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The multi-objective optimisation of high-speed rail profile with small radius curve is studied in the paper. A multi-objective mathematical model for rail profile optimisation of high-speed railway is established. The CN60 rail profile is parameterised into a series of generalised functions of design variables. In order to guarantee the smoothness of the rail profile and meet the maximum grinding depth of rail in China, the constraints are employed. The wheel-rail vertical clearance and equivalent conicity of wheelset are taken as objective functions, and a rail wear prediction programme is compiled. Contact line method is employed to complete the detection algorithm of wheel-rail contact points. Finally, NSGA-II Algorithm is adopted to solve the Pareto-optimal front of the optimisation model. A set of solutions are retrieved from the Pareto optimal front solution as the optimised profile. The optimised rail profile and the original rail profile are matched with the LMA wheel profile (a certain worn type of wheel profiles for EMU in China) respectively. It is testified that the rail profile could effectively reduce the rail wear and improve curving performance. The new method proposed in this paper can provide some reference for the optimisation design of high-speed rail profile with small radius curve. [ABSTRACT FROM AUTHOR]

Service composition is a core issue of cloud manufacturing (CMfg) to integrate distributed manufacturing services for customised manufacturing tasks. Existing studies focus on the quality of service (QoS) in composition by assuming that each service is independent with each other. However, the correlation between services determines whether a composition is feasible in practice and is a primary factor of its QoS. This paper considers two typical correlations, composability-oriented correlation and quality-oriented correlation. The composability-oriented correlation is modelled as a group of constraints to decide whether a solution is feasible. The influence of the quality-oriented correlation between two services on the overall QoS of a composition is quantified by a discount percentage based on their correlation degrees. A mathematical model of correlation-aware service composition is then established. To solve this problem, a many-objective memetic algorithm termed HypE-C (Hypervolume Estimation Algorithm for Multiobjective Optimisation involving Correlation) is designed. Three correlation-based local search strategies are established in the frame of HypE (Hypervolume Estimation Algorithm for Multiobjective Optimisation) to achieve better trade-off among multiple conflicting QoS criteria. Experiments demonstrate the effectiveness of the proposed algorithm HypE-C compared with five many-objective algorithms on eliminating infeasible search space and providing high QoS service composition solutions. [ABSTRACT FROM AUTHOR]

Given that a U-shaped assembly line is usually regarded as a serial flow production system, preventive maintenance (PM) on machines in any station of the line may lead to stoppage of the whole line and sequentially cause great loss of production cost. To improve production continuity during PM, this paper proposes multiple alternative assignment plans with interchangeable abilities to ascertain the production recovery from changes of line structure. A mixed-integer mathematical model is further formulated to make decisions on alternative assignment plans. In this model, cycle time and total assignment plan alteration cost are minimised simultaneously. And an enhanced JAYA algorithm is developed to obtain well-distributed Pareto frontier solutions effectively and efficiently. The proposed algorithm suggests a similarity-based selection and various crossover/neighbourhood search operators to enhance its search ability for global optima. Numerical experiment results prove the effectiveness of the proposed model and enhanced JAYA. This novel methodology of integration optimisation bridges assembly line balancing and PM, and thus promotes production continuity and achieves economic benefits under maintenance within large-scaled assembly lines in real situations. Furthermore, a real-world case study is conducted to illustrate the significance of this novel methodology. [ABSTRACT FROM AUTHOR]

This paper presents a hybrid variable neighbourhood search/tabu search (HVNTS) method and a neighbourhood generation strategy called pairwise inter-reshuffle (PIR) for process planning of 5-axis electrical discharge machining (EDM) drilling processes, which aims to minimize the total non-productive time of the machining process, including tool travelling time, tool switching time and Z-axis compensation moving time. To obtain a mathematical model of the non-productive time, a kinematic transformation and a Chebyshev distance function are then utilized. To solve the mathematical model efficiently, an HVNTS algorithm is applied as the model has a large number of 0–1variables. To improve the solution quality, the PIR method and a dynamic neighbourhood strategy are applied simultaneously and verified. The obtained simulation results demonstrate that the proposed basic and the improved HVNTS have a significant contribution in optimizing the non-productive time of the 5-axis EDM drilling process effectively. [ABSTRACT FROM AUTHOR]

The concept of the sparsest packing is introduced in this paper. The sparsest packing is aimed to place the objects in the container as distant as possible. More specifically, the minimal Euclidean distance between the objects, as well as, between the objects and the boundary of the container is maximised. This new problem statement is motivated by modern clean and energy-saving technologies such as ultrasonic hardening and finishing by detonating gas mixtures. The sparsest packing of two-dimensional objects in a circular container is considered subject to balancing conditions. The objects may have regular or irregular shapes bounded by arcs and line segments. Using the phi-function technique a mathematical model is formulated and a corresponding nonlinear programming problem is stated. A solution algorithm is proposed and computational results are presented to illustrate the approach. [ABSTRACT FROM AUTHOR]

With the rapid development of social platforms and human activities, people are now interested to solve their problems with the help of the social crowd-powered system. So, crowdsourcing has become a promising way of solving problems in a distributed manner within a specific time. Crowd workers pick up a task (simple or complex) and solve it with competing interest or collaboratively and receive an incentive (as monetary or non-monetary). In paid crowdsourcing platforms, crowd workers solve complex tasks and get remunerations through prior bidding or prior announcement of fee decided by the requester (task provider). For decomposable tasks, a single winner may not provide a significant solution to the requester due to the insufficient knowledge. So, we induce collaboration in competitive crowdsourcing markets to better handle decomposable-type tasks. In this paper, we propose an envelope game-based mechanism that ensures if the tasks are decomposable then the workers will be encouraged to collaborate with their subtasks and share their remunerations. This mechanism also increases the chance of receiving more number of cost-effective solutions. Thus both the requester and crowd workers get benefits from the system. The effectiveness of the proposed mechanism is evaluated through empirical analysis on simulated environments. [ABSTRACT FROM AUTHOR]

