Heuristic Procedure Research Articles

Zero-inventory plans, constant workforce, or hybrid approach? Analysing pure production strategies for enhancing factory resilience with demand variability

Historically the notion of resilience capability has primarily been conceptualised as a holistic construct in the domain of supply chain networks – referred to as SCRES. The field of SCRES has since evolved gaining prominence among scholars and practitioners. However, the existing SCRES literature inadequately delves into the embedded resilience harboured by their constituent components, practices, or internal routines, including the aggregate production planning (APP) process. Therefore, the purpose of this article is to examine the interplay between medium-term pure strategies for APP and the resilience of manufacturing facilities, assuming demand to be the sole source of uncertainty. To accomplish this, a realistic Monte-Carlo simulation model integrated with a robust heuristic procedure for pure strategies is merged with a factory resilience index-based Cobb–Douglas function. The results of this research suggest that manufacturing facilities achieve a higher level of resilience when certain ‘zero-inventory plans’ are implemented. Based on this key finding, production/demand planners might incorporate the resilience dimension alongside the customary manufacturing success factors. Thus, to the best of the authors’ knowledge, this study represents the first attempt to establish a clear linkage between the implementation of pure APP strategies and a quantitative measure of factory resilience.

Average Case Subquadratic Exact and Heuristic Procedures for the Traveling Salesman 2-OPT Neighborhood

We describe an exact algorithm for finding the best 2-OPT move that, experimentally, was observed to be much faster than the standard quadratic approach for a large part of a best-improvement local search convergence starting at a random tour. To analyze its average-case complexity, we introduce a family of heuristic procedures and discuss their complexity when applied to a random tour in graphs whose edge costs are either uniform random numbers in [0, 1] or Euclidean distances between random points in the plane. We prove that, for any probability p, there is a heuristic in the family that can find the best 2-OPT move with probability at least p in average-time [Formula: see text]) for uniform instances and [Formula: see text] for Euclidean instances. The exact algorithm is then proved to be even faster in the sense that in those instances in which a heuristic finds the best move, the exact algorithm finds it in a smaller time. We give empirical evidence that a slight variant of our algorithm finds the best move in [Formula: see text] time on both types of instances, achieving the best possible performance for this particular problem. Computational experiments are reported to show the effectiveness of our algorithms, both in best-improvement and in first-improvement 2-OPT local search. History: Accepted by Andrea Lodi, Area Editor for Design and Analysis of Algorithms: Discrete. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2023.0169 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2023.0169 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Code and Data Repository for Average Case Subquadratic Exact and Heuristic Procedures for the Traveling Salesman 2-OPT Neighborhood

The repository contains all files necessary to replicate computational experiments as those reported in the paper "Average case sub-quadratic exact and heuristic procedures for the traveling salesman 2-OPT neighborhood", by Giuseppe Lancia and Paolo Vidoni, INFORMS Journal on Computing, 2024.

Supply Chain Integration and Coordination Applying SAP-ERP: A Case Study in the Fast-moving Consumer Goods Industry

In today’s competitive environment, the pursuit of effective approaches and novel technologies is a key driver for business success. Meanwhile, the coordination of internal and external processes with suppliers and customers is an effective solution for cost reduction, continuous quality improvement, and comprehensive risk management. Therefore, this study proposes a conceptual framework for coordinating and improving supply chain planning workflows using the capabilities of the SAP-ERP solution. The framework encompasses a joint vendor-buyer mathematical model to determine the optimal lot size and purchase quantities. It also provides a heuristic procedure for determining dead-stock in the distribution network. The recommended approach has been applied in a company in the fast-moving consumer goods industry. Key performance indicators, such as inventory volume and inventory turnover rate, were meticulously measured before and after the implementation of the framework. The results show a significant improvement in performance measures, including average total inventory and average inventory turnover, and a considerable impact of using the proposed framework.

