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Showing 15 results for Subject: Production Planning & Control

Danial Khorasanian, Ghasem Moslehi,
Volume 23, Issue 4 (11-2012)
Abstract

In this paper, we propose an iterated greedy algorithm for solving the blocking flow shop scheduling problem with total flow time minimization objective. The steps of this algorithm are designed very efficient. For generating an initial solution, we develop an efficient constructive heuristic by modifying the best known NEH algorithm. Effectiveness of the proposed iterated greedy algorithm is tested on the Taillard's instances. Computational results show the high efficiency of this algorithm with comparison state-of-the-art algorithms. We report new best solutions for 88 instances of 120 Taillard's instances.
Yahia Zare Mehrjerdi, Maryam Dehghan,
Volume 24, Issue 1 (2-2013)
Abstract

Abstract In the dynamic and competitive market, managers seek to find effective strategies for new products development. Since There has not been a thorough research in this field, this study is based on a review on the risks exist in the NPD process and an analysis of risks through FMEA approach to prioritize the existent risks and a modeling behavior of the NPD process and main risks using system dynamics. First, we present new product development concepts and definition. We then based our study on a literature review on the NPD risks and then provide an FMEA approach to define risks priority. Using the obtained main risks, we model the NPD process risks applying system dynamics to analyze the system and the risks effect on. A safety clothing manufacturer is considered as a case study.
Mohammad Jafar Ttarokh, Pegah Motamedi,
Volume 24, Issue 1 (2-2013)
Abstract

This article develops an integrated JIT lot-splitting model for a single supplier and a single buyer. In this model we consider reduction of setup time, and the optimal lot size are obtained due to reduced setup time in the context of joint optimization for both buyer and supplier, under deterministic condition with a single product. Two cases are discussed: Single Delivery (SD) case, and Multiple Delivery (MD) case. These two cases are investigated before and after setup time reduction. The proposed model determines the optimal order quantity (Q*), optimal rate of setup reduction (R*), and the optimal number of deliveries (N*) -just for multiple deliveries case- on the joint total cost for both buyer and supplier. For optimizing our model two algorithm including Gradient Search and Particle Swarm Optimization (PSO), which is a population-based search algorithm, are applied. Finally numerical example and sensitivity analysis are provided to compare the aggregate total cost for two cases and effectiveness of the considered algorithm. The results show that which policy for lot-sizing is leading to less total cost.
Rashed Sahraeian,
Volume 25, Issue 1 (2-2014)
Abstract

In this paper the problem of serial batch scheduling in a two-stage hybrid flow shop environment with minimizing Makesapn is studied. In serial batching it is assumed that jobs in a batch are processed serially, and their completion time is defined to be equal to the finishing time of the last job in the batch. The analysis and implementation of the prohibited transference of jobs among the machines of stage one in serial batch is the main contribution of this study. Machine set-up and ready time for all jobs are assumed to be zero and no Preemption is allowed. Machines may not breakdown but at times they may be idle. As the problem is NP-hard, a genetic algorithm is developed to give near optimal solutions. Since this problem has not been studied previously, therefore, a lower bound is developed for evaluating the performance of the proposed GA. Many test problems have been solved using GA and results compared with lower bound. Results showed GA can obtain a near optimal solution for small, median and large size problems in reasonable time.
Parviz Fattahi, Seyed Mohammad Hassan Hosseini, Fariborz Jolai, Azam Dokht Safi Samghabadi,
Volume 25, Issue 1 (2-2014)
Abstract

A three stage production system is considered in this paper. There are two stages to fabricate and ready the parts and an assembly stage to assembly the parts and complete the products in this system. Suppose that a number of products of different kinds are ordered. Each product is assembled with a set of several parts. At first the parts are produced in the first stage with parallel machines and then they are controlled and ready in the second stage and finally the parts are assembled in an assembly stage to produce the products. Two objective functions are considered that are: (1) to minimizing the completion time of all products (makespan), and (2) minimizing the sum of earliness and tardiness of all products (∑_i▒(E_i∕T_i ) . Since this type of problem is NP-hard, a new multi-objective algorithm is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with two prominent multi-objective genetic algorithms, i.e. NSGA-II and SPEA-II. The computational results show that performance of the proposed algorithms is good in both efficiency and effectiveness criterions.
Mir Saber Salehi Mir, Javad Rezaeian,
Volume 27, Issue 1 (3-2016)
Abstract

