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This research aims at enhancing the performance of scale-model scooter decks by investigating various architected cellular metamaterial and bio-inspired core structure designs, such as honeycomb, tetrachiral, re-entrant, arrowhead, and star-shaped arrangements. An initial effort is made toward the design and rapid prototyping of small-scale deck with a uniform honeycomb core structure. More specifically, polylactic acid is utilized to fabricate complex structures via fused filament fabrication technique. Investigation is then focused on its mechanical performance, such as its bending properties obtained through a three-point bending test. Simulations are also conducted with different core configurations using a geometrically non-linear finite element method which is implemented. Experiments are carried out to verify the numerical results. After validation, various patterns are modeled, and eventually, it is observed that the functionally graded arrowhead structure has the best bending resistance, compared to other bio-inspired and mechanical metamaterial structures. At a constant force of 845 N, the functionally graded arrowhead design lowers the deflection in the middle of the scale model of scooter deck by up to 14.7%, compared to the uniform arrowhead structure. Furthermore, comparing the tetrachiral and functionally graded arrowhead configurations at a constant force, a 30% reduction in central deflection was observed. Due to the lack of similar results and designs in the specialized literature, this work could potentially advance the state-of-the-art scooter core designs and provide designers with architectures that could enhance the performance and safety of scooters. [ABSTRACT FROM AUTHOR]

A computationally effective and physically accurate metamodeling approach is demonstrated to analyze, under uncertainties, the spring-in angle deformation for composite manufacturing processes. Various uncertainties are inevitably present in this manufacturing process due to the heterogeneous thermo-mechanical properties of the composite materials. Analysis of uncertainty propagation using the direct Monte Carlo approach is computationally prohibitive, which calls for the employment of machine learning techniques and surrogate models or metamodels such as Gaussian processes (GP). While these approaches are promising, tuning model parameters and optimizing the hyperparameters are essential to predictive modeling performance. So far, most existing approaches rely on empirical experience through trial and error. Randomly selecting these hyperparameters results in excessive computational cost and poor convergence results. A nature-inspired methodology has been developed to guide the GP in selecting and optimizing the hyperparameters for the uncertainty propagation analysis of composite manufacturing processes. An improved firefly algorithm (iFA) takes account of the environmental factor. It disregards the contribution of a constant attractiveness factor, which in turn accelerates the convergence rate at the early stages of the generation and boosts the immunity of the proposed algorithm. The proposed methodology enabled selection of the proper combination of the factors for the GP and showed its merits over other state-of-the-art deterministic/metaheuristic algorithms, which is further confirmed by various nonparametric, multiple comparison tests. [ABSTRACT FROM AUTHOR]

Due to the rapid change of customized products in Industry 4.0, the operation sequencing (OS) as a core function of CAPP system is frequently needed. In this paper, a novel path-relinking genetic algorithm (PR-GA) is proposed to find the minimal-cost solution of the OS problem. In the PR-GA, the chromosome records the feasible operation sequence (FOS) satisfying the precedence constraints of operations via a permutation, and the designed crossover and mutation involve chromosomes and ensure their feasibility. For a given FOS, the optimal manufacturing resources for every operation are identified by a polynomial-time graph algorithm including a graph building procedure and a dynamic programing procedure. Thus, the PR-GA focuses on searching promising FOSs, and uses path-relinking as a local search around paths between elitists. Moreover, a new framework of GA is established and is characterized by avoiding tuning crossover and mutation rates of the common GA. The PR-GA is compared with state-of-the-art metaheuristic algorithms (MAs) including ant colony optimization, particle swarm optimization, two recent GAs, and a recent exact method to verify its effectiveness and efficiency. The comparison results show that the PR-GA outperforms existing MAs for solution quality, and illustrates the PR-GA's promising efficiency and robust global search ability. [ABSTRACT FROM AUTHOR]

Inspired by cloud computing, cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm on an on-demand and pay-as-you-go business model through the internet. More specifically, new challenges for production planning and decision-making process have emerged in that resource scheduling and have gained the most attention, and there is an urgent need to determine the current status and identify issues and matters to be addressed in the future. This review paper is aiming to discuss aspects of the cloud-based resource scheduling problem through investigating the literature to date to identify the existing gaps and recommending the potential paths moving forward for researchers in this field. So far, literature reviews focused on a broad scope of cloud-based scheduling, as a new approach taking a "narrow scope" by focusing on resource scheduling and various steps of it in the cloud environment are considered. Using the data gathered from the popular databases, a comprehensive statistical analysis on the existing literatures is provided, and the rational sequences of the systematic literature review (SLR) are elaborated. The mathematical models in resource scheduling are thoroughly elucidated. Then, a comprehensive analysis of the main aspects of resource scheduling including the objective functions, constraints, and optimization algorithms is presented. Discussion of the findings of the review paper illustrates that time and cost gain more attention (almost 80%) among all objective functions, and the metaheuristic algorithms are the most widely used in the recent research papers. Finally, suggestions for potential future research to further consolidate this field have been enumerated. [ABSTRACT FROM AUTHOR]

