Multi-objective Simulated Annealing Algorithm Research Articles

A multi-objective optimized OLSR routing protocol.

The rapid development of mobile communication devices has brought challenges to wireless networks, where data packets are able to organize and maintain local area networks more freely without the constraints of wired devices. Scholars have developed diverse network protocols on how to ensure data transmission while maintaining its self-organizational nature. However, it is difficult for traditional network protocols to meet the needs of increasingly complex networks. In order to solve the problem that the better node set may not be selected when selecting the node set responsible for forwarding in the traditional OLSR protocol, a multi-objective optimized OLSR algorithm is proposed in this paper, which incorporating a new MPR mechanism and an improved NSGA-II algorithm. In the process of route discovery, the intermediate nodes responsible for forwarding packets are determined by the new MPR mechanism, and then the main parameters in the OLSR protocol are provided by the multi-objective optimization algorithm. Matlab was used to build a self-organizing network in this study. In addition, the conventional OLSR protocol, NSGA-II algorithm and multi-objective simulated annealing algorithm are selected to compare with the proposed algorithm. Simulation results show that the proposed algorithm can effectively reduce packet loss and end-to-end delay while obtaining better results in HV and Spacing, two multi-objective optimization result evaluation metrics.

Multi-objective simulated annealing algorithm for robotic mixed-model two-sided assembly line balancing with setup times and multiple constraints

Robotic mixed-model two-sided assembly lines (RMTALs) have become increasingly prevalent in manufacturing industries for productivity enhancement. However, only limited attention has been paid to the RMTAL balancing problems that contain both setup times and multiple constraints such as positional constraints, zoning constraints, and synchronism constraints. Given the coexistence of these aspects in real-world scenarios, this study aims to address the RMTAL balancing problem with setup times and the mentioned constraints. To formulate the problem, a mixed integer programming model is proposed to optimize cycle time and total energy consumption. To handle multiple constraints and setup times, a new method based on four-vector encoding is introduced. This method addresses the three constraints during the encoding phase using four intervals and five rules, while setup times are processed during the decoding phase. To solve the problem, a Pareto entropy-based two-mode multi-objective simulated annealing algorithm is developed. The algorithm employs variable neighborhood descent algorithm with switchable objectives to generate the initial Pareto archive. Subsequently, it selects the initial solution with minimal potential in the Pareto archive, alternating between two search modes of exploration and exploitation based on the entropy difference of Pareto solutions. Comparative experiments with four state-of-the-art algorithms on benchmark instances of varying scales demonstrate that the proposed algorithm outperforms the others in over 93% of instances for the hypervolume ratio and inverted generational distance metrics.

An improved solution methodology for the urban transit routing problem

An improved solution methodology is proposed in this paper for the urban transit routing problem (UTRP). This methodology includes a procedure for the generation of improved initial solutions as well as improved metaheuristic search approaches, involving the use of hyperheuristics to manage search operators in both trajectory-based and population-based metaheuristics. The UTRP variant considered in this paper is that of deciding upon efficient bus transit routes. The design criteria embedded in our UTRP model are the simultaneous minimisation of the expected average passenger travel time and minimisation of the system operator’s cost (measuring the latter as the sum total of all route lengths in the system). The model takes as input an origin–destination demand matrix for a pre-specified set of bus stops, along with an underlying road network structure, and returns as output a set of bus route trade-off solutions. The decision maker can then select one of these route sets subjectively, based on the desired degree of trade-off between the aforementioned transit system design criteria. This bi-objective minimisation problem is solved approximately in three distinct stages — a solution initialisation stage, an intermediate analysis stage, and an iterative metaheuristic search stage during which high-quality trade-off solutions are sought. A novel procedure is introduced for the solution initialisation stage, aimed at effectively generating high-quality initial feasible solutions. Two metaheuristics are implemented to solve instances of the problem, namely a dominance-based multi-objective simulated annealing algorithm and an improved non-dominated sorting genetic algorithm, each equipped with a hyperheuristic capable of managing the perturbation operators employed. Various novel operators are proposed for these metaheuristics, of which the most noteworthy take into account the demand of passengers.

