Fuzzy Multi-objective Approach Research Articles

Optimizing Multi-Channel Green Supply Chain Dynamics with Renewable Energy Integration and Emissions Reduction

In response to the global imperative of mitigating greenhouse gas emissions (GHGs) and the shifting landscape of business models toward multi-channel structures, this study delves into the intricacies of a green supply chain. Operating through both online and traditional channels with uncertain demands, the producer’s distribution strategy prompts an exploration of supply chain dynamics. Utilizing an integer programming model, this study calculates optimal prices, optimizes total profit, and minimizes transportation costs to curtail carbon dioxide emissions, depending on the transportation mode. Additionally, this study incorporates renewable energy sources into the production and transportation processes to further minimize carbon dioxide emissions. The integration of renewable energy not only supports environmental goals, but also contributes to the overall profitability of the supply chain by reducing energy costs. Employing a theoretical technique for linearization, the model, resolved through the Jimenez and TH methods, demonstrates efficacy in reconciling economic and environmental goals. The Jimenez method enables the transformation of fuzzy parameters into deterministic equivalents, allowing for a more reliable optimization during uncertainty, while the TH method provides an interactive fuzzy multi-objective approach, aligning the model’s dual objectives for both economic and environmental goals. Notably, when transportation costs to both markets are equal, the model prioritizes devices with a lower environmental impact, showcasing adaptability. Furthermore, the proposed solution empowers decision makers to influence pricing and enhance the entire supply chain’s profitability. In conclusion, this research offers nuanced insights, strategically aligning economic viability with environmental sustainability in the discourse on green supply chains.

Ride-Hailing Matching with Uncertain Travel Time: A Novel Interval-Valued Fuzzy Multi-Objective Linear Programming Approach

This study introduces an innovative approach to tackle multi-objective linear programming (MOLP) problems amidst uncertainty, employing interval-valued fuzzy numbers. The method is tailored to resolve ride-hailing matching challenges encompassing uncertain travel times. Findings reveal that managing uncertainty parameters within interval-valued fuzzy MOLP is achieved through strategic reformulations, focusing on constraint coefficients, resulting in streamlined linear programming formulations conducive to solution simplicity. The efficacy of the proposed model in efficiently handling ride-hailing matching quandaries is demonstrated. Moreover, this study delves into the prospective applications of the developed method, including its potential for generalization to address non-linear programming (NLP) issues pertinent to the ride-hailing domain. This research advances decision-making processes under uncertainty and paves the way for broader applications beyond ride-hailing.

A fuzzy multiobjective linear programming approach for green multiechelon distribution network design

PurposeDistribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.Design/methodology/approachThe problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.FindingsFor the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.Originality/valueThe proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

Multi-skill resource-constrained project scheduling problem considering overlapping: fuzzy multi-objective programming approach to a case study

Resource-constrained project scheduling problem (RCPSP) is a broadly researched issue in the literature. The purpose of the classic form of the problem is scheduling a set of activities considering resource and precedence constraints for minimizing the project completion time. Companies mostly deal with the issue of properly assigning multi-skilled workforces and maintaining the needed skill levels while implementing projects. In this study, a novel MILP model with three objectives is presented to tackle multi-skill RCPSP (MS-RCPSP). This study concentrates on minimizing project makespan, minimizing resource costs as well as tardiness costs, and maximizing quality under uncertainty. However, the standard MS-RCPSP is not able to consider several practical engineering requirements owing to its narrow assumptions. Therefore, key assumptions including overlap between activities, tardiness penalties of activities and the rework duration concept for activities in this model are considered. Due to the complexity of the real world, interval valued fuzzy numbers are taken into account for some of the problem’s parameters. The efficiency of the proposed mathematical framework is represented using both a real case study to construct a railway bridge with 34 activities and large-size problem instances from MMLIB (MM50 and MM100). Since this model is multi-objective, a new extended IVF-ABS approach is presented in this study. Finally, the proposed approach is compared with two methods, namely SO and ABS, from the literature.

