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One of the most common systems in non-automated warehouses, is drive-in pallet racking with a shared storage policy (which is usually based on the duration-of-stay). Such scheme targets towards an efficient use of storage space, since its operation costs are directly related to the size and layout of the warehouse. In this paper, two mathematical programming models and two greedy-randomised based heuristics for finding (nearly) optimal storage and retrieval operation sequences for this type of storage system are proposed. The computational effectiveness of the proposed approaches is measured by considering two sets of synthetic instances. The obtained results show that the proposed heuristics are not only able to compute high-quality solutions (as observed when being compared with the optimal solutions attained by the mathematical programming models), but it is also capable of providing solutions in very short running times even for large instances for which the mathematical programming model failed to find feasible solutions. At the light of these results, the best heuristic is also tested using a rolling-horizon planning strategy in a real-world case study, obtained from a Chilean company. It turns out that the attained results are more effective than the company's current storage policy. [ABSTRACT FROM AUTHOR]

This paper presents a production-planning model for a manufacturing process that undergoes a ramp-up period with learning in production and growth in demand. The labour production and demand functions assumed in this paper are validated using available empirical data. A mathematical programming model is developed with numerical examples presented. The results of the paper indicate that the total costs of production can be minimised if the facility produces without interruption during the ramp-up phase and if the production and demand rates are synchronised as much as possible. The latter can be achieved by producing with the lowest possible production rate and by frequently re-structuring the workforce assigned to the production line. [ABSTRACT FROM PUBLISHER]

In this paper, we address an instance of the dynamic capacitated multi-item lot-sizing problem (CMILSP) typically encountered in steel rolling mills. Production planning is carried out at the master production schedule level, where the various end items lot sizes are determined such that the total cost is minimised. Through incorporating the various technological constraints associated with the manufacturing process, the integrated production-inventory problem is formulated as a mixed integer bilinear program (MIBLP). Typically, such class of mathematical models is solved via linearisation techniques which transform the model to an equivalent MILP (mixed integer linear program) at the expense of increased model dimensionality. This paper presents an alternative branch-and-bound based algorithm that exploits the special structure of the mathematical model to minimise the number of branches and obtain the bound at each node. The performance of our algorithm is benchmarked against that of a classical linearisation technique for several problem instances and the obtained results are reported. [ABSTRACT FROM AUTHOR]

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

This paper studies a periodic review inventory model with random supply capacity and demand, where the retailer is loss-averse. For the single-period problem, it is shown that the retailer will not order unless the initial inventory level is less than a critical value, and the order-up-to level is generally not a constant. Moreover, the critical value and optimal order quantity are only dependent on the random demand and independent on the random capacity. We also investigate the impacts of loss aversion, price and cost on the optimal order quantity and maximum expected utility. Then a dynamic programming approach is used to analyse the retailer’s ordering policy in the multi-period case. In each period, if the initial inventory level is above a given value, an order will not be placed. Otherwise, the upper bound on the optimal order quantity is given. The numerical experiments are conducted to illustrate the optimal ordering policies. [ABSTRACT FROM AUTHOR]

Simulation optimisation has gained a great attention due to its success in the design of complex manufacturing systems. In this paper, we look at manufacturing as a special class of queueing systems and propose the Discrete Event Optimisation (DEO) methodology, which provides a formal way to develop integrated mathematical models for the simultaneous simulation and optimisation. In the case, the obtained model is a mixed integer linear programming model; the methodology provides a formal way to generate approximations of them. The analytical properties of DEO models are analysed for the first time in the framework of sample path optimisation and mathematical programming. The methodology represents a reference for the use of mathematical programming as a way to model simulation optimisation for queueing systems. The applicability of the DEO methodology to complex problems is showed using the task and buffer allocation problem in a production line. [ABSTRACT FROM AUTHOR]

The aim of this paper is to develop a mathematical model for a green product mix decision that incorporates capacity expansion features by using a mathematical programming approach. In order to satisfy customer orders when market demands exceed the company's production capacity, as well as maximise total profits, companies must study the feasibility of expanding capacity with regard to the production of parts. To place efficient controls on green product costs, we present the green product mix decision model for evaluating the benefits of expanding various types of capacity. By applying this model, companies that produce green products can make optimal decisions about further processing and capacity expansion. [ABSTRACT FROM AUTHOR]