In recent years, the interests of disassembly line have increased owing to economic reasons and the increase of environmental awareness. Effective line can provide many advantages in terms of economic aspect and it facilitates competition the companies with others. This study contributes to the relevant literature by a branch, bound and remember algorithm for disassembly line balancing problem with AND/OR precedence. The proposed exact solution method employs the memory-based dominance rule to eliminate the reduplicated sub-problems by storing all the searched sub-problems and to utilise cyclic best-first search strategy to obtain high-quality complete solutions fast. In this paper, minimising the number of stations is taken as the performance measure. The proposed methodology is tested on a set of 260 instances and compared with the mathematical model using CPLEX solver and five well-known metaheuristics. Computational results show that the proposed method is capable of obtaining the optimal solutions for all the tested instances with less than 0.1 seconds on average. Additionally, comparative study demonstrates that the proposed method is the state-of-the-art algorithm and outperforms the CPLEX solver and metaheuristics in terms of both solution quality and search speed aspects. [ABSTRACT FROM AUTHOR]

The assembly line balancing problem can completely vary from one production line to the other. This paper deals with a realistic assembly line for the automotive industry inspired by Fiat Chrysler Automotive in North America and Parskhodro in Iran (both large-scale automotive companies). This problem includes some specific requirements that have not been studied in the literature. For example, the assembly line is five-sided, and workers can move along these sides. Due to the limited workspace, all the sides cannot work simultaneously at one station. First, a mixed integer linear programming model is proposed for the problem. Then, the model is improved to have a tighter linear relaxation. Moreover, an effective logic-based Benders' decomposition algorithm is developed. After careful analysis of problem's structure, three propositions are introduced. The master problem is well restricted by eight valid inequalities. Two different sub-problem types are defined to extract more information from the master problem's solution. In this case, the algorithm adds effective cuts that reduce the solution space to the extent possible at each iteration. Thus, the number of iterations is significantly cut down. The performance of the model and algorithm, as well as improvement made on both, is evaluated. [ABSTRACT FROM AUTHOR]

This paper considers the material supply problem for aircraft moving assembly lines. Distinguished from general automobile assembly lines, multiple parallel jobs are assembled concurrently and durations of assembly jobs are quite long, thus amounts of materials are orderly stored at the line-side space at the same time. In addition, the line-side space should be reused in the time dimension. With these characteristics, decisions on line-side storage of materials were introduced on the basis of material batching and tow-trains scheduling problems. An integrated decision–making mathematical model with the objective of minimising the number of deliveries was established. A hybrid endocrine-immune algorithm (HEIA) was proposed to jointly make decisions on the delivery batch, delivery time and storage positions of each job's materials. Numerical experiments with the real-world data and randomly generated instances validate the effectiveness and efficiency of HEIA. [ABSTRACT FROM AUTHOR]

Humanitarian logistics research has recently received tremendous interest from researchers and practitioners due to its importance in assisting relief operations. While there is an increasing trend for mathematical models related to preparedness and response phases for disaster operations management, recovery-phase models are not as emphasised as other phases due to scarce data and model complication from NP-hard nature of the models. One particular approach that can provide a sufficiently good solution for the NP-hard problems is the metaheuristic approach. In this research, we explore the bi-criteria integrated response and recovery model for making strategic post-disaster decisions in the relief distribution and short-term network restoration. Next, with a focus on considering conflicting objectives between fairness and cost of this problem, we propose a hybrid approach with its evolutionary component based on the non-dominated sorting genetic algorithm-II (NSGA-II) called HNSGA-II. The proposed HNSGA-II is compared against the exact method using the approximate Pareto-front analysis. The proposed algorithm is verified using a case study from a risk assessment tool called Hazus to illustrate how to cope with the aftermath of an earthquake. Finally, results are evaluated using a Hypervolume-based technique and computation time to illustrate the efficiency of the proposed algorithm. [ABSTRACT FROM AUTHOR]

Resource-efficient technologies for cutting sheet materials allow for the contours of cut-off details to be overlapped. This includes the Intermittent Cutting Problem and the Endpoint Cutting Problem. This paper reviews mathematical models of such resource-efficient cutting processes and suggests algorithms for defining the cutter route under technological constraints. As soon as a cutting plan is given, optimisation with shortening the total length of idle passes. No information of the detail shape is required to define the sequence of detail cutting. This is why all curves without self-intersections and contiguities that are a constituent part of detail boundaries may be interpreted as edges of the plane graph G, and all points of intersection and contiguity may be interpreted as vertices of the graph G. Up to homeomorphism, plane graph G can be represented by a list of edges with incident vertices and faces . This allows the restrictions of the planed cutter trajectory to be formalised in terms of graph theory. A series of algorithms for constructing the permitted route in the plane graph G as an image of the cutting plan is suggested. The constructed route for the graph G can be interpreted as a tool trajectory for the cutting plan which is the inverse image of graph G. [ABSTRACT FROM AUTHOR]

In this paper, we demonstrate that the relation between two jobs defined by min{ai, bj} ≤ min{bi, aj}, used in Johnson’s theorem, is not transitive. However, both the theorem and Johnson’s algorithm are correct. [ABSTRACT FROM AUTHOR]

