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The paper aims at the bi‐objective optimization of a two‐echelon distribution network model for facility location and capacity allocation where in a set of customer locations with demands and a set of candidate facility locations will be known in advance. The problem is to find the locations of the facilities and the shipment pattern between the facilities and the distribution centers (DCs) to minimize the combined facility location and shipment costs subject to a requirement that maximum customer demands be met.

To optimize the two objectives simultaneously, the location and distribution two‐echelon network model is mathematically represented in this paper considering the associated constraints, capacity, production and shipment costs and solved using hybrid multi‐objective particle swarm optimization (MOPSO) algorithm.

This paper shows that the heuristic based hybrid MOPSO algorithm can be used as an optimizer for characterizing the Pareto optimal front by computing well‐distributed non‐dominated solutions. These aolutions represent trade‐off solutions out of which an appropriate solution can be chosen according to industrial requirement.

Very few applications of hybrid MOPSO are mentioned in literature in the area of supply chain management. This paper addresses one of such applications.

The purpose of this paper is to realize a multi‐objective location‐routing network optimization in reverse logistics using particle swarm optimization based on grey relational analysis with entropy weight.

Real world network design problems are often characterized by multi‐objective in reverse logistics. This has recently been considered as an additional objective for facility location problem or vehicle routing problem in reverse logistics network design. Both of them are shown to be NP‐hard. Hence, location‐routing problem (LRP) with multi‐objective is more complicated integrated problem, and it is NP‐hard too. Due to the fact that NP‐hard model cannot be solved directly, grey relational analysis and entropy weight were added to particle swarm optimization to decision among the objectives. Then, a mathematics model about multi‐objective LRP of reverse logistics has been constructed, and a proposed hybrid particle swarm optimization with grey relational analysis and entropy weight has been developed to resolve it. An example is also computed in the last part of the paper.

The results are convincing: not only that particle swarm optimization and grey relational analysis can be used to resolve multi‐objective location‐routing model, but also that entropy and grey relational analysis can be combined to decide weights of objectives.

The method exposed in the paper can be used to deal with multi‐objective LRP in reverse logistics, and multi‐objective network optimization result could be helpful for logistics efficiency and practicability.

The paper succeeds in realising both a constructed multi‐objective model about location‐routing of reverse logistics and a multi‐objective solution algorithm about particle swarm optimization and future stage by using one of the newest developed theories: grey relational analysis.

This study aims to propose a new multiobjective optimization metaheuristic based on the tiki-taka algorithm (TTA). The proposed multiobjective TTA (MOTTA) was implemented for a simple assembly line balancing type E (SALB-E), which aimed to minimize the cycle time and workstation number simultaneously.

TTA is a new metaheuristic inspired by the tiki-taka playing style in a football match. The TTA is previously designed for a single-objective optimization, but this study extends TTA into a multiobjective optimization. The MOTTA mimics the short passing and player movement in tiki-taka to control the game. The algorithm also utilizes unsuccessful ball pass and multiple key players to enhance the exploration. MOTTA was tested against popular CEC09 benchmark functions.

The computational experiments indicated that MOTTA had better results in 82% of the cases from the CEC09 benchmark functions. In addition, MOTTA successfully found 83.3% of the Pareto optimal solution in the SALB-E optimization and showed tremendous performance in the spread and distribution indicators, which were associated with the multiple key players in the algorithm.

MOTTA exploits the information from all players to move to a new position. The algorithm makes all solution candidates have contributions to the algorithm convergence.

This study develops an integrated zero waste discharge planning approach for improving resource efficiency in a pulp-and-paper manufacturing firm.

The objectives of this study are to (1) identify the environmental, technical and social metrics in resource efficiency; (2) utilize fuzzy multi-objective programming and the hybrid particle swarm optimization algorithm to solve the fuzzy problem; and (3) develop an assessment for resource efficiency improvement in an industrial case study.

The findings demonstrate the superiority of hybrid particle swarm optimization algorithm in generating optimal results for a pulp-and-paper manufacturing firm.