The problem of designing and balancing assembly lines has been widely studied in the literature. A recently introduced issue is the efficient use of constrained resources with specific assumptions, in which a task needs a resource type (A) or one of two resources (A ∨ B). This paper presents a more general resource-constrained case, in which each task needs resources that may be simple or multiple, alternative and/or concurrent: for instance, (3A), (A ∧ 4B ∧ 3C), (3A ∨ 2B ∨ C), (A ∧ B) ∨ (2C ∧ D) or (A ∨ B) ∧ (2C ∨ D). We also introduce an upper bound on the number of available resources. Finally, we present a computational experiment using the mathematical models that we develop, showing the instances that can be efficiently solved. [ABSTRACT FROM AUTHOR]

The paper presents a new mathematical programming model for optimizing process parameters and subdivisions of depth of cut in multipass turning operations. The model is a direct non-linear mathematical model that solves the optimization problem of multipass turning operations providing all decision variables (cutting speed, federate, depth cut, subdivision of depth of cut, number of passes) for both finishing and rough cutting in a single run. It relaxes restrictive assumptions related to depth of cut and other process parameters. In addition, the developed model eliminates the complexities encountered in existing methods. Illustrative examples from the literature are presented to test and compare the developed model against existing methods. [ABSTRACT FROM AUTHOR]

Multi-manned assembly lines are often designed to produce big-sized products, such as automobiles and trucks. In this type of production lines, there are multi-manned workstations where a group of workers simultaneously performs different operations on the same individual product. One of the problems, that managers of such production lines usually encounter, is to produce the optimal number of items using a fixed number of workstations, without adding new ones. In this paper, such a class of problems, namely, the multi-manned assembly line balancing problem is addressed, with the objective of minimising the cycle time.Amixed-integer mathematical programming formulation is proposed for the considered problem. This model has the primary objective of minimising the cycle time for a given number of workstations and the secondary objective of minimising the total number of workers. Since the addressed problem is NP-hard, two meta-heuristic approaches based on the simulated annealing algorithm have been developed: ISA and DSA. ISA solves the problem indirectly while DSA solves it directly. The performance of the two algorithms are tested and compared on a set of test problems taken from the literature. The results show that DSA outperforms ISA in term of solution quality and computational time. [ABSTRACT FROM AUTHOR]

To enhance the overall performance of supply chains, coordination among production and distribution stages has recently received an increasing interest. This paper considers the coordinated scheduling of production and transportation in a two-stage assembly flowshop environment. In this problem, product components are first produced and assembled in a two-stage assembly flowshop, and then completed final products are delivered to a customer in batches. Considering the NP-hard nature of this scheduling problem, two fast heuristics (SPT-based heuristic and LPT-based heuristic) and a new hybrid meta-heuristic (HGA-OVNS) are presented to minimise the weighted sum of average arrival time at the customer and total delivery cost. To guide the search process to more promising areas, the proposed HGA-OVNS integrates genetic algorithm with variable neighbourhood search (VNS) to generate the offspring individuals. Furthermore, to enhance the effectiveness of VNS, the opposition-based learning (OBL) is applied to establish some novel opposite neighbourhood structures. The proposed algorithms are validated on a set of randomly generated instances, and the computation results indicate the superiority of HGA-OVNS in quality of solutions. [ABSTRACT FROM PUBLISHER]

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]

The flow shop scheduling problem with blocking has important applications in a variety of industrial systems but is under-represented in the research literature. In this paper, a modified fruit fly optimisation (MFFO) algorithm is proposed to solve the above scheduling problem for makespan minimisation. The MFFO algorithm mainly contains three key operators. One is related to the initialisation scheme in which a problem-specific heuristic is adopted to generate an initial fruit fly swarm location with high quality. The second is concerned with the smell-based search in which a neighbourhood strategy is designed to generate a new location. To further enhance the exploitation of the proposed algorithm considered, a speed-up insert-neighbourhood-based local search is applied with a probability. Finally, the last is for the vision-based search in which an update criterion is proposed to induce the fruit fly into a better searching space. The simulation experimental results demonstrated the efficiency of the proposed algorithm, in spite of its simple structure, in comparison with a state-of-the-art algorithm. Moreover, new best solutions for Taillard’s instances are reported for this problem, which can be used as a basis of comparison in future studies. [ABSTRACT FROM PUBLISHER]

