Shahed UniversityJournal of Quality Engineering and Production Optimization4120190601Comparing Two-Echelon and Single-Echelon Multi-Objective Capacitated Vehicle Routing Problems11689310.22070/jqepo.2019.3262.1066ENMehranehEsmaeiliDepartment of Industrial Engineering, Shahed University, Tehran, IranRashedSahraeian ,Department of Industrial Engineering, Shahed University, Tehran, IranJournal Article20180304This paper aims to compare a two-echelon and a single-echelon distribution system. A mathematical model for the Single-Echelon Capacitated Vehicle Routing Problem (SE-CVRP) is proposed. This SE-CVRP is the counterpart of Two-Echelon Capacitated Vehicle Routing Problem (2E-CVRP) introduced in the authors’ previous work. The proposed mathematical model is Mixed-Integer Non-Linear Programming (MINLP) and minimizes 1) the total travel cost, 2) total waiting time of customers, and 3) total carbon dioxide emissions, simultaneously, in distributing perishable products. Applying some linearization methods changes the MINLP model into the Mixed Integer Linear Programming (MILP). In 2E-CVRP, shipments are delivered to customers by using intermediate depots named satellites while in SE-CVRP, direct shipments are used. Considering SE-CVRP, it was assumed that, by eliminating satellites, the large vehicles in depot were used for distribution. Because of the NP-hardness of the Vehicle Routing Problem (VRP) and its extensions, the NSGA-II algorithm was applied to solve the model. The objective functions of both distribution systems were compared in different size issues. The obtained results indicated that by considering large vehicles in an SE-CVRP, this distribution system would outperform the two-echelon one for all objectives of the small-size problems, the first two objectives of medium-size problems, and the first and third objectives of large-size problems.https://jqepo.shahed.ac.ir/article_893_131f8d60f21f37eb1d6c28f9f1c4c0e6.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190601Comparing Mixed-Integer and Constraint Programming for the No-Wait Flow Shop Problem with Due Date Constraints172489410.22070/jqepo.2019.1077.1029ENHamedSamarghandiDepartment of Finance and Management Science, Edwards School of Business,
University of Saskatchewan, Saskatoon, Saskatchewan, Canada, S7N 5A7FarzadFirouzi JahantighDepartment of Industrial Engineering
University of Sistan and Baluchestan
Zahedan, IranJournal Article20181006The impetus for this research was examining a flow shop problem in which tasks were expected to be successively carried out with no time interval (i.e., no wait time) between them. For this reason, they should be completed by specific dates or deadlines. In this regard, the efficiency of the models was evaluated based on makespan. To solve the NP-Hard problem, we developed two mathematical models. Once we solved our problem using Mixed-Integer Programming Model (henceforth MIPM) and then, we applied a Constraint Programming Model (CPM); finally, we compared the optimality of the presented results.https://jqepo.shahed.ac.ir/article_894_3a831fab9adca742dbb8277d46535bab.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190601Optimal Process Adjustment under Inspection Errors Considering the Cycle Time of Production254076810.22070/jqepo.2018.2680.1053ENMohammad SaberFallahnezhaduniversity of yazd0000-0003-3343-2769SomaieAyeenyazd univesityFaezeZahmatkesh SaredorahiYazd universityHasanRasayUniversity of KurdistanJournal Article20190601Process adjustment, also known as process targeting, is one of the classical problems in the field of quality control and production economics. In the process adjustment problem, it is assumed that process parameters are variables and the aim is to determine these parameters such that certain economic criteria are optimally satisfied. The aim of this paper is to determine the optimal process adjustment in a two-stage production system with rework loops. An absorbing Markov chain model is developed in which all items are inspected for conformance with their specification limits. The cycle time of production process is included in the model for optimizing total profit of the system. Also, effects of inspection errors are investigated.<br /> https://jqepo.shahed.ac.ir/article_768_b5c65f134d60ab532ce3b4774b0c8eb7.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190601Determining Maintenance Opportunity Window (MOW) in Job-Shop Systems by Considering Manpower of Maintenance415476910.22070/jqepo.2018.3175.1061ENMahdiMokhtarzadehSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranFariborzJolaiSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranAlirezaNikoubinSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranMohammadbagherAfshar-BakeshlooSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranJournal Article20190212Nowadays, production systems seek to integrate production and maintenance activities. An effective maintenance plan can improve maintenance stability and system performance. Machines that stop for repairing operation impose a high cost on the system. On the other hand, there are always