In this paper, we consider multi-party coordination in a supply chain (SC) that consists of a set of independent producers and a set of resource managers. A decentralised decision-making approach is proposed for a coal SC, with three independent parties – multiple mines, a rail operator and a terminal. The rail operator and the terminal act as common resource managers and connects the independent mines via a rail network. The objective of this SC is to efficiently use an independent rail operator to transport coal from different mines to meet the shipping demand at the terminal. The underlying coordination problem can be seen as a multi-resource constrained scheduling problem. A major part of this paper addresses the key challenges in a decentralised approach based on column generation (CG), which are to compute the value of a column, better upper bounds and to update the multipliers using decentralised methods. We have also discussed the mathematical models for different decision units, the CG algorithm and different strengthening methods. A comprehensive computational experiment based on randomly generated instances highlights the effect of decentralisation and the value of information-sharing. The proposed solution approaches can be extended to a multi-party case with any number of common resources. [ABSTRACT FROM AUTHOR]

In this paper, the extended Resource Renting Problem (RRP/extended) is presented. The RRP/extended is a time-constrained project scheduling problem, in which the total project cost is minimised. In the RRP/extended, this total project cost is determined by a number of extra costs, which are defined in this paper. These costs are based on the costs that are used in the traditional Resource Renting Problem and the Total Adjustment Cost Problem. Therefore, the RRP/extended represents a union of these two problems. To solve the RRP/extended, a scatter search is developed. The building blocks of this scatter search are specifically designed for the RRP/extended. We introduce two crossovers and an improvement method. The efficiency of these building blocks will be shown in the paper. Furthermore, a sensitivity analysis is presented in which the five costs have diverse values. [ABSTRACT FROM AUTHOR]

Distributed arrival time control (DATC) is a heuristic feedback control algorithm for scheduling which has been developed for a real-time distributed scheduling of heterarchical systems. It has been renowned not only for its fast solution searching algorithm but also for its flexibility to changing environment. However, the optimality of this heuristic method has not been analytically explained until recently because it has been designed to discover a near optimal solution instead of the true optimum. In this paper, we provide a novel optimal search method for the DATC scheduling problem by introducing a scalar cost function over the vector space of time and show the existence and location of true optima for the DATC scheduling problem through geometric approach. This geometrical interpretation enables us to find the true optimal by direct projection without iterations like previous DATC approaches. Based on the true optimum found, we evaluate the optimality of DATC algorithms by examining their dependency on initial conditions and explain their intrinsic causality mechanism for the discrepancy with true optimum. The implication of this study is on the new viewpoint over the vector space of DATC, which not only solves the optimality issue of DATC but also provides a new direction of direct search approach like projection method for the true optimum. [ABSTRACT FROM AUTHOR]

This paper examines the capacitated lot-sizing and scheduling problem (CLSP) with sequence-dependent setup times, time windows, machine eligibility and preference constraints. Such a problem frequently arises in the semiconductor manufacturing industry by which this paper is motivated. A mixed integer programming (MIP) model is constructed for the problem. Two MIP-based fix-and-optimise algorithms are proposed in which the binary decision variables associated with the assignment of machines are first fixed using the randomised least flexible machine (RLFM) rule and the rest of the decision variables are settled by an MIP solver. Extensive experiments show that the proposed algorithms outperform the state-of-the-art MIP-based fix-and-optimise algorithms in the literature, especially for instances with high machine flexibility and high demand variation. [ABSTRACT FROM PUBLISHER]

This paper considers an inventory model with non-instantaneous deteriorating item in which demand rate is a function of selling price and time, taking account of time value of money. This paper aids the retailer in maximising the total profit by determining optimal replenishment policies. Shortages are allowed which are partially backlogged. This model also takes into cognizance the fact that in business activities nowadays customers are given delay in payments. Under these assumptions, a mathematical model is formulated over a finite planning horizon and then some useful theoretical results have been framed to characterise the optimal solutions. The necessary and sufficient conditions for the existence and the uniqueness of the optimal solutions are also derived. An algorithm is designed to find the optimum solutions of the proposed model. Numerical examples are included to illustrate the algorithmic procedure and the effects of key parameters are studied to analyse the behaviour of the model. [ABSTRACT FROM AUTHOR]

This paper studies the scheduling problem of minimising total weighted earliness and tardiness penalties on identical parallel machines against a restrictive common due date. This problem is NP-hard in the strong sense and arises in many just-in-time production environments. A fast ruin-and-recreate (FR&R) algorithm is proposed to obtain high-quality solutions to this complex problem. The proposed FR&R algorithm is tested on a well-known set of benchmark test problems that are taken from the literature. Computational results provide evidence of the efficiency of FR&R, which consistently outperform existing algorithms when applied to benchmark instances. This work provides a viable alternative approach for efficiently solving this practical but complex scheduling problem. [ABSTRACT FROM PUBLISHER]

In competitive global markets, it is important to meet customer demands on multiple priorities such as price, quality, customisation and quick delivery. This paper investigates the problems of part input sequencing and scheduling in flexible manufacturing systems in a mass customisation/mass personalisation (MC/MP) environment. Both robot and machine scheduling rules using a state-dependent part input sequencing algorithm are investigated. Simulation experiments and statistical analyses are carried out. Effective rules are identified. The results show interactions between robot scheduling and machine scheduling in the MC/MP environment. Further research suggestions are provided. [ABSTRACT FROM PUBLISHER]

This paper focuses on simultaneous optimisation of production planning and scheduling problem over a time period for synchronous assembly lines. Differing from traditional top-down approaches, a mixed integer programming model which jointly considers production planning and detailed scheduling constraints is formulated, and a Lagrangian relaxation method is developed for the proposed model, whereby the integrated problem is decomposed into planning, batch sequencing, tardiness and earliness sub-problems. The scheduling sub-problem is modelled as a time-dependent travelling salesman problem, which is solved using a dynasearch algorithm. A proposition of Lagrangian multipliers is established to accelerate the convergence speed of the proposed algorithm. The average direction strategy is employed to solve the Lagrangian dual problem. Test results demonstrate that the proposed model and algorithm are effective and efficient. [ABSTRACT FROM PUBLISHER]