Cloud manufacturing (CMfg) is a service-oriented manufacturing paradigm that is striving to produce highly customized products via sharing resources of multiple manufacturing providers. Distributed nature of this paradigm calls for addressing the service composition problem in order to achieve an optimal status in regard to such a collaboration. However, in the majority of research studies done on service composition in CMfg, the sets of candidate services on which the optimal composition is supposed to be conducted are assumed to be predefined. This is an extreme simplification and does not satisfy the actual requirements of cloud manufacturing. This study is aiming to propose a novel approach that first uses TF-IDF (term frequency-inverse document frequency) vectors extracted from the manufacturing capability data and machine learning algorithms to retrieve candidate sets for each corresponding subtask. Then, the optimal composite service is obtained for each scenario by using metaheuristic algorithms. The results prove the efficacy of this method in resulting in a more comprehensive approach for tackling the service composition problem in cloud manufacturing paradigm. [ABSTRACT FROM AUTHOR]

Along with the fourth industrial revolution, different tools coming from optimization, Internet of Things, data science, and artificial intelligence fields are creating new opportunities in production management. While manufacturing processes are stochastic and rescheduling decisions need to be made under uncertainty, it is still a complicated task to decide whether a rescheduling is worthwhile, which is often addressed in practice on a greedy basis. To find a tradeoff between rescheduling frequency and the growing accumulation of delays, we propose a rescheduling framework, which integrates machine learning (ML) techniques and optimization algorithms. To prove the effectiveness, we first model a flexible job-shop scheduling problem with sequence-dependent setup and limited dual resources (FJSP) inspired by an industrial application. Then, we solve the scheduling problem through a hybrid metaheuristic approach. We train the ML classification model for identifying rescheduling patterns. Finally, we compare its rescheduling performance with periodical rescheduling approaches. Through observing the simulation results, we find the integration of these techniques can provide a good compromise between rescheduling frequency and scheduling delays. The main contributions of the work are the formalization of the FJSP problem, the development of ad hoc solution methods, and the proposal/validation of an innovative ML and optimization-based framework for supporting rescheduling decisions. [ABSTRACT FROM AUTHOR]

Robots are used in manufacturing cells for wide purposes including pick and place of the items from a location to a destination. As far as the authors' knowledge in this context, the scheduling problem of a real-life flexible robotic cell (FRC) with intermediate buffers is missing in the literature. Therefore, in this study, the process-sequencing problem of a real-life FRC is considered, aiming to minimize the cyclic operation time of the cell. The problem is mathematically modeled and solved for a real case. Since computation times for solving the problems rise exponentially with increasing the number of machines in the FRC, a genetic, a simulated annealing, and a hybrid genetic algorithms are proposed to solve the large-sized problems. The objective function value of a given solution in metaheuristic algorithms is computed by solving a linear programming model. After tuning the parameters of the proposed algorithms, several numerical instances are solved, and the performance of these algorithms are evaluated and compared. The results show that the performance of the hybrid genetic algorithm was significantly better than both genetic and simulated annealing algorithms. [ABSTRACT FROM AUTHOR]

The dynamic process monitoring is discussed in this paper. Kernel principal component analysis (KPCA) is a nonlinear monitoring method that cannot be applied for dynamic systems. Reduced online KPCA (OR-KPCA)is used for fault detection of dynamic processes, which is developed to built a dictionary according to the process status and then, it update the KPCA model and uses it for process monitoring. Also the Tabu search metaheuristic algorithm is used in order to determine the optimal parameter of the kernel function. In this paper, new approaches for online fault isolation, which is a challenging problem in nonlinear PCA, are formulated. An extension of partial PCA and the elimination sensor identification (ESI) to the case of nonlinear systems are presented in a feature space. The partial OR-KPCA and the elimination sensor identification (ESI-KPCA) are generated based on the OR-KPCA method and they consist of developing a set of sub-models. The sub-models are selected according to a pre-designed fault-to-residual structure matrix and by eliminating sequentially one variable from the set of the variables. The proposed fault isolation methods are applied for monitoring an air quality monitoring network. The simulation results show that the proposed fault isolation methods are effective for KPCA. [ABSTRACT FROM AUTHOR]

In a reconfigurable manufacturing scenario, control system design needs innovative approaches to face the rapid changes in hardware and software modules. The control system should be able to automatically tune its parameters to enhance machine performances and dynamically adapt to different control objectives (e.g., minimize control efforts or maximize tracking performances) while preserving at the same time stability and robustness properties. In this paper, a robust control system for permanent magnet synchronous motors (PMSMs), together with an online self-tuning method, is presented. In particular, a robust discrete-time variable structure control (VSC) has been designed. A heuristic bio-inspired approach has been then implemented on a digital signal processor (DSP) to find the VSC parameter set which minimizes a specific objective function each time a novel speed reference is provided. Experimental results on a PMSM motor show the effectiveness of the proposed controller and tuning method, with noticeable improvements with respect to the original manufacturer-designed controller. [ABSTRACT FROM AUTHOR]

Statistical tolerance optimization is a more practical and economical method for assigning proper tolerances on components in product design. Monte Carlo simulation is the simplest method to simulate probability distributions of random variables (tolerances). However, the precision of this method is proportional to the root of sample size, and a large sample size leads to excessive computational time. Moreover, during the entire process of statistical tolerance optimization, the optimum value cannot be found when the precision of fitness in each iteration is not sufficiently high, and when these inaccurate values are used in many generations with large number of assembly analyses. In this paper, a strategy with multiple sample testing is proposed to solve this problem by improving the precision of fitness and penalty values and reducing the computational time cost. Based on this strategy, high precision of fitness could be obtained for the next generation using metaheuristic algorithms such as genetic algorithm, cuckoo search, and particle swarm optimization, and a feasible solution that satisfies all the constraints could be obtained. A practical industrial application is provided to demonstrate the effectiveness of the statistical tolerance optimization problem, and the experimental results confirm the efficiency of the proposed strategy. [ABSTRACT FROM AUTHOR]

