Good Quality Solutions Research Articles

BargCrEx: A System for Bargaining Based Aggregation of Crowd and Expert Opinions in Crowdsourcing.

Crowdsourcing and crowd voting systems are being increasingly used in societal, industry, and academic problems (labeling, recommendations, social choice, etc.) due to their possibility to exploit “wisdom of crowd” and obtain good quality solutions, and/or voter satisfaction, with high cost-efficiency. However, the decisions based on crowd vote aggregation do not guarantee high-quality results due to crowd voter data quality. Additionally, such decisions often do not satisfy the majority of voters due to data heterogeneity (multimodal or uniform vote distributions) and/or outliers, which cause traditional aggregation procedures (e.g., central tendency measures) to propose decisions with low voter satisfaction. In this research, we propose a system for the integration of crowd and expert knowledge in a crowdsourcing setting with limited resources. The system addresses the problem of sparse voting data by using machine learning models (matrix factorization and regression) for the estimation of crowd and expert votes/grades. The problem of vote aggregation under multimodal or uniform vote distributions is addressed by the inclusion of expert votes and aggregation of crowd and expert votes based on optimization and bargaining models (Kalai–Smorodinsky and Nash) usually used in game theory. Experimental evaluation on real world and artificial problems showed that the bargaining-based aggregation outperforms the traditional methods in terms of cumulative satisfaction of experts and crowd. Additionally, the machine learning models showed satisfactory predictive performance and enabled cost reduction in the process of vote collection.

Length-constrained cycle partition with an application to UAV routing*

This article discusses the Length-Constrained Cycle Partition Problem (LCCP), which constitutes a new generalization of the Travelling Salesperson Problem (TSP). Apart from nonnegative edge weights, the undirected graph in LCCP features a nonnegative critical length parameter for each vertex. A cycle partition, i.e. a vertex-disjoint cycle cover, is a feasible solution for LCCP if the length of each cycle is not greater than the critical length of each vertex contained in it. The goal is to find a feasible partition having a minimum number of cycles. Besides analyzing theoretical properties and developing preprocessing techniques, we propose an elaborate heuristic algorithm that produces solutions of good quality even for large-size instances. Moreover, we present two exact mixed-integer programming formulations (MIPs) for LCCP, which are inspired by well-known modeling approaches for TSP. Further, we introduce the concept of conflict hypergraphs, whose cliques yield valid constraints for the MIP models. We conclude with a discussion on computational experiments that we conducted using (A)TSPLIB-based problem instances. As a motivating example application, we describe a routing problem where a fleet of uncrewed aerial vehicles (UAVs) must patrol a given set of areas.

Iterated local search for the placement of wildland fire suppression resources

We consider the problem of, given a landscape represented by a gridded network and a fire ignition location, deciding where to locate the available fire suppression resources to minimise the burned area and the number of deployed resources as a secondary objective. We assume an estimate of the fire propagation times between adjacent nodes and use the minimum travel time principle to model the fire propagation at a landscape-level. The effect of locating a resource in a node is that it becomes protected and the fire propagation to its unburned adjacent nodes is delayed. Therefore, the problem is to identify the most promising nodes to locate the resources, which is solved by a novel iterated local search (ILS) metaheuristic. A mixed integer programming (MIP) model from the literature is used to validate the proposed method in 32 grid networks with sizes 6x6, 10x10, 20x20 and 30x30, with two different number of fire suppression resources (64 problems). Our ILS produced optimal solutions in 40 cases out of 41 known optimal lower bounds. The proposed method’s effectiveness is also due to its short computing times and small coefficients of variation of the objective function values.We also provide a categorised literature review on fire suppression deterministic optimisation models, from which we conclude that approximate collaborative approaches seldom have been applied in the past and, according to the results obtained, can successfully address the complexity of fire suppression, reaching good quality solutions even for large scale instances.