Biogeography-based optimisation (BBO) algorithm is a new evolutionary optimisation algorithm based on geographic distribution of biological organisms. With probabilistic operators, this algorithm is able to share more information from good solutions to poor ones. BBO prevents the good solutions to be demolished during the evolution. This feature leads to find the better solutions in a short time rather than other metaheuristics. This paper provides a mathematical model which integrates machine loading, part routing, sequencing and scheduling decision in flexible manufacturing systems (FMS). Moreover, it tackles the scheduling problem when various constraints are imposed on the system. Since this problem is considered to be NP-hard, BBO algorithm is developed to find the optimum /near optimum solution based on various constraints. In the proposed algorithm, different types of mutation operators are employed to enhance the diversity among the population. The proposed BBO has been applied to the instances with different size and degrees of complexity of problem adopted from the FMS literature. The experimental results demonstrate the effectiveness of the proposed algorithm to find optimum /near optimum solutions within reasonable time. Therefore, BBO algorithm can be used as a useful solution for optimisation in various industrial applications within a reasonable computation time. [ABSTRACT FROM AUTHOR]

This paper introduces two-dimensional loading time-dependent vehicle routing problem and proposes a bi-objective mathematical model. This problem assesses the process of distributing the rectangular-shaped demanded items over an urban environment; it does not, however, allow items to be loaded on top of each other. In addition to the above assumptions, the presented model also satisfies the first-in-first-out property in the time-dependent vehicle routing problem. Given the NP-hard nature of the problem, a method called elitist non-dominated sorting local search is developed to obtain its solutions. To evaluate the performance of the proposed algorithm, the solutions of this algorithm for small-scale problem instances are compared with the results of an exact method. For the medium-scale problem instances, results of NSGA-II and SPEA2 are used as the basis of comparison. The computational results demonstrate the good performance of the proposed method. [ABSTRACT FROM AUTHOR]

With the wide application of location theory in a variety of industries, the presence of barriers ‎merits the attention of managers and engineers.‎ In this paper, we assess the Weber location problem in the presence of a polyhedral barrier which probabilistically occurs on a given horizontal barrier route in the rectilinear space. A left triangular distribution function is used for the starting point of the barrier and therefore an expected rectilinear barrier distance function is formulated. In addition, a modification of the polyhedral barrier is presented which is equivalent to the original problem. Therefore, a mixed integer nonlinear programming model, which has a nonconvex solution space, is presented. Furthermore, by decomposing the feasible space into a finite number of convex solution spaces, an exact heuristic solution method is proposed. Then, a lower bound problem based on the forbidden region is applied. Some theorems and an example are reported. [ABSTRACT FROM PUBLISHER]

In recent years sampling approaches have been used more widely than optimization algorithms to find parameters of conceptual rainfall–runoff models, but the difficulty of calibration of such models remains in dispute. The problem of finding a set of optimal parameters for conceptual rainfall–runoff models is interpreted differently in various studies, ranging from simple to relatively complex and difficult. In many papers, it is claimed that novel calibration approaches, so-called metaheuristics, outperform the older ones when applied to this task, but contradictory opinions are also plentiful. The present study aims at calibration of two simple lumped conceptual hydrological models, HBV and GR4J, by means of a large number of metaheuristic algorithms. The tests are performed on four catchments located in regions with relatively similar climatic conditions, but on different continents. The comparison shows that, although parameters found may somehow differ, the performance criteria achieved with simple lumped models calibrated by various metaheuristics are very similar and differences are insignificant from the hydrological point of view. However, occasionally some algorithms find slightly better solutions than those found by the vast majority of methods. This means that the problem of calibration of simple lumped HBV or GR4J models may be deceptive from the optimization perspective, as the vast majority of algorithms that follow a common evolutionary principle of survival of the fittest lead to sub-optimal solutions. [ABSTRACT FROM PUBLISHER]

Currently, the analytic network process (ANP) method is widely employed to consider the multiple criteria analysis problems with dependence and feedback effects. However, in order to extend the ANP to resolve the problem of uncertainty or human subjective judgment, the concepts of fuzzy numbers should be incorporated into the ANP to represent the subjective uncertain pairwise judgments. In this paper, therefore, we propose a novel fuzzy analytic network process (FANP) model by solving a mathematical programming problem. Unlike other FANPs, the proposed method does not require the reciprocity assumption of the weight ratios between criteria, and it can derive local and global weights simultaneously in a single model. Two numerical examples of international investment problems are used to demonstrate the proposed method. [ABSTRACT FROM AUTHOR]

