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کاربرد نوع شرط:
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Cross-docking,Metaheuristic,Transhipment,Location of cross-docking centers
- چکیده:
- چکیده انگلیسی: This paper studies multiple cross-dockings where the loads are transferred from origins (suppliers) to destinations (customers) through cross-docking facilities. Products are no longer stored in intermediate depots and incoming shipments are consolidated based on customer demands and immediately delivered to them to their destinations. In this paper, each cross-docking has a covering radius that customers can be served by at least one cross-docking provided. In addition, this paper considers the breakdown of trucks. We present a two-stage model for the location of cross-docking centers and scheduling inbound and outbound trucks in multiple cross-dockings.We work on minimizing the transportation cost in a network by loading trucks in the supplier locations and route them to the customers via cross-docking facilities. The objective, in the first stage, is to minimize transportation cost of delivering products from suppliers to open cross-docks and cross-docks to the customers; in the second-stage, the objective is to minimize the makespans of open cross-dockings and the total weighted summation of completion time. Due to the difficulty of obtaining the optimum solution tomedium- and large-scale problems, we propose four types of metaheuristic algorithms, i.e., genetic, simulated annealing, differential evolution, and hybrid algorithms.The result showed that simulated annealing is the best algorithm between the four algorithms.
- انتشار مقاله: 04-12-1394
- نویسندگان: Javad Behnamian,Seyed Mohammad Taghi Fatemi Ghomi,Fariborz Jolai,Pooya Heidary
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Optimization in Industrial Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Production Planning,Flexible Job-Shop,Finite Scheduling,Part Families
- چکیده:
- چکیده انگلیسی: Tendency to optimization in last decades has resulted in creating multi-product manufacturing systems. Production planning in such systems is difficult, because optimal production volume that is calculated must be consistent with limitation of production system. Hence, integration has been proposed to decide about these problems concurrently. Main problem in integration is how we can relate production planning in medium-term timeframe with scheduling in short-term timeframe. Our contribution creates production planning and scheduling framework in flexible job shop environment with respect to time-limit of each machine in order to produce different parts families in automotive industry. Production planning and scheduling have iterative relationship. In this strategy information flow is transformed reciprocative between production planning and scheduling for satisfying time-limit of each machine. The proposed production planning has heuristic approach and renders a procedure to determine production priority of different part families based on safety stock. Scheduling is performed with ant colony optimization and assigns machine in order of priority to different part families on each frozen horizon. Results showed that, the proposed heuristic algorithm for planning decreased parts inventory at the end of planning horizon. Also, results of proposed ant colony optimization was near the optimal solution .The framework was performed to produce sixty four different part families in flexible job-shop with fourteen different machines. Output from this approach determined volume of production batches for part families on each frozen horizon and assigned different operations to machines.
- انتشار مقاله: 11-06-1390
- نویسندگان: Davod Abachi,Fariborz Jolai,Hasan Haleh
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Information Technology Management
- نوع مقاله: Journal Article
- کلمات کلیدی: Supply Chain,Internet of Things,Hospital Industry,Technology Implementation
- چکیده:
- چکیده انگلیسی: Given the complexities of supply chain networks, the firms consider modern technologies as a potential factor to improve their supply chain performances. One of these technologies is the Internet of Things (IoT). Hence, the main purpose of this study has been to achieve the conceptual model of the IoT implementation in hospital supply chains. Considering the qualitative nature of the study, relevant articles were specified through library research and collecting the related literature. The outputs obtained were analyzed using the meta-synthesis method and the grounded theory. Then the selected articles were extracted and the grounded theory was used to obtain the conceptual model of the IoT implementation in hospital supply chains. The research results indicated that the model featured 7 main categories, 19 subcategories and 86 codes. The results present a conceptual model for the implementation of the IoT in hospital supply chains including an explanation of the main research category, drivers, prerequisites and enablers, environmental and contextual conditions, challenges, technology implementation strategies, and results and outcomes.
