Problem-specific Operators Research Articles

A fair, preference-based posted price resale e-market model and clearing heuristics for circular economy

Resale markets in which secondhand new and used goods are traded play an important role in circular economy with significant economic and environmental benefits. This study proposes a preference-based posted price electronic market model for resale markets which features several mechanisms to improve the market outcome. The proposed model allows market participants to post sales and purchase orders simultaneously inside a trading round which also enables participants to use the revenue obtained from the items to be sold to purchase other items in the market. Besides, each participant is allowed to declare a budget constraint which restricts the amount that the participant will spend in the market to prevent a possible budget deficiency. Furthermore, the model also allows participants to declare their preferences of substitutabilities in their orders. In this study, the proposed model is formally defined, the corresponding market clearing problem is formulated as a hierarchical multi-objective linear integer program to provide fair allocation between the participants. Four different objective functions are proposed, and their outcomes are compared to the current market system. Since the clearing problem is NP-Hard, several heuristic methods including ant colony optimization, artificial bee colony and genetic algorithms along with problem-specific operators are proposed. The performance of the model is statistically analyzed based on several experiments. The genetic algorithm using the proposed problem-specific operators provides solutions within 3% of the optimal objective values and within 1% of the optimal fairness on average. The results also indicate that the model provides improved market outcomes and fair allocation of items among the participants, and thus it has a potential to contribute to the growth of circular economy.

The Vehicle Routing Problem with Availability Profiles

In business-to-consumer (B2C) parcel delivery the presence of the customer at the time of delivery isimplicitly required in many cases. If the customer is not at home, the delivery fails – causing additional costs and effort for the parcel service provider as well as inconvenience for the customer. Typically, parcel service providers report high failed-delivery rates, as they have limited possibilities to arrange a delivery time with the recipient. We address the failed-delivery problem in B2C parcel delivery by considering customer-individual availability profiles (APs) that consist of a set of time windows, each associated with a probability that the delivery is successful if conducted in the respective time window. To assess the benefit of APs for delivery tour planning, we formulate the vehicle routing problem with availability profiles (VRPAP) as a mixed integer program (MIP), including the tradeoff between transportation and failed-delivery costs. We provide analytical insights concerning the model’s cost savings potential by determining lower and upper bounds. In order to solve larger instances we develop a novel hybrid adaptive large neighborhood search (HALNS) with problem-specific operators. We find new best-known solutions for related benchmark instances from the literature and conduct various experiments on self-generated instances. In a case study using real-world data, despite little information on the APs, we were able to reduce failed deliveries by approximately 12% and overall costs by 5%.

An explicit evolutionary approach for multiobjective energy consumption planning considering user preferences in smart homes

Modern Smart Cities are highly dependent on an efficient energy service since electricity is used in an increasing number of urban activities. In this regard, Time-of-Use prices for electricity is a widely implemented policy that has been successful to balance electricity consumption along the day and, thus, diminish the stress and risk of shortcuts of electric grids in peak hours. Indeed, residential customers may now schedule the use of deferrable electrical appliances in their smart homes in off-peak hours to reduce the electricity bill. In this context, this work aims to develop an automatic planning tool that accounts for minimizing the electricity costs and enhancing user satisfaction, allowing them to make more efficient usage of the energy consumed. The household energy consumption planning problem is addressed with a multiobjective evolutionary algorithm, for which problem-specific operators are devised, and a set of state-of-the-art greedy algorithms aim to optimize different criteria. The proposed resolution algorithms are tested over a set of realistic instances built using real-world energy consumption data, Time-of-Use prices from an electricity company, and user preferences estimated from historical information and sensor data. The results show that the evolutionary algorithm is able to improve upon the greedy algorithms both in terms of the electricity costs and user satisfaction and largely outperforms to a large extent the current strategy without planning implemented by users.

