Local Search Metaheuristic Algorithms Research Articles

Metaheuristic-driven extended exergy accounting for sustainable closed-loop food supply chain management

In this research, an exergetic mathematical model is proposed for closed-loop food supply chain network design considering economic, environmental, and social aspects. The studded closed-loop food supply chain consists of six echelons, four echelons for forwarding the products (suppliers, factory, distribution centers, and customers), and two echelons for returning the products (collecting centers and disposal centers). The exergetic model is based on the extended exergy accounting method, which calculates the consumed exergy in all six echelons by considering the equivalents of labor and capital exergy. This model aims to minimize both the economic and exergetic costs associated with the food supply chain. To solve the proposed mathematical model, an ensemble global–local search metaheuristic algorithm utilizing a combined whale optimization algorithm (WOA) and simulated annealing (SA), called CWOASA, is proposed. In the CWOASA algorithm, first, WOA is used to search the entire search space. Next, SA is applied to further improve the best solution obtained by WOA. A real dataset of a dairy supply chain is used to evaluate the efficiency of the proposed metaheuristic-driven exergy analysis model and claim its benefits over the existing methods. Simulation results revealed that the proposed method leads to a 6.74% reduction in the total exergy consumption of the supply chain. The proposed methodology offers valuable insights, shedding light on the prospective gains in terms of mitigated environmental impact for each incremental economic expenditure, thus laying the foundation for promising avenues of future research.

Airline operational crew-aircraft planning considering revenue management: A robust optimization model under disruption

Airline planning involves various issues that, in a general, can be grouped as network planning, schedule design and fleet planning, aircraft planning, and crew scheduling decisions. This study mainly aims to optimize the Crew Scheduling (CS) decisions considering the operational constraints related to Aircraft Maintenance Routing (AMR) regulations. Since, after fuel, crew costs are vital for airlines, and aircraft maintenance constraints are important operationally, the integrated Crew Scheduling and Aircraft Maintenance Routing (CS-AMR) problem is an important issue for the airlines. The present research addresses this problem using the Revenue Management (RM) approach under some disruption scenarios in the initial schedule. The proposed approach enables airlines to make more efficient decisions during disruptions to prevent flight delay/cancellation costs and recaptures an acceptable part of the spilled demand caused by disruption through the fleet stand-by capacity. This approach considers a set of disruptions in the flight schedule under different probable scenarios and provides the optimal decisions. Accordingly, airlines have two decision-making stages: Here-and-Now (HN) decisions related to the initial schedule for crew, aircraft routing and stand-by capacity to face probable disruptions and Wait-and-See (WS) decisions that determine what the executive plan of each crew and aircraft should be under each scenario, and how to use different options for flight cancellation and substitution. To this end, a novel Two-Stage Robust Scenario-based Optimization (TSRSO) model is proposed that considers the HN and WS decisions simultaneously. A numerical example is solved, and its results verify the applicability and evaluate the performance of the proposed TSRSO model. Regarding the complexity of the proposed MILP model categorized as NP-hard problems, we develop a computationally efficient solution method to solve large-scale problem instances. A single-agent local search metaheuristic algorithm, Adaptive Large Neighborhood Search (ALNS), is applied to solve the CS-AMR problem efficiently. According to the result obtained by applying the proposed revenue management approach for the CS-AMR problem, airlines can drive a robust solution under disruption scenarios that not only minimizes the total delay/cancellation costs but also increases the profit by recapturing the spilled demand.