Batch processing machines (BPMs) have important applications in a variety of industrial systems. This paper considers the problem of scheduling two BPMs in a flow shop with arbitrary release times and blocking such that the makespan is minimised. The problem is formulated as a mixed integer programming model. Subsequently, a hybrid discrete differential evolution (HDDE) algorithm is proposed. In the algorithm, individuals in the population are first represented as discrete job sequences, and mutation and crossover operators are applied based on the representation. Second, after using the first-fit rule to form batches, a novel least idle/blocking time heuristic is developed to schedule the batches in the flow shop. Furthermore, an effective local search technique is embedded in the algorithm to enhance the exploitation ability. The performance of the proposed algorithm is evaluated by comparing its results to a commercial solver (CPLEX), a genetic algorithm and a simulated annealing algorithm. Computational experiments demonstrate the superiority of the HDDE algorithm in terms of solution quality, robustness and run time. [ABSTRACT FROM PUBLISHER]

This paper deals with the objective of determining an optimal part sequence on a single-stage multifunctional machining system (SSMS) with a view to achieve the broad objectives of cost minimization and time minimization. SSMS has become a preferred alternative for manufacturers to use the resource efficiently, owing to the flexibility and process variety offered by it. This paper formulates a mathematical model that considers the minimization of both set-up cost and time simultaneously. The option of hiring an additional fixture has also been considered that enables the reduction in tool magazine replenishment and re-fixturing operations, which in turn offers economic advantage by way of reducing set-up cost. This study has proposed a new heuristic by modifying the simulated annealing concept to solve the underlying problem. The conventional simulated annealing search scheme is replaced by a chaotic search that takes into account the ergodic and stochastic properties of chaotic systems. In order to restrict the premature convergence and to diversify the search space, a modified perturbation scheme has been employed. The performance of the proposed algorithm was tested on a simulated case study adopted from the literature and the results obtained reveal the effectiveness and scalability of the proposed algorithm. The results establish that the proposed approach is effective and reactive to severe disturbances and must take place in the manufacturing environment. [ABSTRACT FROM AUTHOR]

This paper deals with Monte Carlo simulation in case of dependent input random variables. We propose an algorithm to generate refined descriptive samples from dependent random variables for estimation of expectations of functions of output variables using the Iman and Conover algorithm to transform the dependent variables to independent ones. Therefore, such estimates obtained through a chosen mathematical model are compared with those obtained using the simple random sampling method, which proved that the former are the most efficient. Besides, using already published work on independent input variables, we can deduce in case of dependent input random variables, that asymptotically the variance of the RDS estimator is less than that of SRS estimator for any simulation function having finite second moment. [ABSTRACT FROM AUTHOR]

This paper is dedicated to the scheduling problem of multi-cluster tools with process module residency constraints and multiple wafer product types. The problem is formulated as a non-linear programming model based on a set of time constraint sets. An effective algorithm called the time constraint sets based (TCSB) algorithm is presented as a new method to schedule the transport modules to minimise the makespan of a number of wafers. In approach, time constraint sets are maintained for all the resources and necessary operations to exploit the remaining production capacities during the scheduling process. To validate the proposed algorithm on a broader basis, a series of simulation experiments are designed to compare our TCSB algorithm with the benchmark with regard to cluster factor, configuration flexibilities and the variation of the processing times and residency constraint times. The results indicate that the proposed TCSB algorithm gives optimal or near optimal scheduling solutions in most cases. [ABSTRACT FROM PUBLISHER]

This paper presents a deterministic nonlinear program in rolling-schedule form for multi-item production planning with uncertain demand and a capacity constraint. In the one-period case, a ranking-based cutoff policy is shown to be optimal. In the multi-period case with scarce capacity, a critical managerial choice is whether to produce quantities meeting mean levels of demand for less important products in immediate periods or to build up safety stocks for more important products in future periods. We address this question by formulating a two-period model and solve it using a multi-component algorithm. Numerical studies are also conducted to gain insights into capacity sharing with regard to product lifecycles and cost differences. [ABSTRACT FROM PUBLISHER]

In condition-based maintenance (CBM) with periodic inspection, the system is preventively replaced if failure risk, which is calculated based on the information obtained from inspection, exceeds a pre-determined threshold. The determination of optimal replacement threshold is often based on the minimisation of average maintenance costs per unit time due to preventive and failure replacements over a long time horizon. It is often assumed that inspections are performed at equal time intervals with no cost. However, in practice, inspections require labour, specific test devices, and sometimes suspension of operations and, thus, it is reasonable to inspect less frequently during the time the system is in its early age and/or in a healthier state and to perform inspections more frequently as time passes and/or as the system degrades. In other words, an age-based inspection scheme. This paper proposes a novel two-phase approach for the determination of an optimal replacement threshold and an optimal age-based inspection scheme for CBM such that the total long-run average costs of replacements and inspections are minimised. First, it takes into account failure and preventive replacement costs to determine the optimal replacement threshold assuming that inspections are performed at equal time intervals with no cost. This assumption is, subsequently, relaxed and its consequences on total average cost are evaluated using a proposed iterative procedure based on A* search algorithm to obtain the optimal age-based inspection scheme. The proposed approach is illustrated through a numerical example. [ABSTRACT FROM PUBLISHER]