A Bi-Population Cooperative Memetic Algorithm for Distributed Hybrid Flow-Shop Scheduling

In the context of globalization and decentralized economies, the distributed manufacturing and scheduling systems have become emerging in large enterprises. This article addresses the distributed hybrid flow-shop scheduling problem (DHFSP) with heterogeneous factories to minimize makespan. Different from single-factory scheduling problem, DHFSP contains several strongly coupled sub-problems, i.e., factory assignment of jobs and the job sequencing in each hybrid flow-shop. In this article, we present a mixed integer linear programming model for the DHFSP and make a first attempt to propose a bi-population cooperative memetic algorithm (BCMA) for solving such a strongly NP-hard problem. The proposed optimization framework comprises collaborative initialization, bi-population cooperation and local intensification. To generate diverse solutions with small makespan, two knowledge-based heuristics are designed for collaborative initialization by utilizing the lower bound of the problem and the historic information of elite solutions during search process. To balance exploration and exploitation, two populations are evolved in a cooperative way. To further enhance the optimization capability, intensification search with multiple problem-specific operators is incorporated. The effect of parameter setting is investigated and extensive computational tests are carried out. The comparative results show that the BCMA is more effective than both the Math solver Gurobi and the existing iterated greedy algorithm, and the effectiveness of each specific design is also verified in solving DHFSP.

Single Item Supplier Selection and Order Allocation Problem with a Quantity Discount and Transportation Costs

In this paper, we address a single item supplier selection, economic lot-sizing, and order assignment problem under quantity discount environment and transportation costs. A mixed-integer nonlinear program (MINP) model is developed with minimization of cost as its objective, while lead-time, the capacity of the supplier and demand of the product are incorporated as constraints. The total cost considered includes annual inventory holding cost, ordering cost, transportation cost and purchase cost. An efficient and effective genetic algorithm (GA) with problem-specific operators is developed and used to solve the proposed MINP model. The model is illustrated through a numerical example and the results show that the GA can solve the model in less than a minute. Moreover, the results of the numerical illustration show that the item cost and transportation cost are the deciding factors in selecting suppliers and allocating orders.
 Keywords: Supplier selection, Economic Order Quantity, Order allocation, Mixed-integer nonlinear programming.

A novel approach for automatic remodularization of software systems using extended ant colony optimization algorithm

Abstract Context Software modularization is extremely important to streamline the inner structure of the program modules without influencing its core functionality. As the framework advances during the upkeep stage, the pristine design of the software package gets disintegrated and hence it is arduous to understand and maintain. There are many existing approaches being carried out to automatically remodularize using optimization techniques to ease the maintenance and improve the quality of the system. The outcomes are rather insufficiently optimal and depend on problem-specific operators, which in turn expands the time multifaceted nature to land at an answer. Apart from these limitations, the issues, such as time complexity, scalability and performance need to be addressed. Objective In this paper, an efficient automatic software remodularization using extended Ant Colony Optimization (ACO) has been proposed to remodularize the software systems. Method The proposed approach mainly includes two phases: optimised traversal of software system using ACO for finding the order of software files to be processed and remodularization of software system using the proposed approach of extended ACO. Results We experimented our proposed approach on seven software systems. The performance is evaluated by using Turbo modularization quality (MQ) which supports Module dependency graph (MDG) that have edge weights. The time complexity of remodularized software system is evaluated based on number of Turbo MQ. Conclusion It can be concluded that when the performance has been compared with the subsisting methodologies, for example, Genetic algorithm (GA), Hill climbing (HC) and Interactive genetic algorithms (I-GAs), the proposed approach has higher Turbo MQ value with lesser time complexity in the evaluated software systems.

A variable neighborhood search for multi-family capacitated lot-sizing problem

The paper presents a novel variable neighborhood search (VNS) algorithm combined with a linear programming solver (LPS) to solve multi-item multi-family capacitated lot-sizing problem with setup times independent of the family sequence. The model has a direct application to real production planning in foundry industry, where the goal is to create the batches of manufactured castings and the sequence of the melted metal loads to prevent delays in delivery of goods to customers. We developed a simple and fast VNS based algorithm with problem-specific operators that are responsible for the neighborhood generating. For large instances of the problem the proposed algorithm is able to provide better results than CPLEX solver.

