A novel multi-objective meta-heuristic model for solving cross-docking scheduling problems

Supplier selection and order allocation constitute vital strategic decisions that must be made by managers within supply chain management environments. In this paper, we propose a multi-objective fuzzy model for supplier selection and order allocation in a two-level supply chain with multi-period, multi-source, and multi-product characteristics. The supplier evaluation objectives considered in this model include cost, delay, and electronic-waste (e-waste) minimization, as well as coverage and weight maximization. A signal function is used to model the price discount offered by the suppliers. Triangular fuzzy numbers are used to deal with the uncertainty of delay and e-waste parameters while the fuzzy Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) is used to obtain the weights of the suppliers. The resulting NP-hard problem, a Pareto-based meta-heuristic algorithm called controlled elitism non-dominated sorting genetic algorithm (CENSGA), is developed. The Non-dominated Sorting Genetic Algorithm (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO) are used to validate the applicability of the CENSGA algorithm and the Taguchi technique to tune the parameters of the algorithms. The results are analysed using graphical and statistical comparisons illustrating how the proposed CENSGA dominates NSGA-II and MOPSO in terms of mean ideal solution distance (MID) and spacing metrics.

In the last decade, multiobjective Particle Swarm Optimization (MOPSO) has been observed as one of the most powerful optimization algorithms in solving Multiobjective Optimization Problems (MOPs). Nowadays, it is becoming increasingly clear that MOPSO can handle complex MOPs based on the competitive-cooperative framework. The goal of this paper is to provide a comprehensive review of MOPSO from the basic principles to hybrid evolutionary strategies. To offer the readers insights on the prominent developments of MOPSO, the key parameters on the convergence and diversity performance of MOPSO were analyzed to reflect the influence on the searching performance of particles. Then, the main advanced MOPSO methods were discussed, as well as the theoretical analysis of multiobjective optimization performance metrics. Even though some hybrid MOPSO methods show promising multiobjective optimization performance, there is much room left for researchers to improve further, in particular in terms of engineering applications. As a result, further in-depth studies are required. This paper should motivate evolutionary computation researchers to pay more attention to this practical yet challenging area.

A metaheuristic particle swarm optimization for enhancing energetic and exergetic performances of hydrogen energy production from plastic waste gasification

Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively.

This paper proposes an optimization method based on Multi-Objective Particle Swarm Optimization (MOPSO) for addressing the operational optimization challenges of Combined Cooling, Heating, and Power (CCHP) integrated energy systems. CCHP systems enhance energy efficiency and reduce environmental pollution, thus possessing significant economic and environmental benefits. Despite existing research progress, current methodologies still inadequately address the simultaneous optimization of economic and environmental aspects. The key contributions of this research include constructing an operational optimization model for the CCHP system, improving the MOPSO algorithm, specifically in terms of inertia weight, learning factors, and individual optimal values, and applying these improvements to solve the model. The theoretical foundations of the CCHP system, multi-objective optimization problems, principles of Particle Swarm Optimization (PSO), and the characteristics and advantages of MOPSO are discussed comprehensively. The optimization model targets minimizing economic costs and optimizing environmental performance, clearly defining decision variables and constraints, and rigorously evaluating MOPSO algorithm applicability. A detailed procedure for constructing and solving the optimization model is provided. A case study is conducted by establishing background information, setting system parameters, configuring MOPSO algorithm parameters, and performing the optimization. Results are thoroughly analyzed, comparing the method's effectiveness against other optimization methods to validate its superiority. The study concludes that this approach effectively optimizes CCHP operations, providing a reference for coordinated planning in integrated energy systems, and discusses future research directions.

Optimizing a bi-objective multi-product EPQ model with defective items, rework and limited orders: NSGA-II and MOPSO algorithms

This paper develops a mathematical model for post-disaster planning with human casualties, which can be considered as operational guidance for the proper use of emergency resources. For this purpose, a stochastic mixed-integer programming model is provided to formulate the problem. The objective functions of the model are (1) maximizing the survival probability of patients, (2) minimizing the maximum of completion time of treatment of all patients, and (3) minimizing the total cost of operations. The model is solved with the ϵ-constraint method. Due to the NP-hardness of the problem which is a significant challenge in the literature, two innovative meta-heuristic algorithms are proposed, i.e. a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective simulated annealing (MOSA). Finally, a comprehensive computational analysis is performed for evaluation purposes. Also, a case study is made on the earthquake in Iran, which illustrates the real-world application of the model. Highlights A stochastic multi-objective mathematical programming model to allocate patients to hospitals and treat them. NSGA-II and MOSA are proposed, in addition to ϵ-constraint method as solution methods. Performance of two meta-heuristic algorithms is measured with five evaluation metrics. The study showed that NSGA-II is more effective than MOSA. The model was implemented on a real-world case, Tabriz earthquake in Iran.

Fuzzy green vehicle routing problem for designing a three echelons supply chain

In this paper, a novel model is presented for machines and automated guided vehicles' simultaneous scheduling, which addresses an extension of the blocking job shop scheduling problem, considering the transferring of jobs between different machines using a limited number of multi-mode automated guided vehicles. Since the model is strictly NP-hard, and because objectives contradict each other, a meta-heuristic algorithm called 'multi-objective invasive weeds optimisation algorithm' with a new chromosome structure which guarantees the feasibility of solutions is developed to solve the proposed problem. Two other meta-heuristic algorithms namely 'non-dominated sorting genetic algorithm' and 'multi-objective particle swarm optimisation algorithm' are applied to validate the solutions obtained by the developed multi-objective invasive weeds optimisation algorithm. A certain method was applied to select the algorithm with the best performance. The result of ranking the algorithms indicated that the developed multi-objective invasive weeds optimisation algorithm had the best performance in terms of solving the mentioned problems. [Received: 7 January 2017; Revised: 30 December 2017; Revised: 17 August 2018; Revised: 22 January 2019; Accepted: 26 July 2019]

