Relocation Problem Research Articles

A three-stage heuristic for optimizing container relocations in maritime container terminals

The Container Relocation Problem (CRP) is one of the most important optimization problems in maritime container terminals. The objective is to minimize the number of relocation operations for retrieving containers in a sequence. If the container to be retrieved next is not at the top of a stack, unproductive relocations have to be carried out. Due to the large number of containers handled by busy terminals, a slight reduction in relocation rates can result in significant savings in operating costs. Most of the existing heuristics make relocation decisions for the blocking containers one by one, based on simple indicators. In this article, we propose a Three-Stage Heuristic (3SH) that extends the decision horizon to multiple containers to achieve a higher-quality solution. Computational experiments are conducted on 3 sets of benchmark instances, and the results show that the proposed heuristic outperforms the state-of-the-art heuristics documented in the research literature.

Integrated optimization of truck appointment quotas and container relocations for multiple blocks considering transshipment containers handling

The container terminal is a crucial transshipment node in the sea and land cargo transport supply chain, and the efficiency of container collection and distribution has always been a research hotspot. In this paper, we consider the impact of transshipment containers resulting from various uncertainties and integrate the operations of external truck pickups with the handling of transshipment containers in the import yard of the container terminal. In light of the varying number of transshipment containers and transit times for different blocks, coupled with the unpredictable arrival order of external trucks leading to the unknown number of relocations, it is proposed to dynamically adjust the upper limit of the appointed quota for each block. We establish a two stage mixed integer programming model for the truck appointment quota and the block relocation problem. The objective functions aim to minimize the number of relocations and minimize the number of yard crane moves under the maximal appointment quota, respectively. This model is utilized for dynamically regulating the upper limit of the appointed quota for each block and determining the time intervals for picking up the appointed and transshipment containers, along with the yard crane scheduling scheme. The problem is solved using CPLEX and the improved variable neighborhood search algorithm (IVNS). The IVNS incorporates the correction strategy, decoding strategy, and scheduling rules designed to align closely with the characteristics of the problem. Through scheme comparison and sensitivity analysis, five management insights are proposed for terminals. Experimental results demonstrate that the approach presented in this paper effectively enhances the utilization efficiency of yard cranes, reduces yard congestion, and yields benefits for both terminals and trucking companies.

Expedited re-design of multi-band passive microwave circuits using orthogonal scaling directions and gradient-based tuning

Geometry scaling of microwave circuits is an essential but challenging task. In particular, the employment of a given passive structure in a different application area often requires re-adjustment of the operating frequencies/bands while maintaining top performance. Achieving this necessitates the utilization of numerical optimization methods. Nonetheless, if the intended frequencies are distant from the ones at the starting point, local search procedures tend to fail, whereas global search algorithms are computationally expensive. As recently demonstrated, a combination of large-scale concurrent geometry parameter scaling with intermittent local tuning allows for dependable re-design of high-frequency circuits at low CPU costs. Unfortunately, the procedure is only applicable to single-band structures due to synchronized modifications of all operating bands under scaling. This article discusses a novel procedure that leverages a similar overall concept, but allows for independent control of all center frequencies. To achieve this goal, an automated decision-making procedure is developed in which a set of orthogonal scaling directions are determined based on their effect on individual circuit bands, and using auxiliary optimization sub-problems. The scaling range is then automatically computed by solving an appropriately-defined least-square design relocation problem. The methodology introduced in the work is illustrated using two planar passive devices. In both cases, wide-range operating frequency re-design has been demonstrated and favorably compared to conventional gradient-based tuning. Furthermore, the presented procedure has been shown to be computationally efficient. It is also easy to implement and integrate with a variety of gradient-based optimization procedures of a descent type.

Real-Time Batch Optimization for the Stochastic Container Relocation Problem

The container relocation problem (CRP) is an important factor affecting the operation efficiency of container terminal yards, and it has attracted much attention for decades. The CRP during the pickup operations of import containers is still an intractable problem for two reasons: the first is that the solution efficiency of the algorithms developed in the existing literature cannot meet the real-time operation requirements; the second is that the pre-optimized operation plan cannot cope with the changes in the real-time operation scenarios caused by the uncertainty of the arrival time of external trucks. This paper proposes an optimization method for the real-time operation scenario which aims to solve the most reasonable operation plan quickly according to the arrivals of external trucks, in which a dynamic upper bound of the optimal solution is derived based on the dynamic programming model of the import containers’ CRP, and an approximate optimal solution can be obtained by minimizing this dynamic upper bound. A heuristic algorithm based on three relocation rules is developed to implement this method, considering the adjustment of the pickup sequence of the target containers. Numerical experiments show that (1) when the number of a batch of target containers is less than 10 (excluding target containers that can be directly picked up), the method proposed in this paper can solve the problem quickly to meet the demand of optimizing real-time pickup operations; (2) compared with other outstanding algorithms, the quality of the solutions obtained by this method is also improved; and (3) this method can be applied to the most container terminals for optimizing real-time pickup operations.