Iterated Local Search for Foundry Lotsizing and Scheduling Problem with Setup Costs

Abstract The paper presents a novel Iterated Local Search (ILS) algorithm to solve multi-item multi-family capacitated lot-sizing problem with setup costs independent of the family sequence. The model has a direct application to real production planning in foundry industry, where the goal is to create the batches of manufactured castings and the sequence of the melted metal loads to prevent delays in delivery of goods to clients. We extended existing models by introducing minimal utilization of furnace capacity during preparing melted alloy. We developed simple and fast ILS algorithm with problem-specific operators that are responsible for the local search procedure. The computational experiments on ten instances of the problem showed that the presence of minimum furnace utilization constraint has great impact on economic and technological conditions of castings production. For all test instances the proposed heuristic is able to provide the results that are comparable to state-of-the art commercial solver.

Selecting Efficient Service-providers in Electric Power Distribution Industry Using Combinatorial Reverse Auction

In this paper, a combinatorial reverse auction mechanism is proposed for selecting the most efficient service-providers for resolving sustained power interruptions in multiple regions of an electric power distribution company’s responsibility area. Through this mechanism, supplying the required service in each region is assigned to only one potential service-provider considering two criteria including cost and service time. So, the corresponding winner determination problem of the proposed auction mechanism is formulated as a bi-objective combinatorial optimization problem. However, finding a feasible solution for the formulated problem as well as its solving is NP-complete. Since exact optimization algorithms are failed in solving this kind of problems in a reasonable time, a problem specific metaheuristic called NSGA-II that is an evolutionary algorithm for solving multi-objective optimization problems is developed to estimate the set of Pareto optimal solutions of the formulated bi-objective winner determination problem. In our developed NSGA-II, two problem-specific operators are proposed for creating initial feasible solutions and converting infeasible solutions to feasible ones. Furthermore, a new method for determining the population size based on the size of problem instance is proposed. We conduct a computational experiment in which several randomly generated problem instances are solved using the proposed NSGA-II in different settings. Computational results of proposed algorithm in different settings are compared using a quality measure and statistical hypothesis tests. The results of performance comparison show that the proposed NSGA-II with a population size determined by proposed method and a different form of binary tournament method performs better in finding non-dominated solutions for different instances of formulated bi-objective optimization problem.

Order matters – A Variable Neighborhood Search for the Swap-Body Vehicle Routing Problem

This article presents investigations on the performance of standard and problem specific neighborhood operators for the Swap-Body Vehicle Routing Problem. More specifically, our work analyzes the contribution of each operator to the solution quality. In addition, an experimental setting is defined that can be utilized to identify promising sequences in a Variable Neighborhood Search. The experiments verify that the sequence matters since two best known solutions can be equaled and 16 out of 18 solutions can be improved, with a maximal improvement of 2.25% and an average improvement of 0.70%.

Nested Multi- and Many-Objective Optimization of Team Track Pursuit Cycling

Team pursuit track cycling is an elite sport that is part of the Summer Olympics. Teams race against each other on special tracks called velodromes. In this article, we create racing strategies that allow the team to complete the race in as little time as possible. In addition to the traditional minimisation of the race times, we consider the amount of energy that the riders have left at the end of the race. For the team coach this extension can have the benefit that a diverse set of trade-off strategies can be considered. For the optimisation approach, the added diversity can help to get over local optima. To solve this problem, we apply different state-of-the-art algorithms with problem-specific variation operators. It turns out that nesting algorithms is beneficial for achieving fast strategies reliably.

Wireless sensors deployment optimization using a constrained Pareto-based multi-objective evolutionary approach

Sensors deployment is one of the most fundamental issues in wireless sensor networks (WSNs) design. One of the major challenges in sensors deployment is to find a tradeoff between conflicting objectives of network, coverage and lifetime, under certain connectivity constraints. This paper proposes a constrained Pareto-based multi-objective evolutionary approach (CPMEA) which aims at finding Pareto optimal layouts that maximize the coverage and minimize the sensors energy consumption for the sake of prolonging the network lifetime, while maintaining the full connectivity between each sensor node and the high energy communication node (HECN). In the proposed CPMEA, certain problem-specific operators are designed to direct the search into feasible regions of the search space. For this purpose, during the evolution, the designed operators are adapted to the objectives as well as constraints of the problem in order to make overall improvements on the CPMEA performance. In this paper, the proposed method is numerically examined in certain WSN test instances and a study of its performance is carried out using certain performance metrics. The results have shown the effectiveness of the designed operators as well as the superiority of the proposed approach over the non-dominated sorting genetic algorithm-II (NSGA-II).