RETRACTED ARTICLE: Effectiveness of Mixed Fuzzy Time Window Multi-objective Allocation in E-Commerce Logistics Distribution Path

The study of logistics distribution network under e-commerce environment is conducive to the establishment of efficient logistics distribution system, but also to promote the further development of e-commerce and improve social benefits of great significance. This study considers multiple fuzzy factors and introduces a customer fuzzy time window with variable coefficients, establishes a multi-objective set allocation integrated multi-level location path planning model, and proposes an archive type multi-objective simulated annealing improvement algorithm based on master–slave parallel framework embedded taboo search to solve the model. Tabu search and large-scale neighborhood algorithm are used to solve the initial solutions of the first level network and the second level network respectively, and archival reception criterion is introduced to deal with the multi-objective problem. The results of the proposed algorithm for the two-level site-routing problem are less than 6% different from the internationally known optimal solution. The master–slave parallel computing framework improves the efficiency of the algorithm by about 6.38%. The experimental results prove the effectiveness and necessity of the improved optimization. In addition, this study simulates the site-routing problem model constructed by the study by extending the data of standard examples. The experimental results prove the correctness and reference significance of the multilevel site-routing problem model with multiple fuzzy factors.

Metaheuristic algorithms for a sustainable saffron supply chain network considering government policies and product quality under uncertainty

Abstract Iranian saffron products hold a unique place in the global market as the most highly valued agricultural and medicinal commodities. The various uses of saffron make it clear that there is a need for special attention to the supply chain network. Unfortunately, the absence of an integrated supply chain network within the saffron industry has resulted in significant challenges related to supply management and demand fulfillment. Addressing real-world uncertainties is paramount when developing models for optimization problems. Therefore, this research proposes a multi-objective optimization model for designing a saffron supply chain network under uncertainty. The model objectives are to decrease the total cost of the supply chain, increase job opportunities and economic development in regions, and improve the quality of products. The proposed mathematical model is solved using the interactive fuzzy method to deal with multiple functions. Furthermore, possibilistic chance constrained programming is employed to effectively manage uncertain variables such as demand, cost, and social parameters within the model. To demonstrate the applicability and validity of the proposed model and solution method, a real case study was conducted in Khorasan Razavi province, Iran. Additionally, because of the complexity of the proposed model in large-scale networks, non-dominated sorting genetic algorithm-II and multi-objective simulated annealing algorithms are proposed. Different parameters are analyzed to determine their impact on the results so that decision-makers can choose values more accurately. The sensitivity analysis and statistical tests performed on the results support the performance of the proposed model. Overall, the results demonstrate that the exact method and metaheuristic algorithms are capable of solving the problem in different dimensions. The computational results derived from this model offer invaluable managerial insights, empowering decision-makers to align their strategies and preferences more effectively.

A Facility Layout Algorithm for Logistics Scenarios Driven by Transport Lines

The layout of facilities in a logistics scenario involves not only the working facilities responsible for processing materials but also the transport lines responsible for transporting materials. The traditional facility layout methods do not take into account the transportation facilities nor calculate the material handling cost by Manhattan distance, thus failing to fulfill the actual requirements of industrial logistics scenarios. In this paper, a facility layout algorithm framework MOSA-FD driven by transport lines is proposed. A multi-objective simulated annealing (MOSA) algorithm is designed for both material handling cost (MHC) and transport facility cost (TFC) objectives. Then, a force-directed (FD) algorithm is applied to correct the unreasonable solutions according to the material transport lines in the logistics workshop, and a better solution is quickly obtained. Finally, by comparing the results with those of other metaheuristic multi-objective algorithms, the acceleration of the force-directed algorithm in this layout problem is demonstrated in experimental instances of different scales, and our method, compared to the MOSA algorithm, can reach optimal ratios of 36% and 80%, respectively, on the multi-objective.

