A novel hybrid meta-heuristic algorithm for a no-wait flexible flow shop scheduling problem with sequence dependent setup times

In this paper, we contemplate the problem of scheduling a set of n jobs in a no-wait flexible flow shop manufacturing system with sequence dependent setup times to minimising the maximum completion time. With respect to NP-hardness of the considered problem, there seems to be no avoiding application of metaheuristic approaches to achieve near-optimal solutions for this problem. For this reason, three novel metaheuristic algorithms, namely population based simulated annealing (PBSA), adapted imperialist competitive algorithm (AICA) and hybridisation of adapted imperialist competitive algorithm and population based simulated annealing (AICA?+?PBSA), are developed to solve the addressed problem. Because of the sensitivity of our proposed algorithm to parameter's values, we employed the Taguchi method as an optimisation technique to extensively tune different parameters of our algorithm to enhance solutions accuracy. These proposed algorithms were coded and tested on randomly generated instances, then to validate the effectiveness of them computational results are examined in terms of relative percentage deviation. Moreover, some sensitive analyses are carried out for appraising the behaviour of algorithms versus different conditions. The computational evaluations manifestly support the high performance of our proposed novel hybrid algorithm against other algorithms which were applied in literature for related production scheduling problems.     

An Artificial Intelligence Approach for Solving Stochastic Transportation Problems

Recent years witness a great deal of interest in artificial intelligence (AI) tools in the area of optimization. AI has developed a large number of tools to solve the most difficult search-and-optimization problems in computer science and operations research. Indeed, metaheuristic-based algorithms are a sub-field of AI. This study presents the use of the metaheuristic algorithm, that is, water cycle algorithm (WCA), in the transportation problem. A stochastic transportation problem is considered in which the parameters supply and demand are considered as random variables that follow the Weibull distribution. Since the parameters are stochastic, the corresponding constraints are probabilistic. They are converted into deterministic constraints using the stochastic programming approach. In this study, we propose evolutionary algorithms to handle the difficulties of the complex high-dimensional optimization problems. WCA is influenced by the water cycle process of how streams and rivers flow toward the sea (optimal solution). WCA is applied to the stochastic transportation problem, and obtained results are compared with that of the new metaheuristic optimization algorithm, namely the neural network algorithm which is inspired by the biological nervous system. It is concluded that WCA presents better results when compared with the neural network algorithm.     

Field services design and management of natural gas distribution networks: a class of vehicle routing problem with time windows approach

Service operations management of metropolitan gas networks at operational level implies the optimisation of decisions related to logistic activities, taking into account multi-objectives and operational constraints. This paper proposes a metaheuristic approach for the operational planning of the daily logistic activities based on vehicle routing with time window model. Experimental results for a real planning case in a gas distribution network demonstrate the approach effectiveness.     

Solving closed-loop supply chain problems using game theoretic particle swarm optimisation

In this paper, we propose a closed-loop supply chain network configuration model and a solution methodology that aim to address several research gaps in the literature. The proposed solution methodology employs a novel metaheuristic algorithm, along with the popular gradient descent search method, to aid location-allocation and pricing-inventory decisions in a two-stage process. In the first stage, we use an improved version of the particle swarm optimisation (PSO) algorithm, which we call improved PSO (IPSO), to solve the location-allocation problem (LAP). The IPSO algorithm is developed by introducing mutation to avoid premature convergence and embedding an evolutionary game-based procedure known as replicator dynamics to increase the rate of convergence. The results obtained through the application of IPSO are used as input in the second stage to solve the inventory-pricing problem. In this stage, we use the gradient descent search method to determine the selling price of new products and the buy-back price of returned products, as well as inventory cycle times for both product types. Numerical evaluations undertaken using problem instances of different scales confirm that the proposed IPSO algorithm performs better than the comparable traditional PSO, simulated annealing (SA) and genetic algorithm (GA) methods.     

