JOURNAL OF INDUSTRIAL ENGINEERING RESEARCH IN PRODUCTION SYSTEMS (IERPS)
Year:2017 | Volume:4 | Issue:8 |Papers Count:7

Jobs scheduling in industries with cyclic procedure on machines, such as perishable products (food industries) or products with a limited lifetime (chemicals, radio actives, etc), is very important. Due to time limitation or competition with other companies, these industries try to minimize the cycle time of jobs processing. Since most productive environments of the industries are cyclic hybrid flow shop and operator’s learning effect is obvious in speed of productions, the aim of this study is to minimize cycle time of each machine with learning effect by consequence of jobs. After proposing a mathematical model and since the cyclic hybrid flow shop environment is NP-hard, three metaheuristics, i.e., genetic algorithm, simulated annealing algorithm and population based simulated annealing algorithm, have been proposed for solving this problem. Results show that on average, population based simulated annealing algorithm due to its population-based structure has a better performance in comparison to other algorithms.

In this paper, a two-stage newsvendor model with one type of product in presence of stochastic demand under quantity flexibility (QF) contract is investigated. Under the proposed model, the manufacturer allows the retailer to update its order size upward or downward. Under this mechanism, the manufacturer is committed to provide a certain level more than the retailer’s primary order to deliver when large demand is observed; in addition, the retailer has the authority to cancel a limited amount of its initial order in the beginning of selling season when the observed demand is small. Under these circumstances, overstocking/shortage risks are shared between two members. By sharing risks, it will be possible to optimize decisions globally. In this paper, a new approach for optimal adjustment of QF parameters (i.e. upward and downward adjustment parameters) is developed. Expected profit functions of both channel members under QF contract is mathematically modeled and optimal closed-form relationship between upward and downward adjustment parameters is determined. The obtained closed-form relation guarantees more profit for the whole supply chain and at the same time assures more profit for both channel members.Under the proposed model, both members benefit from the coordinated decision making while risk of demand uncertainty is shared. Our investigations revealed that increasing flexibility on cancelling initial order causes less flexibility of the manufacturer in providing more products. On the other hand, decrease of cancelling flexibility results in more flexibility for oversupply volume.

Location and routing problems in emergencies are so important. This paper considers a location–routing problem with reliability by the means of considering the probability of failure occurrence. The problem has two objectives that minimizes the total cost and maximizes the reliability.Maximizing the reliability is expressed as minimizing the expected cost of the failure. In this problem, three kinds of failures are considered, which are: failure of centers, routes and vehicles. Since travel costs is dependent on many parameters and hence it is not possible to estimate exactly so they are considered as fuzzy number using linguistic variables. At first, mathematical formulation of the problem is presented, and then because the problem is the NP-hard therefore, meta-heuristics algorithms are used to solve the model.Additionally, a bi-objective discrete firefly algorithm is providedand then in order to evaluate the performance of the algorithm, several test problems are implemented and compared with the NSGA-II. The results show that the biobjective discrete firefly algorithm has a better DM measure; however, it is only suitable for small to medium-sized problems due to the MID measure and it loses its efficiency in larger sizes.

Effective management of distribution of manufactured goods plays an important role in the success and increasing of competition' levels in manufacturing organization. Location routing problem is a problem in which location of distribution center and vehicle routing are considered simultaneously. In this paper, a two-stage stochastic programming model and a meta-heuristic approach are presented for the Transportation Location Routing Problem. Customers can order different products.Capacitated central centers transport different products to open intermediary Distribution Centers (IDCs) and then these products are distributed from IDCs between the customers. A bi-objective optimization model is developed. Two objectives, minimization of the overall costs and maximization of the total served demand, are addressed. Due to the high complexity of the problem, we use the Non-Dominated Sorting Genetic Algorithm to solve the problem. The initial parameters of this algorithm is set with Taguchi method. Computational results show the effectiveness of the proposed solution method to solve problems in different dimensions.

The Transfer Point Location Problem is about locating optimum transfer point between the facility and a set of demand points, such that the maximum distance or the sum of the distances between the customers and the facility through the transfer point is minimized in certain environment. Thus, in this thesis the goal is to construct the modeling of the aforesaid problem, in case of multi objectives with respect to locating the single or multiple transfer point (s), in the certain environment and network topology when one or more facility exist. The objectives are about minimizing total cost of transfer points set up and transportation, minimizing total time of transfer and maximizing demand covering. Also, due to high computational complexity of problem for acquiring a solution near to optimum in limited time, one type of proposed hybrid genetic algorithm is used. At last, the validation and also the application of the developed model in certain environment are shown by a case study of ground wheat distribution system in Andimeshk of Khozestan.

Most of the inventory control models assume that items can be stored indefinitely to meet the future demands. However, certain types of commodities either deteriorate or become obsolete in the course of time and hence are unstable. In this study, a mathematical model is presented for a two-echelon supply chain including a buyer and a producer for an inventory integrated system with non-instantaneous of items that demand is probable and follows a normal distribution. Since, the rate of deterioration describes the condition deterioration the goods and regarding the relation between time and deterioration rate is probable rather than the fixed rate of deterioration. In reality, considering the shortages is necessary in both forms of backlogging and lost sales. Therefore, both kinds of shortages are used in the model.The main goal of this model is determining the optimal ordering policy so that the total cost of supply chain is minimized. The proposed model is solved for some problems by Lingo software. The validity of model is determined by sensitive analysis and the problem is known a NP-hard one, hence a genetic algorithm has been used in order to solve the model problem. The rates of deterioration and confidence level sensitivity analysis have been applied to analyze effect of some important parameters affecting on optimal solution of the inventory model.Finally, the optimal value of the expected cost of supply chain under integrated and non-integrated decision-making has been determined and compared. The results show the efficiency of algorithm.

In this paper, a robust mathematical model for integrated productionrouting-inventory problem of perishable product under uncertain demand in a network consisting of a producer and set of retailers, is presented, where the transshipment among retailers is considered to deal with uncertainty of customers' demand. Moreover, the tradeoff between the solution robustness and model robustness can help in decision making about planning of deliveries, the quantity of production and the quantity of transshipment among retailers. Since the mentioned problem is in category of NP-Hard problems, a heuristic solution algorithm is proposed for solving it that guide the solution to a better solution through conducting the best change in vehicle routes in each step of search. Finally, the proposed algorithm is applied on benchmark instances from literature and a real case study, that results reveal the effectiveness of the algorithm in terms of time and quality of solutions.