Optimization algorithms play a critical role in electromagnetic device designs due to the ever-increasing technological and economical competition. Although evolutionary algorithm-based methods have successfully been applied to different design problems, these methods exhibit deficiencies when solving complex problems with multimodal and discontinuous objective functions, which is quite common in electromagnetic device optimization designs. In this paper, a hybrid multi-objective optimization algorithm based on a non-dominated sorting genetic algorithm (NSGA-II) and a multi-objective particle swarm optimization method (MOPSO) is proposed. In order to enhance the convergence and diversity performance of the algorithm, a new population update mechanism of MOPSO is introduced. Moreover, an adaptive operator involving crossover and mutation is presented to achieve a better balance between global and local searches. The performance of the hybrid algorithm is validated using standard test functions and the multi-objective design of a superconducting magnetic energy storage (SMES) device. Numerical results demonstrate the effectiveness and superiority of the proposed method.

This study proposes a new multi-objective mathematical model in pharmaceutical supply chain for natural disaster response considering quality, green concepts. The proposed model includes three objective functions. The first minimizes total manufacturing costs including production costs, purchasing costs, opening manufacturing plant costs, opening distribution centers costs, transportation costs and cost of poor quality (appraisal and prevention costs). The second minimizes environmental effects of products and transportations. The third maximizes humanitarian forces. Before disaster occurrence, to efficiently predict the objective functions values, we apply the back propagation (BP)—neural network, hybrid genetic algorithm (GA)—artificial neural network and particle swarm optimization (PSO). Finally, the effectiveness of the proposed solution shows the proposed multi objective optimization technique and its feasibility to be adopted as suitable methodology. The obtained results illustrate that the BP had high performance, which its R 2 was 0.99. Managerial implications of this research focus on improving the efficiency and effectiveness of the healthcare supply chain for natural disaster response: saving time, minimizing costs, minimizing environmental impact, utilizing resources more effectively (e.g. financial, human, technical, assets, transportation), showing social responsibility for communities affected by the disaster and continuously improving healthcare supply chain management.

Particle Swarm Optimization(PSO) algorithm is population-based and it is effective for multi-objective optimization problems.For the convergence of the swarm makes the classical algorithm easily converge to local pareto front,the convergence and diversity of the solution are not satisfactory.This paper proposed an independent dynamic inertia weights method for multi-objective particle swarm optimization(DWMOPSO).It changed each particle's inertia weight according to the evolution speed which was calculated by the value of each particle's best fitness in the history.It improved the probability to escape from the local optima.In comparison with Coello's MOPSO through five standard test functions,the solution of the new algorithm has great improvement both in the convergence to the true Pareto front and diversity.

As for the elastic layout of the direct-driven electric wheel system, the electromechanical coupling between the electromagnetic excitation of in-wheel driving motor and the weak damping system gives rise to negative vibration issues, which further deteriorate the dynamic performance of the electric vehicle. This paper presents a multi-objective optimization method for active suspension system to solve these issues by developing an integrated in-wheel motor electrical vehicles model. The unbalanced electromagnetic excitation from in-wheel driving motor is investigated by means of analytical and finite element methods. The Pareto solution set of optimal parameters are generated by the multi-objective particle swarm optimization method, and a comparison in vehicle dynamic performances is made to verify the targeted optimization method. The simulation results indicate that the optimized active suspension system attenuates the sensitivity of the vehicle system to electromagnetic excitation with a satisfactory balancing between vehicle ride comfort and stability as well as active suspension utilization.

BackgroundBlood banks have to be thoughtful about supply chain decisions to effectively satisfy the blood product demand of hospitals. These decisions include the number and locations of distribution centers (DC), as this has a strong impact on both transportation cost and the ability to deliver emergency orders in time.Study Design and MethodsWe propose a mixed‐integer linear programming approach to find optimal DC locations for supplying individual hospitals. The model maximizes the number of hospitals reachable from a DC within a given time‐limit, and minimizes transportation cost. The minimal amount of data required is a set of hospital locations. The model can be further attuned to the user's needs by adding various model extensions. The model's use is demonstrated by two case studies, considering the blood banks of the Netherlands and Finland.ResultsFor both case studies re‐locating the DCs would result in a reduction of transportation cost of about 10% without affecting the reliability of delivery. In addition, to save facility exploitation costs, the number of DCs may be reduced in both countries while maintaining the reliability of delivery. The model was also shown to be robust against changes in hospital ordering behavior.DiscussionWe demonstrated the general usability and added value of the model by successfully optimizing the blood supply chains of the Netherlands and Finland, which differ substantially. Nonetheless, in both countries potential savings in both transportation and facility exploitation cost could be shown. The model code is open source and freely accessible online.

The aim of a Huff location problem is to locate facilities on a competitive environment to maximize the market share. The customer behavior is formulated based on a Huff gravity model. This paper considers a social impact along with economic and environmental aspects in order to construct new facilities in a competitive supply chain. This problem is a multi-objective mixed integer non-linear programming (MINLP). To deal with the problem, two multi-objective meta-heuristics (i.e., non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO)) are proposed to cope with this problem. The results verify the superiority of the NSGA-II over MOPSO in terms of some comparison metrics. Additionally, a two-stage algorithm is developed to sort non-dominated solutions generated by the NSGA-II. In the first stage, Pareto-optimal solutions are obtained by the NSGA-II, then in second stage, a ranking method based on TOPSIS is performed to select the best solutions among Pareto-optimal one.