The findings demonstrate the superiority of hybrid particle swarm optimization algorithm in generating optimal results for a pulp-and-paper manufacturing firm.

Resource efficiency is a multi-objective problem in an uncertain environment. In particular, zero waste discharge planning involves minimizing the total cost and maximizing the waste material recovery rate, wastewater reuse, and waste heat recovery.

The purpose of this article is to develop a cost-effective model for Multi-Criteria ABC Inventory Classification and to measure its performance in comparison to the other existing models.

Particle Swarm Optimization (PSO) algorithm is exclusively designed for Multi-Criteria ABC Inventory Classification wherein the inventory is classified based on the objective of cost minimization, which is achieved through the inventory performance index – total relevant cost. Effectiveness of classification of the proposed model and the other classification models toward two inventory performance measures, that is, cost and inventory turnover has been computed, and the results of all models are relatively compared by arriving at the cumulative performance score of each model.

This study reveals that the ABC Inventory classification based on the proposed PSO approach is more effective toward cost and inventory turnover ratio in comparison to the twenty existing models.

The proposed model can be easily adapted to the industrial requirement of inventory classification by cost as objective as well as other inventory management performance measures.

The conceptual model is more versatile which can be adapted for various objectives and the effectiveness of classification in comparison to the other models can be measured toward each objective as well as combining all the objectives.

The purpose of this paper is to propose an optimal energy management (OEM) method using intelligent optimization techniques applied to implement an optimally hybrid heat and power isolated microgrid. The microgrid investigated combines renewable and conventional power generation.

Five bio-inspired optimization methods include an advanced proposed multi-objective particle swarm optimization (MOPSO) approach which is comparatively applied for OEM of the implemented microgrid with other bio-inspired optimization approaches via their comparative simulation results.

Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization methods. Moreover, the proposed MOPSO is successfully applied to perform 24-h OEM microgrid. The simulation results also display the merits of the real time optimization along with the arbitrary of users’ selection as to satisfy their power requirement.

This paper focuses on the OEM of a designed microgrid using a newly proposed modified MOPSO algorithm. Optimal multi-objective solutions through Pareto front demonstrate that the advanced proposed MOPSO method performs quite better in comparison with other meta-heuristic optimization approaches.

Distribution network design involves a set of strategic decisions in supply chains because of their long-term impacts on the total logistics cost and environment. To incorporate a trade-off between financial and environmental aspects of these decisions, this paper aims to determine an optimal location, among candidate locations, of a new logistics center, its capacity, as well as optimal network flows for an existing distribution network, while concurrently minimizing the total logistics cost and gas emission. In addition, uncertainty in transportation and warehousing costs are considered.

The problem is formulated as a fuzzy multiobjective mathematical model. The effectiveness of this model is demonstrated using an industrial case study. The problem instance is a four-echelon distribution network with 22 products and a planning horizon of 20 periods. The model is solved by using the min–max and augmented ε-constraint methods with CPLEX as the solver. In addition to illustrating model’s applicability, the effect of choosing a new warehouse in the model is investigated through a scenario analysis.

For the applicability of the model, the results indicate that the augmented ε-constraint approach provides a set of Pareto solutions, which represents the ideal trade-off between the total logistics cost and gas emission. Through a case study problem instance, the augmented ε-constraint approach is recommended for similar network design problems. From a scenario analysis, when the operational cost of the new warehouse is within a specific fraction of the warehousing cost of third-party warehouses, the solution with the new warehouse outperforms that without the new warehouse with respective to financial and environmental objectives.

The proposed model is an effective decision support tool for management, who would like to assess the impact of network planning decisions on the performance of their supply chains with respect to both financial and environmental aspects under uncertainty.

The purpose of this study is to propose a new mathematical model that integrates strategic decision-making with tactical-operational decision-making in order to optimize production and scheduling decisions.