This paper considers identical parallel machines scheduling problem with past-sequence-dependent setup times, deteriorating jobs and learning effects, in which the actual processing time of a job on each machine is given as a function of the processing times of the jobs already processed and its scheduled position on the corresponding machine. In addition, the setup time of a job on each machine is proportional to the actual processing time of the already processed jobs on the corresponding machine, i.e., the setup time of a job is past- sequence-dependent (p-s-d). The objective is to determine jointly the jobs assigned to each machine and the order of jobs such that the total completion time (called TC) is minimized. Since that the problem is NP-hard, optimal solution for the instances of realistic size cannot be obtained within a reasonable amount of computational time using exact solution approaches. Hence, an efficient method based on ant colony optimization algorithm (ACO) is proposed to solve the given problem. The performance of the presented model and the proposed algorithm is verified by a number of numerical experiments. The related results show that ant colony optimization algorithm is effective and viable approache to generate optimal⁄near optimal solutions within a reasonable amount of computational time.


Sujit Kumar Jha,
Volume 27, Issue 2 (6-2016)
Abstract

Manufacturing process frequently employs optimization of machining parameters in order to improve product quality as well as to enhance productivity. The material removal rate is a significant indicator of the productivity and cost efficiency of the process. Taguchi method has been implemented for assessing favorable (optimal) machining condition during the machining of nylon by considering three important cutting parameters like cutting speed, feed rate and depth of cut during machining on CNC. The objective of the paper is to find out, which process parameters having more impacts on material removal rate during turning operation on nylon using analysis of variance (ANOVA). An Orthogonal array has been constructed to find the optimal levels of the turning parameters and further signal-to-noise (S/N) ratio has been computed to construct the analysis of variance table. The results of ANOVA shown that feed rate has most significant factor on MRR compare to cutting speed and depth of cut for nylon. The confirmation experiments have conducted to validate the optimal cutting parameters and improvement of MRR from initial conditions is 555.56%.


Rasol Jamshidi,
Volume 27, Issue 3 (9-2016)
Abstract

Most manufacturers use human-machine systems to produce high-quality products. Dealing with human-machine systems is very complicated since not only machines should be utilized in proper condition but also appropriate environment should be provided for human resources. Most manufacturers have a maintenance plan for machines but many of them do not have a proper work-rest schedule for human resources. Considering this fact we emphasis on human role in human-machine systems maintenance and propose a mathematical model that obtains the optimal work-rest schedule for humans based on fatigue-recovery models and the optimal maintenance policy for machines based on reliability level. The performance of proposed model was examined by some instances and the obtained results indicate this model can provide effective maintenance policy for human-machine systems.


Esmaeil Mehdizadeh, Amir Fatehi-Kivi,
Volume 28, Issue 1 (3-2017)
Abstract

In this paper, we propose a vibration damping optimization algorithm to solve a fuzzy mathematical model for the single-item capacitated lot-sizing problem. At first, a fuzzy mathematical model for the single-item capacitated lot-sizing problem is presented. The possibility approach is chosen to convert the fuzzy mathematical model to crisp mathematical model. The obtained crisp model is in the form of mixed integer linear programming (MILP) which can be solved by existing solver in crisp environment to find optimal solution. Due to the complexity and NP-hardness of the problem, a vibration damping optimization (VDO) is used to solve the model for large-scale problems.  To verify the performance of the proposed algorithm, we computationally compared the results obtained by the VDO algorithm with the results of the branch-and-bound method and two other well-known meta-heuristic algorithms namely simulated annealing (SA) and genetic algorithm (GA). Additionally, Taguchi method is used to calibrate the parameters of the meta-heuristic algorithms. Computational results on a set of randomly generated instances show that the VDO algorithm compared with the other algorithms can obtain appropriate solutions.


Ebrahim Asadi Gangraj,
Volume 28, Issue 1 (3-2017)
Abstract

In hybrid flow shop scheduling problem (HFS) with unrelated parallel machines, a set of n jobs are processed on k machines. A mixed integer linear programming (MILP) model for the HFS scheduling problems with unrelated parallel machines has been proposed to minimize the maximum completion time (makespan). Since the problem is shown to be NP-complete, it is necessary to use heuristic methods to tackle the moderate to large scale problems. This article presents a new bottleneck-based heuristic to solve the problem. To improve the performance of the heuristic method, a local search approach is embedded in the structure of the heuristic method. To evaluate the performance of the proposed heuristic method, a new lower bound is developed based on Kurz and Askin [1] lower bound. For evaluation purposes, two series of test problems, small and large size problems, are generated under different production scenarios.  The empirical results show that average difference between lower bound and optimal solution as well as lower bound and heuristic method are equal to 2.56% and 5.23%, respectively. For more investigation, the proposed heuristic method is compared by other well-known heuristics in the literature. The results verify the efficiency of the proposed heuristic method in term of number of best solution.