Adaptive large neighborhood search for the curriculum-based course timetabling problem

In curriculum-based course timetabling, lectures have to be assigned to periods and rooms, while avoiding overlaps between courses of the same curriculum. Taking into account the inherent complexity of the problem, a metaheuristic solution approach is proposed, more precisely an adaptive large neighborhood search, which is based on repetitively destroying and subsequently repairing relatively large parts of the solution. Several problem-specific operators are introduced. The proposed algorithm proves to be very effective for the curriculum-based course timetabling problem. In particular, it outperforms the best algorithms of the international timetabling competition in 2007 and finds five new best known solutions for benchmark instances of the competition.

Multi-Objective Genetic Algorithm for an automatic transmission gear shift map

Abstract: Reductions in engine emissions including carbon dioxide (CO 2 ) can be achieved by analysing the Brake Specific Fuel consumption (BSFC) map and selecting a set of gear ratio and associated gear shift map which will move the engine operating point into the most efficient region. This paper formulates the determination of the most appropriate gear shift map for a Dual Clutch Transmission as a multi objective optimisation problem. A multi-objective genetic algorithm (MOGA) is complemented by a new repair mechanism to enforce acceptable driveability in the solutions produced as well as a new problem specific local search operator exploiting engineering knowledge to determine feasible gear shift maps that result in reduced emissions. The solutions are evaluated against standard as well as novel objective formulations that take into account CO 2 , driveability, gearbox durability and other practical implementation considerations. A correlation analysis, combined with Pareto analysis, is performed to identify and visualise related objectives that can be combined using a traditional weighted sum approach and those that should be handled separately using a Pareto front approach to facilitate the trade-offs identification and resolutions. The first set of objectives aim to reduce CO 2 , the second to maintain driveabil-ity, and the third to consider durability. The novel approach was used to identify trade-off solutions focusing on reducing CO 2 emission. These solutions were validated on a prototype vehicle on a rolling road using the New European Driving Cycle, confirming the expected CO 2 savings whilst conserving good driveability characteristics.

Effective hierarchical optimization by a hierarchical multi-space competitive genetic algorithm for the flexible job-shop scheduling problem

In this paper, we propose a new optimization technique, the hierarchical multi-space competitive distributed genetic algorithm (HmcDGA), which is effective for the hierarchical optimization problem. It is an extension of the multi-space competitive distributed genetic algorithm (mcDGA), which was proposed by the authors. The mcDGA efficiently finds an optimal solution with a low computational cost by increasing the number of individuals in a solution space in which it is likely to exist. An optimization method that is divided into several levels of hierarchy is called a hierarchical optimization. Several hierarchical optimization techniques have been proposed, including the hierarchical genetic algorithm (HGA). In hierarchical optimization, a complex problem is divided into a hierarchical collection of simpler problems, and each level is optimized independently. In this way, complex problems can be solved without the need to develop problem-specific operators. However, in the conventional HGA, this results in a high computational cost because the genetic algorithm (GA) is repeated many times at upper and lower level. The HmcDGA is a hybrid of the mcDGA and HGA, and it has some of the advantages of each one; for example, the HmcDGA can find an optimal solution at low computational cost and without requiring special operations. This allows it to be applied to a wide variety of optimization problems. Therefore, the HmcDGA may become the powerful optimization algorithm that can solve various problems. In this paper, we apply the proposed HmcDGA to the flexible job-shop scheduling problem (FJSP) which is one of the complex combinational optimization problem and confirm its effectiveness. Simulation results show that the HmcDGA can find solutions that are comparable to those found by using GAs developed specifically for the FJSP, the HmcDGA is not required a lot of computational costs comparing to the HGA.