The constrained two-dimensional cutting problem involves maximising the sum of the profits obtained from small rectangular pieces cut from a large rectangular plate where the number of each type of cut piece cannot exceed a prescribed quantity. This paper proposes a best-first branch-and-bound algorithm to find the optimal solution to the problem. The proposed algorithm uses an efficient method to remove the duplicate patterns, and it improves the existing upper bounds. It also prevents the construction of dominated patterns and introduces a new bounding strategy that can prune more than one node at a time. Computational results are compared with a well-known exact algorithm to demonstrate the efficiency of the proposed algorithm. The proposed algorithm is as fast as or faster than the existing algorithm and reduces the average computing time by up to 99% for benchmark problems. For some problems, it can also find optimal solutions that existing algorithms are not able to find. [ABSTRACT FROM PUBLISHER]

This research considers a stochastic lot-sizing problem with multi-supplier and quantity discounts. The objectives are to minimise total costs, where the costs include ordering cost, holding cost, purchase cost and shortage cost, and to maximise service level of the system. In this paper, we first formulate the stochastic lot-sizing problem as a multi-objective programming (MOP) model. We then transform the model into a mixed integer programming (MIP) model. Finally, an efficient heuristic dynamic programming (HDP) model is constructed for solving large-scale stochastic lot-sizing problems. An illustrative example with two cases for a touch panel manufacturer is used to illustrate the practicality of these models, and a sensitivity analysis is applied to understand the impact of the changes in parameters to the outcomes. The results demonstrate that the proposed two models are effective and accurate tools for determining the replenishment of touch panels from multiple suppliers for multi-periods. [ABSTRACT FROM PUBLISHER]

In this paper, we build on and extend Gartner and Kolisch (2014)'s hospital-wide patient scheduling problem. Their contribution margin maximising model decides on the patients' discharge date and therefore the length of stay. Decisions such as the allocation of scarce hospital resources along the clinical pathways are taken. Our extensions which are modeled as a mathematical program include admission decisions and flexible patient-to-specialty assignments to account for multi-morbid patients. Another flexibility extension is that one out of multiple surgical teams can be assigned to each patient. Furthermore, we consider overtime availability of human resources such as residents and nurses. Finally, we include these extensions in the rolling-horizon approach and account for lognormal distributed recovery times and remaining resource capacity for elective patients. Our computational study on real-world instances reveals that, if overtime flexibility is allowed, up to 5% increase in contribution margin can be achieved by reducing length of stay by up to 30%. At the same time, allowing for overtime can reduce waiting times by up to 33%. Our model can be applied in and generalised towards other patient scheduling problems, for example in cancer care where patients may follow defined cancer pathways. [ABSTRACT FROM AUTHOR]

Cut-off grade classifies the available supply of ore (valuable) and waste material within a mineralised deposit. Given the mining, processing and refining limitations of a mining operation, an optimal cut-off grade policy ensures that the flow of ore from the mine to the processing and refining facilities is maintained at the maximum possible throughput. This policy defines a schedule of cut-off grades along with corresponding quantities of mineralised material to be mined, processed and metal refined in each period of the scheduling horizon. The criteria that controls the development of cut-off grade policy aligns with the strategic objectives of an operation in order to maximise the discounted value (net present value or NPV) over the life of operation. This paper proposes a new mixed integer linear programming (MILP) based model that maximises NPV subject to the mining, processing, refining capacity constraints and develops an optimal cut-off grade policy for an open pit mining operation with multiple processing streams. An implementation of the proposed method on hypothetical and realistic data promises a relatively higher NPV as compared to the traditional Lane's approach practiced in the mining industry. [ABSTRACT FROM AUTHOR]

This paper studies the single-hoist cyclic scheduling problem in electroplating systems with two extended features. One extension is that the products must visit some processing tanks more than once (multi-function tanks). Another is that more than one identical tank is used at some stages. These extensions are common in practical electroplating lines and can increase the lines' processing capacity. However, they make the hoist scheduling problem more complicated and little research has been done to optimize the hoist moves in such extended practical systems. In this paper, we develop a comprehensive mixed integer linear programming model to find optimal solutions to the single-hoist cyclic scheduling problem for electroplating lines with these extensions. Examples are given to demonstrate the effectiveness of the model in different types of problems. [ABSTRACT FROM AUTHOR]

Material requirements planning (MRP) is a basic tool for performing detailed material planning function in the manufacture of component parts and their assembly into finished items. MRP's managerial objective is to provide 'the right part at the right time' to meet the schedules for completed products. However satisfying end customer demands faster with lower inventories implies smarter scheduling which must simultaneously reflect actual capacity conditions. Therefore, the need is to schedule both capacity and materials simultaneously. Since MRP does not consider the availability of capacity resources to schedule production, consequently the schedules so developed are usually capacity infeasible. This paper proposes a three-step procedure to develop capacity feasible material and production schedules in a finite capacity environment. In the first step, an LP model produces capacity feasible but lot size relaxed planned order releases for all end products and assembly components which are then fed into a MRP processor, where a bill of material (BOM) explosion process generates material plans. Finally, these material plans are introduced to another LP model which assures that capacity feasibility is again restored. The mathematical models developed consider restrictions on lot sizes as well as alternative production routings and overtime decisions. A numerical example also is provided and some future research directions are outlined. [ABSTRACT FROM AUTHOR]