Nita Shah, Chetan Vaghela,
Volume 28, Issue 2 (6-2017)
Abstract

Abstract

            In this research, an integrated inventory model for non-instantaneous deteriorating items is analyzed when demand is sensitive to changes in price. The price used in this research is a time-dependent function of the initial selling price and the discount rate. To control the deterioration rate of items at the storage facility, investment in preservation technology is incorporated. To provide a general framework to the model, an arbitrary holding cost rate is used. Toward the end of the paper, a numerical case is given to approve the model and the impacts of the key parameters of the model are studied by sensitivity analysis to deduce managerial insights.


Adeleh Behzad, Mohammadali Pirayesh, Mohammad Ranjbar,
Volume 28, Issue 3 (9-2017)
Abstract

In last decades, mobile factories have been used due to their high production capability, carrying their equipment and covering rough and uneven routes. Nowadays, more companies use mobile factories with the aim of reducing the transportation and manufacturing costs. The mobile factory must travel between the suppliers, visit all of them in each time period and return to the initial location of the mobile factory. In this paper, we present an integer nonlinear programming model for production scheduling and routing of mobile factory with the aim of maximization of profit. This problem is similar to the well-known Traveling Salesman Problem (TSP) which is an NP-hard problem. Also at each supplier, the scheduling problem for production is NP-hard. After linearization, we proposed a heuristic greedy algorithm. The efficiency of this heuristic algorithm is analyzed using the computational studies on 540 randomly generated test instances. Finally, the sensitivity analysis of the production cost, transportation cost and relocation cost was conducted.


Parviz Fattahi, Sanaz Keneshloo, Fatemeh Daneshamooz, Samad Ahmadi,
Volume 30, Issue 1 (3-2019)
Abstract

In this research a jobshop scheduling problem with an assembly stage is studied. The objective function is to find a schedule which minimizes completion time for all products. At first, a linear model is introduced to express the problem. Then, in order to confirm the accuracy of the model and to explore the efficiency of the algorithms, the model is solved by GAMS. Since the job shop scheduling problem with an assembly stage is considered as a NP-hard problem, a hybrid algorithm is used to solve the problem in medium to large sizes in reasonable amount of time. This algorithm is based on genetic algorithm and parallel variable neighborhood search. The results of the proposed algorithm are compared with the result of genetic algorithm. Computational results showed that for small problems, both HGAPVNS and GA have approximately the same performance. And in medium to large problems HGAPVNS outperforms GA.


Parviz Fattahi, Mehdi Tanhatalab, Joerin Motavallian, Mehdi Karimi,
Volume 31, Issue 0 (6-2020)
Abstract

The present work addresses inventory-routing rescheduling problem (IRRP) that is needed when some minor changes happen in the time of execution of pre-planned scheduling of an inventory-routing problem (IRP). Due to the complexity of the process of departing from one pre-planned scheduling IRP to a rescheduling IRP, here a decision-support tool is devised to help the decision-maker. This complexity comes from the issue that changes in an agreed schedule including the used capacity of the vehicle, total distance and other factors that need a re-agreements negotiation which directly relates to the agreed costs especially when a carrier contractor is responsible for the distribution of goods between customers. From one side he wants to stick to the pre-planned scheduling and from the other side, changes in predicted data of problem at the time of execution need a new optimized solution. The proposed approached applies mathematical modeling for optimizing the rescheduled problem and offers a sensitivity analysis to study the influence of the different adjustment of variables (carried load, distance, …). 
Saeed Dehnavi, Ahmad Sadegheih,
Volume 31, Issue 1 (3-2020)
Abstract

In this paper, an integrated mathematical model of the dynamic cell formation and production planning, considering the pricing and advertising decision is proposed. This paper puts emphasis on the effect of demand aspects (e.g., pricing and advertising decisions) along with the supply aspects (e.g., reconfiguration, inventory, backorder and outsourcing decisions) in developed model. Due to imprecise and fuzzy nature of input data such as unit costs, capacities and processing times in practice, a fuzzy multi-objective programming model is proposed to determine the optimal demand and supply variables simultaneously. For this purpose, a fuzzy goal programming method is used to solve the equivalent defuzzified multi-objective model. The objective functions are to maximize the total profit for firm and maximize the utilization rate of machine capacity. The proposed model and solution method is verified by a numerical example.

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