An analysis of the interface between evolutionary algorithm operators and problem features for water resources problems. A case study in water distribution network design

Evolutionary Algorithms (EAs) have been widely employed to solve water resources problems for nearly two decades with much success. However, recent research in hyperheuristics has raised the possibility of developing optimisers that adapt to the characteristics of the problem being solved. In order to select appropriate operators for such optimisers it is necessary to first understand the interaction between operator and problem. This paper explores the concept of EA operator behaviour in real world applications through the empirical study of performance using water distribution networks (WDN) as a case study. Artificial networks are created to embody specific WDN features which are then used to evaluate the impact of network features on operator performance. The method extracts key attributes of the problem which are encapsulated in the natural features of a WDN, such as topologies and assets, on which different EA operators can be tested. The method is demonstrated using small exemplar networks designed specifically so that they isolate individual features. A set of operators are tested on these artificial networks and their behaviour characterised. This process provides a systematic and quantitative approach to establishing detailed information about an algorithm's suitability to optimise certain types of problem. The experiment is then repeated on real-world inspired networks and the results are shown to fit with the expected results.

A differential evolution-based memetic algorithm for project scheduling problems

A differential evolution DE-based memetic algorithm MA for solving the project scheduling problem PSP is proposed. In order to balance the abilities of exploration and exploitation, the proposed DE-based MA DEMA combines the DE-based global search with a problem-specific local search operator. In particular, DEMA applies evolutionary search schemes of DE to explore the large search space, where an improved mutation strategy that both uses vector differences for perturbation and learns from the current best solution is adopted. Besides, to make DE suitable for solving PSP which is a combinational optimisation problem, a slackness method is designed to convert between discrete dedications in PSP and continuous vectors in DE. On the other hand, a local search with two neighbourhood structures is utilised to exploit the local information around current solutions so that the solution quality can be further improved. Simulation results on real-world PSP instances validate the superiority of DEMA over state-of-the-art search-based approaches and the effectiveness of new mechanisms designed in DEMA.

Hybrid large neighborhood search for the bus rapid transit route design problem

Due to an increasing demand for public transportation and intra-urban mobility, an efficient organization of public transportation has gained significant importance in the last decades. In this paper we present a model formulation for the bus rapid transit route design problem, given a fixed number of routes to be offered. The problem can be tackled using a decomposition strategy, where route design and the determination of frequencies and passenger flows will be dealt with separately. We propose a hybrid metaheuristic based on a combination of Large Neighborhood Search (LNS) and Linear Programming (LP). The algorithm as such is iterative. Decision upon the design of routes will be handled using LNS. The resulting passenger flows and frequencies will be determined by solving a LP. The solution obtained may then be used to guide the exploration of new route designs in the following iterations within LNS. Several problem specific operators are suggested and have been tested. The proposed algorithm compares extremely favorable and is able to obtain high quality solutions within short computational times.

ROBIL: Robot Path Planning Based on PBIL Algorithm

Genetic algorithm (GAs) have attracted considerable interest for their usefulness in solving complex robot path planning problems. Specifically, researchers have combined conventional GAs with problem-specific operators and initialization techniques to find the shortest paths in a variety of robotic environments. Unfortunately, these approaches have exhibited inherently unstable performance, and they have tended to make other aspects of the problem-solving process (e.g., adjusting parameter sensitivities and creating high-quality initial populations) unmanageable. As an alternative to conventional GAs, we propose a new population-based incremental learning (PBIL) algorithm for robot path planning, a probabilistic model of nodes, and an edge bank for generating promising paths. Experimental results demonstrate the computational superiority of the proposed method over conventional GA approaches.

Multistart Iterated Tabu Search for Bandwidth Coloring Problem

This paper presents a Multistart Iterated Tabu Search (MITS) algorithm for solving Bandwidth Coloring Problem (BCP) and Bandwidth MultiColoring Problem (BMCP). The proposed MITS algorithm exhibits several distinguishing features, such as integrating an Iterated Tabu Search (ITS) algorithm with a multistart method and a problem specific perturbation operator. Tested on two sets of 66 public benchmark instances widely used in the literature, the MITS algorithm achieves highly competitive results compared with the best performing algorithms, improving the previous best known results for 22 instances while matching the previous best known results for 39 ones. Furthermore, two important features of the proposed algorithm are analyzed.