In a virtual cell formation system, machine types are formed for one period during which the machine types of a cluster process the corresponding operations of part types. However, there is one major difference between a virtual cellular manufacturing and a cellular manufacturing and that is the fact that machine types of the same cluster are not necessarily brought to a physical proximity in virtual cellular manufacturing, contrary to a cellular manufacturing. Depending on the variations in the demand for part types, the virtual cells are clustered periodically and merged according to the demand for new part types in a planning horizon. On the other hand, optimal raw material quantity to purchase from qualified suppliers has gained importance recently; this phenomenon is greatly influenced by the significant portion of raw materials costs in a finished product. Consequently, most firms have to pay out most of their revenues on transportation and purchasing. In this paper, the dynamic virtual cellular manufacturing is formulated through a mathematical model developed for this purpose; it is also of utmost importance to consider machine layout and quantity of raw material purchased from qualified suppliers. To solve the real-sized problems of proposed model, a hybrid metaheuristic algorithm is extended. The results demonstrate that the proposed hybrid genetic algorithm is promising. [ABSTRACT FROM AUTHOR]

Electromagnetism-like mechanism (EM) is an emerging and potential metaheuristic based on the attraction and repulsion forces in electromagnetic theory. EMs have been successfully applied to scheduling problems. However, few studies have been conducted regarding the application of EMs in parallel machine scheduling problems. We developed a new EM to solve identical parallel machine scheduling problems under the consideration of family setup times. The objective was to minimize the total weighted flow time. The new EM comprises a matrix representation, a discrete distance measurement, and a proposed attraction-repulsion operator to effectively search for a near-optimal solution. The experiment results indicated that the new EM renewed nearly all best-known solutions in the benchmark problems, and was superior to a genetic algorithm. [ABSTRACT FROM AUTHOR]

The milling process is a widely used conventional machining operation. Due to economic reasons, the multi-pass milling process is more convenient. However, the required time for machining increases and an optimization solution must be undertaken. In this paper, the total production time is minimized by resorting to a powerful bio-inspired algorithm, called the cuckoo optimization algorithm. The constraints are successfully handled and the optimal results are compared with those available in the literature. It is shown that the present results are better. [ABSTRACT FROM AUTHOR]

This paper investigates novel GA-based hybrid artificial immune system (AIS) for the permutation flowshop scheduling problems (PFSP) that minimizes the makespan. The proposed approaches aim to show that the efficiency of GAs in solving flowshop problems can be improved significantly by tailoring the various AIS operators to suit the problem structure. The proposed hybridization scheme is applied in two ways: (1) the first hybrid of GA and AIS introduces vaccination (Jiao and Wang, IEEE Trans Syst Man Sybernetics Part A Syst Hum 30(5):552-561, ) into the field of GAs based on the theory of immunity in biology, (2) the second takes its inspiration on the immune network theory (Perelson, Immunol Rev 110(1):5-36, ), and applied it to the field of GAs. The proposed hybrid metaheuristics produce high quality solutions as proved by the tests performed over Taillard's (Eur J Oper Res 64(2):278-285, ) well-known flowshop scheduling benchmarks and corroborated by the comparisons we did with the most frequently referred in the related literature and recently developed hybrid GAs, including genetic algorithms, particle swarm optimization, and other advanced and recent techniques. Furthermore, the effects of some parameters are discussed. [ABSTRACT FROM AUTHOR]

Many real-world scheduling problems are solved to obtain optimal solutions in term of processing time, cost, and quality as optimization objectives. Currently, energy-efficiency is also taken into consideration in these problems. However, this problem is NP-hard, so many search techniques are not able to obtain a solution in a reasonable time. In this paper, a genetic algorithm is developed to solve an extended version of the Job-shop Scheduling Problem in which machines can consume different amounts of energy to process tasks at different rates (speed scaling). This problem represents an extension of the classical job-shop scheduling problem, where each operation has to be executed by one machine and this machine can work at different speeds. The evaluation section shows that a powerful commercial tool for solving scheduling problems was not able to solve large instances in a reasonable time, meanwhile our genetic algorithm was able to solve all instances with a good solution quality. [ABSTRACT FROM AUTHOR]

As a typical, discrete, and NP-hard problem, assembly sequence planning (ASP) has direct impact on assembly quality and costs. So far, lots of graph-based and meta-heuristic approaches cannot solve this problem effectively. This paper proposes an effective ASP algorithm based on the harmony search (HS) algorithm, which has an outstanding global search ability to obtain the global optimum more efficiently. To solve the ASP problem, an improved harmony search (IHS) algorithm is proposed mainly in four aspects: (1) an encoding of harmony is designed based on ASP problems; (2) an initial harmony memory (HM) is established using the opposition-based learning (OBL) strategy; (3) a particular way to improvise a new harmony is developed; and (4) a local search strategy is introduced to accelerate the convergence speed. Finally, the advantage of the proposed ASP algorithm over the competing algorithms in solving ASP problems is verified by two experiments. Moreover, the proposed ASP algorithm is applied to a practical problem, a propeller of azimuth thruster to verify its practicability. [ABSTRACT FROM AUTHOR]