A new interval-valued fuzzy multi-objective approach for project time–cost–quality trade-off problem with activity crashing and overlapping under uncertainty

PurposeMost projects are facing delays, and accelerating the pace of project progress is a necessity. Project managers are responsible for completing the project on time with minimum cost and with maximum quality. This study provides a trade-off between time, cost, and quality objectives to optimize project scheduling.Design/methodology/approachThe current paper presents a new resource-constrained multi-mode time–cost–quality trade-off project scheduling model with lags under finish-to-start relations. To be more realistic, crashing and overlapping techniques are utilized. To handle uncertainty, which is a source of project complexity, interval-valued fuzzy sets are adopted on several parameters. In addition, a new hybrid solution approach is developed to cope with interval-valued fuzzy mathematical model that is based on different alpha-levels and compensatory methods. To find the compatible solution among conflicting objectives, an arithmetical average method is provided as a compensatory approach.FindingsThe interval-valued fuzzy sets approach proposed in this paper is denoted to be scalable, efficient, generalizable and practical in project environments. The results demonstrated that the crashing and overlapping techniques improve time–cost–quality trade-off project scheduling model. Also, interval-valued fuzzy sets can properly manage expressions of the uncertainty of projects which are realistic and practical. The proposed mathematical model is validated by solving a medium-sized dataset an adopted case study. In addition, with a sensitivity analysis approach, the solutions are compared and the model performance is confirmed.Originality/valueThis paper introduces a new continuous-based, resource-constrained, and multi-mode model with crashing and overlapping techniques simultaneously. In addition, a new hybrid compensatory solution approach is extended based on different alpha-levels to handle interval-valued fuzzy multi-objective mathematical model of project scheduling with influential uncertain parameters.

Optimal energy management in a microgrid with known power from grid based on particle swarm optimization embedded Fuzzy multi-objective approach

The optimal energy management among distributed energy resources (DERs) in a microgrid is pivotal for its satisfactory operation. This paper presents the optimal dispatch of combined heat and power (CHP) units and a heat boiler is conducted in zero bus concept based microgrid (ZBMG) and in grid connected microgrid (GMG). In ZBMG, the utility exchanges a prescheduled amount of power to the microgrid. In this work, the optimal dispatch of various CHP units and boiler in all cases under GMG and ZBMG systems with power and heat demand is examined. The generation cost, emission, and energy loss objectives are fuzzified and solved using particle swarm optimization (PSO) algorithm. The results show that minimum objectives are attained in ZBMG compared to GMG system. The analysis is carried out on a 33-bus and 69-bus distribution systems with a 24-hour electrical and heat load profile pattern.

Towards sustainable project scheduling with reducing environmental pollution of projects: fuzzy multi-objective programming approach to a case study of Eastern Iran

Towards sustainable project scheduling with reducing environmental pollution of projects: fuzzy multi-objective programming approach to a case study of Eastern Iran

A sustainable reverse supply chain network design considering inventory-location-routing problem and cross-docking scheduling under uncertainty

Designing an integrated supply chain network (SCN) by considering economic, social, and environmental aspects will increase its sustainability. This paper proposes a bi-objective mixed-integer linear programming model to design a sustainable closed-loop SCN considering location-inventory-routing problem, cross-docking scheduling, vehicles failure, inflation, routes reliability, impact of load on fuel consumption, discount on purchases from suppliers, supplier selection, and order allocation under uncertainty. The proposed bi-objective model minimises the total costs and maximises the created jobs, simultaneously. A scenario-based approach is used to cope with demand uncertainty while a fuzzy multi-objective solution approach is employed to convert the bi-objective model into a single-objective one. Finally, the performance of the proposed bi-objective mathematical model is evaluated using data from a wire and cable industry in Iran.

A green fuzzy multi-objective approach to the RNP problem for LTE networks

A green fuzzy multi-objective approach to the RNP problem for LTE networks

Spatial water footprint optimization of crop planting: A fuzzy multiobjective optimal approach based on MOD16 evapotranspiration products