Optimizing Attended Home Delivery: Multiple recovery options and customer availability profiles to face synchronization failures

In the growing sector of Attended Home Delivery, unsynchronized deliveries between couriers and recipients affect both customers’ satisfaction and companies’ costs. Hence, reducing such failures improves companies’ service quality and logistics efficiency. To address this issue, we study an Attended Home Delivery Problem with Recovery Options (AHDPRO) in which customers specify their probability of being at home during different timeslots of the day and their preferred recovery option in case of a synchronization failure. The options include leaving the package in a predefined safe location, bringing it to a generic collection point, or scheduling a second delivery attempt. Each alternative involves different costs and, in most cases, additional operational decisions. The AHDPRO aims to complete all customer deliveries while minimizing overall routing times as well as the overall penalty due to the recovery actions implemented and weighted by the probability of a synchronization failure to occur. We propose a branch-and-cut algorithm, including valid inequalities and heuristic procedures, to solve a Mixed-Integer Linear Programming model based on an expanded graph. Using the developed method as a tool for evaluating costs and operations, we conduct an experimental campaign on scenarios adapted from the literature involving lexicographic-based optimization procedures able to address the multiple attributes of the solutions. The results obtained allow us to assess the impact of the different recovery options on the optimal solutions and their values. Additionally, the results yield several managerial insights for companies operating in the Attended Home Delivery sector, such as the timeslot length, perceived service quality, and other key operational factors contributing to efficient planning and improved customer satisfaction.

How can we translate research papers into legislative drafts? A methodological framework of the Translational Legal Approach to Knowledge Utilisation

ABSTRACT One important factor in knowledge utilisation in policy is adapting research papers to better fit policy-makers’ needs. Translating research papers from a variety of genres and types, books to journal articles, into legislative drafts can be understood as a form of adaptation. This was named as the Translational Legal Approach to Knowledge Utilisation (TLA). This article addresses the methodological need to construct a framework for TLA. It proposes a methodological framework which includes definitions, translation heuristic, guiding problems, stages and translation procedures, various translation modes (legislative techniques with different levels of specificity) and various translation forms (legislative genres with different legal status, functions, and other characteristics). By looking at cases from Polish and EU laws and standards, this article proposes a methodology for researchers to better align their research conclusions and recommendations to policy-makers’ needs.

A multi-criteria districting approach with a lexicographic compactness metric: An application to the Chilean postal service

Recent trends in customer habits have greatly affected last-mile logistics, increasing the pressure to effectively serve an increasing demand. For traditional postal mail companies, this change is an opportunity to alleviate the decreasing role of conventional mail within society and to focus on new products and services. Unfortunately, these changes also affect the efficiency of their supply chains, which were focused on delivering letters. In this work we consider the last-mile reorganization problem as faced by Correos de Chile, the Chilean public postal service. While this work is mostly focused on the particular needs of the company, the approach applies to other scenarios where the work allocation can be modeled as a districting problem with multiple simultaneous objectives and special shape conditions on the districts. To solve the proposed model, a multi-criteria heuristic procedure based on a combination of different neighborhood structures, each geared towards specific characteristics of the problem, is put forward. The applicability of the approach on large-size real-life conditions is tested on instances derived from real life operations showing the applicability of the proposed method.

Dual-mask: Progressively sparse multi-task architecture learning

Multi-task architecture learning has achieved significant success by learning optimal sharing architectures for different tasks. However, previous works to learn branched architectures for different tasks can sometimes lead to unsatisfying multi-task performance, as not all detailed branches are relevant to a specific task. Task-relevant architectures can be sparse, including only partial channels or layers in the entire architecture (i.e., a sub-network). In addition, most previous works rely on a heuristic architecture selection procedure that could not support continuous architecture optimization. To this end, in this paper, we propose dual-mask, a progressively sparse multi-task architecture learning method. Starting with a task-free architecture, it identifies the informative features along two-level, channels and layers, for each task, while suppressing conflicting or noisy parts in a differentiable manner, so that better task-specific sub-networks are captured. Specifically, the channel and layer selection modules produce respective hybrid binary and real value masks, designed to pick salient channels and layers for each task, respectively. To jointly optimize masks with model parameters, we propose an importance-guided relaxation method for solving the stochastic binary optimization problem, after which the interference or noise parts can be pruned by masks. Additionally, a progressive training strategy with continuation is provided that gradually sparsity the task-specific sub-networks. Experiments show that dual-mask achieves superior performance than SOTA multi-task methods.