This study presents a multi-objective optimization framework to make production planning, scheduling and maintenance decisions. An epsilon-constraint method is used to solve small instances of the model, while new hybrid optimization algorithms, including multi-objective particle swarm optimization (MOPSO), non-dominated sorting genetic algorithm, multi-objective harmony search and improved multi-objective harmony search (IMOHS) are developed to address the high complexity of large-scale problems.

The computational results demonstrate that the metaheuristic algorithms are effective in obtaining economic solutions within a reasonable computational time. In particular, the results show that the IMOHS algorithm is able to provide optimal Pareto solutions for the proposed model compared to the other three algorithms.

This study presents a new mathematical model that simultaneously determines green production planning and scheduling decisions by minimizing the sum of the total cost, makespan, lateness and energy consumption criteria. Integrating production and scheduling of a shop floor is critical for achieving optimal operational performance in production planning. To the best of the authors' knowledge, the integration of production planning and maintenance has not been adequately addressed.

A set of routers that are connected over communication channels can from network-on-chip (NoC). High performance, scalability, modularity and the ability to parallel the structure of the communications are some of its advantages. Because of the growing number of cores of NoC, their arrangement has got more valuable. The mapping action is done based on assigning different functional units to different nodes on the NoC, and the way it is done contains a significant effect on implementation and network power utilization. The NoC mapping issue is one of the NP-hard problems. Therefore, for achieving optimal or near-optimal answers, meta-heuristic algorithms are the perfect choices. The purpose of this paper is to design a novel procedure for mapping process cores for reducing communication delays and cost parameters. A multi-objective particle swarm optimization algorithm standing on crowding distance (MOPSO-CD) has been used for this purpose.

In the proposed approach, in which the two-dimensional mesh topology has been used as base construction, the mapping operation is divided into two stages as follows: allocating the tasks to suitable cores of intellectual property; and plotting the map of these cores in a specific tile on the platform of NoC.

The proposed method has dramatically improved the related problems and limitations of meta-heuristic algorithms. This algorithm performs better than the particle swarm optimization (PSO) and genetic algorithm in convergence to the Pareto, producing a proficiently divided collection of solving ways and the computational time. The results of the simulation also show that the delay parameter of the proposed method is 1.1 per cent better than the genetic algorithm and 0.5 per cent better than the PSO algorithm. Also, in the communication cost parameter, the proposed method has 2.7 per cent better action than a genetic algorithm and 0.16 per cent better action than the PSO algorithm.

As yet, the MOPSO-CD algorithm has not been used for solving the task mapping issue in the NoC.

One of the practical issues in the area of location and allocation is the location of the hub. In recent years, exchange rates have fluctuated sharply for a number of reasons such as sanctions against the country. Natural disasters that have occurred in recent years caused delays in hub servicing. The purpose of this study is to develop a mathematical programming model to minimize costs, maximize social responsibility and minimize fuel consumption so that in the event of a disruption in the main hub, the flow of materials can be directed to its backup hub to prevent delays in flow between nodes and disruptions in hubs.

A multi-objective mathematical programming model is developed considering uncertainty in some parameters, especially cost as fuzzy numbers. In addition, backup hubs are selected for each primary hub to deal with disruption and natural disasters and prevent delays. Then, a robust possibilistic method is proposed to deal with uncertainty. As the hub location-allocation problem is considered as NP-Hard problems so that exact methods cannot solve them in large sizes, two metaheuristic algorithms including a non-dominated sorting genetic algorithm non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO) are applied to tackle the problem.

Numerical results show the proposed model is valid. Also, they demonstrate that the NSGA-II algorithm outperforms the MOPSO algorithm.

The proposed model was implemented in one of the largest food companies in Iran, which has numerous products manufactured in different cities, to seek the hub locations. Also, due to several reasons such as road traffic and route type the difference in the rate of fuel consumption between nodes, this model helps managers and decision-makers to choose the best locations to have the least fuel consumption. Moreover, as the hub set up increases the employment rate in that city and has social benefits as it requires hiring some staff.

This paper investigates the hub location problem considering backup hubs with multiple objective functions to deal with disruption and uncertainty. Also, this study examines how non-hub nodes are assigned to hub nodes.