The analysis of the principal-agent problem usually requires the classical first-order approach (FOA). However, the validity of the FOA makes restrictive assumptions on the problem under consideration such as the convexity of the distribution function condition. The main aim of this paper is to compute the optimal wages and characterize a closed form solution to the risk-neutral principal-agent problem with limited liability constraints. The development in this paper mainly invokes certain techniques in the semi-infinite programming rather than the FOA. [ABSTRACT FROM AUTHOR]

The objective of assembly line balancing (ALB) is the allocation of tasks to workstations of continuous production lines in order to minimise operating cost or other cycle time-related management criteria. As a consequence of the development of computer technology, mathematical programming models of practical-sized ALB problems can be solved by commercial optimisation software very efficiently. Therefore, the focus of research in ALB can be shifted to practice-driven model formulation and to the investigation of new areas of application. The objective of this paper is to show how cycle-time related operations management decisions can be supported with quick solution of ALB models through the case of a bicycle assembly process. Practical ALB problems related to workforce skill and to production quantity changes are presented and management implications of the results are explained. [ABSTRACT FROM AUTHOR]

Water inrush from coal floor is one of the main disasters in underground coal mining operations. When establishing or selecting control methods, various factors, such as cost, risk and operability, should be taken into consideration. However, due to the lack of effective mathematical models, the selecting process in practice relies solely on engineering experiences, which may not lead to an optimal and effective decision. This paper proposes a method that considers the parameters, variables, and constraints in water inrush control and it adopts economic factors as the objective function to construct a multi-objective optimization model. Using this method, one can not only rank different control measures but also obtain a preferred control effect level, which is a trade-off between efficiency and economy. A case study is used to demonstrate the robustness of the proposed approach. [ABSTRACT FROM AUTHOR]

A cellular manufacturing system (CMS) allocates machines and parts to the cells in order to increase the system performance. Many valuable studies have been conducted in this area to examine various aspects of a CMS and introduce mathematical models for simulating real aspects of manufacturing with this system. The models assume that a factory is monopolistic without any competitor and their behaviour in the real market. Competition has influence on both demand and price, and these have bilateral relations with the production cost. Therefore, the behaviour of competitors to maximise their profits in the market influences the CMS parameters. This paper presents a mathematical programming model incorporated with cell formation and cell layout with the alternative process routing under duopoly Bertrand competition as a game. It is solved by a well-known meta-heuristic algorithm, namely particle swarm optimisation. Additionally, Monte Carlo simulation is used to estimate the production cost of a competitor. Finally, a number of numerical examples are used to demonstrate the presented model. [ABSTRACT FROM PUBLISHER]

In this paper we propose new mathematical models for percutaneous absorption of a drug. The new models are established by introducing, in the classical Fick's law, a memory term being the advection-diffusion equations of the classical models replaced by integro-differential equations. The well-posedness of the models is studied with Dirichlet, Neumann and natural boundary conditions. Methods for the computation of numerical solutions are proposed. Stability and convergence of the introduced methods are studied. Finally, numerical simulations illustrating the behaviour of the model are included. [ABSTRACT FROM AUTHOR]

In this paper, we propose a productivity model for solving the machine-part grouping problem in cellular manufacturing (CM) systems. First, a non-linear 0-1 integer programming model is developed to identify machine groups and part families simultaneously. This model aims to maximize the system productivity defined as the ratio of total output to the total material handling cost. Second, an efficient simulated annealing (SA) algorithm is developed to solve large-scale problems. This algorithm provides several advantages over the existing algorithms. It forms part families and machine cells simultaneously. It also considers production volume, sales price, and maximum number of machines in each cell and total material handling cost. The proposed SA also has the ability to determine the optimum number of manufacturing cells. The performance of the developed models is tested on eight problems of different size and complexity selected from the literature. The results show the superiority of the SA algorithm over the mathematical programming model in both productivity and computational time. [ABSTRACT FROM AUTHOR]

