compromise programming

Concerning global warming and the Greenhouse gas (GHG) effect, clean energy resources have captured researchers' interest recently. Biomass materials are among important biofuels and bioenergy production resources that have the potential to replace fossil fuels. Using biomass materials leads to a decline in GHG emission and air pollution levels, not being dependent on fossil fuels, and provide energy security. Due to the importance of bioenergy and biofuels, a multi-product, multi-period, and green mathematical model has been developed to improve economic and environmental objectives for bioethanol and the electricity supply chain. It includes the following decisions: determining production centers' location and capacity, technology selection, determining inventory holding level, biomass type selection, allocation, amount of material flow, and determining transportation modes. In this study, a scenario-based robust compromise programming approach (SRCP) is developed for the bi-objective solution of the provided mathematical model and determining Pareto optimal points under uncertain conditions. Finally, the performance and effectiveness of SRCP are provided, and the results obtained from the case study in Iran are analyzed. According to the results, Annual electricity and bioethanol production capacity are at least 8000 million kWh and 1250 kton, respectively, satisfying 10% of electricity and 5% of gasoline demand in 6 provinces of Iran. The sensitivity analysis also shows that equal weight for both objectives can be more logical for decision makers.

This paper discusses making decisions in the glass container industry. The production of glass containers for the packaging of food and beverages is one of the most important parts of glass industries. In this research, the decision is made on the production plan for the glass container industries from the perspective of various executive stakeholders. In this regard, two models are initially presented: 1) the first model with a production approach, i.e. considering the objectives and constraints of the production stakeholders, and 2) the second model with a sales approach, taking into account the objectives and constraints of the sales stakeholders. Also, in the sales approach, by defining a new index, the importance of meeting customers’ demands is considered separately and according to different criteria. TOPSIS technique as one of the multi-attribute decision making methods is employed to calculate the noted parameter. Then, a multi-objective integrated model with a managerial approach for decision making on the production planning in the glass container industry is proposed, in which it is attempted to consider the viewpoints of various stakeholders. Finally, the proposed approach is implemented in one of the largest companies producing glass containers in Iran. In this regard, compromise programming is used to solve the final model. It is one of the multi-objective optimization methods which is classified under non-preferred methods. The obtained results show the efficiency of the proposed integrated approach for the studied company. It is also worth noting that the obtained results are presented for the management of the studied company and the results are found to be useful.

The transportation of hazardous materials such as gasoline has unique features due to the nature of these type of materials and the importance role of these material in human life. Therefore, determining the routing of these materials is more challengeable than non-hazardous one due to more dangerous and risky condition besides the transportation costs. Since the distribution of gasoline take places in boarding (24 hours), logistics system should be able to meet these requirements in all of days. In this paper, a new model is developed for planning the route of vehicles as 7×24 (boarding) in order to minimizing the risk of hazardous materials. The proposed vehicle routing model in this paper has been attempted to consider minimization of transportation costs, reduce emissions due to gasoline distribution, consumption, diminishing the route’s risk and suggesting suitable time for servicing customers. The proposed model turns in to a single objective model using compromise programming and then solved by GAMS software using the data of case study and then its results have been reported.

Designing highly reliable and economical systems is of interest in today’s competitive world. In this paper, enhancing system reliability through redundancy allocation is investigated, where the supplier selection is taken into account and redundant components are provided from appropriate suppliers with the most suitable offers such as discount on purchasing price of components, warranty length of components, things like that, so that the system reliability, profit and the warranty length proposed by suppliers are simultaneously maximized. The resulting multi-objective model is then solved with the well-known compromise programming approach and the performance of the proposed approach is investigated through a numerical example.

One of the primary concerns in any system design problem is to prepare a highly reliable system with minimum cost. One way to increase the reliability of systems is to use redundancy in different forms such as active or standby. In this paper, a new nonlinear multi- objective integer programming model with the choice of redundancy strategy and component type is developed where standby strategy is of cold type. In the proposed model, system’s reliability is maximized along with minimizing system’s cost and weight. The proposed model contributes to the literature by determining the redundancy strategies concurrently with determining redundancy levels and component types. The multi-objective model is solved using the mathematical compromise programming technique for different Lp metrics and produces different Pareto solutions.