing inclination. Obtained results are based on a case study in plastic recycling industry. Simultaneous consideration of financial decisions and uncertainty in supply chain network design can lead to an improvement in the profit of the supply chain.

In this research, mathematical programming approach is applied to determine healthcare facility locations and to partition areas in defense systems in order to extend future studies. For this purpose, a mathematical model is presented whose goal is to optimize the process of transmitting injured persons in war time. In this model, population zones are partitioned into large groups, called districts. In any district, a healthcare center is present to service to injured persons. The objective functions of the problem are minimizing construction and reconstruction costs, and minimizing costs of transmitting injured persons. The problem contains three general classes of constraints: 1) constraints of sending injured persons between centers 2) constraints of assigning population zones to districts 3) constraint of satisfying feasible structures of districts. In order to solve the problem, the solver CPLEX, an efficient tool of optimization, is used. The results obtained from solving the problem can be used for future studies whenever operating defenses occur.

Coordination and integration between components and subsystems in complex products such as computers, special simulators, aircraft has especial importance. Therefore, it is necessary to pay special attention to the selection of the suppliers for components and subsystems of these products. In other words, in the selection of suppliers for critical and complex components and subsystems, other criteria such as levels of compliance and coordination between suppliers should be considered. The main reason for the inclusion of these criteria in the supplier selection problem is that in the occurrence of supply disruption in sensitive and complex components and subsystems, it’s not easy to switch to other similar suppliers. In this article the amount of compatibility and co-evolutionary between suppliers and their ability to establish interaction with each other as a criteria in supplier selection is modeled using mathematical programming and past performance of suppliers in disruption situation to preserve the most compatibility.

In competitive markets, attracting potential customers and keeping current customers is a survival condition for each company. So, paying attention to the requests of customers is important and vital. In this paper, the problem of order acceptance and scheduling has been studied, in which two types of customers or agents compete in a single machine environment. The objective is maximizing sum of the total profit of first agent's accepted orders and the total revenue of second agent. Therefore, only the first agent has penalty and its penalty function is lateness and the second agent's orders have a common due date and this agent does not accept any tardy order. To solve the problem, a mathematical programming, a heuristic algorithm and a pseudo-polynomial dynamic programming algorithm are proposed. Computational results confirm the ability of solving all problem instances up to 70 orders size optimally and also 93.12% of problem instances up to 150 orders size by dynamic programming.

In order to manage the expenditures in a road transportation network in which the transport demands between cities are less than a truckload capacity, one needs to determine the location of hubs at first, and then collect the cargo from the cities in some routes which are assigned to the appropriate hubs. In this paper, a special case of hub location-routing issue was considered that is suitable for the particular conditions of Iran as cities are located in the sparse and distant places. A mixed integer mathematical programming model was proposed. As the model is NP-hard in nature, a two-phase hybrid method including genetic algorithms and simulated annealing was designed to solve the model. The results of the comparison between the model and the outputs demonstrated the accuracy and speed of the proposed solution method. Finally, a real case including all 31 capital cities of Iran provinces was solved to illustrate the appropriate performance of the solution method.

This paper aims to model and resolve single allocation multi-product hierarchical hub location problem with considering of uncertainty and quality of service. The designed hierarchical hub network has three levels that the top one consists of fully connected central hub nodes and second and third levels are star network of non-central hub nodes with central hub nodes and demand centers to hub nodes (central and non-central), respectively. In the proposed model, objective functions minimizes the sum of transportation and delay costs beside the cost of activating the inactive airline routs, and thereby optimal decision is made on location of hubs, allocation of non-hub nodes to hub nodes and the type of transportation vehicle. For evaluation of the proposed model a collected dataset of Iran is used. Sensitivity analysis of model’s behavior with parameters’ change is done and resulted in management implications.

Parcel delivery is a complex logistic service, as it serves many small or medium-sized customers who may send or receive parcels. Modeling such delivery system needs to integrate two different research areas of hub location and vehicle routing. As it totally depends on the network and the linkage of the nodes, in this paper, some door-to-door service providers are taken into account to provide suitable information for modeling parcel deliveries of sparse and wide countries. Since the proposed mixed-integer programming model is NP-hard, a new multi-steps solution method based on a simulated annealing algorithm and local search is presented. The results of the proposed model and the solution method are evaluated based on some small test problems. The performance of the solution method is illustrated by solving a real case with all capital cities of 31 provinces in Iran.

In this research, a new fuzzy streaming data classification method is presented. Fuzzy data classification is one of the most challenging areas in data analysis due to high-volume and sophisticated computation engaged in data analysis techniques. However, in some other areas such as mathematical programming, great achievements are obtained in modeling of systems measured by fuzzy data. So, in this research classification based on data envelopment analysis (DEA) is selected to handle fuzzy data classification. On the other hand, the proposed method can be applied for dealing with streaming data by using an updating schema. This feature makes the proposed method suitable for non-stationary environment in which data pattern varies over the time. Experiments using simulated data demonstrate the capability of the proposed method in dealing with uncertain and non-stationary environment.

Preventive maintenance scheduling is to perform a series of tasks that prevents or minimizes production breakdowns and improves the reliability. Mathematical models have been developed to solve the preventive maintenance scheduling problem. There are several limitations in the prior work in this area of research. Craft combinations are assumed to be given. The craft combination problem concerns the computation of all combinations of assigning multi skilled workers to accomplishing a particular task. Some research provides heuristic and artificial intelligence approach for integrated solution for the preventive maintenance scheduling problem with multi skilled workforce constraints. The purpose of this study is scheduling the preventive maintenance with constraint programming. Constraint programming is used in varied range of techniques such as artificial intelligence and operations research. Two novel preventive maintenance scheduling model bases on constraint programming are formulated to automatically produce the optimal solution and craft combination in multiple resource problems. Preventive maintenance scheduling problem with multiple and single resource solved with mathematical programming and constraint programming. The solution of these two approaches compared in numerical examples.

This research proposes a mathematical programming model for measurement of the relative efficiency of Iranian industries. Four inputs and three outputs were used to estimate the performance of 9 Kind of industries for the period between 1358 and 1377. The statistical and theoretical data of the study approves that our proposed model can be employed as reliable and valid pattern. The result implies that any of 9 kind of industries is not to be preferred to each other. On the other word; there is not any meaningful differences among this industries by considering the relative efficiency.