The vehicle routing problem with capacitated cross-docking

A key decision for every product consolidation node (e.g., warehouse or cross-dock) in a distribution network is the synchronization of inbound and outbound product flows. Among other operational issues, the physical space restrictions as well as the restricted availability of resources (e.g., forklifts) limit the number of vehicles that can be processed at the same time. As a result, the coordination and scheduling of inbound and outbound vehicles becomes a hard problem to solve with significant impact on the overall network performance. This paper introduces a new Vehicle Routing Problem with capacitated Cross-Docking. In particular, the cross-dock capacity represents the maximum number of products that can be handled at the cross-dock for unloading, consolidation, and loading operations, at any point in time. We also consider a First-In First-Out (FIFO) policy for serving the inbound vehicles at the cross-dock. This operational aspect has not been thoroughly examined in the literature. At first, a mathematical formulation is provided. Next, we present a local search metaheuristic algorithm that is tested on existing as well as new benchmark data sets. Various computational experiments are performed to explore the effect of the cross-dock processing capacity on the total transportation costs. From the methodological perspective, the handling of capacity constraints at the cross-dock has several challenges. To deal with the additional complexity, a heuristic mechanism and a Constraint Programming optimizer have been used as alternative approaches for efficiently evaluating the feasibility of tentative solutions. A performance comparison of these approaches is presented and algorithmic insights are provided.

Polynomial analogy‐based software development effort estimation using combined particle swarm optimization and simulated annealing

SummarySoftware development effort estimation is an effective factor in the success or failure of software projects. There are several methods to estimate the effort of software projects, the most common of which is analogy‐based estimation (ABE). In this article, a polynomial version of ABE (named PABE) is presented, in which, the project effort is calculated based on a polynomial ensemble of different ABE models. To optimize the controllable parameters of the PABE model, a combined global–local search metaheuristic algorithm based on particle swarm optimization and simulated annealing is utilized in two steps. At the first step, for each similarity and adaptation function, the optimized ABE model is determined by exploiting the optimal value of feature weights, the number of similar projects, and other parameters of the ABE model. Then, at the second step, the amount of effort attained by the optimized models is used for estimating the final effort by the proposed polynomial equation. The proposed PABE method has been successfully executed on five well‐known software effort estimation datasets: Maxwell, Albrecht, Cocomo81, Desharnais, and Kemerer. Obtained results show the superiority of the proposed PABE model in terms of accuracy and efficiency compared to other techniques.

Supply chain sustainability improvement using exergy analysis

Sustainable supply chain management (SSCM) is an improved version of supply chain management, in which, not only economic issues but also social and environmental issues are considered. It can be achieved through extended exergy analysis which is a powerful thermodynamic tool for evaluating the sustainability of an industrial system. This paper provides an exergetic analysis to model and calculate the consumed exergy for sustainable supply chains. The model considers different objectives of financial, social and environmental aspects on selecting the more sustainable supply chain to produce and distribute productions. The proposed model is solved using a hybrid global- and local-search metaheuristic algorithm based on genetic algorithm and simulated annealing (named GLGASA). By minimizing the exergy cost, the authors provide an insight about the potential of the environmental destruction saving per unit of additional cost. This quantification of the cost and saving would be needed at the time of business case calculation for the new projects or modification and upgrade of the existing processes to achieve a better decision that guarantees both return of the investment and environmental protection. In order to validate the proposed methodology, a real food supply chain is presented and discussed to show the usability of the model and claim the benefits over the previously available models. According to the obtained results, the proposed method provides 4.48% saving in the consumed exergy of the supply chain by accepting additional economic costs.

A variant of the split vehicle routing problem with simultaneous deliveries and pickups for inland container shipping in dry-port based systems

In this paper, we will study a typical problem in inland container shipping, concerning the barge transportation of maritime containers between a dry port and a set of seaport terminals. The barges depart from the dry port and visit a set of sea terminals, where containers need either to be dropped off or picked up. The goal is to achieve economies of scale with barges and avoid trucking as much as possible. The decision thus involves finding the best allocation of containers to barges in order to guarantee on-time delivery and meet capacity restrictions. The problem will be modeled as a variant of the split vehicle routing problem with simultaneous pickups and deliveries coupled with time features. The model includes parameters that can be tuned to improve barge utilization and travelling distance. A hybrid local search meta-heuristic algorithm, combined with a branch-and-cut solver, will be developed to solve the model. Numerical experiments have been conducted to test the performance of the algorithm and provide solution analysis for practical insights. Real-world data has been collected from a local barge operator based in the Port of Rotterdam region and will be used as input for the experiments. This will result in an in-depth analysis into current planning practices. The proposed framework complements existing models in the literature and contributes to the development of a comprehensive set of decision support tools, which help in the decision-making process for inland terminals.