Purchasing managers face a complex problem when determining order quantities for multiple items in the presence of quantity discounts and resource limitations. In this paper, the problem is formulated as a mathematical programming model. An efficient solution algorithm is developed utilizing the Lagrangian relaxation approach. Extensive computational experiments are performed and the results are presented. [ABSTRACT FROM AUTHOR]

Copyright of INFOR is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of INFOR is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper offers a credibility-constrained programming model for reliable design of an integrated forward–reverse logistics network with hybrid facilities under uncertainty and random facility disruptions. To tackle with this problem, a novel mathematical model is first developed that integrates the network design decisions in both forward and reverse flows and utilises reliability concepts to deal with facility disruptions. Then, the developed model is enhanced based on the credibility-constrained programing to cope with the epistemic uncertainties embedded in the model parameters. Since the hybrid distribution-collection facilities play an important role in both forward and reverse flows, it is supposed that they might be randomly disrupted. Several effective reliability strategies are considered to hedge against random facility disruptions. First, locating two types of hybrid facilities, namely, reliable and unreliable, is taken into account in the concerned logistics network when disruptions strike. Second, unreliable hybrid facilities are allowed to be partially disrupted, and thus a percentage of their capacities may be lost. However, they can still serve their customers with their remaining capacities. To compensate the lost capacity at unreliable hybrid facilities, a sharing strategy is also considered, in which goods can be shipped from reliable hybrid facilities to unreliable ones. Finally, several numerical experiments along with a sensitivity analysis are conducted to illustrate the significance and applicability of the developed model as well as the effectiveness of the credibility-based solution approach. [ABSTRACT FROM AUTHOR]

This paper considers the NP-hard problem of scheduling printed circuit packs (PCPs) on sequencers to minimize the total number of changeovers of input tapes required to produce PCPs. A mathematical model for this problem is given. However, when the size of the problem increases, the model will not be computationally feasible. An effective and fast heuristic to solve this problem is presented. The heuristic has shown its effectiveness over two previous heuristics found in the literature. It has been tested on a problem with 45 PCPs, two sequencers, each with seven dispensing heads, and a total of 20 input tape types. Comparison results showed that the heuristic reduced the total number of changeovers by 34% and 23% over previous heuristics. In addition, the proposed heuristic provided balanced workload across available sequencers. To investigate the efficiency of our heuristic further, we tested it against data sets from the literature. The proposed heuristic performs reasonably well compared with the reported results. In printed circuit packs' environment, set-up reduction is a main concern. Set-up is composed of the time required to changeover input tapes on sequencers when switching from PCP type to another. The proposed heuristic is more effective in reducing the number of changeovers of input tapes than heuristics found in the literature. The same heuristic can be used to schedule jobs on CNC machines so that total amount of tool switching is minimized. [ABSTRACT FROM AUTHOR]

Fuzzy logic programming tries to introduce fuzzy logic into logic programming in order to provide new generation computer languages which incorporate comfortable programming resources for helping the development of applications where uncertainty could play an important role. In this sense, the mathematical concept of multi-adjoint lattice has been successfully exploited into the so-called Multi-Adjoint Logic Programming approach, MALP in brief, for modelling flexible notions of truth-degrees beyond the simpler case of true and false. In this paper, we focus on two relevant mathematical concepts for this kind of domains useful for evaluating MALP. On the one side, we adapt the classical notion of Dedekind–MacNeille completion in order to relax some usual hypothesis on such kind of ordered sets, and next we study the advantages of generating multi-adjoint lattices as the Cartesian product of previous ones. On the practical side, we show that the formal mechanisms described before have direct correspondences with interesting debugging tasks into the ‘Fuzzy Logic Programming Environment for Research’, FLOPER in brief, developed in our research group. [ABSTRACT FROM PUBLISHER]

In this paper, the approximate analytical solutions of the mathematical model of vibration equation with fractional-order time derivative β (1<β≤2) for very large membranes are obtained with the help of powerful mathematical tools like homotopy perturbation method and homotopy analysis method. Both the methods perform extremely well in terms of efficiency and simplicity. The validity and applicability of the techniques are shown for obtaining approximate numerical solutions for different particular cases which are presented through figures and tables. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Intersection Graph Method for solving the AGV Flow Path Optimization Model developed by Kaspi and Tanchoco (1990). A branch-and-bound procedure is described wherein only a reduced subset of all nodes in the flow path network is considered. Only intersection nodes are used to obtain optimal solutions. Two examples are given to illustrate the proposed method. [ABSTRACT FROM AUTHOR]