Production routing for perishable products

This paper introduces the production routing problem for perishable products with fixed shelf life and gradual decay, where the age of products impacts the price that can be obtained when satisfying customer demands. In this problem, a single supplier is responsible for the production and distribution of perishable products to a set of customers. Fixed setup and variable production costs occur at the supplier location when production takes place, and routing costs are charged for the distribution activities. Additionally, inventory holding costs are incurred at both the production facility and the customer locations. Transshipments between customer locations are allowed on the basis of a unit transshipment cost. The objective is to maximise the total profit given by the sales revenue minus the sum of production, routing, inventory and transshipment costs. The problem is formulated as a mixed integer linear program and solved using a branch-and-cut and a hybrid, iterated local search-based heuristic. Based on the results of the computational experiments, we analyse the impact of perishability on the structure of the solutions. We explore perishability in the form of both product shelf life and decay, observing that larger decay rates lead to a reduction in total profit, as a result of both a decrease in revenues and an increase in the total cost. We further analyse the impact of different decay rates on the cost structure of the problem and the significance of transshipments for the management of perishability across the system. Finally, we adapt our hybrid method to solve the standard production routing problem. Comparison of our results with state-of-the-art solution methods on benchmark instances from the literature shows that our heuristic provides good quality solutions that are competitive with the scientific literature.

Exact and heuristic methods for the berth allocation problem with multiple continuous quays in tidal bulk terminals

The Berth Allocation Problem (BAP) is a primary seaside operations planning problem in bulk terminals. It consists of allocating quayside space to incoming vessels. In this article, the BAP for multiple continuous quays and dynamic arrivals is considered. The formulation considers the tidal constraints typical to exporting bulk terminals and restrictions regarding each vessel's possible quays. To solve the problem, a mixed-integer linear model is first presented. As the complexity of the problem grows exponentially with the size of the instances, an Iterated Local Search (ILS) approach is proposed to solve industrial-sized instances. The ILS approach works on sequences of vessels that are decoded using a bottom-left constructive heuristic. The initial sequences are obtained using a greedy scheduling heuristic that provides suitable starting solutions, especially for congested terminals. Extensive numerical experiments are carried on randomly generated instances for tidal bulk terminals and reported benchmark sets in literature. The results prove that the proposed ILS can provide good-quality solutions.

A hybrid arithmetic optimization algorithm and differential evolution for optimization of truss structures subjected to frequency constraints

A new hybrid arithmetic optimization algorithm (AOA) associated with differential evolution (DE) is developed for truss optimization. The development is named as ADE with the goal of maintaining a balance between low computational cost and good solution quality. Besides, several limitations of the AOA, which include the inefficiency of the exploration phase and the inconvenient use of two parameters MOA and MOP to find the optimal solution, as well as how to overcome them are also discussed. In terms of AOA in ADE, the exploration phase is removed, and both math optimizer accelerated (MOA) and math optimizer probability (MOP) parameters are adjusted to be independent of the maximum number of iterations. Moreover, the exploitation phase is modified to exploration which helps to limit local solutions and maintain a balance between exploitation and exploration in ADE algorithm. Through a probability parameter, the DE with DE/best/1 operator is executed in ADE to improve exploitation capability as well as convergence rate. Four truss structures with continuous design variables are considered to demonstrate the performance of the current algorithm. The obtained results show that the developed algorithm has a low computational cost, indicating its computational efficiency.

A partial evaluation approach for the School Bus Routing Problem

Several real-life optimization problems, such as the case of several instances of the School Bus Routing Problem (SBRP), are very complex and expensive to solve with exact algorithms. Metaheuristics are a good alternative in these situations because they are capable of generating good quality solutions to these problems in a reasonable time. Metaheuristics iterate thousands of times by introducing changes concerning the previous solutions. Each new solution must be evaluated, and sometimes, the new solutions have elements unchanged that are unnecessarily re-evaluated. However, an approach avoids repeatedly evaluating parts of different solutions known as partial evaluation. This work applies this technique to the SBRP to reduce its execution time. To apply the partial evaluation approach in this problem, each solution contains the information of the change that was made concerning the solution from which it originates. With this information, when evaluating the objective function, it will be only necessary to analyze the routes that changed. In the literature reviewed, no previous work was found in which the partial evaluation approach has been applied in the context of SBRP. In this paper we apply it in order to reduce the computational cost of SBRP solutions based on metaheuristics. The results show that it is possible to decrease the execution time in 80% of the instances, reducing the execution time on average by 73.6%.