Hub location problems appear at the core of strategic planning for many transportation and logistics problems. This class of optimization problems has received a wide gamut of research attention during the last few decades. However, the associated mathematical formulations are mainly developed with deterministic parameters to avoid the complexities and computational difficulties. In this paper, we put forward an extension to the uncapacitated single allocation p- hub median problem under uncertainty in which demands and travel times are stochastic. A robust optimization approach is used to deal with uncertainty in parameters and design a robust hub-and-spoke network. The developed model minimizes the total expected transportation costs, while bounding the relative regret in each scenario. To efficiently solve the model, a two-stage approach is investigated to design two hybrid heuristics. We firstly apply a meta-heuristic (i.e., variable neighborhood search (VNS) or particle swarm optimization (PSO)) to find the best combination for the hubs location. Then, by fixing the hubs location, the original model easily decomposes by scenario for which an efficient tabu search (TS) is designed. Encouraging results on a wide-range of test problems are reported. [ABSTRACT FROM AUTHOR]

The cell formation problem determines decomposition of the manufacturing cells of a production system. Machines are assigned to the cells to process one or more part families so that each cell is operated independently and the inter-cellular movements are minimized. This paper proposes a new algorithm for grouping problems (bin packing, graph coloring, scheduling, etc.) which is a grouping version of an almost new algorithm (league championship algorithm (LCA)), and we used it to solve benchmarked instances of cell formation problem posing as a grouping problem. To evaluate the effectiveness of our approach, we borrow a set of 35 most widely used benchmark problem instances from literature and compare the performance of grouping LCA (GLCA) and several well-known algorithms published. The proposed algorithm can reach the best solution for 29 of the 35 benchmark problems and differs with the best-known solution of three benchmark problems only with 0.7 % average gap. We also used a new method to find the number of initial cells. The results show that GLCA may hopefully be a new approach for such kinds of difficult-to-solve problems. Moreover, a real-world industrial case is provided to show how the proposed algorithm works. Considering the performance of the GLCA algorithm on all test problems, the proposed algorithm should thus be useful to both practitioners and researchers. [ABSTRACT FROM AUTHOR]

The flexible job-shop scheduling problem is known to be an (Non-deterministic Polynomial-time hard) combinatorial problem and has become a challenge in optimization and manufacturing control. Although flexibility is important in order to respond effectively to higher product variety, shorter lead times, and smaller batch sizes, industrialists also require just-in-time scheduling strategies to increase customer satisfaction. The aim of this paper is to find adequate job release times to meet production demands in relation to specific due dates. Since large deviations in job completion times are undesirable, the scheduling objective for just-in-time production is translated into the minimization of the mean-square due date deviation (MSD), quadratically penalizing inventory (earliness) costs and backlogging (tardiness) costs. Given the computational complexity of the problem, two meta-heuristics are proposed: a genetic algorithm (GA) and particle swarm optimization (PSO), as well as two different approaches to handle job release times. In the GA, job release times are treated as dependent variables, whereas the PSO enables the integration of job release times as independent variables within the particle encoding. These meta-heuristic approaches were compared using three benchmarks, two adapted from the literature and one inspired from a real manufacturing cell. The simulation results show that the GA and PSO attained similar performances, each one with advantages and disadvantages for constrained and unconstrained MSD problems. [ABSTRACT FROM AUTHOR]

Reducing expensive raw material waste is an important goal in the industry. In this paper, two-dimensional irregular cutting stock problem-a nesting problem that differs from other in their irregular shape of the pieces-with demand is studied, in which the required pieces has to be produced from large rectangular sheet minimizing material waste. Structure of this problem made it intractable for practical applications such that exact algorithms are not able to solve it in a reasonable time. Greedy randomized adaptive search procedure (GRASP) meta-heuristic algorithm is adapted to tackle the problem by providing high-quality solution in an appropriate time. The algorithm does not depend on the shape (convexity and regularity) of pieces and is able to deliver an optimum solution for instances up to 30 pieces of 7 different types. In addition, computational results are provided for different test problems from the related literature. [ABSTRACT FROM AUTHOR]

To benefit from globalization, the single-facility scheduling problem is extended to a distributed multi-facility level. This paper tackles both the mathematical modeling and solution techniques for the problem of distributed job shop scheduling. The problem is first mathematically formulated by a mixed integer linear programming model. This model supersedes the best available model in the literature in terms of both size and computational complexities. Moreover, novel simulated annealing algorithms are developed. These algorithms propose an advanced move operators matching the special encoding scheme used. The developed near-optimal search method has been enhanced by combining it with a local search method and adding advanced features to it such as the introduced restart phase. Also, we have hybridized the developed metaheuristic further with a greedy algorithm. Using the Taguchi method, the algorithm is finely tuned. Numerical experiments are conducted to evaluate the performance of the algorithms against an available genetic and a greedy algorithm. The results show that the proposed algorithm outperforms both algorithms. [ABSTRACT FROM AUTHOR]