Consider scheduling n independent, single operation jobs, all available at time zero, on m identical processors. Further, assume that each job must be processed by exactly one of these processors, and it is desired to minimize makespan. This paper reports a computationally efficient procedure for treating this problem, which utilizes human ingenuity to improve on a traditional solution procedure. Computational experience with the procedure indicates that good solutions to large problems are easily obtained; in fact out of 240 test problems, the optimal solution was found in every case. The entire procedure is simple enough for a shop foreman to be able to solve large problems by hand. [ABSTRACT FROM AUTHOR]

This paper proposes a new memetic evolutionary algorithm to achieve explicit learning in rule-based nurse rostering, which involves applying a set of heuristic rules for each nurse's assignment. The main framework of the algorithm is an estimation of distribution algorithm, in which an ant-miner methodology improves the individual solutions produced in each generation. Unlike our previous work (where learning is implicit), the learning in the memetic estimation of distribution algorithm is explicit, that is, we are able to identify building blocks directly. The overall approach learns by building a probabilistic model, that is, an estimation of the probability distribution of individual nurse-rule pairs that are used to construct schedules. The local search processor (ie the ant-miner) reinforces nurse-rule pairs that receive higher rewards, A challenging real-world nurse rostering problem is used as the test problem. Computational results show that the proposed approach outperforms most existing approaches. It is suggested that the learning methodologies suggested in this paper may be applied to other scheduling problems where schedules are built systematically according to specific rules. [ABSTRACT FROM AUTHOR]

Nonlinear systems of equations often represent mathematical models of chemical production processes and other engineering problems. Homotopic techniques (in particular, the bounded homotopies introduced by Paloschi) are used for enhancing convergence to solutions, especially when a good initial estimate is not available. In this paper, the homotopy curve is considered as the feasible set of a mathematical programming problem, where the objective is to find the optimal value of the homotopic parameter. Inexact restoration techniques can then be used to generate approximations in a neighborhood of the homotopy, the size of which is theoretically justified. Numerical examples are given. [ABSTRACT FROM AUTHOR]

The purpose of this note is to describe a classroom activity that can be used in introductory Operations Research courses. It consists of modelling a one-dimensional puzzle using mathematical programming to produce glider models. The exercise is designed to motivate the students in solving a problem with the opportunity of producing by themselves a real product. The activity can be performed in pairs where each student will have a glider model if an optimal solution is found. [ABSTRACT FROM AUTHOR]

Multiple Criteria Quadratic Programming (MCQP), a mathematical programming-based classification method, has been developed recently and proved to be effective and scalable. However, its performance degraded when learning from imbalanced data. This paper proposes a cost-sensitive MCQP (CS-MCQP) model by introducing the cost of misclassifications to the MCQP model. The empirical tests were designed to compare the proposed model with MCQP and a selection of classifiers on 26 imbalanced datasets from the UCI repositories. The results indicate that the CS-MCQP model not only performs better than the optimization-based models (MCQP and SVM), but also outperforms the selected classifiers, ensemble, preprocessing techniques and hybrid methods on imbalanced datasets in terms of AUC and GeoMean measures. To validate the results statistically, Student’sttest and Wilcoxon signed-rank test were conducted and show that the superiority of CS-MCQP is statistically significant with significance level 0.05. In addition, we analyze the effect of noisy, small disjunct and overlapping data properties on the proposed model and conclude that the CS-MCQP model achieves better performance on imbalanced data with overlapping feature than noisy and small disjunct data. [ABSTRACT FROM PUBLISHER]

Building on the work of Noam Chomsky (1963), this paper presents a hierarchy of grammars and associated computational automata in order to inform social theory construction and method. A detailed exposition of linguistic forms within the grammar hierarchy reveals clear analogues with common social scientific paradigms. Two of these paradigms (which are termed structural and process approaches) are already being widely exploited by formal methodological techniques. A third paradigm, which is rooted in a tradition of interpretive sociology, has been more resistant to formalization. Using arguments from theoretical computer science, the paper suggests that existing quantitative methodologies can be extended to accommodate qualitative arguments which subsume empirical domains as diverse as natural language and structurational phenomena. [ABSTRACT FROM AUTHOR]

The conventional approach to the modeling and solution of most scheduling problems involves the development of a mathematical model which (I) employs discrete variables (e.g., linear integer programs), and (2) includes only a single objective to be maximized or minimized (e.g., minimization of makespan). Unfortunately, models involving discrete variables are inherently combinatorially explosive (i.e., methods such as branch-and-bound will exhibit computation times which grow exponentially with problem size). Further, scheduling problems encountered in the real world invariably involve multiple conflicting objectives, and thus using a single-objective representation can lead to gross oversimplification. in this paper we address a specific class of scheduling problem encountered in several real-world applications that may be efficiently addressed as a linear multiobjective model having only continuous variables. The model and its solution are compared with those of a highly acclaimed recent approach, and they appear to provide significant improvement. [ABSTRACT FROM AUTHOR]

