Operations Research Algorithms Research Articles

Estimating electrical distribution network length and capital investment needs from real-world topologies and land cover data

Green technologies such as solar photovoltaic systems and electric vehicles play a fundamental role in the global decarbonization effort. To enable their diffusion, electricity distribution networks need to be upgraded, which is a complex and expensive endeavor. However, utilities face budgetary constraints and seek to reduce planning uncertainty. Here, we utilize 7,527 real-world grid topologies and land cover data to develop a model for estimating conductor and capital investment needs for electrifying a specific area. Our work yields three main contributions: First, we demonstrate the important role of land cover data in power line planning. We show that, for medium and large networks, distinct methodologies are needed due to the significant impact of land cover, particularly buildings and roads. Second, we introduce a parsimonious model of power line length and identify the number of consumption points as the primary determinant of network investment costs. Third, we present a cost assessment model tailored for regulators and investors, offering valuable insights for network planning, policymaking, due diligence, and research. Our work highlights the importance of combining land cover data and operations research algorithms in distribution network planning and provides policymakers with a tool to ensure cost-efficient network expansion.

Regression Machine Learning Models for the Short-Time Prediction of Genetic Algorithm Results in a Vehicle Routing Problem

Machine learning techniques have advanced rapidly, leading to better prediction accuracy within a short computational time. Such advancement encourages various novel applications, including in the field of operations research. This study introduces a novel way to utilize regression machine learning models to predict the objectives of vehicle routing problems that are solved using a genetic algorithm. Previous studies have generally discussed how (1) operations research methods are used independently to generate optimized solutions and (2) machine learning techniques are used independently to predict values from a given dataset. Some studies have discussed the collaborations between operations research and machine learning fields as follows: (1) using machine learning techniques to generate input data for operations research problems, (2) using operations research techniques to optimize the hyper-parameters of machine learning models, and (3) using machine learning to improve the quality of operations research algorithms. This study differs from the types of collaborative studies listed above. This study focuses on the prediction of the objective of the vehicle routing problem directly given the input and output data, without optimizing the problem using operations research algorithms. This study introduces a straightforward framework that captures the input data characteristics for the vehicle routing problem. The proposed framework is applied by generating the input and output data using the genetic algorithm and then using regression machine learning models to predict the obtained objective values. The numerical experiments show that the best models are random forest regression, a generalized linear model with a Poisson distribution, and ridge regression with cross-validation.

Constrained optimization algorithms for the computation of investable portfolios analytics: evaluation of economic-capital parameters for performance measurement and improvement

PurposeThis paper aims to empirically test, from a regulatory portfolio management standpoint, the application of liquidity-adjusted risk techniques in the process of getting optimum and investable economic-capital structures in the Gulf Cooperation Council financial markets, subject to applying various operational and financial optimization restrictions under crisis outlooks.Design/methodology/approachThe author implements a robust methodology to assess regulatory economic-capital allocation in a liquidity-adjusted value at risk (LVaR) context, mostly from the standpoint of investable portfolios analytics that have long- and short-sales asset allocation or for those portfolios that contain long-only asset allocation. The optimization route is accomplished by controlling the nonlinear quadratic objective risk function with certain regulatory constraints along with LVaR-GARCH-M (1,1) procedure to forecast conditional risk parameters and expected returns for multiple asset classes.FindingsThe author’s conclusions emphasize that the attained investable economic-capital portfolios lie-off the efficient frontier, yet those long-only portfolios seem to lie near the efficient frontier than portfolios with long- and short-sales assets allocation. In effect, the newly observed market microstructures forms and derived deductions were not apparent in prior research studies (Al Janabi, 2013).Practical implicationsThe attained empirical results are quite interesting for practical portfolio optimization, within the environments of big data analytics, reinforcement machine learning, expert systems and smart financial applications. Furthermore, it is quite promising for multiple-asset portfolio management techniques, performance measurement and improvement analytics, reinforcement machine learning and operations research algorithms in financial institutions operations, above all after the consequences of the 2007–2009 financial crisis.Originality/valueWhile this paper builds on Al Janabi’s (2013) optimization algorithms and modeling techniques, it varies in the sense that it covers the outcomes of a multi-asset portfolio optimization method under severe event market scenarios and by allowing for both long-only and combinations of long-/short-sales multiple asset. The achieved empirical results, optimization parameters and efficient and investable economic-capital figures were not apparent in Al Janabi’s (2013) paper because the prior evaluation were performed under normal market circumstances and without bearing in mind the impacts of the 2007–2009 global financial crunch.