- انتشار مقاله: 02-08-1398
- نویسندگان: Ali Mohaghar,Mohammad Reza Taghizadeh Yazdi,Fariborz Jolai,Mehdi Mohammadi,Shayan Atashin Panjeh
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial Engineering International
- نوع مقاله: Journal Article
- کلمات کلیدی: particle swarm optimization,Markov model,Bi-product multi-echelon inventory planning,Backordering,Exponential lead time
- چکیده:
- چکیده انگلیسی: In this paper, we apply continuous review (S-1, S) policy for inventory control in a three-echelon supply chain (SC) including r identical retailers, a central warehouse with limited storage space, and two independent manufacturing plants which offer two kinds of product to the customer. The warehouse of the model follows (M/M/1) queue model where customer demands follow a Poisson probability distribution function, and customer serving time is exponential random variable. To evaluate the effect of considering bi-product developed model, solution of the developed model is compared with that of the two (M/M/1) queue models which are separately developed for each product. Moreover, and in order to cope with the computational complexity of the developed model, a particle swarm optimization algorithm is adopted. Through the conducted numerical experiments, it is shown that total profit of the SC is significantly enhanced using the developed model.
- انتشار مقاله: 10-07-1399
- نویسندگان: Maryam Alimardani,Fariborz Jolai,Hamed Rafiei
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Environmental,Co-Firing Biomass Plants,location optimization,Z-Number Data Envelopment Analysis (DEA),economic and social indicators,Fuzzy Data Envelopment Analysis (FDEA),perturbation analysis
- چکیده:
- چکیده انگلیسی: Co-firing biomass plants are of extensive demand due to utilization of both agricultural residues (main) and natural gas (stand-by). Researchers have shown that one strategic decision in establishment of agricultural residues based plants, is location optimization problem. Moreover, mismatch between agricultural lands and biomass plants can lead to high transportation costs and related carbon dioxide emissions. Standard indicators are considered and used for the stated multi-objective mathematical problem. This article presents a novel approach based on Z-number data envelopment analysis (DEA) model to handle severe uncertainty associated with actual data. The multi-objective mathematical model considers environmental, economic and social aspects of biomass plants. Moreover, fuzzy DEA model is utilized to verify and validate the results of Z-number DEA model through 30 independent experiments. The obtained results indicate that “accessibility to water”, “population”, “cost of land”, and “unemployment rate” are the most significant factors in location optimization of co-firing power plants. The obtained results also indicate that “Ilam”, “Semnan”, “Kohgiluyeh and Boyer-ahmad”, “South Khorasan”, and “Chaharmahal and Bakhttiari” are the optimum locations. This is the first unique approach for location optimization of co-firing plants based on combined agricultural residues and natural gas under uncertainty. Second, a unique fuzzy mathematical optimization approach is presented. Third, it is a practical approach for biomass power plants.
- انتشار مقاله: 20-05-1397
- نویسندگان: Niloufar Akbarian Saravi,Reza Yazdanparast,Omid Momeni,Delaram Heydarian,Fariborz Jolai
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Cross-docking,heuristics,Facilities planning and design,mixed integer model
- چکیده:
- چکیده انگلیسی: This paper proposes an integrated network design model for a post-distribution cross-docking strategy, comprising multi product production facilities with shared production resources, capacitated cross docks with setup cost and customer zones with time windows constraints. The model is dynamic in terms of time-varying uncertain demands, whereas uncertainty is expressed with scenario approach and contains both ‘‘wait-and-see’’ and ‘‘here-and-now’’ decisions. Inventory is just permitted in plants and over several time periods. The objective of the model is to minimize the sum of the fixed location costs for establishing cross docking centers and inventory related costs across the supply chain while ensuring that the limited service rate of cross docking centers and production facilities, and also the lead time requirements of customers are not violated. The problem is formulated as a mixed-integer linear programming problem and solved to global optimality using CPLEX. Due to the difficulty of obtaining the optimum solution in medium and large-scale problems, two heuristics that generate globally feasible, near optimal solution, Imperialistic competitive algorithm (ICA) and simulated annealing (SA), are also proposed as heuristics. We find that CPLEX is not able to solve some of the sets to optimality and turned out to run out of memory, but it performs quite well for small test sets, as compared with the two heuristics. While SA is a faster heuristic method in terms of runtime, ICA generates better results on average, but in more time.