some intangible situations during a production process in which repairing activities can be carried out. If they are detected, system productivity can be improved. The main purpose of this study is specifying Maintenance Opportunity Window (MOW) in job-shop production systems. For this purpose, mathematical models and formulae were developed in order to determine the MOW in a way that they could provide maximum repairing time for the machine and, as a result, the lowest disturbance occurring in production. This model also determines the number of lost products during PM. Considering the manpower of maintenance and M/M/1//k queueing model, the terms required for repairs are addressed. Finally, numerical experiments on and sensitivity analysis of critical parameters of the model, such as the initial level of the buffers and processing rates of the machines, are considered. Model validation is carried out by comparison of the results with a simulation model. In this study, some suggestions for improving the system are proposed.https://jqepo.shahed.ac.ir/article_769_8ebd3eda3dfe08ebae76384c83450bc6.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190601Robust Economic-Statistical Design of Acceptance Control Chart557277010.22070/jqepo.2018.3646.1078ENSamradJafarian-NaminPhD Candidate, Industrial Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran0000-0002-9275-2837Mohammad SaberFallahnezhadAssociate Professor, Industrial Engineering Department, Faculty of Engineering, Yazd University, Yazd, Iran0000-0003-3343-2769RezaTavakkoli-MoghaddamProfessor, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran0000-0002-6757-926XMehrdadMirzabaghiPhD Candidate, School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranJournal Article20181222Acceptance control charts (ACC), as an effective tool for monitoring highly capable processes, establish control limits based on specification limits when the fluctuation of the process mean is permitted or inevitable. For designing these charts by minimizing economic costs subject to statistical constraints, an economic-statistical model is developed in this paper. However, the parameters of some processes are in practice uncertain. Such uncertainty could be an obstacle to getting the best design. Therefore, the parameters are investigated by a robust optimization approach. For this reason, a solution procedure utilizing a genetic algorithm (GA) is presented. The algorithm procedure is illustrated based on numerical studies. Additionally, sensitivity analysis and some comparisons are carried out for more investigations. The results indicate better performance of the proposed approach in designing ACC and more reliable solutions for practitioners.https://jqepo.shahed.ac.ir/article_770_3119541fa56e5838e17120826a577a40.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190601Stock Evaluation under Mixed Uncertainties Using Robust DEA Model738489610.22070/jqepo.2019.3652.1080ENPejmanPeykaniFaculty of Industrial Engineering, Iran University of Science & Technology, Tehran, IranEmranMohammadiIran university of science and technologyFatemeh SadatSeyed EsmaeiliFaculty of Mathematics, Science and Research Branch, Islamic Azad University, Tehran, IranJournal Article20180812Data Envelopment Analysis (DEA) is one of the popular and applicable techniques for assessing and ranking the stocks or other financial assets. It should be noted that in the financial markets, most of the times, the inputs and outputs of DEA models are accompanied by uncertainty. Accordingly, in this paper, a novel Robust Data Envelopment Analysis (RDEA) model, which is capable to be used in the presence of discrete and continuous uncertainties, is presented. The proposed novel RDEA model in the paper was implemented in a real case study of Tehran Stock Exchange (TSE). The results showed that the proposed new RDEA model was effective in the assessment and ranking of the stocks under different scenarios with interval values.https://jqepo.shahed.ac.ir/article_896_80e09a0155f8381788f12f738266b805.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190601A Bi-objective Mathematical Model for Closed-loop Supply Chain Network Design Problem859893710.22070/jqepo.2019.3688.1086ENMehdiBoronoosSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranSeyed AliTorabiSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranMohammadMousazadehSchool of Industrial Engineering, College of Engineering, University of Tehran, Tehran, IranJournal Article20180825In this paper, a bi-objective mixed-integer linear optimization model for Closed-loop Supply Chain Network Design Problem (CLSCND) is developed. The proposed model includes both the forward and reverse directions and includes different types of facilities, namely, manufacturing/remanufacturing centers, warehouses, and disassembly centers. The first objective function tried to minimize the total cost of the supply chain, while the second one was aimed at maximizing the responsiveness of the network in both forward and reverse directions, simultaneously. To solve the proposed bi-objective model, an augmented ε-constraint method was implemented by which a set of Pareto-optimal solutions for the problem were generated. An illustrative numerical example is given in the study to show the applicability and efficiency of the presented optimization model.https://jqepo.shahed.ac.ir/article_937_8e7f60d65b46edeef9a305e44a20735d.