Genetic algorithm-based approach for fixed and switchable capacitors placement in distribution systems with uncertainty and time varying loads

Installation of capacitors in primary and secondary networks of distribution systems is one of the efficient methods for energy and peak load loss reduction. Also voltage profile in the feeder is improved and static voltage stability is enhanced. The main challenge is the determination of optimal location and size of fixed and switchable capacitors with respect to network configuration, distribution of load in the feeder, time variation of load and uncertainty in load forecasting or load allocation process. To solve this complex problem, an efficient method for simultaneous allocation of fixed and switchable capacitors in radial distribution systems is presented. Energy and peak load loss reduction, and capacitor cost are considered in the cost function. Time variation and uncertainty of load are also involved in problem formulation. Genetic algorithm with a new coding as two rows chromosomes is used for optimisation. Numerical studies show the effectiveness of the proposed procedure     

Autonomous decentralized finite element method and its applications

In this paper, a new solution procedure using the finite element technique in order to solve problems of structure analysis is proposed. This procedure is called the autonomous decentralized finite element method because it is based on the characteristic autonomy and decentralization in life or biological systems (life‐like approach). The fundamental approach is developed according to an idea of cellular automata manipulation by the new neighbourhood model. The finite element method with an algorithm of the relaxation method is adopted as the solution procedure in this approach. The proposed procedure demonstrates that it is a powerful means of numerical analysis for many kinds of structural problems that are structural morphogenesis, structural optimization and structural inverse problems. Our procedure is applied to numerical analysis of three simple plane models: (1) The structural shape analysis problem for the prescribed displacement mode of a truss structure, (2) An adaptive structure remodelling problem on an elastic continuum, (3) An identification problem of thermal conductivity on a continuum. The effectiveness and validity of our idea are shown from their numerical results. Copyright © 2003 John Wiley & Sons, Ltd.     

Cyclical parthenogenesis algorithm for layout optimization of truss structures with frequency constraints

Structural optimization with frequency constraints is seen as a challenging problem because it is associated with highly nonlinear, discontinuous and non-convex search spaces consisting of several local optima. Therefore, competent optimization algorithms are essential for addressing these problems. In this article, a newly developed metaheuristic method called the cyclical parthenogenesis algorithm (CPA) is used for layout optimization of truss structures subjected to frequency constraints. CPA is a nature-inspired, population-based metaheuristic algorithm, which imitates the reproductive and social behaviour of some animal species such as aphids, which alternate between sexual and asexual reproduction. The efficiency of the CPA is validated using four numerical examples.     

Development of a hybrid metaheuristic to minimise earliness and tardiness in a hybrid flowshop with sequence-dependent setup times

This paper presents an efficient hybrid metaheuristics for scheduling jobs in a hybrid flowshop with sequence-dependent setup times. The problem is to determine a schedule that minimises the sum of earliness and tardiness of jobs. Since this problem class is NP-hard in the strong sense, there seems to be no escape from appealing to metaheuristic procedures to achieve near-optimal solutions for real life problems. This paper proposes the hybrid metaheuristic algorithm which comprises three components: an initial population generation method based on an ant colony optimisation, a simulated annealing algorithm as an evolutionary algorithm that employs certain probability to avoid becoming trapped in a local optimum, and a variable neighbourhood search which involves three local search procedures to improve the population. A design of experiments approach is employed to calibrate the parameters of the algorithm. Results of computational tests in solving 252 problems up to 100 jobs have shown that the proposed algorithm is computationally more effective in yielding solutions of better quality than the adapted random key genetic algorithm and immune algorithm presented previously.     

Ant colony optimisation with parameterised search space for the job shop scheduling problem

The job-shop scheduling problem (JSSP) is known to be NP-hard. Due to its complexity, many metaheuristic algorithm approaches have arisen. Ant colony metaheuristic algorithm, lately proposed, has successful application to various combinatorial optimisation problems. In this study, an ant colony optimisation algorithm with parameterised search space is developed for JSSP with an objective of minimising makespan. The problem is modelled as a disjunctive graph where arcs connect only pairs of operations related rather than all operations are connected in pairs to mitigate the increase of the spatial complexity. The proposed algorithm is compared with a multiple colony ant algorithm using 20 benchmark problems. The results show that the proposed algorithm is very accurate by generating 12 optimal solutions out of 20 benchmark problems, and mean relative errors of the proposed and the multiple colony ant algorithms to the optimal solutions are 0.93% and 1.24%, respectively.     