Sensibility Analysis with Genetic Algorithm to Allocate Distributed Generation and Capacitor Banks in Unbalanced Distribution Systems

In the last years, many authors have dedicated themselves to solving the problem of distributed resources allocation in Distribution Systems (DS). The optimal siting and sizing of such equipment can improve the efficiency of the provided service, promoting technical and economic gains. Several papers use artificial intelligence algorithms and methods based on population evolutions to achieve this goal, due to the increasing complexity of the simulated systems and the highly combinatorial characteristics of this problem. Reducing the search space and/or setting the initial population are critical factors for improving the performance of population-based optimization methods. In the literature, the adjustment of the initial population does not receive the attention it should, especially when dealing with three-phase large-scale unbalanced systems – when computational time becomes primary adversity. The objective of this paper is to propose a novel approach for DS optimization, using Genetic Algorithms (GA) to map which areas are more sensitive to equipment allocation to reduce losses. In this work, capacitor banks and distributed photovoltaic (PV) generation are considered. The method adopted to carry out the allocation considers proper modeling of unbalanced DS through multiphase Optimal Power Flow frameworks. Its development takes advantage of the probabilistic nature of the GA to perform a sensitivity analysis to determine the positions that may reduce the losses in a daily planning horizon. For this, the execution of GA is done sequentially, under different conditions of generation and load, selected at random, setting in optimization a 24-hour load and PV generation curves that take into consideration the uncertainties, representing each hour of the day individually for better detail of the planning horizon. It was seen that the best positions are always concentrated in specific regions, called Optimal Allocation Zones (OAZ), which may favor the planning of Utilities. Furthermore, the OAZ method can be used to define an initial population of evolutionary algorithms, thus achieving a reduction in computational time and a better quality of solutions, which is a major contribution to literature. Through this approach, the computational time can be reduced up to 60% in large-scale systems. Simulations were carried out on four unbalanced distribution test-feeders: IEEE 4, IEEE 13, IEEE 37, and IEEE 123 Node Test Feeders.

Capability-based machine layout with a matheuristic-based approach

Capability-based machine layout (CB-ML) problem is firstly introduced in this paper. In the conventional machine layout problem, part flow matrix is generated from parts’ machine routes to minimize total part flows. However, defining part flow matrix based on the machines’ routes (instead of processing capability requirements of parts) restricts facility designers to utilize available flexibility in manufacturing systems. In this research, parts’ processing requirements are defined in terms of Resource Elements (REs), which describe unique processing capabilities and the processing capability overlaps of machines. If part flow matrix is defined in terms of REs, it becomes possible to utilize available flexibility in a more effective manner. However, physical part flows cannot be identified directly from the RE-based flow matrices. Because, the processing requirements of manufactured parts can be satisfied from alternative machines that contain the required REs. Therefore, RE-based part flow matrix must be mapped into the machine flow matrix, which requires defining the machine flow matrix as a decision variable. This makes the proposed CB-ML problem much more complicated than the conventional machine layout problem. We firstly developed an integer non-linear programming model for the proposed CB-ML problem. Because of its NP-completeness and nonlinear structure, a matheuristic-based solution approach is also developed. The extensive computational analysis have shown that the proposed approach is able to provide good quality solutions for the larger problem instances within reasonable computation times.

Robust optimization approaches for purchase planning with supplier selection under lead time uncertainty

Supplier reliability is a critical issue for manufacturing companies. Delivery delays from suppliers create backlog and firefighting on the shop floors. To avoid disruption and hedge against supplier lead time uncertainty, companies rely on diversification, multi-sourcing, and safety lead times. In multi-sourcing, the buyer might order the same product (raw material) from different suppliers. The design of a robust and cost-efficient purchasing/ordering plan in multi-sourcing is a complex task which has a strong impact on the performance of the company. In this study, we investigate the use of robust optimization for the integrated lot-sizing and supplier selection problem under lead time uncertainty. More specifically, we use polyhedral budgeted uncertainty sets. The resulting model determines the ideal lot sizes to minimize the total costs taking into consideration suppliers’ reliability and prices. To solve this problem, a row and column generation approach is proposed. To alleviate scalability issues, we enhance the row and column generation through a robust counterpart formulation, and we propose an efficient fix-and-optimize approach. Our extensive computational experiments show that the fix-and-optimize approach yields good quality solutions within a reasonable amount of computational time. We provide insights into supplier diversification based on the risk profile of the decision-maker. One of the conclusions is that an extremely risk-averse decision-maker selects a single supplier, namely the most reliable one even if it does not offer the lowest price.