Multi-objective optimisation for sustainable few-to-many pickup and delivery vehicle routing problem

ABSTRACT E-commerce is a continuously growing sector significantly affected by sustainability issues during the last few years. To deal with economic, environmental and social sustainability aspects, e-commerce platforms consolidate orders to pick-up several requests from the same location, defining the so-called Few-to-Many Pick-up and Delivery Vehicle Routing Problem (F-M VRPPD). The proposed contribution addresses the optimisation of this problem by developing a multi-objective simulated annealing algorithm distinguished by four tailored Local Search (LS) operators specifically developed to increase the probability to identify feasible solutions and decrease the computational time. This algorithm is validated with several instances of a case study e-commerce platform based in an European mountain region. Firstly, the original LS operators are compared to benchmark literature ones to solve identical problems, reporting better performance in 84% of these instances. Furthermore, for the most relevant scenarios significant results are presented and discussed concerning the economic, environmental and social performance of the defined solutions according to the characteristics of the instances, as the routes height profile and the drivers’ metabolic energy consumption. The tri-dimensional Pareto frontiers suggest how through a slight worsening in the economic objective function it is possible to improve the social one by up to 18.3% on average.

Modified multi-objective simulated annealing algorithm for scheduling a flow shop production system with setup times

The presence of setup times and multiple objectives makes the scheduling problem complex. This paper presents two algorithms namely: multi-objective simulated annealing (MOSA) and modified MOSA for the simultaneous minimisation of makespan and mean tardiness in scheduling a flow shop production system wherein setup times depend on processing sequence. The modified MOSA algorithm is developed by hybridising MOSA with variable neighbourhood search. Benchmark problems are used to conduct computational experiments. The analysis of the algorithms reveals that modified MOSA outperforms MOSA for the measures considered. Hybridisation improves the performance of MOSA. The proposed algorithms are applied on scheduling a real-world SDST flow shop and the results indicate superior performance of modified MOSA.

Half-open time-dependent multi-depot electric vehicle routing problem considering battery recharging and swapping

In order to promote green and sustainable development of the transportation industry, an increasing number of logistics companies have begun to deploy electric vehicles (EVs) to provide urban distribution services. This paper studies a Half-Open Time-Dependent Multi-Depot Electric Vehicle Routing Problem Considering Battery Recharging and Swapping (HOTDMDEVRPBRS) in last-mile delivery. Based on the calculation functions of EV energy consumption, travel time, and carbon emissions under the time-dependent road network, a mixed integer programming model is formulated. The goal of the model is to minimize the economic cost and environmental cost of logistics companies. Given the complexity of the problem, this paper designs a multi-objective simulated annealing algorithm (SAA). Finally, this paper carries out comprehensive computational experiments to verify and evaluate the performance of the proposed model and method and examines the economic and environmental benefits brought by the Half-Open Joint Distribution Mode (HOJDM). According to the results, SAA shows good performance and provides a high-quality solution. Meanwhile, the HOJDM significantly reduces the total cost and carbon emissions of logistics enterprises and provides valuable suggestions for enterprise managers and government decision-makers.

Modelling Cross-Docking in a Three-Level Supply Chain with Stochastic Service and Queuing System: MOWFA Algorithm

In today’s competitive world, it is essential to provide a new method through which maximum efficiency can be created in the production and supply cycle. In many production environments, sending goods directly from the producer to the consumer brings many problems. Therefore, an efficient transport system should be established between producers and consumers. Such a system is designed in the field of supply chain management knowledge. Supply chain management is the evolutionary result of warehousing management and is one of the important infrastructural foundations of business implementation, in many of which the main effort is to shorten the time between the customer’s order and the actual delivery of the goods. In this research, the supply chain consists of three levels. Suppliers are placed on the first level, cross-docks on the second level, and factories on the third level. In this system, a number of suppliers send different raw materials to several different cross-docks. Each channel is assigned to a cross-dock for a specific product. The main goal of this article is to focus on optimizing the planning of incoming and outgoing trucks with the aim of minimizing the total operation time within the supply chain. The arrival rate of goods from suppliers to the cross-dock is stochastic with a general probability distribution. On the other hand, the time required to prepare and send the goods is random with a general probability distribution. The service time in each cross-dock depends on the number of its doors. Therefore, each cross-dock can be modeled as a G/G/m queueing system where m represents the number of doors. The mathematical model of the research has been developed based on these assumptions. Since the problem is NP-hard, the time to solve it increases drastically with the increase in the dimensions of the problem. Therefore, three metaheuristics, including multi-objective water flow, non-dominated sorting genetic, and a multi-objective simulated annealing algorithm have been used to find near-optimal solutions to the problem. After adjusting the parameters of the algorithms using the Taguchi method, the results obtained from the algorithms were analyzed with a statistical test and the performance of the algorithms was evaluated. The results vividly demonstrate that non-dominated sorting genetics is the best of all.