Learning-Based Branch-and-Price Algorithms for the Vehicle Routing Problem with Time Windows and Two-Dimensional Loading Constraints

A capacitated vehicle routing problem with two-dimensional loading constraints is addressed. Associated with each customer are a set of rectangular items, the total weight of the items, and a time window. Designing exact algorithms for the problem is very challenging because the problem is a combination of two NP-hard problems. An exact branch-and-price algorithm and an approximate counterpart are proposed to solve the problem. We introduce an exact dominance rule and an approximate dominance rule. To cope with the difficulty brought by the loading constraints, a new column generation mechanism boosted by a supervised learning model is proposed. Extensive experiments demonstrate the superiority of integrating the learning model in terms of CPU time and calls of the feasibility checker. Moreover, the branch-and-price algorithms are able to significantly improve the solutions of the existing instances from literature and solve instances with up to 50 customers and 103 items. Summary of Contribution: We wish to submit an original research article entitled “Learning-based branch-and-price algorithms for a vehicle routing problem with time windows and two-dimensional loading constraints” for consideration by IJOC. We confirm that this work is original and has not been published elsewhere, nor is it currently under for publication elsewhere. In this paper, we report a study in which we develop two branch-and-price algorithms with a machine learning model injected to solve a vehicle routing problem integrated the two-dimensional packing. Due to the complexity brought by the integration, studies on exact algorithms in this field are very limited. Our study is important to the field, because we develop an effective method to significantly mitigate computational burden brought by the packing problem so that exactness turns to be achievable within reasonable time budget. The approach can be generalized to the three-dimensional case by simply replacing the packing algorithm. It can also be adapted for other VRPs when high-dimensional loading constraints are concerned. Broadly speaking, the study is a typical example of adopting supervised learning to achieve acceleration for operations research algorithms, which expands the envelop of computing and operations research. Hence, we believe this manuscript is appropriate for publication by IJOC.

A machine learning approach to enable bulk orders of critical spare-parts in the shipping industry

Purpose: The main purpose of this paper is to propose a methodological approach and a decision support tool, based on prescriptive analytics, to enable bulk ordering of spare parts for shipping companies operating fleets of vessels. The developed tool utilises machine learning and operations research algorithms, to forecast and optimize bulk spare parts orders needed to cover planned maintenance requirements on an annual basis and optimize the company’s purchasing decisions.Design/methodology/approach: The proposed approach consists of three discrete methodological steps, each one supported by a decision support tool based on clustering and machine learning algorithms. In the first step, clustering is applied in order to identify high interest items. Next, a forecasting tool is developed for estimating the expected needs of the fleet and to test whether the needed quantity is influenced by the source of purchase. Finally, the selected items are cost-effectively allocated to a group of vendors. The performance of the tool is assessed by running a simulation of a bulk order process on a mixed fleet totaling 75 vessels.Findings: The overall findings and approach are quite promising Indicatively, shifting demand planning focus to critical spares, via clustering, can reduce administrative workload. Furthermore, the proposed forecasting approach results in a Mean Absolute Percentage Error of 10% for specific components, with a potential for further reduction, as data availability increases. Finally, the cost optimizer can prescribe spare part acquisition scenarios that yield a 9% overall cost reduction over the span of two years.Originality/value: By adopting the proposed approach, shipping companies have the potential to produce meaningful results ranging from soft benefits, such as the rationalization of the workload of the purchasing department and its third party collaborators to hard, quantitative benefits, such as reducing the cost of the bulk ordering process, directly affecting a company’s bottom line.

Quantifying net benefits of intervention programmes to enable their digitalised generation

Interventions on infrastructure networks are required to ensure that they continue to provide the service expected of them. Although all existing methods for determining intervention programmes take into consideration, in some form, the costs and benefits associated with the interventions, there is a wide variation in exactly how it is done. With increasing strides to digitalise the determination of optimal intervention programmes, a systematic and quantitative method for determining their net benefits is required. The novelty in the proposed method is how the provided service and intervention costs over time are considered when determining optimal intervention programmes. It considers the difference between candidate intervention programmes and a reference intervention programme. In other words, it makes clear whether network-level considerations – for example, network-level synergies and constraints – should result in the intervention on an asset being executed earlier or later than indicated by the optimal asset life cycle. The method enables the use of advanced operations research algorithms in infrastructure-management systems and, therefore, helps enable the automated generation of optimal intervention programmes. For illustration, the method is used in the determination of the optimal intervention programme for a small railway network.