FP-SMA: an adaptive, fluctuant population strategy for slime mould algorithm.

In this paper, an adaptive Fluctuant Population size Slime Mould Algorithm (FP-SMA) is proposed. Unlike the original SMA where population size is fixed in every epoch, FP-SMA will adaptively change population size in order to effectively balance exploitation and exploration characteristics of SMA’s different phases. Experimental results on 13 standard and 30 IEEE CEC2014 benchmark functions have shown that FP-SMA can achieve significant reduction in run time while maintaining good solution quality when compared to the original SMA. Typical saving in terms of function evaluations for all benchmarks was between 20 and 30% on average with a maximum being as high as 60% in some cases. Therefore, with its higher computation efficiency, FP-SMA is much more favorable choice as compared to SMA in time stringent applications.

A MILP model and two heuristics for the Bin Packing Problem with Conflicts and Item Fragmentation

Bin Packing Problem with Conflicts and Item Fragmentation (BPPC-IF) is a variant of the classical bin packing problem in which fragments of the same item can be packed in different bins (item fragmentation), and some pairs of items cannot be packed in the same bin (item conflicts). To solve the problem, we propose a new mixed-integer linear programming (MILP) model and two heuristics. The MILP model represents BPPC-IF as a generalized transportation problem in which sources correspond to items, and sinks are defined for all maximal independent sets in the graph representing conflicts between items. The proposed heuristics, called Sequential Packing Heuristics 1 and 2 (SPH1 and SPH2, respectively) are modifications of the previously proposed Sequential Maximum Degree Packing Heuristic (SMDPH) for BPPC-IF, which fills bins by solving a series of MILP subproblems. Both new heuristics use improved criteria for selecting items to pack. Additionally, in SPH1, bins are filled heuristically without solving any MILP subproblems. Computational experiments carried out on a large set of benchmark problem instances from the literature show that (i) SPH1 obtains good quality solutions and is orders of magnitude faster than the MILP-based heuristics, (ii) compound heuristic SPH1+SPH2 finds on average better solutions than any other heuristic and is on average faster than any other MILP-based heuristic, and (iii) the proposed MILP model greatly outperforms the best previously proposed MILP model.

The collaborative batching problem in multi-site additive manufacturing

Additive Manufacturing (AM) is a technology with the potential to disrupt entire supply chains - one reason why AM attracted attention from both academia and practitioners in the last decade. In recent years, researchers have focused on increasing the efficiencies of AM operations as the technology has reached new levels of maturity. Collaborative production (CP) is a proven approach for decreasing the costs of operations in conventional fields of production. However, CP has not been sufficiently studied in the context of AM. Our study aims to close this research gap by demonstrating the impact of collaborative planning in the field of AM. We introduce the collaborative multi-site batching problem in the context of AM, wherein we assume that production orders have to be batched and scheduled at several geographically dispersed manufacturing sites, by a central authority. We devise a quadratic model and develop an efficient solution approach. The model is solved by combining mixed integer programming with Genetic Algorithms, wherein batching and scheduling problems are solved sequentially. An extensive computational study reveals that the proposed approach yields very good solution quality within short computational times. Managerial insights emphasise that cross-site collaborative production planning can significantly decrease the overall costs of AM operations.