Agriculture is required to produce more food while reduce water consumption at the same time. To improve the irrigation water use efficiency in agricultural production, this study proposed a fuzzy multiobjective optimal modeling approach based on Moderate-resolution Imaging Spectroradiometer (MODIS) global evapotranspiration products for optimizing spatial water footprint of crop planting. This study attempts to accurately estimate the spatial crop water footprint based on remote sensing information, and effectively plan crop planting structure considering conflicting targets, spatial water footprint, and limited irrigation water resources under fuzzy uncertainty. To test its feasibility, the proposed approach is employed to a typical semi-arid region in Northwest China. Three performance evaluation indicators, including synthetic degree (SD), sustainability index (SI), and approximation degree of ideal objective value (AD), are applied for measuring model performance and try to give a more comprehensive picture of agricultural land use strategies. Results show that (1) the remote sensing data can effectively improve the accuracy of water footprint estimation, and identify crop planting advantage distribution in spatial; (2) the planting area of crops with high evapotranspiration should be increased in irrigation districts near the upstream with high precipitation, which can improve economic benefit, increase utilization rate of green water, and fairness of allocation; (3) through optimization, the average total, blue, and gray water footprint among IDs are drop while green water utilization rates is raised comparing with status quo (2014); (4) Water scarcity will not only damage the coordinated development degree of economy, society and environment, but also hurt the regional sustainable development ability and the realization of development objectives. The proposed optimal modeling framework can help optimize the spatial water footprint of crop planting and improve irrigation water use efficiency, which can be applied to similar regions that suffer from water scarcity.

Material requirements planning with a fuzzy model

There are numerous uncertainty forms and modes capable of affecting the Material requirements planning (MRP) systems. The aim of this study was to provide a fuzzy multi-objective decision-making model for MRP problem. In this paper, a fuzzy multi-objective linear programming approach was proposed for MRP model with a fuzzy Lead Time (LT), in which, the MRP models of classic numbers were combined up to the probability of occurrence of each of the possible LTs as far as possible. Then, an objective function was considered, which maximizes the probability of occurrence of LTs. The initial values ​​of the MRP model were applied to this objective function and some decision makers were shown that with their risky behavior accept some LTs, which improve other goals, but their probability of occurrence is poor.

An Intuitionistic Fuzzy Multi-Objective Goal Programming Approach to Portfolio Selection

Portfolio selection can be regarded as a type of multi-objective decision problem. However, traditional solution methods rarely discussed the decision maker’s nonsatisfaction and hesitation degrees with regard to multiple objectives and they require many extra binary variables, which lead to tedious computational burden. Based on the above, the aim of this paper is to develop a new and unified intuitionistic fuzzy multi-objective linear programming (IFMOLP) model for such portfolio selection problems. The nonmembership functions are constructed by the pessimistic, optimistic, and mixed approaches so as to perfect the traditional intuitionistic fizzy (IF) inequalities and IF theory. The decision maker’s hesitation degrees with regard to multiple objectives are represented by using IF inequalities, and the new IFMOLP model based on IF inequalities is proposed. The IFMOLP problems are solved by the S-shaped membership functions without extra binary variables required by the piecewise-linear method. Finally, the portfolio selection model under IF environments based on IFMOLP is established, and a real example is analyzed to demonstrate its validity and superiority. The developed unified IFMOLP model and method can not only effectively solve multi-objective decision problems with nonsatisfaction and hesitation degrees but also remarkably reduce the complexity of the nondeterministic polynomial-hard problems.

Sustainable Closed-Loop Mask Supply Chain Network Design Using Mathematical Modeling and a Fuzzy Multi-Objective Approach

The outbreak of the deadly coronavirus, which is increasing the number of victims every day, has created many changes in today’s world. The use of various masks is the most important social tool against this virus. Given the importance of rapid and quality supply of masks in the current situation, it is necessary to study supply chain in particular. In this research, the design of a closed chain supply chain network for different types of masks is assessed. The studied supply chain includes suppliers, manufacturers, distributors, and retailers in the forward flow and collection centers, separate centers, recycling centers, and disposal centers in the backward flow. In this regard, a multi-objective mathematical model with the objectives of increasing the total profit and reducing the total environmental impact, and maximizing social responsibility is presented. The optimization of this mathematical model has been done using a fuzzy optimization approach in GAMS software. The results of this study show that maximizing the total profit and minimizing the environmental effects and maximizing social responsibility are in contrast to each other. In addition, the sensitivity analysis indicated that the customers’ demand can affect all aspects of the sustainable supply chain simultaneously.

A location-inventory-routing model for green supply chains with low-carbon emissions under uncertainty.

