hassan haleh

Since the development of a strong platform architecture is considered a competitive advantage for companies and is effective in improving the future generations of the product, therefore there is a need for a kind of diversified product design that simultaneously manages the costs and supply chain process and thus help to develop the architecture of the product platform. In this research, the design for vareity (DFV) method and two generational variety index (GVI) and coupling index (CI) are used to measure a product architecture and by using the quality improvement function (QFD) and the design structure matrix ( DSM), design indicators for variety are identified and ranked. Also, the DFV approach is simultaneously modeled with the categories of design for cost (DFC) and design for supply chain (DFSC) and a mathematical model applied to the development of the product platform architecture is obtained, which seeks to diversify the product and reduce costs and Supply chain process management. The case study of the current research is the phased array antenna system, in which the problem is solved using one of the new optimization techniques (LP metric) and GAMS software. After the implementation of the model, its validation was carried out and considering three objectives including the total cost and the evaluation score (competence) of the suppliers and the objective of variety and the seven main parameters of the model, sensitivity analysis and other comparisons and results. A review is provided. Regarding the comparison of the goals with each other, the findings show the inverse relationship of the total cost goal with the variety and the evaluation score goals and the direct relationship between the two variety and the evaluation score goals. Also, the results of the sensitivity analysis showed the higher effectiveness of the goal of variety among the investigated parameters, and the evaluation score (competence) of suppliers and the total cost were ranked next.

The achievement of chain greening objectives, besides costs minimization, can be realized when both reverse and forward flows are taken into account in the design of the supply chain network. It is possible to decrease the chain costs and have a greener chain by means of different strategies like vehicular routing, hub location, inventory management, and simultaneous pickup and delivery. The development of green reverse supply chains and the practice of the above-mentioned strategies are becoming increasing important with the appearance of perishable product chains. Considering the mentioned points, the current study attempts to design a green reverse supply chain network for the purpose of distributing dairy items such as yogurt drink where the strategy of simultaneous pickup and delivery under uncertainty is taken into consideration. This model focuses on the simultaneous costs reduction and also decrease of lost demands and presents a fuzzy solution for solving the bi-objective model.

Order picking operation is one of the most well-known labor and cost intensive internal logistics processes. Withdrawal of the order in response to customer need is defined in order to collect a set of orders from storage zone in the shortest possible time. The purpose of this research is to provide a scientific and practical basis considering the constraints that enforce to achieve an acceptable level of performance in order picking systems. This is done by building a Mixed Integer Linear Programming (MILP) formulation and developing an adapted solution method suited to the structure of the problem

First, by reviewing the literature in the field of order picking systems, sufficient knowledge has been obtained at the operational level, and with emphasis on warehouse management constraints, a MILP formulation is proposed by integrating order batching and picker routing. After validating the model and solving it through GAMS software, due to the nature of the problem, which is an NP-hard type, the problem is solved with an efficient algorithm, which is a grouping version of the league championship algorithm, and the results are compared. To develop the algorithm, operators are fit to the specific structure of the problem, i.e., the assignment of orders (items) to order pickers (groups)

Developing a multi-period MILP formulation for multi-trip picker routing, assuming for the first time the possibility of product replenishment and limited access to pickers. For large-scale problem instances, the league championship algorithm is used. The results indicate the effective capability and efficiency of this algorithm for solving large test problem instances.

The issue of multi-period order picking and multi-trip routing of pickers is considered for the first time ‎in this paper. Because of the limited number of pickers, this must be taken into account in modeling. ‎The assumption of product replenishment is also considered for the first time in this article and its ‎modeling has been done. In this way, orders enter the warehouse over time, during different periods, ‎and are placed in a predetermined positions. The limited access to pickers in each period is also ‎discussed for the first time in this paper. Finally, the objective function of minimizing the total ‎tardiness, which is in line with the needs of the industry, is also introduced in this paper. Regarding the ‎solution method, a league championship metaheuristic algorithm is presented which takes into ‎account the problem structure (which corresponds to the structure of grouping problems) and ‎solution generation operators have been developed to maintain the new solution.‎

An integrated optimization framework, including location assignment under grouping class-based storage policy and schedule of dual shuttle cranes, is offered by presenting a new optimization programming model. The objective functions, which are considered at this level, are the minimization of total costs and energy consumption. Scheduling of dual shuttle cranes among specified locations, which were determined in the upper-level, is conducted in the lower-level by considering time windows and balance constraints under multi-period planning conditions. A modified nested differential evolution-based algorithm is introduced to solve the proposed model because it is an Np-hard bi-level bi-objective optimization model. Eventually, with the intention of illustrating the validation of the presented optimization model and solution methodology, various numerical experiments are tailored, and different comparative numerical examples are provided based on two current algorithms in the literature. Sensitivity analyses illustrate that grouping class-based storage policy could be rendered superior planning of operations in both levels of the investigated problem.

A flexible cell scheduling problem (CSP) under time-of-use (TOU) electricity tariffs are developed in this study. To apply a kind of energy-conscious policy, over cost of on-peak period electricity consumption, limitations on total energy consumption by all facilities, set up time available on each cell, part defect (pert) percentage and the total number of automated guided vehicles (AGV) are considered. Additionally, an ant colony optimization (ACO) algorithm is employed to find a near-optimum solution of proposed mixed integer linear programming (MILP) model with the objective of minimizing the total cost of CSP model. Since no benchmark is available in the literature, a lower bound is implemented as well to validate the result achieved. Moreover, to improve the quality of the results obtained by meta-heuristic algorithms, two hybrid algorithms (HGA and HACO) was proposed to solve the model. For parameter tuning of algorithms, Taguchi experimental design method is applied. Then, numerical examples are presented to prove the application of the proposed methodology. Our results compared with the lower bound and as a result it confirmed that HACO was capable to find better and nearer optimal solutions.