Capacitated production lot-sizing problems (LSPs) are challenging problems to solve due to their combinatorial nature. We consider a multi-item capacitated LSP with setup times, safety stocks, and demand shortages plus lost sales and backorder considerations for various production methods (i.e., job shop, batch flow, or continuous flow among others). We use multi-objective mathematical programming to solve this problem with three conflicting objectives including: (i) minimizing the total production costs; (ii) leveling the production volume in different production periods; and (iii) producing a solution which is as close as possible to the just-in-time level. We also consider lost sales, backorders, safety stocks, storage space limitation, and capacity constraints. We propose two novel Pareto-based multi-objective meta-heuristic algorithms: multi-objective vibration damping optimization (MOVDO) and a multi-objective harmony search algorithm (MOHSA). We compare MOVDO and MOHSA with two well-known evolutionary algorithms called the non-dominated sorting genetic algorithm (NSGA-II) and multi-objective simulated annealing (MOSA) to demonstrate the efficiency and effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Multiple parts process planning (MPPP) is a hard optimization problem that requires the rigor and intensity of metaheuristic-based algorithms such as simulated annealing and genetic algorithms. In this paper, a solution method for this problem is developed based on genetic algorithms. Genetic algorithms solve problems by exploring a given search space. To do this, a landscape over which the search traverses is constructed based on a number of algorithm choices. Key algorithm choices include (a) type of chromosome representation, which affects the efficiency of an algorithm, and (b) type and form of genetic operators, which affect the effectiveness of an algorithm. More specifically, the suitability of a variable-length chromosome (VLC) representation for encoding a solution to a MPPP problem is investigated. The effectiveness and efficiency of implementing the VLC algorithm is analyzed and compared with: (a) the commonly used fixed-length chromosome representation, (b) a variant of the simulated annealing algorithm, and (c) a knowledge-informed simulated annealing algorithm. The scalability of the algorithms is analyzed and their effectiveness demonstrated by experimental results based on four problem sizes. Obtained results show that, although there are variances in performances, all algorithms investigated are capable of obtaining good solutions. In addition, variances were observed for different aspects of the MPPP problem. The results indicate that the VLC algorithm is effective in solving MPPP problems that consider multiple aspects in the search for optimal process planning solutions. [ABSTRACT FROM AUTHOR]

This paper considers the single batch-processing machine scheduling problem with aging effect and multi-maintenance activity concept. Real processing time of jobs increases due to machine's aging effect. Maintenance time is constant and the machine returns to its initial condition after each maintenance activity and the aging effect starts anew. Our scheduling problem is under assumptions: non identical job sizes and dynamic job arrivals. We have to determine jointly (1) the maintenance frequencies, (2) the position of the maintenance activities on the machine, (3) the grouping of the jobs into the batches, and (4) the sequence of the batches on the machine, to minimize makespan. At first, we formulate the problem as a mathematical model. Since this problem has been proved to be NP-hard; therefore, a heuristic and two meta-heuristic algorithms are offered for the real-sized problems. The performance of the proposed algorithms is analyzed on the various test problems based on the considered measures. [ABSTRACT FROM AUTHOR]

In this paper, an unrelated parallel machine scheduling problem with sequence and machine-dependent setup times and makespan minimization is studied. A new makespan linearization and several mixed integer formulations are proposed for this problem. These formulations outperform the previously published formulations regarding size of instances and computational time to reach optimal solutions. Using these models, it is possible to solve instances six times larger than what was previously solved and to obtain optimal solutions on instances of the same size up to four orders of magnitude faster. A metaheuristic algorithm based on multi-start algorithm and variable neighbourhood descent metaheuristic is proposed. Composite movements were defined for the improvement phase of the proposed metaheuristic algorithm that considerably improved the performance of the algorithm providing small deviations from optimal solutions in medium-sized instances and outperforming the best known solutions for large instances. [ABSTRACT FROM AUTHOR]

One of the most important and yet complex decision-making problems in the area of transportation programming issues is vehicle routing problem. There are various exact, heuristic, and metaheuristic methods presented for solving different vehicle routing problems. In this manuscript, a mathematical model and a new heuristic solution method are proposed for solving multi-depot vehicle routing problem with time windows and different types of vehicles. In this problem, depots must serve customers between their fuzzy time windows with vehicles having different capacities, velocities, and costs. For this purpose, the mathematical model for multi-depot routing problem is developed to consider the mentioned circumstances. The objectives of this model is travel distance reduction and customers' service level increscent which leads to cost and service time reduction. For complexity of this problem and much computational time of exact solutions of developed model, a heuristic approach is proposed. This systematic approach has some steps as: customer clustering, routing, vehicle type determination, scheduling, and routes improvement using simulated annealing and customer service level improvement. The efficiency of the proposed method is analyzed by a case study in ISACO Co. Results show that the method is efficient and applicable in industries. [ABSTRACT FROM AUTHOR]

This research extends the hybrid particle swarm optimization-based metaheuristic to solve the fuzzy parallel machine scheduling problems with bell-shaped fuzzy processing times. In this paper, we propose a discrete particle swarm optimization (DPSO) which comprises two components: a particle swarm optimization and genetic algorithm. In this paper, fuzzy arithmetic on bell-shaped fuzzy numbers is used to determine the completion time of jobs. We also use a defuzzification function to rank the fuzzy numbers. Under this ranking concept among fuzzy numbers, we plan to minimize the fuzzy makespan. An extensive numerical study on large-scale scheduling problems up to 100 jobs is conducted to assess the performance of the DPSO algorithm. The results show the proposed algorithm in comparison with lower bound to be very efficient for different structure instances. [ABSTRACT FROM AUTHOR]

