International Journal of Industrial Engineering & Production Research

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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.

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In this paper, a stochastic cell formation problem is studied using queuing theory framework and considering reliability. Since cell formation problem is NP-Hard, two algorithms based on genetic and modified particle swarm optimization (MPSO) algorithms are developed to solve the problem. For generating initial solutions in these algorithms, a new heuristic method is developed, which always creates feasible solutions. Moreover, full factorial and Taguchi methods are implemented to set crucial parameters in the solutions procedures. Deterministic method of branch and bound (B&B) algorithm is used to evaluate the results of modified particle swarm optimization algorithm and the genetic algorithm. The results indicate that proposed algorithms have better performance in quality of the metaheurstic algorithms final answer and solving time compared with the method of Lingo software’s B&B algorithm. The solution of two metaheurstic algorithms is compared by t test. Ultimately, the results of numerical examples indicate that considering reliability has significant effect on block structures of machine-part matrixes.

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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.

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In this paper, a new model of hub locating has been solved considering reliability and importance of flow congestion on hub nodes in a dynamic environment. Each of nodes considered as hubs and their communication paths with other non-hubs nodes have specific reliability. In order to reduce input flow to any hub and avoid creation unsuitable environmental and traffic conditions in that area, efficiency capacity is allocated to each hub, which is subject to a penalty in case of exceeding this amount. Another capability of this model is the ability of deciding whether hubs are active or inactive in each period, so hub facilities can be established or closed due to different conditions (such as changes in demand, legislative, etc.). The model is non-linear and bi-objective that the first goal is reducing transportation costs, hub rental fees and extra flow congestion penalties on hub nodes and the second goal is to increase the minimum designed network reliability. After linearization of the model, using ε-constraint method, optimal boundary is obtained. Also, to demonstrate the performance of the model, we use IAD dataset for solving problem. To evaluate the model, sensitivity analysis is presented for some of important parameters of the model.

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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, …). 

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This paper aims to introduce a joint optimization approach for maintenance, quality, and buffer stock policies in single machine production systems based on a P control chart. The main idea is to find the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval, and the control limits simultaneously, such that the expected total cost per time unit is minimized. In the considered system, we have a fixed rate of production and stochastic machine breakdowns which directly affect the quality of the product. Periodic preventive maintenance (PM) is performed to reduce out-of-control states. In addition, corrective maintenance is required after finding each out-of-control state. A buffer is used to reduce production disturbances caused by machine stops. To ensure that demand is met during a preventive and corrective maintenance operation. All features of three sub-optimization problems including maintenance, quality control, and buffer stock policies are formulated and the proposed integrated approach is defined and modeled mathematically. In addition, an iterative numerical optimization procedure is developed to provide the optimal values for the decision variables. The proposed procedure provides the optimal values of the preventive maintenance period, the buffer stock size, the sample size, the sampling interval, and the control chart limits simultaneously, in a way that the total cost per time unit is minimized. Moreover, some sensitivity analyses are carried out to identify the key effective parameters.

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Venture capital (VC) financing is associated with the challenges of double-sided moral hazard, and uncertainty, which leads to the difficulty in estimating the venture's value accurately and consequently the impossibility of determining the optimal equity sharing between the entrepreneur and investor. Traditionally, convertible preferred equity mechanisms used to be implemented as an incentive to decline moral hazard. However, despite the emphasis on investor risk-taking, such mechanisms transfer the investor risk to the entrepreneur and do not mitigate the incentive of opportunistic behaviors. Furthermore, according to the literature review, and to the best of the authors’ knowledge, there has not been developed any practical mechanism for equity sharing in VC financing up to now. This paper proposes a fair equity sharing mechanism, which alleviates the above-mentioned deficiencies. It adjusts both parties' share during the equity dilution in each stage of financing, regarding the difference between the venture's ex-ante and ex-post values. Moreover, it manages uncertainty by applying staged financing and the option of abandonment at the end of each stage. The proposed mechanism has been verified by using the mathematical tools and drawing its curves for a case study.