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190601A Multi-Objective Optimization Model for Split Pollution Routing Problem with Controlled Indoor Activities in Cross Docking under Uncertainty9912693510.22070/jqepo.2019.4113.1096ENHesamKargari Esfand AbadDepartment of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranBehnamVahdani1Department of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranManiSharifiDepartment of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranFarhadEtebariDepartment of Industrial Engineering, Faculty of Industrial and Mechanical Engineering, Qazvin Branch, Islamic Azad University, Qazvin, IranJournal Article20190128Cross docking is a logistics strategy that strives to reduce inventory holding costs, shipping costs, and delays in delivering the products. In this research, an optimization model is presented for split loading and unloading products by suppliers and customers, vehicle routing with fuzzy possibilistic time window constraints among them, assignment of vehicles to cross dock, consolidation and integration of products in cross dock, and allocation of sorted products to outbound vehicles. The mathematical model provided in this study has three objective functions. The first and second objectives minimize total cost and fuel consumption, and the third one maximizes satisfaction degrees of suppliers and customers. With the intention of solving the model, two multi-objective meta-heuristic algorithms, namely Multi-Objective Grey Wolf Optimizer (MOGWO) and Multi-Objective Imperialist Competitive Algorithm (MOICA) were utilized. With the intention of illustrating the accuracy of the suggested model and solution approaches, a broad range of numerical instances were considered and the results were investigated.https://jqepo.shahed.ac.ir/article_935_b9b9f9dc6ee2e6c31bbb3ac719dfc1dc.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190601An Interactive Possibilistic Programming Approach to Designing a 3PL Supply Chain Network Under Uncertainty12715293410.22070/jqepo.2019.3644.1076ENMahtabAsoodehDepartment of Industrial Engineering &amp; Management Systems,Amirkabir University of technology, Tehran, IranSeyed Mohammad JavadMirzapour Al-e-hashemAmirKabir University0000-0002-3235-2698Journal Article20180811The design of closed-loop supply chain networks has attracted increasing attention in recent decades with environmental concerns and commercial factors. Due to the rapid growth of knowledge and technology, the complexity of the supply chain operations is increasing daily and organizations are faced with numerous challenges and risks in their management. Most organizations with limited resources, capabilities, and knowledge outsource their logistics services to reduce costs and increase customer satisfaction. The Third-Party Logistics (3PL) Providers have been set up to outsource various supply chain activities to specialized companies. This paper proposes a bi-objective possibilistic mixed-integer nonlinear programming model for designing a closed-loop supply chain network from the perspective of 3PL. To solve the proposed multi-objective model, a two-stage solving approach was applied first to converting the possibilistic model into its equivalent crisp counterpart and second, to converting the crisp multi-objective model into a single-objective one. Using this approach, a single-objective equivalent auxiliary crisp model was obtained and solved optimally byIBMILOGCPLEX software. Solving numerical examples proved the effectiveness of the proposed bi-objective, possibilistic framework. Several sensitivity analyses were performed to gain managerial insights.https://jqepo.shahed.ac.ir/article_934_887d4ce0410888c82f6cbbf2a2709f50.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190601Using Metaheuristic Algorithms Combined with Clustering Approach to Solve a Sustainable Waste Collection Problem15317498810.22070/jqepo.2019.3684.1085ENMasoudRabbaniSchool of Industrial Engineering, College of Engineering, University of TehranHamedFarrokhi-AslSchool of Industrial Engineering, Iran University of Science &amp; Technology, Tehran, IranJournal Article20181224Sustainability is a monumental issue that should be considered in designing a logistics system. In order to incorporate sustainability concepts in our study, a waste collection problem with economic, environmental, and social objective functions was addressed. The first objective function minimized overall costs of the system, including establishment of depots and treatment facilities. Addressing environmental concerns, greenhouse gases emission was minimized by the second objective function and the third one maximized distances between each customer and treatment facilities. Treatment facility is noxious for human health and should be located