Penerapan Algoritma Tabu Search pada Capacitated Vehicle Routing Problem Pengangkutan Sampah di Kota Padang Sidempuan

Waste transportation activities are an example of a form of Capacitated Vehicle Routing Problem (CVRP) related to finding the minimum route. The Tabu Search algorithm is one of the metaheuristic methods that can guide the heuristic local search procedure to explore the solution area outside the local optimal point. The Tabu Search algorithm can be used to find the optimal VRP solution, namely the route that has the minimum total mileage by considering vehicle capacity. The purpose of this research is to determine the optimal route for garbage transportation in the Capacitated Vehicle Routing Problem (CVRP) model in Padang Sidempuan City using the Tabu Search algorithm. Based on the results of the study, it is concluded that the optimal route for transporting waste in the Capacitated Vehicle Routing Problem (CVRP) model in Padang Sidempuan City using the Tabu Search algorithm obtained the shortest route in iteration 1 with the route (12-11-10-9-8-7-6-5-4-3-2-1-0) and route length 16.55 km.

A biased random-key genetic algorithm for the knapsack problem with forfeit sets

This work addresses the Knapsack Problem with Forfeit Sets, a recently introduced variant of the 0/1 Knapsack Problem considering subsets of items associated with contrasting choices. Some penalty costs need to be paid whenever the number of items in the solution belonging to a forfeit set exceeds a predefined allowance threshold. We propose an effective metaheuristic to solve the problem, based on the Biased Random-Key Genetic Algorithm paradigm. An appropriately designed decoder function assigns a feasible solution to each chromosome, and improves it using some additional heuristic procedures. We show experimentally that the algorithm outperforms significantly a previously introduced metaheuristic for the problem.

Solution approaches to the three-index assignment problem

This paper delves into the axial Three-index Assignment Problem (3IAP), alternatively known as the Multi-dimensional Assignment Problem, defined as an extension of the classical two-dimensional assignment problem. The 3IAP entails allocating $n$ tasks to $n$ machines in $n$ factories, ensuring one task is completed by one machine in one factory at a minimum total cost. This combinatorial optimization problem is classified as \emph{NP}-hard due to its inherent complexity and being the subject of much scholarly research and investigation. The study employs an algorithmic approach to devise rapid and effective solutions for the 3IAP. A new heuristic Greedy-style Procedure (GSP) is introduced for solving the 3IAP, achieving feasible solutions within polynomial time. Particular configurations of cost matrices enable us to reach quality solutions. Examining tie-cases and matrix ordering unveiled innovative variants. Further investigation of cost matrix attributes facilitates the development of two new heuristic categories, offering optimal or nearly optimal solutions for the 3IAP. Extensive numerical experiments validate the effectiveness of the heuristics, generating quality solutions in a short computational time. Furthermore, we implement two potent methods using optimization solvers, achieving optimal solutions for the 3IAP within competitive CPU times.

A Heuristic Procedure for Improving the Routing of Urban Waste Collection Vehicles Using ArcGIS

This paper proposes a heuristic procedure created to improve the collection routes obtained with the support of the ArcGIS software and its complement, Network Analyst. After a series of experiments, it was found that the software presents several inconsistencies with logistical and operational management concepts, such as the unnecessary realization of U-turns in a dead end and unnecessary access to areas with difficult access to a single customer. These are issues that a collection route planner must consider to make a good decision that considers the cost of visiting areas with difficult access and the benefits of reaching that area. In this sense, our heuristic procedure considers a set of best practices in operational and logistical strategies that could be programmed within the Network Analyst. As it is well known in the science of vehicle routing, U-turns and sub-tours in the routes travelled by vehicles increase distances and operating costs, so in our work, we propose a systematic heuristic procedure to reduce the number of U-turns performed by a municipal waste collection truck, while using the ArcGIS-Network Analyst add-on to reduce the number of sub-tours in the route under the Directed-Capacitated Arc Routing Problem approach. It is then shown how the routes improved using our conceptual heuristic procedure, which provides better quality than the original routes obtained with ArcGIS and Network Analyst. Specifically, reducing the total distances travelled by the vehicle fleet, increasing the coverage of sidewalks visited by the truck in the urban perimeter of a city, and minimizing the time used in municipal solid waste collection operations. The importance of our work lies in the fact that effective MSW management is an essential municipal service whose regulation can drive innovation, sustainability, and efficiency in the MSW sector.