The location-inventory-routing modeling is an integrated and comprehensive approach to the interconnected location planning, inventory management, and vehicle routing problems in supply chain management. Supplier selection and order allocation are critical operational and strategic decisions in green supply chains. Green supply chain management is an environmental approach to sourcing and production that considers sustainability in every supply chain stage. In this study, a novel bi-objective mixed-integer linear programming model is formulated to solve the location-inventory-routing problems in green supply chains with low-carbon emissions under uncertainty. The proposed model is used for supplier selection and order allocation by considering the location priorities, heterogeneous vehicle routing, storage needs, uncertain demand, and backorder shortage. The formulated bi-objective model is solved with a weighted fuzzy multi-objective solution approach coupled with a novel intelligent simulation algorithm to ensure the feasibility of the solution space. We generate and solve different-sized problems to demonstrate the applicability and efficacy of the proposed model.

Shapley-Shubik Index incorporated debottlenecking framework for sustainable food-energy-water nexus optimised palm oil-based complex

Palm oil mill effluent (POME) elimination has been considered recently as the alternative to expensive biogas recovery to conquer environmental criticisms in the palm oil industry involving the release of biogas (i.e., a primary contributor towards climate change), during POME treatment. With POME elimination and mill-refinery integration considered, the integrated palm oil-based complex (POBC) serves as an appropriate system for sustainable food-energy-water (FEW) nexus evaluations. In this respect, multi-objective optimisation and debottlenecking of POBC has been suggested to address the economic and environmental trade-offs in palm oil mill (POM) for cleaner production. In this study, a stepwise debottlenecking framework was proposed to include previous fuzzy multi-objective optimisation approach and a new debottlenecking method to improve the fuzzy optimal POBC design and evaluate the impact of process on FEW nexus contributions. Each process creates distinctive impact or influence on the POBC performance. Shapley-Shubik Power Index (SSI) has been applied in the notion of power for yes-no voting systems. By evaluating the operate-fail possibilities of internal processes, SSI can be utilised to allocate the power of each process in achieving or failing the POBC performance target, prior to identifying the system bottleneck (SB) in terms of process stage. Therefore, a novel process-level debottlenecking approach with SSI incorporation for a sustainable FEW system was proposed. The SB of greatest impact on overall POBC performance within the fuzzy optimal flowsheet was identified based on SSI allocation and weightage assignment considering decision-maker's interest in multiple goals. Subsequently, the debottlenecked POBC flowsheet with improvement in SB process parameter was generated subjected to profit maximisation and environmental constraints. In this study, the nut/kernel separation and biomass cogeneration system was identified as the SB of fuzzy optimal POBC. The debottlenecked results show 0.8% and 51% of profit and net energy improvement upon 30% increment of boiler efficiency. The debottlenecked flowsheet is validated in the benefit-to-drawback ratio analysis with a feasible score of 3.9. The POME-eliminated POBC generated via the integrated multi-objective optimisation and debottlenecking framework is applicable for POM retrofit to secure 13%, 97% and 47% of improvements in terms of economic potential, greenhouse gas emissions and water footprint compared to the status quo biogas utilisation scenario. The results of the study could aid palm oil holders in real-life planning of budget distribution and maintenance schedule for process stages in the retrofitted POM.

The role of bank affiliation in bank efficiency: a fuzzy multi-objective data envelopment analysis approach

This paper examines differences in bank efficiency between banks affiliated with single-bank holding companies and those affiliated with multi-bank holding companies by applying a fuzzy multi-objective two-stage data envelopment analysis technique. Using a sample of U.S. commercial banks covering 1994–2018, the results show that banks affiliated with multi-bank holding companies are more efficient than those affiliated with single-bank holding companies, suggesting that the former takes advantage of their parents’ resources to enhance their efficiency, consistent with the internal capital market theory. They also show that banks with a powerful CEO exhibit lower efficiency than others. Moreover, there is an inverted U shape relationship between multi-bank holding company structure and bank efficiency, suggesting the presence of an optimal number of multi-bank holding subsidiaries that maximizes efficiency.