in the maximum distance from the urban area. Initially, the locations of depots and treatment facilities were determined. Then, heterogeneous vehicles started to collect waste from the location of each customer and take it to treatment facilities. The problem included two types of open and close routes. Moreover, each vehicle had a capacity restriction, servicing time, and route length. There were different types of waste and each vehicle had a different capacity for them. Three metaheuristic algorithms combined with clustering approach were proposed to look for the best solutions in rational time. The Non-dominated Sorting Genetic Algorithm-II (NSGA-II), improved Strength Pareto Evolutionary Algorithm (SPEA-II), and Multi-Objective Evolutionary Algorithm based on Decomposition (MOEA/D) were compared in terms of performance metrics. According to the results, NSGA-II outweighed other algorithms in the presented model.https://jqepo.shahed.ac.ir/article_988_ec4daea81d90f4b1c244c0607d73b048.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190601Optimizing and Solving Project Scheduling Problem for Flexible Networks with Multiple Routes in Production Environments17519689510.22070/jqepo.2019.3870.1091ENA.BirjandiFaculty of Industrial Engineering, South Tehran Branch, Islamic Azad UniversityS. MeysamMousaviDepartment of Industrial Engineering, Faculty of Engineering, Shahed UniversityM.HajirezaieFaculty of Industrial Engineering, South Tehran Branch, Islamic Azad UniversityBehnamVahdaniazad university of ghazvinJournal Article20190202In production environments, multi-route Resource-Constrained Project Scheduling Problem (RCPSP) is more complex and consists of two types of flexible and fixed parts. The flexible parts comprise the semi-finished products and each part has multiple routes denoted independently with activities and predictive relationships. This research develops a new Mixed‐Integer Nonlinear Programming (MINLP) model to minimize the makespan. The proposed mathematical model identifies the optimal routes and, consequently, determines the optimal project network. Also, it allocates renewable resources to each production activity. Production sequencing of activities is optimized by the proposed model. A new hybrid approach by regarding GA and PSO in a binary solving space is introduced to handle two main sub-problems of RCPSP-MR in production environments, namely route selection and production scheduling. To evaluate the presented optimization model and algorithm, 60 test problems in various sizes are reported in detail.https://jqepo.shahed.ac.ir/article_895_8136778a25d98d062681e3505d078040.pdfShahed UniversityJournal of Quality Engineering and Production Optimization4120190622Determining Economic Order Quantity (EOQ) with Increase in a Known Price under Uncertainty through Parametric Non-Linear Programming Approach19721993810.22070/jqepo.2019.4126.1098ENJavadTaheri-tolgariindustrial engineering, kharazmi university, shiraz, iran0000-0001-9024-9261AbolfazalMirzazadehDepartment of Industrial Engineering, Faculty of Engineering. Kharazmi University, Tehran, IranJournal Article20190102Constant unit procurement cost is one of the main assumptions in the classic inventory control policies. In the realistic world and practice, suppliers sometimes face increase in the price of a known item. In this paper, an inventory model for items with a known one-time-only price increase under fuzzy environment is presented by employing trapezoidal fuzzy numbers to find the optimal solution. We developed three different policies on the basis of methods such as α-cuts, for defuzzification of internal parameters before solving the model, and Vujosevic, for difuzzification of the external parameters after solving it. In the first policy, we integrated α-cuts method and Parametric Non-Linear Programming ( ) problems to attain the Membership Functions ( ) of external variables in the primary model for achieving the optimal solution. These variables were reached by internal parameters through two-phase maximum/minimum non-linear programming problems and the external variables were approximate fuzzy numbers. Under the other two policies, we used defuzzification techniques of Centroid of Gravity ( ), Signed Distance ( ), and the Maximum Degree of Membership ( ) to attain crisp numbers. The optimal order policies by the three methods were compared and numerical computations showed that the efficiency of the first method (i.e., the presented one) was considerably better than that of the other two methods. In fact, the first method selected the optimal and attractive strategies by allocating membership functions to different α-cuts and provided the Decision Maker ( ) with great information to decide and select the best strategies. The methods were validated by a numerical example. The main aim of this model was determining the special ordering range and net costs saving quantity (involving ordering, holding, and purchasing costs). The time of ordering for positive net costs saving was calculated.https://jqepo.shahed.ac.ir/article_938_7caecc7b7231016a9b427d82153aab92.pdf