Cost Control of Material Substitution in Steel Enterprise

For dealing with cost control problems of material substitution in steel production process in make-to-order steel enterprise, we established the substitution rules of material substitution and built up the cost optimized controlling model for material substitution. The objective function of this model was to minimize the total cost of both stock and task lateness. We made use of Operations Research and Genetic Algorithm to solve this model. Finally, we also conducted the application research for the model and algorithm, and its promising future was revealed.

Development and Application of the Bat Algorithm for Optimizing the Operation of Reservoir Systems

AbstractOptimal utilization of water resources by means of water transfers and reservoirs in semiarid and arid regions is used to mitigate natural water scarcity. In this context, metaheuristic algorithms for optimum reservoir system operation have become an attractive alternative to traditional operations research algorithms such as linear programming (LP), nonlinear programming (NLP), and dynamic programming (DP). This paper presents the metaheuristic bat algorithm (BA) and its application to the optimal operation of the Karoun-4 reservoir system in Iran and to a hypothetical four-reservoir system. The merits of the performance of the BA in the optimization of reservoir operation are demonstrated by comparison to those of LP, NLP, and genetic algorithm (GA) in terms of the convergence to global optima and of the variance of results about global optima for reservoir optimization problems.

Enhanced assessment of limb neuro-mechanics via a haptic display

Current state-of-the-art quantitative assessments of abnormal neuro-mechanics (e.g., spasticity, rigidity, dystonia) require sophisticated measurement systems that, together with the lengthiness of the data acquisition, make these approaches impractical for the clinical setting. Our long-term goal is to create a tool that enhances the capability of therapists to diagnose abnormal neuro-mechanics without the need of lengthy procedures and sophisticated equipment. Towards this direction, we present a haptic display of abnormal limb neuro-mechanics. This haptic display can be used as a training device for clinicians to understand the causality of the haptic sensation associated with neurological impairments and abnormal biomechanics during manipulation of a patients' limb. Other uses include the testing of operational research algorithms to maximize the exchange of haptic information, reproducing conditions of human-human interaction, and a test bed for developing novel assessment techniques and targeted interventions. Experimental results support the hypothesis that the best assessment of abnormalities can be obtained via specific manipulation strategies.

Semantic Traffic-Aware Routing Using the LarKC Platform

The popularity of location-based services and automotive navigation systems calls for a new generation of intelligent solutions to support users in mobility. This article presents a traffic-aware semantic routing service for mobile users based on the Large Knowledge Collider (LarKC) Semantic Web pluggable platform. It proposes a technique for integrating conceptual query answering with statistical learning and operations research algorithms. The presented prototype of a traffic-aware semantic routing service works efficiently with large, heterogeneous information sources and delivers value-added services to mobile users.

An new approach for the automatic analysis and control of material handling systems: integrated layout flow analysis (ILFA)

In the majority of small and medium sized enterprises (SEMs), the direct costs of material handling cannot be clearly measured. There are several reasons for this, including the large number of product types, complexity of their production cycle, and continuous change in markets. Therefore, production managers require flexible tools to create a suitable material handling system model which explicitly and rapidly calculates the indices required as these are traditionally neglected or laboriously approximated, (i.e., time and cost in material flow inside the factory, storage area requirements, and MH utilization percentage). This paper proposes an integrated approach to analyzing and controlling material handling operations in an industrial manufacturing plant from a “full quantitative” point of view. The model presented unites quite different fields of research into a unique methodology. This material handling model rapidly and automatically provides production managers with extensive and significant information. As a result, integrated layout flow analysis interrelates systematic layout planning with operational research algorithms and visual interactive simulation, using a complete software platform to implement them. This integrated layout flow analysis approach focuses on determining the space requirement for manufacturing department buffers, the transportation system requirements, the performance indices, and the time and cost of material flows spent in the layout and in MH traffic jams.

