MASALAH TRANSPORTASI MULTI-OBJECTIVE FUZZY DUA TAHAP MENGGUNAKAN METODE ZERO SUFFIX DAN PENDEKATAN FUZZY PROGRAMMING

Masalah transportasi multi-objective fuzzy merupakan masalah transportasi dengan fungsi tujuan lebih dari satu yang supply dan demand diberikan dalam bilangan fuzzy yaitu salah satunya bilangan fuzzy heksagonal. Dalam masalah transportasi ada keadaan di mana gudang penerima barang tidak mampu menampung permintaan barang sehingga dilakukan pengiriman secara 2 tahap. Untuk menyelesaikan masalah transportasi dengan parameter bilangan fuzzy diubah menggunakan teknik Robust Ranking sehingga dapat dicari solusi optimal. Pada penelitian ini, metode transportasi yang digunakan adalah metode Zero Suffix. Pengoptimalan semua fungsi tujuan pada masalah transportasi multi-objective tidak dapat diselesaikan hanya dengan metode transportasi biasa karena terdapat fungsi tujuan yang saling konflik. Pendekatan Fuzzy Programming diperlukan untuk menyelesaikan masalah dengan fungsi tujuan yang saling konflik dengan menentukan solusi optimal pareto dalam masalah transportasi multi-objective fuzzy dua tahap. Solusi akhir dari masalah transportasi multi-obejctive fuzzy dua tahao diperoleh dari penjumlahan solusi optimal pareto tahap pertama dan tahap kedua. Kata kunci : masalah transportasi, multi-objective, bilangan fuzzy heksagonal, masalah transportasi dua tahap, zero suffix , pendekatan fuzzy programming. ******** Multi-objective fuzzy transportation problem are transportation problem with multi objective function which supply and demand were given in hexagonal fuzzy number. There is situation in transportation problem due to the unable storage to receive the quantity of product, then the shipment was implemented in two stage. To solve the fuzzy transportation problem, fuzzy number parammeter are changed using Robust Ranking technique so that the optimal solution can be resolved. In this thesis using Zero Suffix method. To optimizing all objective function on multi-objective transportation problems can not be solved only by ordinary transportation method because there are conflicting objective function. Fuzzy programming approach is used to determine the pareto solution of multi-objective fuzzy two stage transportation problem. The final solution of multi-objective fuzzy two stage transportation problem is sum of pareto solution first stage with pareto solution second stage. Keywords : transportation problem, multi-objective, hexagonal fuzzy numbers, two-stage transportation problem, zero suffix , fuzzy programming approach

Retrofitting Transportation Network Using a Fuzzy Random Multiobjective Bilevel Model to Hedge against Seismic Risk

This paper focuses on the problem of hedging against seismic risk through the retrofit of transportation systems in large-scale construction projects (LSCP). A fuzzy random multiobjective bilevel programming model is formulated with the objectives of the retrofit costs and the benefits on two separate levels. After establishing the model, a fuzzy random variable transformation approach and fuzzy variable approximation decomposition are used to deal with the uncertainty. An approximation decomposition-based multi-objective AGLNPSO is developed to solve the model. The results of a case study validate the efficiency of the proposed approach

Reverse Logistics Network Design with a 3-Phase Interactive Intuitionistic Fuzzy Goal Programming Approach: A Case Study of Covid-19 in Pathum Thani, Thailand

During outbreaks, a vast quantity of Infected Medical Waste (IMW) can be substantially generated in a short period, which poses a massive risk to medical personnel and surrounding communities. This study proposes an Intuitionistic Fuzzy Multi-Objective Multi-Period Mixed-Integer Linear Programming (IFMOMILP) model for effective IMW management in outbreaks under uncertainty, considering financial and risk factors subject to a priority from Decision Makers (DMs). The primary emphasis is on determining the optimal locations and capacity levels for temporary facilities, including temporary storage and treatment centers, as well as the optimal transportation routes. A 3-phase interactive Intuitionistic Fuzzy Goal Programming (i-IFGP) approach is developed to solve this IFMOMILP model. First, the Jiménez approach is applied to handle the uncertainties. Then, the problem is solved by Intuitionistic Fuzzy Goal Programming (IFGP). An actual case study of the COVID-19 outbreak in Pathum Thani province in Thailand was carried out to demonstrate the effectiveness of the proposed approach. The proposed approach yields solutions with varying feasibility degrees and scaling factors, providing alternatives for DMs. Then, the score function is utilized to imply DMs’ satisfaction with the outcomes, which is a concrete measure since it can reflect the intention of the DMs