Sustainable microgrid design considering blockchain technology for real-time price-based demand response programs

In recent years, rapidly increasing electric power demand and organizational concerns regarding social and environmental issues have resulted in significant attention being given to microgrids. However, sustainable microgrids that simultaneously address economic benefits and environmental and social issues have not been broadly explored by researches. This study addresses the sustainable microgrid design problem by leveraging blockchain technology to provide the real time-based demand response programs. Three sustainable objectives (economy, environment, and society) are formulated by a multi-objective mixed integer-linear programming model. A robust fuzzy multi-objective optimization approach is proposed to determine the optimal number, location, and capacity of renewable distributed generation units as well as the equilibrium supply and dynamic pricing decisions under uncertain demand, capacity, and economic, environmental, and social parameters. The proposed model and solution approach are then applied to a case study in Vietnam. We find that blockchain technology-based sustainable microgrid can result in a 1.68% and 2.61% increase of profitability and consumer satisfaction, respectively, and a 0.97% reduction of environmental impacts.

An integrated open-loop supply chain network configuration model with sustainable supplier selection: fuzzy multi-objective approach

Supply chain management is an essential part of most companies to reduce costs and increase the profit for a competitive business environment. The success of companies is directly connected to the performance of the supply chain. The sustainability of businesses broadly depends on the purchasing decision, and the process becomes more complex with resource limitations. In addition, relationships with suppliers are considered for optimal production and risk minimization in the supply chain network. This research aims to design and optimize a multi-echelon, multi-period, and multi-objective open-loop supply chain (OLSC) network design. The objectives are deemed to be the total costs, the minimization of environmental costs, and the maximization of the importance level of suppliers. Furthermore, a fuzzy approach based on interval type-2 fuzzy sets is designed to evaluate the supplier performance according to sustainable qualitative criteria. After determination of the weights of criteria and evaluation of suppliers by interval type-2 fuzzy numbers, the final score of suppliers is calculated based on the weights of criteria and evaluation scores. Three fuzzy programming approaches are utilized to solve the developed OLSC model. A numerical example is provided to validate the model and effectiveness of the proposed evaluation procedure. The results demonstrate the feasibility of the proposed evaluation procedure and the achievement of a compromise in the developed model.

A new weighted fuzzy programming model for supplier selection and order allocation in the food industry

Purpose The purpose of this paper is to develop a combined intuitionistic fuzzy analytic hierarchy process (IFAHP) and fuzzy multi-objective optimization approach to select suppliers and allocate the orders to them in the bottled water production context. Design/methodology/approach First, the primary weights of criteria associated with the supplier selection problem are calculated using the IFAHP technique. Then a fuzzy multi-objective optimization model is developed to allocate the appropriate amount of orders to each supplier. Findings The proposed methodology has been successfully implemented in the case of an Iranian food company in its bottled water factory. Results demonstrate our model is capable of practically handling the uncertainty in DMs’ preference that leads to effective and efficient supplier selection and order allocation decisions. Originality/value The authors develop a novel hybrid decision-making tool to tackle the uncertainty in decision-makers’ opinions with a demonstrated applicability and some promising outcomes in efficiently allocating the order quantity to suppliers in the area of bottled water production.

Grasshopper optimization algorithm based two stage fuzzy multiobjective approach for optimum sizing and placement of distributed generations, shunt capacitors and electric vehicle charging stations

Abstract In this paper a two stage Grasshopper Optimization Algorithm (GOA) based Fuzzy multiobjective approach is proposed for optimum sizing and placement of Distributed Generations (DGs), Shunt Capacitors (SCs) and Electric Vehicle (EV) charging stations for distribution systems. In the first stage Fuzzy GOA approach is used for optimum sizing and allocation of DGs and SCs for improving the substation power factor, real power loss reduction and voltage profile improvement of the distribution system. In the second stage distribution system integrated with DGs and SCs is considered and fuzzy GOA approach is used for identifying optimum locations for EV charging stations and number of vehicles at the charging stations. EV battery charging load models are developed from the Lithium ion battery charging characteristic curves for load flow analysis. Simulation results are shown to show the advantages of fast converging properties of GOA over GA and PSO techniques. Simulation results are demonstrated on 51 bus and 69 bus distribution networks to show the advantages of proposed methodology compared to conventional objective based simultaneous optimization approach. The effect of EV load growth and the effect of uncertainties in DGs and distribution system load are shown on the distribution system performance.