The Ontario Ministry of Natural Resources recently commissioned a research project aimed at developing a suitable planning method for the optimal allocation of crown land among various uses — agriculture, timber, mining, urban development, and recreation. This paper describes, in detail, the pilot project that was started in the summer of 1072. An area of 1100 square miles to the north and east of the city of Sault Ste Marie was chosen and divided into 635 equal sections. Data from this area were used to develop and validate the model. Given the "capability" (i.e., the inherent capacity to produce output) of each "section" or "parcel" of land, the concentration of population, the communication patterns, prices for products, transportation costs, economies of scale, etc., the model develops an "optimal" allocation of the land "parcels" to various uses, by maximizing the "net benefit." The model involved the solution of a very large mathematical programming problem with a matrix size of 820 rows x 72,000 columns including 71 zero-one integer variables. This was successfully done using the UMPIRE mathematical programming code. [ABSTRACT FROM AUTHOR]

This paper is written with three objectives in mind. First, to point out that the problem of grouping, where a larger number of elements n are combined into m mutually exclusive groups (m ~n) should be recognized as a problem in Integer Programming, and that such recognition can help us in avoiding complete enumeration of stages in grouping from n to n - 1 ... to m, and of alternative possibilities in each stage. Second, to formulate mathematically some simple versions of the relevant Integer Programming Problem, so that the available computer codes can solve it. When the grouping attempts to minimize the within groups sums of squares, the so-called string property is proved to be necessary for the minimum (Lemma 1). It is shown that the string property can be exploited to write the non-linear within group sums of squares as a linear function (Lemma 2). An attempt is made to generalize the string property for the higher dimensional case. Third, to give some numerical examples to clarify the mathematical models and to illustrate the advantages of Integer Programming formulation by comparing with the numerical examples appearing in the literature [8], [13 ]. Zero-one programming where the integer variables can take values of zero or one only appears to be most efficient from the limited experience of the author. The paper is divided into three sections which closely correspond to the three objectives noted above. [ABSTRACT FROM AUTHOR]

In a variety of discrete manufacturing environments, it is common to experience a nonlinear production rate. In particular, our interest is in the case of an increasing production rate, where learning creates efficiencies. This leads to greater output per unit time as the process continues. However, the advantages of an increasing production rate may be offset by other factors. For examples, JIT policies typically lead to smaller lot sizes, where the value of an increasing production rate is largely lost. We develop a general model that balances the impact of various competing effects. Our research focuses on determining lot sizes that satisfy demand requirements while minimising production and holding costs. We extend our prior work by developing a multi-product, multi-machine method for modelling and solving this class of production problems. The solution method is demonstrated using the production function from the PR#2 grinding process for a production plant in Carlisle, PA. The solution heuristic provides solution times that are on average only 0.22 to 0.55% above optimum as the solution parameters are varied and the ratio of heuristic solution times to optimal solution times varies from 18.16 to 14.15%. [ABSTRACT FROM AUTHOR]

This paper is primarily concerned with data envelopment analysis (DEA) of systems where negative outputs and negative inputs arise naturally. Examples of situations in which both negative inputs and negative outputs occur are given. More attention has been paid, in the literature, to the former type of problem. Most available DEA software does not solve this type of problem or copes with negative outputs and possibly negative inputs by assigning zero weights to them. A modified slacks-based measure (MSBM) model is presented, in which both negative outputs and negative inputs occur. The MSBM model overcomes the lack of translation invariance in the slacks-based measure model by drawing on the ideas from the range directional model (RDM). The MSBM model takes into account individual input and output slacks, which provides more precise evaluation of inefficient decision-making units (DMUs). It therefore, generally leads to lower efficiencies for inefficient DMUs than the RDM. [ABSTRACT FROM AUTHOR]

Preference voting and aggregation require the determination of the weights associated with different ranking places. This paper proposes three new models to assess the weights. Two of them are linear programming (LP) models which determine a common set of weights for all the candidates considered and the other is a nonlinear programming (NLP) model that determines the most favourable weights for each candidate. The proposed models are examined with two numerical examples and it is shown that the proposed models cannot only choose a winner, but also give a full ranking of all the candidates. [ABSTRACT FROM AUTHOR]