- انتشار مقاله: 30-11-1394
- نویسندگان: Javad Behnamian,Seyed Mohammad Taghi Fatemi Ghomi,Fariborz Jolai,M. Telgerdi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Finite planning horizon,Flow shop,Determining lot sizes,Hybrid particle swarm optimization,Basic period
- چکیده:
- چکیده انگلیسی: Many authors have examined lot sizing, scheduling and sequence of multi-product flow shops, but most of them have assumed that set up times are independent of sequence. Whereas dependence of set up times to sequence is more common in practice. Hence, in this paper, we examine the discussed problem with hypothesis of dependence of set up times to sequence and cyclic schedule policy in basic period form. To do so, a mixed integer non-linear programming (NLP) model is developed for this problem. To solve the model these techniques are applied: Heuristic G-group for determining the frequency of item production and assigning product to periods and three meta heuristic methods including hybrid Particle swarm optimization , hybrid Vibration damping optimization hybrid genetic algorithm are used to determine the sequence and economic lot sizes of each item. In addition, to compare these methods, some random problems are produced and computation of them shows the substantial superiority of hybrid Particle swarm optimization.
- انتشار مقاله: 15-12-1388
- نویسندگان: Mohammad Aliabadi,Fariborz Jolai,Esmaeil Mehdizadeh,Masoud Jenabi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Genetic Algorithm,Two Dimensional Bin Packing Problem,priority,Continuous Lower Bound
- چکیده:
- چکیده انگلیسی: In this paper a two-dimensional non-oriented guillotine bin packing problem is studied when items have different priorities. Our objective is to maximize the total profit which is total revenues minus costs of used bins and wasted area. A genetic algorithm is developed to solve this problem where a new coding scheme is introduced. To evaluate the performance of the proposed GA, first an upper bound is presented. Then, a series of computational experiments are conducted to evaluate the quality of GA solutions comparing with upper bound values. From the computational analysis, it appears that the GA algorithm is able to give good solutions.
- انتشار مقاله: 14-12-1386
- نویسندگان: Majid Shakhsi-Niyaei,Fariborz Jolai,Jafar Razmi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Industrial and Systems Engineering
- نوع مقاله: Journal Article
- کلمات کلیدی: Routing problem,time windows,Single vehicle,Multiple routes,Goal programming
- چکیده:
- چکیده انگلیسی: The single vehicle routing problem with multiple routes is a variant of the vehicle routing problem where the vehicle can be dispatched to several routes during its workday to serve a number of customers. In this paper we propose a goal programming model for multi-objective single vehicle routing problem with time windows and multiple routes. To solve the model, we present a heuristic method which exploits an elementary Shortest Path Algorithm with Resource Constraints. Computational results of the proposed algorithm are discussed.
- انتشار مقاله: 31-01-1386
- نویسندگان: Fariborz Jolai,Mehdi Aghdaghi
- مشاهده
- جایگاه : پژوهشی
- مجله: Journal of Algorithms and Computation
- نوع مقاله: Journal Article
- کلمات کلیدی: multi,Total Weighted Tardiness,objective scatter search,Parallel Machine Scheduling Problem,Total Weighted Earliness
- چکیده:
- چکیده انگلیسی: The parallel machine scheduling problem is an important and difficult problem to be considered in the real-world situations. Traditionally, this problem consists of the scheduling of a set of independent jobs on parallel machines with the aim of minimizing the maximum job completion. In today's manufacturing systems, in which both early and tardy finishing of job processing are undesired, the objectives related to earliness and tardiness penalties have become increasingly popular. In this paper, two major goals are considered as follows: (1) total weighted earliness; (2) total weighted tardiness. Due to the complexity of such a hard problem, a new multi-objective meta-heuristic method, i.e. multi-objective scatter search (MOSS), is proposed to obtain the locally Pareto-optimal frontier where the simultaneous minimization of the above-mentioned objectives is desired. In order to validate the performance of the proposed MOSS method, in terms of solution quality and diversity level, various test problems are considered and the reliability of this method, based on different comparison metrics, is compared with the Elite Tabu Search (ETS) devised in this paper. The computational results show the high capability of the proposed MOSS method.
- انتشار مقاله: 17-02-1392
- نویسندگان: Reza Tavakoli Moghadam,Fariborz Jolai,Somayyeh Ghandi Beygi
- مشاهده