Modelling open pit dynamics using discrete simulation

The objective in any mining operation is to exploit ore at the lowest possible cost with the prospect of maximizing profits. The planning of an open pit mine is an economic exercise, constrained by certain geological, operating, technological and local field factors. Heuristic methods, economic parametric analysis, operations research and genetic algorithms have been used to formulate periodic open pit planning problems. Open pit design, optimization and subsequent materials scheduling problems are governed by stochastic dynamic process. Thus, current algorithms are limited in their abilities to address problems arising from these random and dynamic field processes. The primary objective of this study is to use a discrete stochastic simulation to capture the random field processes associated with open pit design and materials scheduling. An open pit production simulator (OPPS), implemented in MATLAB, based on a modified elliptical frustum is used to model the geometry of open pit layout expansion. The simulator mimics the periodic expansion of the open pit layouts. The interaction of the open pit expansion model with the geological and economic block model returns the respective amount of ore, waste, stockpile materials, and the net present value of the venture. A case study of an iron ore deposit with 114 000 blocks was carried out to verify and validate the model. The optimized pit limit was designed using the Lerchs – Grossman algorithm. The best-case annual schedule, generated by the shells node in Whittle Four-X, yielded a net present value (NPV) of $414 million over a 21-year mine life at a discount rate of 10% per annum. The best scenario out of 5000 simulation iterations using OPPS resulted in an NPV of $422 million over the same time span. Further research, based on hybrid stochastic simulation in conjunction with reinforcement learning, can provide a powerful tool for addressing the random field and dynamic processes in long-term open pit planning.

Applying Markowitz's Critical Line Algorithm

We provide a Matlab quadratic optimization tool based on Markowitz's critical line algorithm that significantly outperforms standard software packages and a recently developed operations research algorithm. As an illustration: For a 2000 asset universe our method needs less than a second to compute the whole frontier whereas the quickest competitor needs several hours. This paper can be considered as a didactic alternative to the critical line algorithm such as presented by Markowitz and treats all steps required by the algorithm explicitly. Finally, we present a benchmark of different optimization algorithms' performance.

Scheduling the Resident 80-Hour Work Week: An Operations Research Algorithm

The resident 80-hour work week requires that programs now schedule duty hours. Typically, scheduling is performed in an empirical "trial-and-error" fashion. However, this is a classic "scheduling" problem from the field of operations research (OR). It is similar to scheduling issues that airlines must face with pilots and planes routing through various airports at various times. The authors hypothesized that an OR approach using iterative computer algorithms could provide a rational scheduling solution. Institution-specific constraints of the residency problem were formulated. A total of 56 residents are rotating through 4 hospitals. Additional constraints were dictated by the Residency Review Committee (RRC) rules or the specific surgical service. For example, at Hospital 1, during the weekday hours between 6 am and 6 pm, there will be a PGY4 or PGY5 and a PGY2 or PGY3 on-duty to cover Service "A." A series of equations and logic statements was generated to satisfy all constraints and requirements. These were restated in the Optimization Programming Language used by the ILOG software suite for solving mixed integer programming problems. An integer programming solution was generated to this resource-constrained assignment problem. A total of 30,900 variables and 12,443 constraints were required. A total of man-hours of programming were used; computer run-time was 25.9 hours. A weekly schedule was generated for each resident that satisfied the RRC regulations while fulfilling all stated surgical service requirements. Each required between 64 and 80 weekly resident duty hours. The authors conclude that OR is a viable approach to schedule resident work hours. This technique is sufficiently robust to accommodate changes in resident numbers, service requirements, and service and hospital rotations.

Incomplete Dynamic Backtracking for Linear Pseudo-Boolean Problems

Many combinatorial problems can be modeled as 0/1 integer linear programs. Problems ex- pressed in this form are usually solved by Operations Research algorithms, but good results have also been obtained using generalised SAT algorithms based on backtracking or local search, after transformation to pseudo-Boolean form. A third class of SAT algorithm uses non-systematic backtracking to combine con- straint propagation with local search-like scalability, at the cost of completeness. This paper describes such an algorithm for pseudo-Boolean models. Experimental results on a variety of problems are encouraging, in some cases yielding improved solutions or performance compared to previous algorithms.

Design of an IT-driven decision support system for vehicle routing and scheduling

This paper presents the development of a decision support system used by an oil downstream company for routing and scheduling purposes. The studied problem refers to a complex delivery process of oil products from a number of distribution centers to all customers. The latest rapid advance of operations research (OR) applications, in the form of advanced planning and scheduling (APS) systems, has shown that OR algorithms can be applied in practice if (a) they are embodied in packaged information technology (IT) solutions, (b) the interface problems to mainstream ERP software applications are solved. In this study the utilisation of advanced IT systems supports effectively the planning and management of distribution operations. The combination of a supply chain management (SCM) application with a geographical information system (GIS) integrated with an enterprise resource planning (ERP) software resulted to this innovative decision support tool. The objectives of this new tool are: optimum use of the distribution network resources, transportation cost reduction and customer service improvement. The paper concludes with the benefits of the new system, emphasising at how new technologies can support transportation processes with the help of operations research algorithms embedded in software applications.

