Optimizing multi-supplier multi-item joint replenishment problem for non-instantaneous deteriorating items with quantity discounts

Purpose This paper aims to propose an improved Moth Flame Optimization (I-MFO) algorithm to optimize the cost-oriented two-sided assembly line balancing (2S-ALB). Prior to the decision to assemble a new product, the manufacturer will carefully study and optimize the related cost to set up and run the assembly line. For the first time in ALB, the power cost is modeled together with the equipment, set up and labor costs. Design/methodology/approach I-MFO was proposed by introducing a global reference flame mechanism to guide the global search direction. A set of benchmark problems was used to test the I-MFO performance. Apart from the benchmark problems, a case study from a body shop assembly was also presented. Findings The computational experiment indicated that the I-MFO obtained promising results compared to comparison algorithms, which included the particle swarm optimization, Cuckoo Search and ant colony optimization. Meanwhile, the results from the case study showed that the proposed cost-oriented 2S-ALB model was able to assist the manufacturer in making better decisions for different planning periods. Originality/value The main contribution of this work is the global reference flame mechanism for MFO algorithm. Furthermore, this research introduced a new cost-oriented model that considered power consumption in the assembly line design.

An improved moth-flame optimization (IMFO) algorithm is proposed to increase the location accuracy of a vision measurement system. This algorithm can optimize the initial pose parameters by improving a series of random solutions to the required precision. A measurement experiment system of space manipulator is designed to precision test. The IMFO algorithm is evaluated on 23 benchmark functions and measurement experiments for pose, and the results are verified by a comparative study with self-adaptive differential evolution (SaDE), moth-flame optimization (MFO), and proactive particle swarm optimization (PPSO). The statistical results of the benchmark functions show that the IMFO algorithm can provide very promising and competitive results. Additionally, the experimental results of pose measurement show that the accuracy of the IMFO algorithm is approximately twice higher than that of other three algorithms. All in all, the experiments indicate that the IMFO algorithm has a good optimization ability to complete the visual identification accurately.

Time series prediction of wind speed has been widely used in wind power generation. The volatility and instability of wind speed have a large negative impact on wind turbines and power systems, which can lead to grid collapse in severe cases. Therefore, accurate wind speed prediction is crucial for wind power generation. In this paper, considering the influence of different parameters on algorithm training and prediction, an improved moth flame optimization algorithm is constructed to optimize the LSTM wind energy prediction system to obtain better performance. The system consists of three modules: data preprocessing, optimization, and prediction. The data preprocessing module uses fuzzy information granulation to blur the input data. On this basis, the combination of swarm intelligent optimization algorithm and prediction model can effectively predict wind speed time series. Taking the California wind farm as an example, the MAPE of the experiment in the short-term forecast is 3.15%, the MAPE of the medium-term forecast is 4.38%, and the MAPE of the long-term forecast is 18.28%. The experimental results show that the proposed model has obvious advantages over the previous model.

This paper is aimed at suggesting a methodology leading to effective supplier management processes utilizing information obtained from the supplier selection processes. For this methodology, we propose the supplier selection and management system (SSMS) that includes purchasing strategy system, supplier selection system, and supplier management system, and we explain how the SSMS is applied to a real supply chain. The methodology identifies the managerial criteria using information derived from supplier selection process and makes use of them in the supplier management process. These managerial criteria include key criteria that are major criteria required by the manufacturer for the best quality of parts from suppliers according to the character of each part, and weak criteria that show the shortcomings of selected suppliers as compared with alternative suppliers with regard to each criterion. The effectiveness of supplier management with managerial criteria was verified by a t-test and a correlation analysis with the example of a Korean air conditioner manufacturer.

The Moth flame optimization algorithm (MFO) is a nature-inspired optimization algorithm which imitates moths darts into the fire. Similarly, to other meta-heuristic algorithm, MFO still has the disadvantage of getting into the local best and the convergence rate cannot be satisfying. In this paper a Multi-operator Moth Flame Optimization algorithm (MOMFO) is proposed to improve the MFO algorithm, which combines Elite Search strategy, Adaptive control strategy and Chaos search strategy to enhance MFO.A two-stage strategy is used to balance the global search and local search ability, which makes the algorithm have better search performance and accuracy. The proposed MOMFO is benchmarked on eight well-known test functions and real-world problem, and the results show that the algorithm is effective in solving complex nonlinearity problems.