This paper presents a study on the two-stage assembly flow shop scheduling problem for minimising the weighed sum of maximum makespan, earliness and lateness. There are m machines at the first stage, each of which produces a component of a job. A single machine at the second stage assembles the m components together to complete the job. A novel model for solving the scheduling problem is built to optimise the maximum makespan, earliness and lateness simultaneously. Two optimal operation sequences of jobs are determined and verified. As the problem is known to be NP-hard, a hybrid variable neighbourhood search – electromagnetism-like mechanism (VNS-EM) algorithm is proposed for its handling. To search beyond local optima for a global one, VNS algorithm is embedded in each iteration of EM, whereby the fine neighbourhood search of optimum individuals can be realised and the solution is thus optimised. Simulation results show that the proposed hybrid VNS-EM algorithm outperforms the EM and VNS algorithms in both average value and standard deviation. [ABSTRACT FROM AUTHOR]

In this paper, multi-objective flow-shop scheduling with parallel machines is considered, and an algorithm for production-scheduling optimisation is proposed. The objective is to design an algorithm to optimise production scheduling, taking into consideration customer changes to delivery dates and quantities, materials requisitioned from the supplier and inventory management. Manufacturing lead time for the production order is minimised, and, finally, resource use is considered as an auxiliary criterion. Numerical examples, modelled with data from a Mexican enterprise, and computational results are reported. The paper indicates that the mathematical model, as well as the program that solves it, provides flexibility with minimum modifications. Thus it can provide useful information for multiple decision processes. [ABSTRACT FROM AUTHOR]

Remanufacturing has been widely studied for its potential to achieve sustainable production in recent years. In the literature of remanufacturing research, process planning and scheduling are typically treated as two independent parts. However, these two parts are in fact interrelated and often interact with each other. Doing process planning without considering scheduling related factors can easily introduce contradictions or even infeasible solutions. In this work, we propose a mathematical model of integrated process planning and scheduling for remanufacturing (IPPSR), which simultaneously considers the process planning and scheduling problems. An effective hybrid multi-objective evolutionary algorithm (HMEA) is presented to solve the proposed IPPSR. For the HMEA, a multidimensional encoding operator is designed to get a high-quality initial population. A multidimensional crossover operator and a multidimensional mutation operator are also proposed to improve the convergence speed of the algorithm and fully exploit the solution space. Finally, a specific legalising method is used to 'legalise' possible infeasible solutions generated by the initialisation method and mutation operator. Extensive computational experiments carried out to compare the HMEA with some well-known algorithms confirm that the proposed HMEA is able to obtain more and better Pareto solutions for IPPSR. [ABSTRACT FROM AUTHOR]

Effective solutions to the cell formation and the production scheduling problems are vital in the design of virtual cellular manufacturing systems (VCMSs). This paper presents a new mathematical model and a scheduling algorithm based on the techniques of genetic algorithms for solving such problems. The objectives are: (1) to minimize the total materials and components travelling distance incurred in manufacturing the products, and (2) to minimize the sum of the tardiness of all products. The proposed algorithm differs from the canonical genetic algorithms in that the populations of candidate solutions consist of individuals of different age groups, and that each individual's birth and survival rates are governed by predefined aging patterns. The condition governing the birth and survival rates is developed to ensure a stable search process. In addition, Markov Chain analysis is used to investigate the convergence properties of the genetic search process theoretically. The results obtained indicate that if the individual representing the best candidate solution obtained is maintained throughout the search process, the genetic search process converges to the global optimal solution exponentially. The proposed methodology is applied to design the manufacturing system of a company in China producing component parts for internal combustion engines. The performance of the proposed age-based genetic algorithm is compared with that of the conventional genetic algorithm based on this industrial case. The results show that the methodology proposed in this paper provides a simple, effective and efficient method for solving the manufacturing cell formation and production scheduling problems for VCMSs. [ABSTRACT FROM AUTHOR]

This paper analyzes the single-source capacitated facility location problem (SSCFLP) with soft capacity constraints. Hence, the maximum capacity at each facility can be potentially exceed by incurring in a penalty cost, which increases with the constraint-violation gap. In some realistic scenarios, this penalty cost can be modelled as a piecewise function. As a result, the traditional cost-minimization objective becomes a non-smooth function that is difficult to optimise using exact methods. A mathematical model of this non-smooth SSCFLP is provided, and a biased-randomized iterated local search metaheuristic is proposed as a solving method. A set of computational experiments is run to illustrate our algorithm and test its efficiency. [ABSTRACT FROM AUTHOR]

Location and routing problems belong to well-known optimization ones, whose combinations have merged as a location-routing problem (LRP). Arc routing has recently become a popular interest of researchers. Accordingly, the location-arc routing problem (LARP) is defined. This paper deals with a location-arc routing problem when there are transportation decisions from suppliers to established depots (TLARP). It is addressed by developing a bi-objective mathematical model to optimize the total costs and makespan. An augmented ε-constraint algorithm is employed to find the true Pareto solutions. Then two meta-heuristics are considered: non-dominated sorting genetic algorithm II (NSGA-II) and multi-objective late acceptance hill-climbing (MOLAHC) algorithm. There are four heuristics regarding NSGA-II, its combination with MOLAHC, and having/not having a local search (LS). Results demonstrate that NSGA-II+LS is the best and hybrid+LS is the second best heuristics while the solving time of hybrid+LS is significantly less than the time of NSGA-II+LS. [ABSTRACT FROM AUTHOR]

In this paper, thinned concentric circular antenna arrays (CCAAs) with low sidelobe levels and fixed half-power beamwidths are synthesized by using the Slime Mold Algorithm (SMA). SMA is a novel stochastic optimization method inspired by the oscillation mode of the slime mold in nature. Entering the literature as a new optimization technique, SMA is based on a matchless mathematical model that finds the most suitable way to find food in the search space thanks to the negative and positive oscillations of the slime mold. SMA is also used to obtain thinned CCAAs having different ring numbers. It is seen that the results obtained by SMA are very good. Besides, the flexibility of SMA in different values of parameters is a promising feature for the other antenna array synthesis problems. The results obtained by SMA are compared with several meta-heuristic algorithms in the literature. SMA shows a better performance in synthesizing CCAAs than the other compared algorithms. [ABSTRACT FROM AUTHOR]