Development of an Interactive Tool based on Combining Graph Heuristic with Local Search for Examination Timetable Problem

Every university faces a lot of challenges to solve the examination timetabling problem because the problem is NP-hard and contains numerous institutional constraints. Although several attempts have been taken to address the issue, there are scarcities of interactive and automated tools in this domain that can schedule exams effectively by considering institutional resources, different constraints, and student enrolment in courses. This paper presents the development of a system as a graphical and interactive tool for examination timetabling problem. To develop the system, combining graph coloring heuristic and local search meta-heuristic algorithms are employed. The graph heuris-tic ordering is incorporated for constructing initial solution(s), whereas the local search meta-heuristic algorithms are used to produce quality exam timetables. Different constraints and objective functions from ITC2007 exam competition rules are adopted, as it is a complex real word exam timetabling problem. Finally, the system is tested on the ITC2007 exam benchmark dataset, and test results are presented. The main aspect of the system is to deliver an easy-to-handle tool that can generate quality timetables based on institutional demands and smoothly manage several key components. These components are collecting data associated with the enrolment of students in exams, defining hard and soft constraints, and allocating times and resources. Overall, this software can be used as a commercial scheduler in order to provide institutions with automated, accurate, and quick exam timetable.

Solving vehicle routing problem for multistorey buildings using iterated local search

Vehicle routing problem (VRP) which is a well-known combinatorial optimisation problem that has many applications used in industry is also a generalised form of the travelling salesman problem. In this study, we defined and formulated the VRP in multistorey buildings (Multistorey VRP) for the first time and proposed a solving method employing iterated local search metaheuristic algorithm. This variant of VRP has a great potential for turning the direction of optimisation research and applications to the vertical cities area as well as the horizontal ones. Routes of part picking or placing vehicles/humans in multistorey plants can be minimised by this way. VRP can also be applied to the optimisation of delivering the packages (goods, meals, folders, mails, etc.) to rooms or locations of the structures such as buildings, and skyscrapers for travelling robots/humans using elevators and stairs. The first detailed multistorey building optimisation experiments were conducted by designing a series of scenarios with different parameter values (number of storeys, connections between storeys and customers). The results were presented and the effects of the various building structures over the performance were discussed.

Breakout Local Search for the Travelling Salesman Problem

The travelling salesman problem (TSP), a famous NP-hard combinatorial optimisation problem (COP), consists of finding a minimum length tour that visits n cities exactly once and comes back to the starting city. This paper presents a resolution of the TSP using the breakout local search metaheuristic algorithm (BLS), which is based on the iterated local search (ILS) framework and improves it by introducing some fundamental features of several well-established metaheuristics such as tabu search (TS) and variable neighbourhood search (VNS). BLS moves from a local optimum of a neighbourhood to another by applying perturbation jumps whose type and number are determined adaptively. It has already been applied to many COP and gives good results. This innovative hybridisation resolved well 41 instances from the commonly used benchmark library TSPLIB. The high quality of experimental results shows the competitiveness of the proposed algorithm compared to other algorithms based on local search.

Investigating the enhanced Best Performance Algorithm for Annual Crop Planning problem based on economic factors.

The Annual Crop Planning (ACP) problem was a recently introduced problem in the literature. This study further expounds on this problem by presenting a new mathematical formulation, which is based on market economic factors. To determine solutions, a new local search metaheuristic algorithm is investigated which is called the enhanced Best Performance Algorithm (eBPA). eBPA’s results are compared against two well-known local search metaheuristic algorithms; these include Tabu Search and Simulated Annealing. The results show the potential of the eBPA for continuous optimization problems.