In this article, the optimal allocation of compensation devices in power systems is determined using Improved Moth-flame Optimization (IMFO) algorithm. The main objective of this study is to maximize power system loadability, reduce total power system losses and enhance the voltage profile through finding the optimal locations and sizes of Static VAR Compensator (SVC) and Thyristor Controlled Compensator (TCSC). The methodology of this article is based on Continuation Power Flow (CPF) which is merged with IMFO in Optimum Power Flow (OPF) problem restricted by equality and inequality constraints. The proposed algorithm is implemented to regulate the required control variables. Both of single and multi-objective functions are applied on 57-IEEE bus test system to prove the performance of the proposed algorithm. The acquired simulation results by IMFO are compared with Moth Flame Optimization technique (MFO) and Multi Verse Optimizer (MVO) to emphasize the robustness of the proposed algorithm.

In this research we study the optimization problem of path planning in an oceanic ecosystem with a Static current in the presences of various obstacles with the autonomy of Autonomous underwater vehicles (AUV). We introduce an improved Moth flame optimization algorithm called refraction principle and opposite-based-learning Moth flame optimization (ROBL-MFO) in a Static current environment. This algorithm addresses the differences between the traditional Moth flame optimization (MFO) algorithm. First, it adjusts the moth position update formula we deal with the term of the historically optimal flame average, and improves the convergence velocity of the algorithm. Secondly, it uses a random inverse learning strategy to narrow down the algorithm search. Finally, the principle of refraction provides the algorithm with the ability to jump from the local optima and help the algorithm avoid premature convergence. The numerical results show that the ROBL-MFO improves the classical MFO, in terms of optimization criteria.KeywordsAutonomous underwater vehiclesPath planningStatic currentRefraction principle and opposite-based learning - Moth flame optimization

PT Out Of Asia is an exporting company that produces handicraft products. All products in the company are semi-finished products from suppliers who performed the finishing and packing stage. The production process system is done by the company varies according to the demand of the consumer, so the raw material of packing needed also vary. However, the increasing number of consumer demand is increasingly binding, it will also increase the demand for packing raw materials, so it is necessary to control the supply of raw materials packing to minimize the cost of raw material inventory according to storage capacity and packing production capability. In addition, ordering of raw materials can be done in combination to save raw material inventory. The method used in this research is Joint Replenishment Problem (JRP) and (Economic Order Quantity (EOQ) method, to compare total inventory cost using Joint Replenishment Probe (JRP) and Economic Order Quantity (EOQ) method. The research shows that total cost the minimum inventory using Joint Replenishment Problem (JRP) method is Rp 1,180,246,601 / year compared to use the Economic Order Quantity (EOQ) method of Rp 4,326,163,356 / year, with the difference of comparison using JRP and EOQ of Rp 3,145,916,750/ year

In recent days, swarm intelligent (SI) optimization algorithms have been proved to be a powerful framework for finding tradeoff solutions of multi-objective optimization problems (MOPs). Many researchers have proposed various SI optimization algorithms. Multi-objective artificial flora (MOAF) optimization algorithm is a recently proposed algorithm for solving MOPs. However, problems of decreased population diversity and uniformity of solutions distribution in the late evolutionary period is existed in the algorithm. Hence, this paper proposes a knee point-driven MOAF (kpMOAF) optimization algorithm to address the vulnerability of MOAF optimization algorithm. Knee points of the non-dominant solutions are taken by the proposed algorithm as criterion to guide the population evolution. Researchers have proved that select knee points equals to select a large hypervolume. Therefore, using it as criterion is an effective way to enhance the population convergence rate and maintain the diversity of solutions. In addition, adaptive neighborhood control method is introduced in the evolution process to improve the algorithm development capability. Simulation results on 10 benchmark functions demonstrate the competitiveness of kpMOAF optimization algorithm.