This paper considers the no-wait job shop (NWJS) problem with makespan minimisation criteria. It is well known that this problem is strongly NP-hard. Most of the previous studies decompose the problem into a timetabling sub-problem and a sequencing sub-problem. Each study proposes a different sequencing and timetabling algorithm to solve the problem. In this research, this important question is aimed to be answered: is the timetabling or the sequencing algorithm more important to the effectiveness of the developed algorithm? In order to find the answer, three different sequencing algorithms are developed; a tabu search (TS), a hybrid of tabu search with variable neighbourhood search (TSVNS), and a hybrid of tabu search with particle swarm optimisation (TSPSO). Afterwards, the sequencing algorithms are combined with four different timetabling methods. All the approaches are applied to a large number of test problems available in the literature. Statistical analysis reveals that although some of the sequencing and timetabling algorithms are more complicated than the others, they are not necessarily superior to simpler algorithms. In fact, some of the simpler algorithms prove to be more effective than complicated and time-consuming methods. [ABSTRACT FROM PUBLISHER]

The permutation flow shop problem (PFSSP) is an NP-hard problem of wide engineering and theoretical background. In this paper, a cuckoo search (CS)-based memetic algorithm, called HCS, is proposed for the PFSSP. To make CS suitable for the PFSSP, a largest-ranked-value (LRV)-rule-based random key is used to convert the continuous position in CS into a discrete job permutation. The Nawaz-Enscore-Ham (NEH) heuristic is then combined with the random initialisation to initialise the population with a certain quality and diversity. After that, CS is employed to evolve nest vectors for exploration, and a fast local search is embedded to enhance the local exploitation ability. In addition, simulations and comparisons based on PFSSP benchmarks are carried out, which shows that our algorithm is both effective and efficient. [ABSTRACT FROM PUBLISHER]

A sparse layout problem for clusters formed by irregular 3D objects is introduced. The shape of a 3D cluster is represented as a convex hull of the objects inside the cluster. The objects in the cluster may have different irregular shapes, can be freely translated and rotated and must be placed in the cluster without mutual overlapping. Each irregular 3D object in the cluster is composed by a union of basic convex 3D objects. The clusters must be placed in a container without mutual overlapping. The objective is to maximize the distance between the 3D clusters. New geometric tools to describe analytically nonoverlapping, containment and distance constraints for 3D clusters are introduced. The sparse layout problem is formulated as a nonlinear nonconvex continuous programming problem. A solution algorithm is proposed, and computational results are provided. [ABSTRACT FROM AUTHOR]

This paper proposes a model-based approach for identifying rotordynamic parameters using full spectrum. First, for developing a mathematical model a cracked shaft system with an offset-disc integrated with an active magnetic bearing (AMB) is considered. Moreover, external viscous and internal (rotating) damping is considered in the mathematical model to analyze their effect on the dynamics of the cracked rotor system. Subsequently, an identification algorithm is developed based on the proposed mathematical model to estimate the critical parameters of the model with the help of displacements and AMB currents obtained from full spectrum analysis. Eight parameters, such as the external and internal damping, translational as well as rotational additive crack stiffness, disc eccentricity and its phase, and AMB parameters, are the main parameters affecting the dynamic behaviour of a rotor system estimated. To overcome the practical difficulty in the measurement of rotational displacements during the identification, a gyroscopic dynamic reduction scheme is used to remove the rotational degrees of freedom. The developed algorithm is tested against various random noise in responses and modelling or bias errors in physical parameters to check the sensitiveness and robustness of the model as in the real case. [ABSTRACT FROM AUTHOR]

In modern manufacturing systems, especially assembly lines, human input is a critical resource to provide dexterity and flexibility. However, the repetitive precision tasks common in assembly lines can have adverse effects on workers and overall system performance. We present a data-driven approach to evaluating task performance using wearable sensor data (kinematics, electromyography and heart rate). Eighteen participants (gender-balanced) completed repeated cycles of maze tracking and assembly/disassembly. Various combinations of input data types and classification algorithms were used to model task performance. The use of the linear discriminant analysis (LDA) algorithm and kinematic data provided the most promising classification performance. The highest model accuracy was found using the LDA algorithm and all data types, with respective levels of 62.4, 88.6, 85.8 and 94.1% for predicting maze errors, maze speed, assembly/disassembly errors and assembly/disassembly speed. The presented approach provides the possibility for real-time, on-line and comprehensive monitoring of system performance in assembly-lines or similar industries. Practitioner summary: This paper proposed models the repetitive precision task performance using data collected from wearable sensors. The use of the LDA algorithm and kinematic data provided the most promising classification performance. The presented approach provides the possibility for real-time, on-line and comprehensive monitoring of system performance in assembly lines or similar industries. Abbreviations: AD: anterior deltoid; BB: biceps brachii; ECR: extensor carpi radialis; ECU: extensor carpi ulnaris; FCR: flexor carpi radialis; FCU: flexor carpi ulnaris; FN: false negatives; FP: false positives; HR: heart rate; HRR: heart rate reserve; IMUs: inertial measurement units; kNN: k-nearest neighbors; LDA: linear discriminant analysis; MD: medial deltoid; MF: median power frequency; MNF: mean power frequency; MVIC: maximum voluntary isometric contraction; nRMS: normalized root-mean-square amplitudes; PD: posterior deltoid; RandFor: random forests; RHR: resting heart rate; RMS: root-mean-square amplitudes; sEMG: surface electromyographic; SVM: support vector machines; TB: triceps brachii medial; TN: true negatives; TP: true positives; t-SNE: t-distributed Stochastic Neighbor Embedding; UT: upper trapezius [ABSTRACT FROM AUTHOR]