Offline optimization of sensor configuration and integration architecture for efficient cooperative navigation

Multi-agent cooperative navigation can improve navigation performance by taking advantage of inter-agent communication and relative-sensing capabilities. For Multi-Agent Systems (MAS), the achievable navigation performance, communication cost, computation complexity, and sensor cost are directly influenced by the sensor configuration and the integration architecture. To reduce hardware cost and computation/communication load, this paper proposes a feasible method to realize the offline design of sensor configuration and integration architecture for cooperative navigation. Specifically, this goal is achieved by solving a multi-objective combinatorial optimization problem, where the sensor configuration and integration architecture are considered as the optimization variables; the navigation performance requirement is the constraint; and the communication cost, computation complexity, and sensor cost are considered as the optimization objectives. This optimization problem is solved by a Multi-Objective Simulated Annealing (MOSA) algorithm. Using a MAS with three unmanned aerial vehicles as an example, simulations are carried out to validate the proposed method, and the results show its feasibility and effectiveness. The Pareto solutions reveal the characteristics and applicability of different integration architectures in different scenarios, which is meaningful for practical applications.

A Multi-Objective Optimization Approach for Emergency Medical Service Facilities Location-Allocation in Rural Areas.

PurposeThe location of emergency medical service (EMS) facilities is a basic facility location problem. Many scholars have examined this kind of problem, but research on the location of EMS facilities in rural areas is still lacking. Different from urban areas, the location optimization of EMS facilities in rural areas must consider the accessibility of roads. The objective of this study conducted the optimal locations of new EMS stations and construction/upgrading of transfer links aiming to improve the medical emergency efficiency of mountain rural areas.MethodsThree multi-objective models were constructed to examine the effects of varying assumptions (suppose existing roads cannot be upgraded, existing roads can be upgraded, and existing roads can be upgraded and new roads can be constructed) about minimizing the population considered uncovered (response time from the residential to the EMS station less than or equal to 0.5 h), time spent traveling from the residential area to the EMS station, construction costs for building new emergency facilities, and costs for improving or building new roads. Furthermore, we developed an improved multi-objective simulated annealing algorithm to examine the problem of optimizing the design of rural EMS facilities.ResultsWe tested the models and algorithm on the Miao Autonomous County of Songtao, Guizhou Province, China. According to the actual situation of the case area, the models and algorithm were tested with the assumption that only three new EMS stations would be constructed. The number of people not covered by EMS stations decreased from 30.7% in Model 1 to 22% in Model 2, and then to 18.9% in Model 3.ConclusionOur study showed that the traffic network had a significant impact on the location optimization of EMS stations in mountainous rural areas. Improving the traffic network conditions could effectively improve the medical emergency efficiency of mountain rural areas.

A novel model for sustainable waste collection arc routing problem: Pareto-based algorithms.