A Study on the Enhanced Best Performance Algorithm for the Just-in-Time Scheduling Problem

The Just-In-Time (JIT) scheduling problem is an important subject of study. It essentially constitutes the problem of scheduling critical business resources in an attempt to optimize given business objectives. This problem is NP-Hard in nature, hence requiring efficient solution techniques. To solve the JIT scheduling problem presented in this study, a new local search metaheuristic algorithm, namely, the enhanced Best Performance Algorithm (eBPA), is introduced. This is part of the initial study of the algorithm for scheduling problems. The current problem setting is the allocation of a large number of jobs required to be scheduled on multiple and identical machines which run in parallel. The due date of a job is characterized by a window frame of time, rather than a specific point in time. The performance of the eBPA is compared against Tabu Search (TS) and Simulated Annealing (SA). SA and TS are well-known local search metaheuristic algorithms. The results show the potential of the eBPA as a metaheuristic algorithm.

On the performance of new local search heuristics for annual crop planning: case study of the Vaalharts irrigation scheme

This paper investigates the capabilities of three new local search (LS) metaheuristic algorithms in determining solutions to an annual crop planning (ACP) problem at an existing Irrigation Scheme. ACP is an optimisation problem in agricultural planning which involves determining resource allocation solutions amongst the various crops that are required to be grown at an irrigation scheme, within a year. The LS algorithms investigated are the best performance algorithm (BPA), the iterative best performance algorithm (IBPA) and the largest absolute difference algorithm (LADA). To determine the relative merits of the solutions found by these algorithms, their solutions have been compared against the solutions of two well-known LS metaheuristic algorithms and four population-based metaheuristic algorithms in the literature. The results show that BPA, IBPA and LADA were competitive in determining solutions for this particular optimisation problem.

GPU Computing for Parallel Local Search Metaheuristic Algorithms

Local search metaheuristics (LSMs) are efficient methods for solving complex problems in science and industry. They allow significantly to reduce the size of the search space to be explored and the search time. Nevertheless, the resolution time remains prohibitive when dealing with large problem instances. Therefore, the use of GPU-based massively parallel computing is a major complementary way to speed up the search. However, GPU computing for LSMs is rarely investigated in the literature. In this paper, we introduce a new guideline for the design and implementation of effective LSMs on GPU. Very efficient approaches are proposed for CPU-GPU data transfer optimization, thread control, mapping of neighboring solutions to GPU threads, and memory management. These approaches have been experimented using four well-known combinatorial and continuous optimization problems and four GPU configurations. Compared to a CPU-based execution, accelerations up to \times 80 are reported for the large combinatorial problems and up to \times 240 for a continuous problem. Finally, extensive experiments demonstrate the strong potential of GPU-based LSMs compared to cluster or grid-based parallel architectures.

A local search metaheuristic algorithm for the vehicle routing problem with simultaneous pick-ups and deliveries

This article proposes a local search metaheuristic solution approach for the vehicle routing problem with simultaneous pick-ups and deliveries (VRPSPD), which models numerous practical transportation operations in the context of reverse logistics. The proposed algorithm is capable of exploring wide solution neighborhoods by statically encoding moves into special data structures. To avoid cycling and induce diversification, the overall search is coordinated by the use of the promises concept which is based on the aspiration criteria of tabu search. The proposed promises implementation is applied to basic solution features, namely solution arcs. In terms of the challenging capacity constraints imposed by the VRPSPD model, we present a constant-time feasibility checking procedure for the employed local search operators. The presented metaheuristic development was tested on eighteen large-scale VRPSPD benchmark instances derived from the literature. It proved to be both robust and effective, improving most of the previously best-known solutions of the examined test problems.