A binarization approach to model interactions between categorical predictors in Generalized Linear Models

In this paper, our goal is to enhance the interpretability of Generalized Linear Models by identifying the most relevant interactions between categorical predictors. Searching for interaction effects can quickly become a highly combinatorial, and thus computationally costly, problem when we have many categorical predictors or even a few of them but with many categories. Moreover, the estimation of coefficients requires large training samples with enough observations for each interaction between categories. To address these bottlenecks, we propose to find a reduced representation for each categorical predictor as a binary predictor, where categories are clustered based on a dissimilarity. We provide a collection of binarized representations for each categorical predictor, where the dissimilarity takes into account information from the main effects and the interactions. The choice of the binarized predictors representing the categorical predictors is made with a novel heuristic procedure that is guided by the accuracy of the so-called binarized model. We test our methodology on both real-world and simulated data, illustrating that, without damaging the out-of-sample accuracy, our approach trains sparse models including only the most relevant interactions between categorical predictors.

Mapping paleostress trajectories by means of the clustering of reduced stress tensors determined from homogeneous and heterogeneous data sets

It is easy to draw stress trajectories to investigate the present stress field by interpolating stress orientations determined at control points. However, challenges arise when we deal with the trajectories of paleostresses, because neighboring control points may have the stress orientations of different tectonic phases. We must choose coeval stresses to draw the trajectories. Recent stress inversion techniques can separate stresses from heterogeneous data from fault, dilational fractures, etc. Natural data sets from those structures are often heterogeneous, and age data are usually not enough to classify the stresses by age. As a result, an unsupervised classification problem of the inversion results must be solved to draw the trajectories. Here, we propose a simple and heuristic procedure for this problem. We assume smooth trajectories during each of the phases. The smoothness makes density-based clustering adoptable to solve the problem. The heterogeneity of data sets allows the additional partition of the clusters. As a worked exercise for this technique, the trajectories of minimum horizontal stress orientations were drawn based on the paleostresses determined from the attitudes of felsic dikes and quartz veins formed in mid Cretaceous orogeny in the North Kitakami Terrain, northern Japan. The orogen-parallel and orogen-perpendicular extensional stress fields delineated by the present technique were probably the manifestations, respectively, of the gravitational collapse of the orogen and of regional extensional tectonics in the Far East.

Improving the Robustness of the Theil-Sen Estimator Using a Simple Heuristic-Based Modification

One of the most widely used robust regression methods for solving simple linear regression problems is the Theil-Sen (TS) estimator. This estimator has some notable advantages; however, it does not belong to the most robust estimation methods (called high-breakdown estimators) and is prone to outliers whose distribution is highly asymmetric with respect to the correct data points. This paper presents a modification of the TS estimator, the Robustified Theil-Sen (RTS) estimator. The new method uses a heuristic-based selection procedure to reduce the number of initial estimates of the regression function parameters computed with at least one outlier, thereby improving the regression results. The use of this heuristic procedure only slightly increases the computational time required for using the RTS estimator compared to the TS estimator. Preliminary results of two numerical experiments presented in the paper show that the RTS estimator outperforms other comparable estimators, i.e., the TS estimator and the repeated median estimator, in terms of robustness. The results presented also suggest that the breakpoint value (which is a measure of the robustness of estimators) of the RTS estimator is higher than the breakpoint value of the TS estimator and equal to the breakpoint value of the high-breakpoint estimators.

An Improved DeNSE Methodology for Optimal Sectorization of Water Distribution Networks