Two-stage based dynamic earth-rock transportation assignment problem under fuzzy random environment to earth-rock dam construction

This paper discusses a two-stage based dynamic transportation assignment problem (TS-based DTAP) under a fuzzy random environment in an earth-rock transportation system. This problem is a multi-objective dynamic programming optimization process for minimizing total operational cost, transportation duration and total waste. Triangular fuzzy random numbers are used for the uncertain parameters, and a hybrid crisp approach and an expected value operator are introduced to deal with these uncertainties. A dynamic programming based contraction particle swarm optimization is developed to solve the proposed expected value model for TS-based DTAP. Then, the earth-rock dam construction at Pubugou Hydropower project is used as a practical application to verify the proposed approach. Results and analysis are presented to highlight the performance of the proposed TS-based DTAP model and the optimization method, which proves to be effective and relatively efficient compared to the models under other environments and a standard PSO algorithm

Robust Multi-Objective Sustainable Reverse Supply Chain Planning: An Application in the Steel Industry

In the design of the supply chain, the use of the returned products and their recycling in the production and consumption network is called reverse logistics. The proposed model aims to optimize the flow of materials in the supply chain network (SCN), and determine the amount and location of facilities and the planning of transportation in conditions of demand uncertainty. Thus, maximizing the total profit of operation, minimizing adverse environmental effects, and maximizing customer and supplier service levels have been considered as the main objectives. Accordingly, finding symmetry (balance) among the profit of operation, the environmental effects and customer and supplier service levels is considered in this research. To deal with the uncertainty of the model, scenario-based robust planning is employed alongside a meta-heuristic algorithm (NSGA-II) to solve the model with actual data from a case study of the steel industry in Iran. The results obtained from the model, solving and validating, compared with actual data indicated that the model could optimize the objectives seamlessly and determine the amount and location of the necessary facilities for the steel industry more appropriately.This article belongs to the Special Issue Uncertain Multi-Criteria Optimization Problem

Developing an Overbooking Fuzzy-Based Mathematical Optimization Model for Multi-Leg Flights

Overbooking is one of the most vital revenue management practices that is used in the airline industry. Identification of an overbooking level is a challenging task due to the uncertainties associated with external factors, such as demand for tickets, and inappropriate overbooking levels which may cause revenue losses as well as loss of reputation and customer loyalty. Therefore, the aim of this paper is to propose a fuzzy linear programming model and Genetic Algorithms (GAs) to maximize the overall revenue of a large-scale multi-leg flight network by minimizing the number of empty seats and the number of denied passengers. A fuzzy logic technique is used for modeling the fuzzy demand on overbooking flight tickets and a metaheuristics-based GA technique is adopted to solve large-scale multi-leg flights problem. As part of model verification, the proposed GA is applied to solve a small multi-leg flight linear programming model with a fuzzified demand factor. In addition, experimentation with large-scale problems with different input parameters’ settings such as penalty rate, show-up rate and demand level are also conducted to understand the behavior of the developed model. The validation results show that the proposed GA produces almost identical results to those in a small-scale multi-leg flight problem. In addition, the performance of the large-scale multi-leg flight network represented by a number of KPIs including total booking, denied passengers and net-overbooking profit towards changing these input parameters will also be revealed

Stochastic multi-period multi-product multi-objective Aggregate Production Planning model in multi-echelon supply chain

In this paper a multi-period multi-product multi-objective aggregate production planning (APP) model is proposed for an uncertain multi-echelon supply chain considering financial risk, customer satisfaction, and human resource training. Three conflictive objective functions and several sets of real constraints are considered concurrently in the proposed APP model. Some parameters of the proposed model are assumed to be uncertain and handled through a two-stage stochastic programming (TSSP) approach. The proposed TSSP is solved using three multi-objective solution procedures, i.e., the goal attainment technique, the modified ε-constraint method, and STEM method. The whole procedure is applied in an automotive resin and oil supply chain as a real case study wherein the efficacy and applicability of the proposed approaches are illustrated in comparison with existing experimental production planning method