The paper provides for the first time a comprehensive introduction into the mechanisms through which the method of separation achieves risk reduction and into the ways it can be implemented in engineering designs. The concept stochastic separation of critical random events on a time interval, which consists of guaranteeing with a specified probability a specified degree of distancing between the random events, is introduced. Efficient methods for providing stochastic separation by reducing the duration times of overlapping critical random events on a time interval are presented. The paper shows that the probability of overlapping of critical events, randomly appearing on a time interval, is practically insensitive to the distribution of their duration times and to the variance of the duration times as long as the mean of the duration times remains the same. A rigorous proof is presented that this statement is valid even for two random events on a time interval. The paper also provides insight into various mechanisms through which deterministic separation improves reliability and reduces risk. It is demonstrated that the separation on properties is an efficient technique for compensating the drawbacks associated with homogeneous properties. It is demonstrated that improving reliability by including redundancy, improving reliability by segmentation and some of the deliberate weak link techniques and stress limiters techniques for reducing risk are effectively special cases of a deterministic separation. Finally, the paper demonstrates that in a number of cases, the way to extract benefit from the method of separation is to build and analyse a mathematical model based on the method of separation. A comprehensive classification of the discussed methods for stochastic and deterministic separation is also presented. [ABSTRACT FROM AUTHOR]

In this paper, we introduce two new algorithms for solving classical variational inequalities problem with Lipschitz continuous and monotone mapping in real Hilbert space. We modify the subgradient extragradient methods with a new step size, the convergence of algorithms are established without the knowledge of the Lipschitz constant of the mapping. Finally, some numerical experiments are presented to show the efficiency and advantage of the proposed algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a general inspection plan for critical multicharacteristic components is presented. In this plan the assumption in the literature that characteristics are inspected equal number of times for such components is relaxed and allowed for different number of inspections for different characteristics depending on characteristic's defective rates and inspection cost. A mathematical model that depicts and represents the plan has been developed. A decent type algorithm is proposed to determine the optimal number of repeat inspections and sequence characteristics for inspection that minimizes the expected total cost. The expected total cost consists of the cost of false acceptance (cost of type II error), cost of false rejection (cost of type I error), and the cost of inspection. Empirical comparisons with the model in the literature on randomly generated problems has been conducted. The results have shown that the proposed plan performs better in terms of the expected total cost on 87 percent of the generated problems for the assumed specific parameters. The reduction in the expected total cost is up to 23.5%. [ABSTRACT FROM AUTHOR]

Setup planning is one of the crucial exercises that affects process planning, scheduling and their integration. In a reconfigurable environment where more than one machine is available to process a part, setup planning assumes an important position which involves a complex optimisation problem facing several constraints. In this research machining features are sequenced and grouped into certain setup based on tool approach direction (TAD). Therefore considering the criteria such as machine utilisation, cost and makespan, near optimal solutions are selected to satisfy the requirements of the scheduling system. We have also addressed in this paper adaptive characteristics of process plan associated with setup, i.e. a cross machine setup planning to capture the different configuration of machines. Exploiting the stress of the immune system, a well known intelligent search technique known as artificial immune system (AIS) has been applied to solve two problems associated with the research issue discussed here. The proposed AIS based approach is demonstrated to resolve an example problem. It is found that the proposed approach is successfully applicable to machines having several configurations and accommodative to different setup requirements from a scheduling system where the frequent changes in machines are witnessed. By the way of combining the setup for alternative machines with process planning, we can demonstrate clearly a true relation of integration of functionalities of process planning and scheduling. [ABSTRACT FROM PUBLISHER]

This paper develops a three-phase procedure to measure the performance of a highly value-added footwear manufacturing system taking reworking actions into account, in which the system consists of multiple production lines. We mainly address the system reliability as a performance indicator to evaluate the possibility of demand satisfaction. First, we construct the manufacturing system as a manufacturing network by graphical transformation and decomposition. Second, capability analysis is implemented to determine the input flow of each station based on the manufacturing network. Third, a simple algorithm is proposed to generate all minimal capacity vectors that stations should provide to satisfy the given demand. We evaluate the system reliability in terms of the minimal capacity vectors. A further decision making issue is discussed to decide a reliable production strategy. Whenever the system state changes, the proposed performance evaluation procedure can be implemented easily and flexibly. [ABSTRACT FROM PUBLISHER]

This paper presents a distributed scheduling procedure for a large-scale single-machine problem with precedence constraints, and identifies phenomena using large-scale distributed decision-making for a decomposable and distributed situation. The approach proposed exhibits efficient computational performance over a large-sized work load (23,000 jobs and 2,209,536 precedence constraints) in a distributed computing environment. We highlight three findings: (1) the communication burden originating from the large-scale problem can lead to performance loss while distributed agents collaborate to solve the problem, but after a threshold the computational gain by distribution offsets the loss; (2) when the problem size is sufficiently large, the real computation gain outperforms the expected gain by the number of agents (super-linear effect); and (3) when the number of precedence constraints of each agent differs, the slowest agent processing the largest number of precedence constraints restrains the computational performance (load imbalance). We believe that our research is the first distributed decision-making model that meets the requirements of distributed information, distributed decision authority, and distributed computation in a large-scale single-machine scheduling situation with precedence constraints. [ABSTRACT FROM PUBLISHER]