Sectorization of a water distribution network (WDN) into district meter areas (DMAs) is considered a key strategy for an efficient management of WDNs. Typically, it involves a two-stage procedure: a clustering stage, in which the division of the WDN into clusters is performed, and a dividing stage, which involves the placement of valves and flow meters on the cluster’s boundary pipes to define the DMAs. While recently published methods attempt to enhance both the clustering and dividing stages, they fail to provide decision-making flexibility. They also neglect to consider the presence of existing valves in the WDN, which can significantly affect the evaluated implementation cost, often considered the primary decision-driving factor. This paper presents improvements to the previously introduced DeNSE method for sectorization of WDNs, aiming to address these deficiencies. The methodology consists of a clustering stage, based on the network uniformity index, and a dividing stage, in which the originally used heuristic procedure is replaced with Genetic Algorithm (GA) optimization, minimizing implementation cost. Consideration of existing valves in WDN and criteria for water supply security are also included in the dividing stage to offer a better estimate of implementation costs and post-sectorized operational efficiency of the WDN. Finally, GIS visualization is implemented, and a hydraulic model of the sectorized WDN (EPANET file) is generated, providing practitioners with valuable insights and decision-making flexibility. The methodology is tested on a part of the Amsterdam WDN in the Netherlands, serving as a pilot for methodology evaluation. A range of feasible sectorization solutions is generated and compared based on implementation cost and three performance indicators (PIs). The paper provides an in-depth discussion on the selection of preferable sectorization solution. The reported results demonstrate the method’s efficiency in optimizing sectorization solutions with minimum implementation cost whilst preserving the WDN operational efficiency and meeting the local design criteria.

Heuristic Procedures for the Synthesis of Cutting Hyperplanes in Iterative Algorithms for Solving Mixed Linear Programming with Boolean Variables Problems

The main provisions for one of the promising directions of methods development of mixed integer linear programming are presented. The method is based on an iterative application of a cutting-plane procedure that takes into account, as fully as possible, the properties of the problems being solved. Heuristic procedures are applied for the synthesis of cutting-planes as an intermediate step substantiating the construction of a solution tree. The properties of the generated algorithms are experimentally investigated, the prospects and ways of developing the method and expanding the classes of discrete optimization problems to be solved are determined. The material is illustrated with simple visual examples.

Model and heuristics for the multi-manned assembly line worker integration and balancing problem

This paper examines the balancing of assembly lines with multi-manned stations and a heterogeneous workforce. Both topics received considerable attention in the literature, but not in an integrated fashion. Combining these two characteristics gives rise to a highly combinatorial Multi-manned Assembly Line Worker Integration and Balancing Problem. When considering multi-manned stations, the already coupled decisions on assigning tasks to heterogeneous workers and workers to stations must be further linked with task scheduling assessments. We propose a Mixed-Integer Linear Programming model and develop two heuristic solution procedures, which tackle the problem with a hierarchical decomposition approach. Computational tests on a large dataset indicate that the proposed method can obtain good primal bounds in short computational times. We demonstrate that these results can be applied to the monolithic model either as a warm start or in a proximity search procedure to obtain synergistic gains with statistically significant differences. From a managerial perspective, we show that multi-manned stations can reduce the assembly line's length even in the presence of a heterogeneous workforce, which is crucial for many industries manufacturing large-size products.

Integrated charging scheduling and operational control for an electric bus network

The last few years have seen the massive deployment of electric buses in many existing transit networks. However, the planning and operation of an electric bus system differ from that of a bus system with conventional vehicles, and some key problems have not yet been studied in the literature. In this work, we address the integrated operational control and charging scheduling problem for a network of electric buses with a limited opportunity charging capacity. Operational control is carried out through speed control of the vehicles and bus holding at the terminal. We propose a hierarchical control framework to solve this integrated problem, where the charging and operational decisions are taken jointly by solving a mixed-integer linear program in the high-level control layer. Since this optimization problem might become very large as more bus lines are considered, we propose to apply Lagrangian relaxation in such a way as to exploit the structure of the problem and enable a decomposition into independent subproblems. A local search heuristic is then deployed in order to generate good feasible solutions to the original problem. This entire Lagrangian heuristic procedure is shown to scale much better on transit networks with an increasing number of bus lines than trying to solve the original problem with an off-the-shelf solver. The proposed procedure is then tested in the high-fidelity microscopic traffic environment Vissim on a bus network constructed from an openly available dataset of the city of Chicago. The results show the benefits of combining the charging scheduling decisions with the real-time operational control of the vehicles as the proposed control framework manages to achieve both a better level of service and lower charging costs over control baselines with predetermined charging schedules.

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators

Nonlinear optimal control for robotic exoskeletons with electropneumatic actuators