Distributed project scheduling with information sharing in supply chains: part I—an agent-based negotiation model

Distributed project scheduling problem (DPSP) in supply chains is concerned with configuration and scheduling of multiple projects in a network of independent and autonomous enterprises. Individual enterprises must collaborate with each other during two main stages: the configuration of a project—selection of contractors for performing project operations and the project scheduling—determining when the operations start. However, the collaboration is especially difficult because none of these enterprises holds the global information about the entire supply chain and all constituent enterprises. Instead, they have to capitalize whatever information is shared between them in order to solve their own local problems in a distributed and autonomous fashion. It is essential for the solution process to strike an overall balance between effectiveness and efficiency. The research, reported in this two-part paper, is aimed at proposing a negotiation-based algorithm for solving DPSP. Its emphasis is how to improve the convergence and quality of the solution by taking advantage of inter-enterprise information sharing especially the sharing of schedule flexibility information (SFI). The first part of this paper describes a new agent-based approach to DPSPs in supply chains while the second part will present detailed discussion on the theoretical and experimental analysis.     

A scatter search algorithm for stacking sequence optimisation of laminate composites

This paper explores the metaheuristic approach called scatter search for lay-up sequence optimisation of laminate composite panels. Scatter search is an evolutionary method that has recently been found to be promising for solving combinatorial optimisation problems. The scatter search framework is flexible and allows the development of alternative implementations with varying degree of sophistication. The main objective of this paper is to demonstrate the effectiveness of the proposed scatter search algorithm for the combinatorial problem like stacking sequence optimisation of laminate composite panels. Preliminary investigations have been carried out to compare the optimal stacking sequences obtained using scatter search algorithm for buckling load maximisation with the best known published results. Studies indicate that the optimal buckling load factors obtained using the proposed scatter search algorithm found to be either superior or comparable to the best known published results.

Later, two case studies have been considered in this paper. Thermal buckling optimisation of laminated composite plates subjected to temperature rise is considered as the first case study. The results obtained are compared with an exact enumerative study conducted on the problem to demonstrate the effectiveness and performance of the proposed scatter search algorithm. The second case study is optimisation of hybrid laminate composite panels for weight and cost with frequency and buckling constraints. The two objectives are considered individually and also collectively to solve as multi-objective optimisation problem. Finally the computational efficiency of the proposed scatter search algorithm has been investigated by comparing the results with various implementations of genetic algorithm customised for laminate composites. It was shown in this paper through numerical experiments that the scatter search is capable of finding practical solutions for optimal lay-up sequence optimisation of composite laminates and results are comparable and sometimes even superior to genetic algorithms.     

Optimising online review inspired product attribute classification using the self-learning particle swarm-based Bayesian learning approach

Bowing to the burgeoning needs of online consumers, exploitation of social media content for extrapolating buyer-centric information is gaining increasing attention of researchers and practitioners from service science, data analytics, machine learning and associated domains. The current paper aims to identify the structural relationship between product attributes and subsequently prioritise customer preferences with respect to these attributes while exploiting textual social media data derived from fashion blogs in Germany. A Bayesian Network Structure Learning model with the K2score maximisation objective is formulated and solved. A self-tailored metaheuristic approach that combines self-learning particle swarm optimisation (SLPSO) with the K2 algorithm (SLPSOK2) is employed to decipher the highest scored structures. The proposed approach is implemented on small, medium and large size instances consisting of 9 fashion attributes and 18 problem sets. The results obtained by SLPSOK2 are compared with the particle swarm optimisation/K2score, Genetic Algorithm/K2 score and ant colony optimisation/K2 score. Results verify that SLPSOK2 outperforms its hybrid counterparts for the tested cases in terms of computational time and solution quality. Furthermore, the study reveals that psychological satisfaction, historical revival, seasonal information and facts and figure-based reviews are major components of information in fashion blogs that influence the customers.     