Parallel genetic approach for routing optimization in large ad hoc networks

<span>This article presents a new approach of integrating parallelism into the genetic algorithm (GA), to solve the problem of routing in a large ad hoc network, the goal is to find the shortest path routing. Firstly, we fix the source and destination, and we use the variable-length chromosomes (routes) and their genes (nodes), in our work we have answered the following question: what is the better solution to find the shortest path: the sequential or parallel method?. All modern systems support simultaneous processes and threads, processes are instances of programs that generally run independently, for example, if you start a program, the operating system spawns a new process that runs parallel elements to other programs, within these processes, we can use threads to execute code simultaneously. Therefore, we can make the most of the available central processing unit (CPU) cores. Furthermore, the obtained results showed that our algorithm gives a much better quality of solutions. Thereafter, we propose an example of a network with 40 nodes, to study the difference between the sequential and parallel methods, then we increased the number of sensors to 100 nodes, to solve the problem of the shortest path in a large ad hoc network.</span>

A multi-agent approach to the truck multi-drone routing problem

In this work, we address the Truck-multi-Drone Team Logistics Problem (TmDTL), devoted to visit a set of points with a truck helped by a team of unmanned aerial vehicles (UAVs) or drones in the minimum time, starting at a certain location and ending at a different one. It is an enhanced version of the multiple Flying Sidekicks Traveling Salesman Problem (mFSTSP) presented in Murray and Raj (2020) wherein drones are allowed to visit several customers per trip.In order to cope with large instances of the complex TmDTL, we have developed a novel agent-based method where agents represent the points that are going to be visited by vehicles. Agents evolve by means of movement inside a grid (locations vs. vehicles) according to a set of rules in the seek of better objective function values. Each agent needs to explore only a fraction of the complete problem, sharing its progress with the rest of the agents which are coordinated by one central agent which helps to maintain an asynchronous memory of solutions – e.g. on the control of the mechanism to escape from local minima.Our agent-based approach is firstly tested using the largest instances of the single TDTL problem reported in the literature, which additionally serves as upper bounds to the TmDTL problem. Secondly, we have solved instances up to 500 locations with up to 6 drones in the fleet. Thirdly, we have tested the behavior of our approach in 500 locations problems with up to 8 drones in order to test the fleet size sensitivity.Our experiments demonstrate the ability of the proposed agent-based system to obtain good quality solutions for complex optimization problems that arise. Further, the abstraction in solutions coding applied makes the agent-based approach scalable and flexible enough to be applied to a wide range of other optimization problems.

Comparative study of whale optimization algorithm and flower pollination algorithm to solve workers assignment problem

Many important problems in engineering management can be formulated as Resource Assignment Problem (RAP). The Workers Assignment Problem (WAP) is considered as a sub-class of RAP which aims to find an optimal assignment of workers to a number of tasks in order to optimize certain objectives. WAP is an NP-hard combinatorial optimization problem. Due to its importance, several algorithms have been developed to solve it. In this paper, it is considered that a manager is required to provide a training course to his workers in order to improve their level of skill or experience to have a sustainable competitive advantage in the industry. The training cost of each worker to perform a particular job is different. The WAP is to find the best assignment of workers to training courses such that the total training cost is minimized. Two metaheuristic optimizations named Whale Optimization Algorithm (WOA) and Flower Pollination Algorithm (FPA) are utilized to final the optimal solution that reduces the total cost. MATLAB Software is used to perform the simulation of the two proposed methods into WAP. The computational results for a set of randomly generated problems of various sizes show that the FPA is able to find good quality solutions.

An evolution strategy approach for the distributed permutation flowshop scheduling problem with sequence-dependent setup times