One of the major concerns in a reverse supply chain management (RSCM) system is dealing with returned products (due to being defective or obsolete) through a reverse logistics, such that the returned items reach their final destinations with minimum cost. In this paper, for managing returned products, a comprehensive seven-layer recovery network is designed, including primary customers, collection/redistribution centers, recovery, recycling and disposal centers, and secondary customers. The network is mathematically modeled as a mixed integer linear programming (MILP) model whose optimal solution determines the proper collection and recycling centers for the reverse and forward logistics of returned and recovered products, such that the total cost is minimized. Since the problem belongs to the network design class of problems which is NP-hard, the time for obtaining an optimal solution grows exponentially as the number of binary variables increases. Therefore, a new Tabu search-based heuristic method is developed for computing optimal or near-optimal solutions for the recovery system. Also, the Taguchi experimental design technique is employed for parameter tuning of the heuristic and coming up with a robust design. The efficiency and effectiveness of the proposed heuristic method has been evaluated through comparisons with a recently developed SA method, as well as the global optimal solutions of the model. Experimental results showed that the new Tabu search-based approach outperforms the SA method and has an average solution gap of 3.28 % with optimal solutions and a robustness of 2.18 %. [ABSTRACT FROM AUTHOR]

This paper proposes a colonial competitive algorithm which is improved by variable neighborhood search algorithm for the simultaneous effects of learning and deterioration on hybrid flowshop scheduling with sequence-dependent setup times. By the effects of learning and deterioration, the processing time of a job is determined by position in the sequence and its execution start time. In addition, it is assumed that the processing time of any job depends on the number of workers assigned to the job on a particular stage, and the more workers assigned to a stage, the shorter the job processing time. These additional traits that are added to the scheduling problem coexist in many realistic scheduling situations. This problem consists of two basic questions of job scheduling and worker assignment. Minimization of the earliness, tardiness, makespan, and total worker employing costs is considered as the objective function. To evaluate the performance of the hybrid colonial competitive algorithm, the random key genetic algorithm, immune algorithm, variable neighborhood search, and hybrid simulated annealing metaheuristic presented previously are investigated for comparison purposes, and computational experiments are performed on standard test problems. Results show that our proposed algorithm performs better than the other algorithms for various test problems. [ABSTRACT FROM AUTHOR]

The heat sinks are utilized in electronic devices to eliminate heat from the chip and efficiently transmit it to the environment. Therefore, the optimal geometry sizes of fin heat sinks are the point of concern for manufacturers and designers. For this reason, the importance of optimization techniques particularly metaheuristics is understood. The design variables are width of heat sink, number of fins, fin height, and fin diameter. The various responses that have been considered are electromagnetic emitted radiations, thermal resistance, and mass of the heat sink investigated separately and simultaneously (multi-objective). Mine blast algorithm (MBA), as a recently developed optimizer, is inspired from explosion of mines. The optimum dimensions and values for each response have been obtained by the MBA and have been compared with other optimization methods in the literature. In terms of thermal resistance and mass responses, the MBA has offered better values, while for the emitted radiations, the obtained results obtained by Taguchi-based gray relational analysis (TGRA) was preferred. For manufacturing point of view, the MBA and TGRA both suggested better and efficient design. In addition, the value path analysis has been carried out to compare the trade-off among the considered responses. Finally, parametric sensitivity analyses have been implemented for design parameters, and discussions and comparisons have been carried out for the effects of each decision variable. By considering all responses, width of heat sink and fin height are considered as the most important and effective design parameters, respectively. [ABSTRACT FROM AUTHOR]

During the last few years, most production-based businesses have been under enormous pressure to improve their top-line growth and bottom-line savings. As a result, many companies are turning to systems and technologies that can help optimise their supply chain activities. In this paper, we discuss a real-world application of scheduling in the mining industry. This is a highly constrained problem with some of the constraints changing over time. The objective is to generate a plan that meets the quality and tonnage targets by utilising the provided equipment and machines. The mathematical model is described in detail, and a three-module complex algorithm based on computational intelligence is proposed. The main software functionality is also described. The developed software is currently in the production use. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of no-wait two-stage flexible flow shop scheduling problem (NWTSFFSSP) considering unrelated parallel machines, sequence-dependent setup times, probable reworks and different ready times to actualize the problem. The performance measure used in this study is minimizing maximum completion time (makespan). Because of the complexity of addressed problem, we propose a novel intelligent hybrid algorithm [called hybrid algorithm (HA)] based on imperialist competitive algorithm (ICA) which are combined with simulated annealing (SA), variable neighborhood search (VNS) and genetic algorithm (GA) for solving the mentioned problem. The hybridization is carried out to overcome some existing drawbacks of each of these three algorithms and also for increasing the capability of ICA. To achieve reliable results, Taguchi approach is used to define robust parameters' values for our proposed algorithm. A simulation model is developed to study the performance of our proposed algorithm against ICA, SA, VNS, GA and ant colony optimization (ACO). The results of the study reveal the relative superiority of HA studied. In addition, potential areas for further researches are highlighted. [ABSTRACT FROM AUTHOR]