Reliability of the cellular manufacturing systems and automated guided vehicles (AGVs) in the production systems has been recently become one of the most challenging issues. This paper focuses on proposing a multi-objective mathematical model in order to optimize three objectives including the minimization of production costs, minimization of the intracellular and intercellular part transportations and finally maximization of the reliability of AGVs. The failure rates of intracellular AGVs at any of the cells follow either an exponential or a Weibull distribution whereas, this rate is considered to be constant for the intercellular AGVs. As reliability calculation in the Weibull case is very hard (if not impossible), a simulation approach is applied to estimate the reliability of system in this case. It is assumed that all the intracellular and intercellular transportations are done through the AGVs. This means that the production system halts when all AGVs are failed. In order to validate the proposed model, some numerical examples are generated and solved by implementation of a non-dominated sorting cuckoo search (NSCS) and a multi-objective invasive weeds optimization (MOIWO) algorithm. Finally, a hybrid analytic hierarchy process and TOPSIS method (AHP-TOPSIS) is utilized to select the better algorithm in terms of some multi-objective metrics, simultaneously.

A dual command (DC) cycle “dynamic sequencing” method in unit-load multiple-rack AS/RS system under time-of-use (TOU) electricity tariffs are applied in this paper. To make a type of energy efficient model, over cost of on-peak period electricity consumption, penalty cost for over power consumption, bounds on total consumed energy and accessible times of all facilities are considered in the model. Moreover, a genetic algorithm (GA) is developed to achieve a near-optimum solution of suggested energy-based mathematical model with the objective of minimizing the total cost of the AS/RS system under TOU tariffs. Considering that no benchmark is obtainable in the literature, a simulation annealing (SA) algorithm is developed in addition to certify the outcome gained. For supplementary confirmation, we comparing the total cost of our model with the single tariff model and also doing a sensitivity analysis for allowable amount of power consumption. The system throughput in terms of time and cost is calculated for the model too. In the last part, sixteen numerical examples with different number of required storage/retrieval orders are suggested to display the function of the proposed procedure. Our outcomes verified that GA was able to obtain well and closer optimal solutions and the TOU tariffs model get minimum total cost.

Scheduling is an important subject of production and operations management area. In flow-shop scheduling, the objective is to obtain a sequence of jobs which when processed in a fixed order of machines, will optimize some well defined criteria. The concept of transportation time is very important in scheduling. Transportation can be done by robots. In situations that robots are used to transport materials (material handler), breakdown of the machines and robots have a significant role in the production concern. This paper deals with minimization of the total elapsed time for flow shop scheduling problem (number of machine=3) in which the effect of machine and robots breakdown interval are considered simultaneously. Furthermore, by providing an example, the proposed algorithm is described. A summary and future works conclude the paper

This study tries to enhance imperialist colony algorithm (ICA) in the context of travel salesman problem (TSP). By adding new learning operator, imperialist learns form colonies that have suitable cost in which manner that improves solution of problems. We believe that controlled learning improvement is better than uncontrolled one. The efficiency of new operator represented with variety of instances from TSPLIB. We evaluate the approach on standard TSP test problems and show that it performs better, with respect to solution quality and computation time than ICA without new learning operator.

One of the most important decisions that must be made in production systems is determining the product mix. That means which and how much of the product should be made from it in order to increase the final output of the system. Often, in previously existing algorithms, all problem parameters are assumed to be certain and decision-making used to be carried out. The situation studied in this paper is that all the production parameters are in the form of triangular fuzzy numbers. Those production parameters include weekly demand, selling price, cost of raw materials, processing time of products and the available capacity of resources. In the proposed algorithm, considering the multi-bottleneck, with the help of fuzzy Vikor, the prioritization of production is calculated. Finally in order to explain the aforementioned method, a numerical example has been discussed.

Supplier Selection is a Multiple-Attribute Decision- Making (MADM). The Grey Decision-Making method is a proper technique for ranking suppliers in cases where the existing qualitative attribute is in an uncertain environment. Taking Advantages of the grey theory concept, linguistic variables areturned in togrey numbers and then used in MADM techniques. But an important issue in most MADM techniques such asTOPSISmethod is independencerequest of attributes, which if notconsidered, may lead to false results. The proposed approach in this article is Factor Analysis(FA) technique for determination of independent factors and their substitution with attributes in grey TOPSIS method.In the proposed approach,independent factors areobtained by FA technique after determination ofthe weights of attributes and making initial grey decision- making matrix. Weight of any factor is obtained from average of the attribute's weight related to its factor. Alsocomponents of the grey decision- making matrix after FA rank will be obtainedfrom average of attribute components related to any factor in initial decision- making matrix. Finally, the suppliers are being ranked by using grey possibility degree. In this paper, the suggested approach has been used to ranking the five suppliers of SazehGostarSaipa company.

In this study, an outsourcer evaluation and management system is developed for a manufacturing company by use of Fuzzy goal programming (FGP). A first phase of the methodology evaluation criteria for outsources and the objectives of the company are determined. Considering the fuzziness in the decision data, linguistic variables that can be expressed in generalized fuzzy number are used. The propose approach is utilized from fuzzy sets, Analytic Network Process (ANP), fuzzy TOPSIS and Preference Ranking Organization method for enrichment evaluations (PROMETHEE) approaches. Evaluation criteria for this problem are weighted by Fuzzy ANP approach then in the Fuzzy TOPSIS and Fuzzy PROMETHEE approaches. At the second phase the FGP model developed selects the most appropriate outsourcers suitable to be strategic partners with the company and simultaneously allocates the quantities to be ordered to them. At the end, gives the computational results.