Scientia Iranica
Year:2024 | Volume:31 | Issue:6|Papers Count:5

The discrete grey modelling technique is a novel methodology of grey predictionmodels, which is effective to improve the effectiveness and applicability of greymodels. In order to build a more general and effective univariate grey predictionmodel, the discrete grey modelling technique is utilised in this paper to builda quadratic polynomial discrete grey model, abbreviated as the QPDGM. Theproperties of the QPDGM model have been discussed, which indicate that thenew model can be regarded as an extension of the conventional discrete greymodel and nonhomogeneous grey model, and it is also coincidence with threeclasses of exponential sequences. The QPDGM model is finally applied to predictthe energy consumption of China, including the electric power, crude oil andnatural gas consumptions. The results have been compared to some commonlyused univariate grey prediction models, which indicates the QPDGM model isgenerally more accurate than other models.

Following the need for joint optimization of decisions in supply chains, this paper newly provides an integrated framework to efficiently fulfill a production-planning-routing problem (PPRP). In such integrated scheme, a set of perishable family-products are manufactured on a single batch-processing machine. These products are dispatched to the customers by a third-party logistics service provider with only two types of licensed eco-friendly transportation facilities. In order to efficiently deliver the manufactured products before they become unusable, we propose a combined shipment structure. To accomplish this, we formulate the problem in the context of a MILP model. In particular, we aim to establish two manufacturing policies based on both increasing and decreasing rates of production and also two delivery policies expressing distinct preferences in fulfilling the customers’ demands. In this regards, we investigate the cost structures obtained from the established integrated planning and the resulting distribution configurations as well. Further, four heuristic algorithms are developed for solving the problem with respect to each hybrid production/distribution scheme derived from the former policies. Finally, to compare the mentioned procedures, it is conducted a numerical study illustrating the preferable efficiency of the plan gained according to the hybrid of the increasing-production-rate and decreasing-delivery-distance policies.

In this paper, we address the weighted multi-objective re-entrant flow-shop scheduling problem considering release dates in order to minimize makespan, total completion time, total tardiness, maximum idle time, and number of tardy jobs. Each job is taken into account with deterministic processing times, and release dates. The flow-shop comprised of two workshops in whose jobs are entered to the main workshop and after the first part of the processing, they are transferred to the second workshop and after this stage, the jobs are returned to the main workshop for the last part of the processing. We model the problem by a new mixed integer programming based on formulating sum of idle time as a new concept. Moreover, a hybrid evolutionary algorithm is proposed based on some dispatching rules, ant colony optimization, and genetic algorithm. The performance of the proposed algorithm on some test instances is compared to the mixed integer linear programming model as well as the state-of-the-art algorithms called genetic algorithm, tabu search, bio-geography based optimization, and artificial bee colony. The computational experiments show that our proposed approach outperforms other algorithms and the results indicate efficiency and capability of the proposed algorithm in comparison with the traditional algorithms.

The decompositions of soft sets and soft matrices are important tools for theoretical and practical studies. In this paper, firstly, we study the decomposition of soft sets in detail. Later, we introduce the concepts of $\alpha$-upper, $\alpha$-lower, $\alpha$-intersection and $\alpha$-union for soft matrices and present some decomposition theorems. Some of these operations are set-restricted types of existing operations of soft sets/matrices, others are $\alpha$-oriented operations that provide functionality in some cases. Moreover, some relations of decompositions of soft sets and soft matrices are investigated and the newfound relations are supported with numerical examples. Finally, two new group decision making algorithms based on soft sets/matrices are constructed, and then their efficiency and practicality are demonstrated by dealing with real life problems and comparison analysis. By using these proposed approaches, solutions can be presented to soft set-based multi-criteria decision making problems, both ordinary and involving primary assessments. These allow to handle soft set-based multi-criteria decision making from different perspectives.

The 0{1 knapsack polyhedron as the most basic relaxation of a 0{1 integer program has attracted the attention of many researchers over the years. We present a very fast method that is guaranteed to generate one facet for the 0{1 knapsack polyhedron. Unlike lifting of cover inequities, our method does not require an initial minimal cover or a predetermined lifting sequencing, and its worst-case complexity is linear in some variables. Therefore, with minimal computational burden, it can be used to generate a potentially strong valid inequality based on any 0{1 relaxation of a general (Mixed) Integer Program (M)IP. Such valid inequalities can be added to the (M)IP problem prior to solving, or given their low computational cost, can be generated during solving the (M)IP, checked to see if they separate the incumbent fractional solution, and added to the problem if they do