Innovative vendor-managed inventory strategy exploiting interconnected logistics services in the Physical Internet

Traditional supply chain networks are often designed in the interests of a company. Once the network has been defined, the storage and distribution of goods are usually fixed and restricted within the network. This is assumed to be an inherent limit of current inventory control research. Instead of specialised hierarchical storage networks, this paper proposes an innovative vendor-managed inventory strategy exploiting the Physical Internet (PI), which is an open, universal, interconnected logistics system. In such a system, facilities and means of transport are shared and can be allocated according to demands of users. As a result, the PI allows users to stock anywhere in the network and also provides open multisourcing options for orders with on-demand warehousing services within the PI. Inventory decisions can be made dynamically by each player to minimise networkwide inventory levels. A non-linear, simulation-based optimisation model was developed for the vendors’ inventory decision-making when confronted with stochastic demands. A metaheuristic using simulated annealing was applied to solve the problem, and then, the optimised inventory decisions were validated using simulation. The results suggest that the proposed PI inventory model can reduce the total logistics cost while maintaining a comparable or better level of end customers’ services.     

Cost Optimisation for Underground Mining Networks

In this paper we consider the problem of optimising the construction and haulage costs of underground mining networks. We focus on a model of underground mine networks consisting of ramps in which each ramp has a bounded maximum gradient. The cost depends on the lengths of the ramps, the tonnages hauled through them and their gradients. We model such an underground mine network as an edge-weighted network and show that the problem of optimising the cost of the network can be described as an unconstrained non-linear optimisation problem. We show that, under a mild condition which is satisfied in practice, the cost function is convex. Finally we briefly discuss how the model can be generalised to those underground mine networks that are composed not only of ramps but also vertical shafts, and show that the total cost in the generalised model is still convex under the same condition. The convexity of the cost function ensures that any local minimum is a global minimum for the given network topology, and theoretically any descent algorithms for finding local minima can be applied to the design of minimum cost mining networks.This work was supported by the Australian Research Council     

Improved mine blast algorithm for optimal cost design of water distribution systems

The design of water distribution systems is a large class of combinatorial, nonlinear optimization problems with complex constraints such as conservation of mass and energy equations. Since feasible solutions are often extremely complex, traditional optimization techniques are insufficient. Recently, metaheuristic algorithms have been applied to this class of problems because they are highly efficient. In this article, a recently developed optimizer called the mine blast algorithm (MBA) is considered. The MBA is improved and coupled with the hydraulic simulator EPANET to find the optimal cost design for water distribution systems. The performance of the improved mine blast algorithm (IMBA) is demonstrated using the well-known Hanoi, New York tunnels and Balerma benchmark networks. Optimization results obtained using IMBA are compared to those using MBA and other optimizers in terms of their minimum construction costs and convergence rates. For the complex Balerma network, IMBA offers the cheapest network design compared to other optimization algorithms.     

A highly optimised tolerance-based approach for multi-stage,multi-product supply chain network design

In this paper we propose an algorithm called Highly Optimised Tolerance (HOT) for solving a multi-stage, multi-product supply chain network design problem. HOT is based on power law and control theory. The proposed approach takes its traits from the local incremental algorithm (LIA), which was initially employed to maximise the design parameter (i.e. yield), particularly in the percolation model. The LIA is somewhat analogous to the evolution by natural selection schema. The proposed methodology explores a wide search space and is computationally viable. The HOT algorithm tries to make the system more robust at each step of the optimisation. The objective of this paper is to reduce the total cost of supply chain distribution by selecting the optimum number of facilities in the network. To examine the effectiveness of the HOT algorithm we compare the results with those obtained by applying simulated annealing on a supply chain network design problem with different problem sizes and the same data sets.     

Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time

This paper considers the design and balancing of mixed-model disassembly lines with multi-robotic workstations under uncertainty. Tasks of different models are performed simultaneously by the robots which have different capacities for disassembly. The robots have unidentical task times and energy consumption respectively. Task precedence diagrams are used to model the precedence relations among tasks. Considering uncertainties in disassembly process, the task processing times are assumed to be interval numbers. A mixed-integer mathematical programming model is proposed to minimise the cycle time, peak workstation energy consumption, and total energy consumption. This model has a significant managerial implication in real-life disassembly line systems. Since the studied problem is known as NP-hard, a metaheuristic approach based on an evolutionary simulated annealing algorithm is developed. Computational experiments are conducted and the results demonstrate the proposed algorithm outperforms other multi-objective algorithms on optimisation quality and computational efficiency.     

Optimal Implementation of Photovoltaic and Battery Energy Storage in Distribution Networks

Recently, implementation of Battery Energy Storage (BES) with photovoltaic (PV) array in distribution networks is becoming very popular in overall the world. Integrating PV alone in distribution networks generates variable output power during 24-hours as it depends on variable natural source. PV can be able to generate constant output power during 24-hours by installing BES with it. Therefore, this paper presents a new application of a recent metaheuristic algorithm, called Slime Mould Algorithm (SMA), to determine the best size, and location of photovoltaic alone or with battery energy storage in the radial distribution system (RDS). This algorithm is modeled from the behavior of SMA in nature. During the optimization process, the total active power loss during 24-hours is used as an objective function considering the equality and inequality constraints. In addition, the presented function is based on the probabilistic for PV output and different types of system load. The candidate buses for integrating PV and BES in the distribution network are determined by the real power loss sensitivity factor (PLSF). IEEE 69-bus RDS with different types of loads is used as a test system. The effectiveness of SMA is validated by comparing its results with those obtained by other well-known optimization algorithms.     

A novel approach for efficient stance detection in online social networks with metaheuristic optimization

In the 19th and 20th centuries, social networks have been an important topic in a wide range of fields from sociology to education. However, with the advances in computer technology in the 21st century, significant changes have been observed in social networks, and conventional networks have evolved into online social networks. The size of these networks, along with the large amount of data they generate, has introduced new social networking problems and solutions. Social network analysis methods are used to understand social network data. Today, several methods are implemented to solve various social network analysis problems, albeit with limited success in certain problems. Thus, the researchers develop new methods or recommend solutions to improve the performance of the existing methods. In the present paper, a novel optimization method that aimed to classify social network analysis problems was proposed. The problem of stance detection, an online social network analysis problem, was first tackled as an optimization problem. Furthermore, a new hybrid metaheuristic optimization algorithm was proposed for the first time in the current study, and the algorithm was compared with various methods. The analysis of the findings obtained with accuracy, precision, recall, and F-measure classification metrics demonstrated that our method performed better than other methods.     

A multi-phase covering Pareto-optimal front method to multi-objective parallel machine scheduling

This paper considers the problem of parallel machine scheduling with sequence-dependent setup times to minimise both makespan and total earliness/tardiness in the due window. To tackle the problem considered, a multi-phase algorithm is proposed. The goal of the initial phase is to obtain a good approximation of the Pareto-front. In the second phase, to improve the Pareto-front, non-dominated solutions are unified to constitute a big population. In this phase, based on the local search in the Pareto space concept, three multi-objective hybrid metaheuristics are proposed. Covering the whole set of Pareto-optimal solutions is a desired task of multi-objective optimisation methods. So in the third phase, a new method using an e-constraint hybrid metaheuristic is proposed to cover the gaps between the non-dominated solutions and improve the Pareto-front. Appropriate combinations of multi-objective methods in various phases are considered to improve the total performance. The multi-phase algorithm iterates over a genetic algorithm in the first phase and three hybrid metaheuristics in the second and third phases. Experiments on the test problems with different structures show that the multi-phase method is a better tool to approximate the efficient set than the global archive sub-population genetic algorithm presented previously.