Using Operational Research and Advanced Informatics for Classifying: Application for Road Accidents

This paper presents a method for combining different sources of information - data file, expert knowledge, statistics, additional (possibly imprecise) information brought by the user- in order to classify an event (say a road accident) in predefined classes, which could be required before action. The interest of operational research algorithms, aiming at helping the operator is stressed. The application of the software “CRlQUE” on 35 road accidents is assessed.

Embedding Relaxations in Global Constraints for Solving TSP and TSPTW

Constraint Programming (CP) has been successfully applied to several combinatorial optimization problems. One of its advantages is the availability of complex global constraints performing efficient propagation and interacting with each other through shared variables. However, CP techniques have shown their limitations in dealing with optimization problems since the link between the objective function and problem decision variables is often quite loose and does not produce an effective propagation. We propose to integrate optimization components in global constraints, aimed at optimally solving a relaxation corresponding to the constraint itself. The optimal solution of the relaxation provides pieces of information which can be exploited in order to perform pruning on the basis of cost-based reasoning. In fact, we exploit reduction rules based on lower bound and reduced costs calculation to remove those branches which cannot improve the best solution found so far. The interest of integrating efficient well-known Operations Research (OR) algorithms into CP is mainly due to the smooth interaction between CP domain reduction and information provided by the relaxation acting on variable domains which can be seen as a i>communication channel among different techniques. We have applied this technique to symmetric and asymmetric Traveling Salesman Problem (TSP) instances both because the TSP is an interesting problem arising in many real-life applications, and because pure CP techniques lead to disappointing results for this problem. We have tested the proposed optimization constraints using ILOG solver. Computational results on benchmarks available from literature, and comparison with related approaches are described in the paper. The proposed method on pure TSPs improves the performances of CP solvers, but is still far from the OR state of the art techniques for solving the problem. However, due to the flexibility of the CP framework, we could easily use the same technique on TSP with Time Windows, a time constrained variant of the TSP. For this type of problem, we achieve results that are comparable with state of the art OR results.

Optimization-Oriented Global Constraints

In this paper, we propose a way of exploiting Operations Research techniques within global constraints for cost-based domain filtering. In Constraint Programming, constraint propagation is aimed at removing from variable domains combinations of values which are proven infeasible. Pruning derives from i>feasibility reasoning. When coping with optimization problems, pruning can be performed also on the basis of costs, i.e., i>optimality reasoning. Cost-based filtering removes combination of values which are proven sub-optimal. For this purpose, we encapsulate in global constraints optimization components representing suitable relaxations of the constraint itself. These components embed efficient Operations Research algorithms computing the optimal solution of the relaxed problem and a gradient function representing the estimated cost of each variable-value assignment. We exploit these pieces of information for pruning and for guiding the search. We have applied these techniques to a couple of ILOG Solver global constraints (a constraint of difference and a path constraint) and tested the approach on a variety of combinatorial optimization problems such as Timetabling, Travelling Salesman Problems and Scheduling Problems with sequence dependent setup times. Comparisons with pure Constraint Programming approaches and related literature clearly show the benefits of the proposed approach.

The traveling salesman problem: a hierarchical model.

Our review of prior literature on spatial information processing in perception, attention, and memory indicates that these cognitive functions involve similar mechanisms based on a hierarchical architecture. The present study extends the application of hierarchical models to the area of problem solving. First, we report results of an experiment in which human subjects were tested on a Euclidean traveling salesman problem (TSP) with 6 to 30 cities. The subject's solutions were either optimal or near-optimal in length and were produced in a time that was, on average, a linear function of the number of cities. Next, the performance of the subjects is compared with that of five representative artificial intelligence and operations research algorithms, that produce approximate solutions for Euclidean problems. None of these algorithms was found to be an adequate psychological model. Finally, we present a new algorithm for solving the TSP, which is based on a hierarchical pyramid architecture. The performance of this new algorithm is quite similar to the performance of the subjects.