Damage assessment and relocation as tools for preserving business during martial law

The article is devoted to studying the problems of damage assessment and business relocation under martial law in Ukraine. The article uses literature and document analysis methods, including a review of recent studies on damage assessment and business relocation and an analysis of relevant legal acts and international standards. The authors examine the legal framework governing the process of assessing the damage caused by armed aggression and the mechanisms for compensating it.The methodological aspects of evaluation, including standardised and independent approaches, are analysed, emphasising the importance of adherence to national and international standards. The main stages of the valuation process are described: preparation, actual valuation, and report. Particular attention is paid to the problems of accounting for changes in the value of money over time and regional differences in economic conditions. The article examines the role of international organisations and standards in ensuring transparency and reliability of damage assessment. The importance of establishing the International Register of Losses in The Hague as an important tool fordocumenting losses and ensuring accountability for armed aggression is emphasised. Particular attention is paid to the issues of business risk insurance in wartime and investment insurance against war risks. The role of such organisations as the Multilateral Investment Guarantee Agency in supporting the investment climate in Ukraine is analysed. The importance of effective damage assessment and business relocation as key tools for maintaining economic stability, minimising losses and preserving the potential for future recovery and development of Ukraine under martial law isemphasised. The need to further improve the methodology for assessing losses and compensation mechanisms to ensure fair compensation and increase confidence in the country's economic recovery process is highlighted.

Carsharing equitable relocation problem: A two-stage stochastic programming approach with learning-embedded endogenous uncertainty in demand

Uncertain demand may exacerbate the imbalance of the supply–demand for a one-way carsharing system and complicate vehicle relocation decisions. To consider the effect of the uncertainty, this study proposes a two-stage stochastic nonlinear programming model, integrating long-term and short-term decisions and maximizing the profit of the carsharing companies. Specifically, in the first stage, tactical decisions of fleet sizing and initial vehicle distribution are determined before the realization of the uncertain demand. Operational decisions of both operator-based and user-based relocation are optimized in the second stage. Moreover, this paper first studies the user-based relocation incentives, which affect the distribution of uncertain demand with an endogenous relationship. A learning-embedded optimization method is introduced to learn such a distribution, enabling the decision-making optimization model to achieve higher performance under the guidance of the demand uncertainty. Second, we envision an equitable relocation issue that considers an uneven distribution of the unsatisfied demand with two different equity criteria measured from the aspects of stations and OD pairs, respectively. Third, the large problem scale, the nonlinear objective function and constraints, and the endogenous demand uncertainty constitute the nontrivial challenges to the feasible solution. For solving the problem efficiently, we linearize the nonlinear terms and develop a dedicated two-phase solution algorithm with a learning-embedded trust-region method in phase I to solve the continuous relaxation problem and a mixed-integer linear programming guided iterative rounding in phase II to obtain the integer solutions of carsharing operations. The solution algorithm adaptively bridges the learning and optimization process via the trust-region method with flexible sample generation. Finally, we conduct numerical experiments based on a real-world one-way carsharing system in Beijing to demonstrate the effectiveness and applicability of the proposed method and reveal some insights for the carsharing service.

Designing relocation rules with genetic programming for the container relocation problem with multiple bays and container groups

The container relocation problem (CRP) is an NP-hard combinatorial optimisation problem that arises in yard management. The problem is concerned with loading all containers from the storage yard to the ship in a certain order. The yard layout consists of bays where containers are placed in stacks on top of each other, and each container has a due date that determines their retrieval order. Due to its complexity, heuristic methods are used to solve CRP, ranging from relocation rules to metaheuristics. Relocation rules (RRs) are used when the goal is to obtain a solution of acceptable quality in short time. Manually designing RRs is difficult and time-consuming, which motivates the use of different methods to automatically design RRs. In this study, we investigate the application of genetic programming (GP) to design RRs for CRP with multiple bays and container groups. The GP algorithm was adapted for generating RRs by proposing a new set of terminals and several solution construction methods. The proposed method was evaluated on an extensive benchmark of existing problems. The results obtained with automatically developed RRs were compared with the results of manually designed RRs and it was found that the automatically designed RRs performed significantly better in all cases.

