This paper introduces the effect of task deterioration in simple assembly line balancing problem. In many realistic assembly lines, a deterioration task is considered when a task is started earlier than the assigned time since the station time is constant and the earliness of the task does not reduce the cycle time. This phenomenon is known as deteriorating tasks. Therefore, we seek an optimal assignment and schedule of tasks in workstations, in order to minimize the number of stations for a given cycle time, which is known as SALBP-1. For this purpose, a mathematical model is proposed. Since the pure SALBP-1 is proved to be NP-hard and considering task deterioration complicates problem further, we propose a genetic algorithm for solving such problem. Several well-known test problems are solved to study the performance of the proposed approach.

This paper focuses on routing flexibility, which is the ability to manufacture a part type via several routes and/or to perform different operations on more than one machine. Specifically, the paper presents a comprehensive method for the measurement of routing flexibility, in a generic manufacturing system. The problem is approached in a modular way, starting from a basic set of flexibility indexes. These are progressively extended to include more comprehensive and complex routing attributes, such as: the average efficiency, the range and the homogeneous distribution of the alternative routes. Two procedures are finally proposed to compare manufacturing systems in terms of routing flexibility. The first one uses a vectorial representation of the previously defined indexes and the second one is based on data envelopment analysis, a multi-criteria decision making approach. The paper concludes with a numerical example, supported by discrete event simulation, which validates the proposed approach.

Group scheduling problem in cellular manufacturing systems consists of two major steps. Sequence of parts in each part-family and the sequence of part-family to enter the cell to be processed. This paper presents a new method for group scheduling problems in flow shop systems where it minimizes makespan (Cmax) and total tardiness. In this paper, a position-based learning model in cellular manufacturing system is utilized where processing time for each part-family depends on the entrance sequence of that part. The problem of group scheduling is modeled by minimizing two objectives of position-based learning effect as well as the assumption of setup time depending on the sequence of parts-family. Since the proposed problem is NP-hard, two meta heuristic algorithms are presented based on genetic algorithm, namely: Non-dominated sorting genetic algorithm (NSGA-II) and non-dominated rank genetic algorithm (NRGA). The algorithms are tested using randomly generated problems. The results include a set of Pareto solutions and three different evaluation criteria are used to compare the results. The results indicate that the proposed algorithms are quite efficient to solve the problem in a short computational time.

The selection of appropriate alternative in the industrial environment is an important but, at the same time, a complex and difficult problem because of the availability of a wide range of alternatives and similarity among them. Therefore, there is a need for simple, systematic, and logical methods or mathematical tools to guide decision makers in considering a number of selection attributes and their interrelations. In this paper, a hybrid decision making method of graph theory and matrix approach (GTMA) and analytical hierarchy process (AHP) is proposed. Three examples are presented to illustrate the potential of the proposed GTMA-AHP method and the results are compared with the results obtained using other decision making methods.

Random variates play key role in any simulation system and there are different algorithms to generate random variates. One of the best algorithms for generating random variates is uniform fractional part algorithm. The algorithm has high performance in terms of efficiency, speed and simplicity. Although the algorithm has useful results, it is an approximate algorithm. In this article, the approximate form of the algorithm has been studied, and some suggestions have also been presented. Through acceptance-rejection approach and hat and squeeze function, the approximate algorithm is transformed to near exact algorithm. The proposed model of this paper has been examined and compared with the traditional one and the preliminary results indicate that it performs better than the other existing algorithms.

In this research, the integrated inventory models are developed for price–sensitive stock– dependent demand and delay in payments are permissible. Two level trade credit police in the vendor–buyer and buyer–customer is considered. An easy–to–use solution algorithm is derived for the integrated models to determine the buyer’s optimal pricing and ordering strategy. A negotiation scenario is incorporated to distribute the extra profit between the vendor and buyer. A numerical example and sensitivity analysis are given to validate the proposed models. It is observed that the total joint profit of the integrated system can increase even if the price discount is offered to the buyer in proposed models.

The hub location problem appears in a variety of applications, including airline systems, cargo delivery systems and telecommunication network design. Hub location problems deal with finding the location of hub facilities and the allocation of demand nodes to these located hub facilities. In this paper, a new model for the capacitated single allocation hub covering location problem is presented. Instead of using capacity constraints to limit the amount of flow received by the hubs, the second objective function is introduced to minimize service times in the hubs. The service time in the hubs includes the waiting time of received flows in a queue and the time to get services. Due to the NP-hardness of the problem, a new weight-based multi-objective imperialist competitive algorithm (MOICA) is designed to find near-optimal solutions. To validate the performance of the proposed algorithm, the solutions obtained by the MOICA are compared by the exact solutions of the mathematical programming model.

This paper discusses the mathematical modeling and performance optimization of CO2 cooling system of a fertilizer plant using genetic algorithm. The fertilizer plant comprises of various systems viz. shell gasification and carbon recovery, desulphurization, co-shift conversion, decarbonation- CO2 cooling, CO2 removal, nitrogen wash and ammonia synthesis, etc. One of the most important functionaries of a fertilizer plant is CO2 cooling system. The CO2 cooling system of a fertilizer plant has five main subsystems, arranged in series. We propose a mathematical model, which considers exponential distribution for the probable failures and repairs. We also use probabilistic approach and derive differential equations based on Markov birth-death process. These equations are then solved using normalizing conditions to determine the steady state availability of the CO2 cooling system. The performance of each subsystem of CO2 cooling system of a fertilizer plant is also optimized using genetic algorithm. The results of the proposed model of this paper is useful to the plant management for the timely execution of proper maintenance decisions and hence to enhance the system performance.

This paper proposes a mathematical model for retailer’s optimal inventory policy for deteriorating items under the conditions of permissible delay in payment in supply chain coordination. In this paper, supplier offers full trade credit to his/her retailer and the retailer, in turn, provides the partial trade credit to his/her customer. This research article helps the retailer determine the optimal cost, more accurately. The primary objective is to minimize the retailer’s cost and to determine the optimal inventory policy for retailer. The results of this article generalize some already published results by incorporating deterioration on inventory items. We use some numerical examples to analyze the effects of various parameters on retailer’s inventory policy.

In this paper, a multi products single machine economic production quantity model with
discrete delivery is developed. A unique cycle length is considered for all produced items with
an assumption that all products are manufactured on a single machine with a limited capacity.
The proposed model considers different items such as production, setup, holding, and
transportation costs. The resulted model is formulated as a mixed integer nonlinear
programming model. Harmony search algorithm, extended cutting plane and particle swarm
optimization methods are used to solve the proposed model. Two numerical examples are used
to analyze and to evaluate the performance of the proposed model.