This paper deals with the flexible job shop scheduling problem with the objective of minimizing the makespan. An efficient heuristic based on a constructive procedure is developed to obtain high-quality schedules very quickly. The algorithm is tested on benchmark instances from the literature in order to evaluate its performance. Computational results show that, despite its simplicity, the proposed heuristic can obtain effective solutions in very short and nearly zero time and is comparable with even metaheuristic algorithms and promising for practical problems. [ABSTRACT FROM AUTHOR]

Cross docking is a distribution strategy to efficiently control the physical flow of inventory. In this approach, the concept of warehouse shifts from a place for holding the inventory to a site for receiving, classifying, and shipping to the product destination in a very short time. Shipments are typically kept for less than 24 h in the cross dock. The major advantage of this strategy is to reduce the cycle time and inventory level while fulfilling customer's demands. In general, relevant studies are based on one-product type delivery and pickup assumption whereas in reality, customers are likely to order different types of products in various amounts. This paper focuses on considering the scheduling problem of cross docking multi-product type deliveries and pickups, so that the objective, minimizing the summation of transportation cost and holding cost, can be satisfied. The efficiency and capability of the mathematical model is evaluated by presenting a case study and sensitivity analysis of the parameters influencing the problem. In order to solve the model, as the problem is NP-hard, an efficient heuristic procedure to provide effective initial solution within reasonable computational times is applied and then improved by an imperialist competitive algorithm. Computational results which consist of comparing the proposed heuristic and meta-heuristic with optimal solution obtained by Generalized Algebraic Modeling System show that the suitability of the proposed solving approach is quite sensible for solving such complicated problems. [ABSTRACT FROM AUTHOR]

In this paper the capacitated lot sizing and scheduling problem with sequence-dependent setups, setup carryover, and backlogging has been studied. The problem can be formulated as a mixed-integer program. Most lot sizing problems are hard to solve, especially in medium and large scale. In recent years, to deal with the complexity and find optimal or near-optimal results in reasonable computational time, a growing number of researchers have employed metaheuristic approaches to lot sizing problems. One of the most popular metaheuristics is genetic algorithm which has been applied to different optimization problems successfully. Therefore, we have developed a genetic algorithm to solve this model. To test the accuracy of the genetic algorithm, a lower bound is developed and compared against the genetic algorithm. In computational experiments, proposed genetic algorithm performed extremely well. It is concluded that the genetic algorithm is efficient and effective for this problem. [ABSTRACT FROM AUTHOR]

This paper presents a meta-heuristic method for a resource-constrained project scheduling problem. In this problem, the activities are given with multiple executive modes and there are minimum as well as maximum time lags between different activities. The objective here is to determine a mode and a start (or finish) time, for each activity such that total tardiness/earliness cost of activities could be minimized. The meta-heuristic approach was developed, as a two-phased genetic algorithm. The process of solving the proposed problem includes two stages (phases). In the first stage, through applying a genetic algorithm, the main problem is simplified so that each activity has only one executive mode. In the second phase, through developing another genetic algorithm, the best answer to the problem is achieved. Finally, the computational results obtained from the solution algorithms of this research were compared with the results existing in the Project Scheduling Problem Library (PSPLIB). These algorithms were provided in the MATLAB programming language. The findings show that our algorithm improved some of the best recorded solutions in the PSPLIB. [ABSTRACT FROM AUTHOR]

In the manufacturing process, loop layout problems (LLPs) are preferred to configurations like single row layout, double row layout, cluster layout, and circular layout for their relative lower initial investment costs because they contain a minimal number of required material links to connect all workstations while providing a high degree of material handling flexibility. The performance of a system is based on the impact of the loop layout. Most of these LLPs are non-polynomial hard. Numerous research works related to loop layout have been published. The literature analysis given here is not restricted to specific considerations about loop layout design. This paper aims to deal with the current and future trends of research on LLPs based on previous research including formulations, solution methodologies. A trend toward bidirectional assumption, large-sized test problems, mixed integer programming, shortcuts instead of additional loop, loading and waiting time for shortcuts, integration of the supply chain management with the data envelopment analysis and also integrate exact, decomposition algorithm with metaheuristics, and developing loop layout using metaheuristics such as scatter search algorithm, improved tabu search, and artificial bees colony algorithm to loop layout is observed. Several research directions are pointed out and discussed. [ABSTRACT FROM AUTHOR]

In this paper, a multi-item multiperiod inventory control problem with all-unit and/or incremental quantity discount policies under limited storage capacity is presented. The independent random demand rates of the items in the periods are known and the items are supplied in distinct batch sizes. The cost consists of ordering, holding, and purchasing. The objective is to find the optimal order quantities of all items in different periods such that the total inventory cost is minimized and the constraint is satisfied. A mixed binary integer programming model is first developed to model the problem. Then, a parameter-tuned genetic algorithm (GA) is employed to solve it. Since there is no benchmark available in the literature, a memetic algorithm (MA) is utilized as well to validate and verify the results obtained. The model implementation is next presented using some numerical examples and finally the performances of the proposed GA and MA are compared using two statistical tests and a simple additive weighting method. The results show that GA has better performance than MA in terms of average objective function value and average run time using the two comparison procedures. [ABSTRACT FROM AUTHOR]

