Scientia Iranica
Volume:30 Issue: 4, Jul-Aug 2023

An integrated decision in supply chain is a significant principle in order to compete in today’s market. This paper proposes a novel mathematical model in a two-stage supply chain scheduling to cooperate procurement and manufacturing activities. The supply chain scheduling along with the production approach of cellular manufacturing under demand, processing time, and transportation time uncertainties makes business environment sustainably responsive to the changing needs of customers. Uncertainties are formulated by queuing theory. In this paper, a new mixed-integer nonlinear programming formulation is used to determine types of vehicles to carry raw materials, suppliers to procure, priority of each part in order to process, and cell formation to configure work centers. The goal is to minimize total tardiness. A linearization method is used to ease tractability of the model. A genetic algorithm is developed due to the NP-hard nature of the problem. The parameters of the genetic algorithm are set and estimated by Taguchi’s experimental design. Numerous test problems are employed to validate the effectiveness of the modeling and the efficiency of solution approaches. Finally, a real case study and a sensitivity analysis are discussed to provide significant managerial insights and assess the applicability of the proposed model.

This paper discusses a special situation in project management in which an analyst wants to prioritize several independent activities to handle all them one after another, in such a way that there are no precedence relationships over the activities. As a novel idea, in this research, the notion is that the structure of prioritized activities is a linear arrangement, and therefore it could be taken into account as a combinatorial optimization problem. The paper formulates a mathematical model, develops a row-generation solving procedure, and reports the computational results for the problem instances of size up to 300 activities. The results demonstrate the applicability and efficiency of the proposed methodology.

Timetabling problems are among the commonly encountered problems in real life, from education institutions to airline companies. It is generally difficult to obtain optimal solutions for the timetabling problems that vary in terms of structures of constraints and objective functions, and these problems are considered being in NP-hard category, which cannot be solved in polynomial time in real life. In this study, a bi-objective mathematical model is proposed for a course scheduling problem in Kutahya Dumlupinar University Department of Industrial Engineering. While it is aimed in the first objective function to maximize the sum of the preferences of instructors determined by using the Analytic Hierarchy Process Method, it is aimed to minimize the students’ course overlap in the other. Conic scalarization method is used to combine the objective functions. Due to NP-hard nature of the problem, the Tabu Search Algorithm, one of metaheuristic approaches is used to solve it. Using the obtained data, the Tabu Search Algorithm by considering the proposed bi-objective mathematical model is designed for the problem and a software is developed in Excel Visual Basic program. The experimental results are evaluated with Analysis of Variance by using Minitab Program, comparing the results, satisfactory solutions are obtained.

The q-rung orthopair fuzzy sets (q-ROFSs) are increasingly valuable to express fuzzy and vagueinformation, as the generalization of intuitionistic fuzzy sets (IFSs) and Pythagorean fuzzy sets(PFSs). In this paper, we propose complex $q$-rung orthopair fuzzy sets (C$q$-ROFSs) as a new tool to deal with vagueness, uncertainty and fuzziness by extending the range of membership and non-membership function of $q$-ROFS from real to complex number with the unit disc.We develop some new complex $q$-rung orthopair fuzzy Hamacher operations and complex $q$-rung orthopair fuzzy Hamacher aggregation operators, i.e., the complex $q$-rung orthopair fuzzy Hamacher weighted average (C$q$-ROFHWA) operator, and the complex $q$-rung orthopair fuzzy Hamacher weighted geometric (C$q$-ROFHWG) operator. Subsequently, we introduce the innovative concept of a complex $q$-rung orthopair fuzzy graphs based on Hamacher operator called complex $q$-rung orthopair fuzzy Hamacher graphs (C$q$-ROFHGs) and determine its energy and Randi\'{c} energy.In particular, we present the energy of a splitting C$q$-ROFHG and shadow C$q$-ROFHG. Further, we describe the notions of complex $q$-rung orthopair fuzzy Hamacher digraphs (C$q$-ROFHDGs).Finally, a numerical instance related to the facade clothing systems selection is presented to demonstrate the validity of the proposed concepts in decision making (DM).

This paper presents a multi-objective mathematical model which aims to optimize and harmonize a supply chain to reduce costs, improve quality, and achieve a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to increase quality and customer satisfaction and reduce production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was firstly validated. The algorithm's parameters are then adjusted to solve the model with the multi-objective simulated annealing (MOSA) algorithm. To validate the designed algorithm's performance, we solve some examples with General Algebraic Modeling System (GAMS). The MOSA algorithm has achieved an average error of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in average less than 1 minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS couldn't reach an optimal solution for the large problem in a reasonable computational time. The designed algorithm's average error was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this paper.

The increasing severity and frequency of disasters have posed major challenges for people. Amongst, the risks of fatalities and injuries of people with disabilities (PWDs) have significantly increased. The Sendai Framework for Disaster Risk Reduction (SFDRR) initiated a movement to create a "disability-accessible and inclusive environment" which highlighted the problems PWDs faced during disasters. One of the most important issues is providing evacuation and accommodation according to the special needs of PWDs. In this study, a MILP model is proposed to pick up PWDs from different locations and transfer them to shelters. Throughout this research, diverse disabilities, heterogonous vehicles, compatibility types of disabilities and vehicles, multi-depot and adept and amateur operators were considered to help evacuate PWDs. Additionally, 27 problems are solved to examine the efficacy of (μ+1) EA algorithm in large scale problems. Subsequently, a real case study with 500 nodes including pick up, shelters, and depot nodes are analyzed. The computational results illustrate that by adding small-sized (car) and medium-sized (Van) vehicles to the current fleet, the time for tours traveled significantly reduces. Finally, a sensitivity analysis has been conducted to prepare some managerial implications for crisis managers during the occurrence of disasters to help PWDs during evacuation.

This paper focuses on a closed-loop supply chain that deals with disruptions in the distribution centers, and optimizes the network in two dimensions of sustainability: economic and environmental. Economically, the proposed network maximizes the profits of the customers, manufacturers and distributors. Three avenues for cost minimization are designed for the customer by adding the warranty periods, the reworking options, and the incentives for returning the used items. Non-dominated solutions via the Reservation Level-driven Tchebycheff procedure are found by appropriate choice of facility establishment and suitable allocation links considering the disruption in the distribution centers.Environmentally, the model adopts a zero-waste strategy by embedding various return-segmentation policies and a secondary chain. The backward flow depends on the customers' choice of reworking, the validity of the warranty contract, and the quality of the returns. The test results indicate that due to various revenue options, the manufacturing and distribution centers prefer returns with medium-range quality, while due to the incentives offered for the recyclable items, the customers benefit the most from returning the items with the lowest quality. The tests on the probability of disruptions indicate that establishing a minimum number of the manufacturing and/or distribution sites without disruption leads to better overall performance.