Municipal solid waste (MSW) management is known as one of the most crucial activities in municipalities that requires large amounts of fixed/variable and investment costs. The operational processes of collection, transportation and disposal include the major part of these costs. On the other hand, greenhouse gas (GHG) emission as environmental aspect and citizenship satisfaction as social aspect are also of particular importance, which are inevitable requirements for MSW management. This study tries to develop a novel mixed-integer linear programming (MILP) model to formulate the sustainable periodic capacitated arc routing problem (PCARP) for MSW management. The objectives are to simultaneously minimize the total cost, total environmental emission, maximize citizenship satisfaction and minimize the workload deviation. To treat the problem efficiently, a hybrid multi-objective optimization algorithm, namely, MOSA-MOIWOA is designed based on multi-objective simulated annealing algorithm (MOSA) and multi-objective invasive weed optimization algorithm (MOIWOA). To increase the algorithm performance, the Taguchi design technique is employed to set the parameters optimally. The validation of the proposed methodology is evaluated using several problem instances in the literature. Finally, the obtained results reveal the high efficiency of the suggested model and algorithm to solve the problem.

Efficient multi-objective simulated annealing algorithm for interactive layout problems

This paper presents an efficient simulated annealing algorithm for solving multi-objective layout problems where several rectangular components are placed, respecting non-overlap and non-protrusion constraints in the given space. Resolving layout problems can be very hard in some industrial cases because problems are over-constrained and computing feasible optimal layout designs are time consuming. In most practical problems, both real and virtual components exist. The virtual components represent the required accessible space allowing the user to access to the component. The virtual components can overlap with each other, while the overlap is not allowed for the real components. Considering the limited layout design space, the capacity of the layout problem is analyzed using constructive placing techniques. To explore the feasible layout space, a hybrid simulated annealing is proposed to determine the order of placement; then, a constructive placing strategy based on empty maximal space is developed. What’s more, an interactive visualization environment is introduced between the optimization and expert. The proposed algorithm is the first attempt to search feasible space of multi-objective layout design by constructive placing method.

Optimizing A Fuzzy Multi-Objective Closed-loop Supply Chain Model Considering Financial Resources using meta-heuristic

This paper presents a multi-objective mathematical model which aims to optimize and harmonize a supply chain to reduce costs, improve quality, and achieve a competitive advantage and position using meta-heuristic algorithms. The purpose of optimization in this field is to increase quality and customer satisfaction and reduce production time and related prices. The present research simultaneously optimized the supply chain in the multi-product and multi-period modes. The presented mathematical model was firstly validated. The algorithm's parameters are then adjusted to solve the model with the multi-objective simulated annealing (MOSA) algorithm. To validate the designed algorithm's performance, we solve some examples with General Algebraic Modeling System (GAMS). The MOSA algorithm has achieved an average error of %0.3, %1.7, and %0.7 for the first, second, and third objective functions, respectively, in average less than 1 minute. The average time to solve was 1847 seconds for the GAMS software; however, the GAMS couldn't reach an optimal solution for the large problem in a reasonable computational time. The designed algorithm's average error was less than 2% for each of the three objectives under study. These show the effectiveness of the MOSA algorithm in solving the problem introduced in this paper.

Optimizing locations of waste transfer stations in rural areas.

Many studies have considered the location of rural waste transfer stations, but most have omitted the impact of transportation network conditions. Traffic accessibility must be considered in optimizing the location of rural waste transfer stations, which is an important difference from the location of rural waste transfer stations. On the basis of previous studies, this study will consider the impact of traffic network on the optimization locations of waste transfer station in the rural areas. The objective of this study was to ensure the minimum Euclidean distance between the waste transfer station and the population center is the maximum, minimize the garbage transportation cost of each population center, construction costs for waste transfer stations, construction and upgrade costs for roads on a traffic network. A multi-objective facility location-network design model and an improved multi-objective simulated annealing algorithm was used to solve the problem. A detailed practical case study was used to illustrate the application of the proposed mathematical model. The results show that transportation network plays an important role in facility location optimization, and the improvement of traffic network conditions can greatly reduce waste transportation costs.