In this paper, the flexible flow shop sequence-dependent group scheduling problem (FFSDGS) with minimisation of makespan as the criterion (FFm | fmls, Splk | Cmax) is investigated. For the first time a mathematical model for the proposed research problem is developed. Since the problem is shown to be NP-hard, six metaheuristic algorithms based on tabu search (TS) are developed to efficiently solve the problem. The proposed metaheuristics are different to the only available metaheuristic algorithm in the literature based on TS. By applying randomised complete block design and using available test problems in the literature, the best of the proposed TS algorithms in this research is identified. The performance of the best developed metaheuristic algorithm is then compared with the existing algorithm in the literature by solving the test problems, also available in the literature, ranging in size from small, medium, to large. A comparison based on paired t-test at 95% confidence interval, shows that the best proposed algorithm in this research has a better performance than the existing algorithm in the literature with an average percentage deviation of around 1.0% for medium and large size problems. [ABSTRACT FROM AUTHOR]

Spatial scheduling pursues optimal sequencing and dynamic spatial layouts that satisfy traditional restrictions in resource capacity and due dates. In shipbuilding, block spatial scheduling can be regarded as arranging some polygonal objects within rectangular work plates in an optimal sequence. This problem is important because of its impact on shipbuilding productivity and it is a NP-hard problem due to the complex constraints. In this paper, detailed analysis and mathematical description are made on block shape and block intersection. Based on the investigations of the characteristics of block shapes, the corresponding processing techniques and typical constraints, a mathematical model for block spatial scheduling is developed. Subsequently, a block spatial scheduling greedy algorithm with the objective of minimising makespan is presented using several proposed spatial scheduling strategies. The algorithm is implemented by developing a block spatial scheduling system at a block assembly shop, through which visual scheduling results of daily block layouts and the progress charts for all blocks can be obtained. Moreover, several main spatial scheduling performance indicators are proposed for comparing the proposed greedy algorithm with the existing grid algorithm and manual methods. A case study is made with real data from a shipyard, and the result shows that better performance and higher efficiency can be achieved in a short computational time. [ABSTRACT FROM AUTHOR]

Cellular manufacturing is an effective alternative to batch-type production systems where different products are intermittently produced in small lot sizes with frequent setups, large in-process storage quantities, long production lead times, decreasing throughputs, and complex planning and control functions. An effective approach to forming manufacturing cells and introducing families of similar parts, consequently increasing production volumes and machine utilisation, is the use of similarity coefficients in conjunction with clustering procedures. In a similarity coefficients-based approach, the results of the clustering analysis depend on the minimum admissible level of similarity adopted for the generic group of clustered items. This is the so-called threshold value of group similarity. The aim of this paper is to identify effective values of the threshold value of group similarity to help practitioners and managers of manufacturing systems form machine groups and related part families. The proposed threshold values for a given similarity coefficient are based on calculation of the percentile of aggregations generated by the adopted clustering algorithm. The importance of the proposed measure of group similarity has been demonstrated by experimental analysis conducted on a large set of significant instances of the cell formation problem in the literature. This analysis can also support the best determination of this percentile-based cut value especially when the number of manufacturing cells is not known in advance. [ABSTRACT FROM AUTHOR]

Subdivision surfaces combine smooth spline surfaces and polygonal meshes together, therefore, a smooth design model and discrete machining models may be unified and subdivision surfaces may be used as a common representation for geometric design and machining. Motivated by the idea, this paper presents the study of finish machining of objects represented by subdivision surfaces with emphasis on geometric error control involved in tool-path generation. First, given a design model, chordal error is controlled during finishing model building. A chordal error-driven adaptive subdivision method is used to build finishing models with less data. Second, a surface decomposition machining strategy is used to control the cusp height error. A simple iso-slope curve tracing and surface decomposition algorithm is presented to partition the model into flat and steep regions. Contour-map tool-paths are generated in the steep regions while iso-planar tool-paths are generated in the flat regions. The gouge problem is easily handled through two-dimensional (2D) tool-path correction algorithms. The implementation results demonstrate that subdivision is capable of serving as a unified representation for both geometric modelling and machining. [ABSTRACT FROM AUTHOR]

In typical production-inventory models of deteriorating items, deterioration of the production process has not been considered. In this paper, a model is proposed in which both the produced items and the production equipment deteriorate. When the production system deteriorates, it shifts to an out-of- control state and begins to produce a proportion of defective items, necessitating corrective maintenance action. A model is formulated to integrate several realistic aspects, including item and process deterioration, varying demand and production rates, quality, inspection, and maintenance. A heuristic solution algorithm is developed to determine the production and inspection schedules and a numerical example is solved. [ABSTRACT FROM AUTHOR]

This research presents a rule-based Anti-lock Braking System (ABS) algorithm towards active vehicle safety in Heavy Commercial Road Vehicles (HCRVs). Wheel Slip Regulation (WSR) algorithms, that are constituents of an ABS, are typically either model-based or rule-based. Model-Based Algorithms (MBAs) utilise mathematical models that characterise vehicle dynamics and are intensive in their demand for real-time information. On the other hand, Rule-Based Algorithms (RBAs) operate on set rules with pre-defined thresholds and utilise data from sensors that are typically available on the vehicle. A great deal of advancement has been reported in literature related to MBAs. However, most commercially available RBAs are proprietary in nature and the finer details are seldom revealed. Hence, this work proposes an RBA, which is a combined Slip and Wheel Acceleration Threshold Algorithm (SWATA), including a framework for identifying the importance of thresholds and their magnitudes. SWATA was tested on a Hardware-in-Loop (HiL) setup across varying road and loading conditions, and it provided a maximum of 36% braking distance improvement compared to a case where it was inactive. An adaptive version – ASWATA – is also proposed that can adapt the thresholds according to the particular tyre-road interface. Additionally, a quantitative comparison of the proposed RBA with a Sliding Mode Control (SMC) based MBA, which was developed and tested on the same HiL setup, is presented. It was observed that the performance of the RBA was on par with that of the MBA for most test cases, but with minimal data requirements. [ABSTRACT FROM AUTHOR]