This paper considers a distributed permutation flowshop scheduling problem with sequence-dependent setup times (DPFSP-SDST) to minimize the maximum completion time among the factories. The global economy has enabled large companies to have distributed production centers to become widespread, and effective production scheduling between these centers plays a vital role in the competitiveness of companies. To provide effective scheduling for the DPFSP-SDST, we propose a new mixed-integer linear programming (MILP) model and a new constraint programming (CP) model, which is presented for the first time in literature to the best of our knowledge. As the CP has become a solid competitor to the MILP in the literature, this study aims to exploit the effectiveness of CP to solve such a complex DPFSP-SDST. Since the problem is NP-hard, we also offer an evolution strategy (ES_en) algorithm that employs a self-adaptive scheme to obtain high-quality solutions in a short time. A ruin-and-recreate procedure is also embedded into the developed ES_en. We calibrate the parameters of the proposed ES_en using a design of experiment approach. We also compare the proposed ES_en algorithm's performance with three state-of-the-art metaheuristic algorithms from the literature, i.e., the IG2S (a variant of an iterated greedy algorithm with NEH2_en initialization), IGR (another variant of an iterated greedy algorithm with a restart scheme), and discrete artificial bee colony (DABC) algorithm. A detailed computational experiment is carried out to evaluate the performance of the mathematical models (MILP and CP) and the heuristic algorithms (ES_en, IG2S, IGR, and DABC). A comprehensive benchmark set is generated for the DPFSP-SDST from the well-known PFSP instances from the literature, considering various combinations of jobs, machines, factories, and SDST settings, resulting in 2880 benchmark instances. For 216 out of 240 small-size instances, optimal results are reported by solving the proposed MILP and CP models, whereas time-limited model results are reported for the rest. The computational results show that the CP model outperforms the MILP model in terms of the solution time for small-size instances. Initially, the performance of the heuristic algorithms is verified concerning the optimal results on small-size instances. Then, the performance of the heuristic algorithms is evaluated for large instances. ES_en algorithm significantly outperforms the IG2S, IGR, and DABC algorithms for solving large instances. The computational results show that the proposed ES_en algorithm is robust and provides good-quality solutions for the DPFSP-SDST in a short computational time.

A Lagrangian heuristic for the multicommodity capacitated location problem with balancing requirements

We consider the multicommodity capacitated location problem with balancing requirements. This problem has applications in transportation and was introduced in the context of planning the movements of containers between ports and terminals, identified as depots in the problem formulation. We propose a Lagrangian relaxation based on a reformulation of the problem that allows the subproblem to be decomposed by depot, giving rise to a series of continuous knapsack subproblems. We optimize the Lagrangian dual by a bundle method and we develop a Lagrangian heuristic based on slope scaling. Numerical results are presented on a set of large-scale benchmark instances. We obtain good quality solutions within short computing times using the Lagrangian heuristic approach.

Metaheuristic, models and software for the heterogeneous fleet pickup and delivery problem with split loads

This paper addresses a rich variant of the vehicle routing problem (VRP) that involves pickup and delivery activities, customer time windows, heterogeneous fleet, multiple products and the possibility of splitting a customer demand among several routes. This variant generalizes traditional VRP variants by incorporating features that are commonly found in practice. We present two mixed-integer programming models and propose a metaheuristic based on Adaptive Large Neighborhood Search for the addressed problem. Additionally, to facilitate the use of the proposed approaches in real-world decision-making, we develop an open-source, publicly available web interface that allows one to set and solve VRP variants with the mentioned features. Computational experiments using benchmark instances with up to 150 customers show that the approaches can be used to obtain good-quality solutions in a reasonable time frame in practice.

Configuration and Evaluation of Multi-CQF Shapers in IEEE 802.1 Time-Sensitive Networking (TSN)

Time-Sensitive Networking (TSN) is a task group of the IEEE 802.1 standardization working group (WG) developing the IEEE 802.1 TSN communication standards. TSN is developing a “toolbox” of many standards to provide support for enabling the separation of critical and non-critical traffic, the timeliness and dependability, i.e., reliability, fault-tolerance, and security, of critical traffic. In this paper we focus on the Cyclic Queuing and Forwarding (CQF) traffic shapers, such as the original CQF, the Cycle Specified Queuing and Forwarding (CSQF) and an extension of those, the so-called Multi-CQF shaper. We define formally the problem of configuring CQF-based networks. We have developed a Constraint Programming (CP) formulation for Multi-CQF, as well as a Simulated Annealing (SA)-based metaheuristic solution. These solutions can also obtain results for CQF and CSQF, which can be seen as a special case of Multi-CQF. The CQF configuration problem is NP-hard. We evaluate our solutions on several test cases and scenarios. The CP formulation can find optimal solutions for small problem sizes but does not scale for realistic test cases. However, SA is able to handle large test cases and to find good quality solutions, making it suitable for both design-time and runtime network configuration. We also present comprehensive evaluation results comparing the CQF-based variants (CQF, CSQF, Multi-CQF) on industrial use cases, contrast them to the Time Aware Shaper (TAS, 802.1Qbv), and discuss their advantages and disadvantages.