A Framework for Time-Cost-Quality Optimization in Project Management Problems Using an Exploratory Grid Concept in the Multi-Objective Simulated-Annealing

Time, cost, and quality are the three indispensable factors for the realization and success of a project. In this context, we propose a framework composed of a multi-objective approach and multi-criteria decision-making methods (MCDM) to solve time-cost-quality trade-off optimization problems. A multi-objective Simulated Annealing (MOSA) algorithm is used to compute an approximation to the Pareto optimal set. The concept of the exploratory grid is introduced in the MOSA to improve its performance. MCDM are used to assist the decision-making process. The Shannon entropy and AHP methods assign weights to criteria. The first methodology is for the inexperienced decision-makers, and the second concedes a personal and flexible weighting of the criteria weights, based on the project manager’s assessment. The TOPSIS and VIKOR methods are considered to rank the solutions. Although they have the same purpose, the rankings achieved are different. A tool is implemented to solve a time-cost-quality trade-off problem on a project activities network. The computational experiments are analyzed and the results with the exploratory grid in Simulated Annealing (SA) are promising. Despite the framework aims to solve multi-objective trade-off optimization problems, supporting the decisions of the project manager, the methodologies used can also be applied in other areas.

Task Planning Considering Location Familiarity in Spatial Crowdsourcing

Spatial crowdsourcing (SC) is a popular distributed problem-solving paradigm that harnesses the power of mobile workers (e.g., smartphone users) to perform location-based tasks (e.g., checking product placement or taking landmark photos). Typically, a worker needs to travel physically to the target location to finish the assigned task. Hence, the worker’s familiarity level on the target location directly influences the completion quality of the task. In addition, from the perspective of the SC server, it is desirable to finish all tasks with a low recruitment cost. Combining these issues, we propose a Bi-Objective Task Planning (BOTP) problem in SC, where the server makes a task assignment and schedule for the workers to jointly optimize the workers’ familiarity levels on the locations of assigned tasks and the total cost of worker recruitment. The BOTP problem is proved to be NP-hard and thus intractable. To solve this challenging problem, we propose two algorithms: a divide-and-conquer algorithm based on the constraint method and a heuristic algorithm based on the multi-objective simulated annealing algorithm. The extensive evaluations on a real-world dataset demonstrate the effectiveness of the proposed algorithms.

Solving the location problem of front distribution center for omni-channel retailing

Consumer demand and retailing models nowadays are being upgraded more frequently. More and more retailers are switching to the Omni-channel retailing model. Choosing a reasonable location for a front distribution center (FDC) helps control an enterprise's cost and improves its service level. This is especially true in the existence of fierce competition. In this paper, two important and contradictory objectives are proposed for the first time in the FDC location problem: minimizing the distribution costs from the facility and minimizing the fixed cost of the facility's location. For these objectives, a bi-objective programming model is established by considering the factors of a facility's capacity, demand and rent fluctuation. Meanwhile, the FDC location problem has been solved by compromising programming and elite set multi-objective simulated annealing algorithm respectively. Taking the FDC locations set of an e-commerce enterprise in a region of Beijing as an empirical sample, this paper uses the above algorithms to re-plan the FDC locations of the enterprise. This algorithm provides support for retail enterprises by helping find the best FDC location. Based on the empirical results, some comments and future research directions are also proposed.

Multi-objective simulated annealing for hyper-parameter optimization in convolutional neural networks.

In this study, we model a CNN hyper-parameter optimization problem as a bi-criteria optimization problem, where the first objective being the classification accuracy and the second objective being the computational complexity which is measured in terms of the number of floating point operations. For this bi-criteria optimization problem, we develop a Multi-Objective Simulated Annealing (MOSA) algorithm for obtaining high-quality solutions in terms of both objectives. CIFAR-10 is selected as the benchmark dataset, and the MOSA trade-off fronts obtained for this dataset are compared to the fronts generated by a single-objective Simulated Annealing (SA) algorithm with respect to several front evaluation metrics such as generational distance, spacing and spread. The comparison results suggest that the MOSA algorithm is able to search the objective space more effectively than the SA method. For each of these methods, some front solutions are selected for longer training in order to see their actual performance on the original test set. Again, the results state that the MOSA performs better than the SA under multi-objective setting. The performance of the MOSA configurations are also compared to other search generated and human designed state-of-the-art architectures. It is shown that the network configurations generated by the MOSA are not dominated by those architectures, and the proposed method can be of great use when the computational complexity is as important as the test accuracy.