Traditionally, the model of a resource-constrained project-scheduling problem (RCPSP) contains a single objective function of either minimizing project makespan or maximizing project net present value (NPV). In order to be more realistic, in this paper, two multi-objective meta-heuristic algorithms of multi-population and two-phase sub-population genetic algorithms are proposed to find Pareto front solutions that minimize the project makespan and maximize the project NPV of a RCPSP, simultaneously. Based on standard test problems constructed by the RanGen project generator, a comprehensive computational experiment is performed, where response surface methodology is employed to tune the parameters of the algorithms. The metaheuristics are computationally compared, the results are analyzed, and conclusions are given at the end. [ABSTRACT FROM AUTHOR]

For environmentally conscious and sustainable manufacturing, manufacturers need to incorporate product recovery by designing manufacturing systems to include reverse manufacturing by considering both assembly and disassembly systems. Just as the assembly line is considered the most efficient way to assemble a product, the disassembly line is seen to be the most efficient way to disassemble a product. While having some similarities to assembly, disassembly is not the reverse of the assembly process. The challenge lies in the fact that it possesses unique characteristics. In this paper, we consider a sequence-dependent disassembly line balancing problem (SDDLBP) that is concerned with the assignment of disassembly tasks to a set of ordered disassembly workstations while satisfying the disassembly precedence constraints and optimizing the effectiveness of several measures considering sequence-dependent part removal time increments. SDDLBP is not a trivial problem since it is proven to be NP-complete. Further complications occur by considering multiple objectives including environmental and economic goals that are often contradictory. Therefore, it is essential that an efficient methodology be developed. A new approach based on the particle swarm optimization algorithm with a neighborhood-based mutation operator is proposed to solve the SDDLBP. Case scenarios are considered, and comparisons with ant colony optimization, river formation dynamics, and tabu search approaches are provided to demonstrate the superior functionality of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Dynamic facility layout problem deals with the problem of arranging and rearranging the layout plan of a system throughout several periods. In each period, the material handling costs are different from the past period due to the change in the market demand and product mix. In this paper, the uncertainty that exists in transportation values' forecast is modeled by fuzzy theory. In this paper, departments have unequal areas. This means that in each period, departments can be placed only in certain places due to different spacing requirements. In addition, closeness rating matrix is considered in order to model the goodness of different layout plans with regard to qualitative factors according to decision maker. Accordingly, fuzzy dynamic facility layout problem with unequal areas and closeness rating matrix is considered that aims to minimize the uncertain material handling costs as well as the shifting costs, and maximize closeness rating with regard to space requirements of unequal area departments. A number of fuzzy algorithms are developed in order to deal with the problem. A number of ranking criteria from the literature are implemented in order to compare the performance of the developed algorithms. [ABSTRACT FROM AUTHOR]

One of the most important operational management problems in a cross-docking terminal is truck scheduling problem. In this paper, we consider a truck scheduling problem for three types of trucks (inbound-only trucks, outbound-only trucks, and compound trucks) in a multi-door cross-docking terminal. The objective of the problem is to determine the door assignments and the docking sequences of all three types of trucks simultaneously to minimize the makespan. A mathematical model for optimal solution is derived and two meta-heuristics, genetic algorithm and self-evolution algorithm, are proposed. The performance of the meta-heuristic algorithms are evaluated using randomly generated several examples. [ABSTRACT FROM AUTHOR]

The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature's algorithm. [ABSTRACT FROM AUTHOR]

This paper aims to determine simultaneously the amount, timing, and location of progress payments in projects in order to achieve a set of equitable solutions in the client–contractor negotiation process. The objective function tries to minimize the distance between the final solutions and the best achievable solutions of the client and the contractor. Due to combinatorial nature of the proposed problem, an iterative two-stage search method is proposed. In the first stage, a set of payments is determined to maximize the contractor's net present value (NPV), another set to maximize the client's NPV, and another one to minimize the objective function. In the second stage, activities are rescheduled to improve the solutions by fixing the amount and location of progress payments. A hybrid meta-heuristic algorithm named GASA, along with a simulated annealing (SA) and a genetic algorithm (GA), is introduced for the first stage, while the activities are optimally scheduled in the second stage. It has been shown that SA is better for the contractor's objective function and GA for the client's objective function, but GASA is the best in all situations; besides, the proposed method has represented to be an efficient approach to obtain non-dominated solutions in the client–contractor negotiation process. [ABSTRACT FROM AUTHOR]

This paper presents the results of a simulation study of a typical flexible manufacturing system that consists of seven machining centres, a loading and an unloading area, and six different part types. Owing to the existence of identical machining centres, the part types have alternative routings (their number varies between two and eight). One of the objectives of this work is to show how the following metaheuristics: ant colony optimisation, genetic algorithms, simulated annealing, tabu search, particle swarm optimisation and electromagnetism-like method, are adapted for solving the alternative routing selection problem in real time in order to reduce the congestion in the system by selecting a routing for each part among its alternative routings. The other goal is to highlight the impact of the real-time rescheduling of parts contained in the loading station on system performances when these metaheuristics are applied. The simulation results jugged by the production rate, machines and material handling utilisation rate show that for an overloaded system, the real-time rescheduling outperforms the case without rescheduling, but it has a negative impact on the work in process. [ABSTRACT FROM AUTHOR]