Air target threat assessment is a key issue in air defense operations. Aiming at the shortcomings of traditional threat assessment methods, such as one‐sided, subjective, and low‐accuracy, a new method of air target threat assessment based on gray neural network model (GNNM) optimized by improved moth flame optimization (IMFO) algorithm is proposed. The model fully combines with excellent optimization performance of IMFO with powerful learning performance of GNNM. Finally, the model is trained and evaluated using the target threat database data. The simulation results show that compared with the GNNM model and the MFO‐GNNM model, the proposed model has a mean square error of only 0.0012 when conducting threat assessment, which has higher accuracy and evaluates 25 groups of targets in 10 milliseconds, which meets real‐time requirements. Therefore, the model can be effectively used for air target threat assessment.

As the move towards Grid Integrated-Photovoltaic (GI-PV) system is proposed to improve the power quality development. A novel Adaptive Neuro-Fuzzy Inference System (ANFIS) based on improved Moth Flame Optimization (MFO) algorithm is described for grid integrated approach. The solar integration of Maximum Power Point (MPP) fed into modified Switched Boost Inverter (SBI) is presented, this GI-PV connected circuit has become prominent research in a recent scenario for energy demand. Proposed MFOA-ANFIS controller has generated the duty cycle pulses to each converter circuit. The benefit of grid-tied SBI is direct control outer-loop employed to obtain MFO-ANFIS techniques. To maintain a constant voltage DC-link is employed for inner-loop, this presence of constant DC-power to grid loads with support of MFO-ANFIS assists Proportional Integral Differential (PID) method. The results acquired by the simulation expressed that the proposed controller is addressed to maintain active and reactive power exchange, regulate DC bus-link voltages, grid voltage, and grid current. The effectiveness of the practical implication research is achieved by the output as represented as minimum grid harmonics, load current, and compensator current as verified in MATLAB/Simulink platform.

Joint replenishment problems constitute an important class of models in inventory management. They exhibit aspects of possible coordination among multiple products to save costs. Their computational complexity had been open even if there are just two products that need to be synced. In “Integer factorization: Why two-item joint replenishment is hard,” Schulz and Telha present a simple framework based on integer factorization to establish the computational hardness of two variants of the joint replenishment problem with two items. Whereas difficult to solve in practice and not believed to be solvable in polynomial time, integer factorization is not as difficult as NP-complete problems. The authors show that a similar technique can be used to show even the NP-completeness of one variant of the joint replenishment problem (again with just two items).

An IMFO-LSTM_BIGRU combined network for long-term multiple battery states prediction for electric vehicles

Renewable Energy Resources (RES) can supply a clean and a smart solution to satisfy the rapidly growing load demand. The Photovoltaic (PV) and wind turbines are considered as resources of Distributed Generation (DG). The integration of PV - DG units into distribution systems has a significant effect on reducing the system active power losses. In this paper, a new technique based on the Moth Flame Optimization (MFO) algorithm, is used to optimally allocate the PV -DG units in Radial Distribution Systems (RDSs) and determine the optimal size of each unit. The objective of the technique is to minimize the total active power losses of the system, while satisfying several operating constraints. The proposed optimization algorithm is tested using three different IEEE standard RDSs of various sizes, which are 33, 69 and 118-bus systems. In order to demonstrate the applicability and the performance of the MFO algorithm, numerical simulations are obtained when applying the MFO algorithm, and compared with different existing optimization techniques. This comparison shows the validity of the proposed MFO algorithm.

A recently proposed swarm inspired optimization algorithm based on the navigation approach of Moths in space entitle as Moth-Flame Optimization (MFO) algorithm, is used for solve equality and inequality constrained optimization, real challenging layout problems. The navigating strategy of moths in universe entitles transverse orientation, a well active mechanism for travel so far distance in the straight direction. In fact, artificial lights trick moths, so they follow a deadly spiral path. MFO algorithm gives the competitive results with both continuous and discrete control variables. Real Challenging Constrained Optimization is a way of optimising an objective function in presence of constraints on some control variables. MFO have an ability to solve both constraints that may be either hard constrained or soft constrained. A statical representation of MFO algorithm expresses the best objective function value with reference to accuracy and standard deviation over recently proposed and most popular optimization algorithms. Fourteen constrained benchmark function of real engineering problems have been calculated and gained solutions were compared with the solution obtained by various recognized algorithms. The results obtained through MFO algorithm represent better solutions in the field of engineering design problems among many recently developed algorithms.