Optimizing Ambulance Allocation in Dynamic Urban Environments: A Historic Data-Driven Approach

In this study, we present a methodology to solve the multi-period ambulance relocation problem based on historical data. We present a methodology to convert historical data in latitude–longitude coordinates into cell-based network data. Then, we propose a mixed-integer programming model that utilizes the converted data for the concomitant problem. Patient incidence is highly uncertain. Rather than simply covering historical demand, we propose a methodology that allows ambulances to reach as many locations as possible at any given time within a limited amount of time, the golden time. We experimented with real data from Seoul, South Korea, and show that the proposed mathematical model can derive an efficient ambulance operation policy with fewer ambulances.

Solution strategies for integrated distribution, production, and relocation problems arising in modular manufacturing

Recently, there has been a paradigm shift by certain energy companies towards modular manufacturing, whereby transportable modular production units can be relocated between production facilities to meet the spatial and temporal changes in the availabilities, demands, and prices of the underlying commodities. We refer to the optimal distribution, production, and storage of commodities, including intermediary commodities, and the relocation and operation of the modular production units as the dynamic multiple commodity supply chain problem with modular production units. To this end, we present a “flow-based” and a “path-based” mixed-integer linear programming formulation to model the problem. In an effort to solve large-scale instances of the problem, we propose an iterative three-stage matheuristic for the “path-based” formulation. In the first stage of the matheuristic, a feasible solution to the problem is generated by an integrated column generation and Lagrangian relaxation based heuristic. In the second stage of the matheuristic, a path-relinking procedure is utilized as a local search heuristic to further improve the solution. And in the final stage of the matheuristic, the Lagrangian multipliers are updated via a subgradient method. The effectiveness of the matheuristic is illustrated through numerical experiments with a set of randomly generated test instances. For the large-scale test instances, the results show that the matheuristic produces quality solutions orders of magnitude faster than a “state-of-the-art” mixed-integer linear programming solver.

Automated design of relocation rules for minimising energy consumption in the container relocation problem

The container relocation problem (CRP) is a combinatorial optimisation problem in which the sequence of container relocations must be determined to retrieve all containers from the yard while optimising a given objective. Prior to now, the primary objective of CRP was to minimise the number of container relocations; however, due to environmental concerns, optimising energy consumption is gaining importance. This criterion was not considered extensively in the literature about CRP; therefore, there is a lack of suitable methods to tackle this problem variant. Primarily, there is a lack of relocation rules (RRs), simple heuristics that efficiently solve large problems in negligible time. Unfortunately, RRs are challenging to design manually since this process requires significant domain knowledge and is time-consuming. Using hyperheuristics to evolve new RRs automatically is one way to circumvent this problem. In this study, we evolved new RRs that aim to minimise energy consumption using genetic programming. We consider both the scenario in which only energy consumption is optimised and a multi-objective scenario where energy consumption is optimised together with the number of container relocations. The proposed approach is compared with an existing approach from the literature that uses a genetic algorithm to design RRs. The results show that RRs designed using genetic programming perform significantly better than the existing method, especially in multi-objective scenarios.

Vehicle relocation and staff rebalance problem in one-way free-floating car-sharing system: A novel multi-iteration probabilistic greedy algorithm

This research aims to address the staff-based vehicle relocation problem that arises in a one-way car-sharing system operating in a combined mode, in which both on-street parking slots and parking hubs are utilized. The objective of the problem is to determine the relocation plan for vehicles and travel routes for staff with the minimum operational costs. A novel mathematical model is proposed to control the model size, in which the parking spaces constraint is considered without introducing dummy nodes. Then, based on the structure of the model, a heuristic algorithm with tailored operators and the initial solution generation method is proposed to solve the problem. The experimental results demonstrate that, with the tailored operators and solving strategies, the proposed method outperforms commercial solvers and other existing heuristic algorithms in terms of both solving efficiency and solution quality. Furthermore, a group of test cases generated from real cases is utilized to examine the performance of the relocation plan in different scenarios. The results indicate that the overall operation costs of the car-sharing system could be effectively controlled by distributing parking capacities among parking hubs evenly. In addition, the proposed model and high-efficiency algorithm also enable decision-makers to test the performance of the car-sharing system in various scenarios and help them to get a better understanding of the system.

Evaluation Method for Industrial Concentration District in The Area

In areal scope, certain concentration of industry brings positive effects that raise the effectiveness of regional economic development, but it also results in various negative aftereffects such as ecological environmental destruction in the area when it is over certain limit. Furthermore, it loses dominance of the economic geographical location of the area and causes serious loss from the state viewpoint by resulting the problem of industrial relocation in the economic geographical space. In this paper, we solved methodological problem of evaluating industrial concentration district in the area in order to protect negative effects due to high concentration of industry that causes acute issues with environment and resources recently. In order to solve this problem, we established evaluation index system for evaluation of the industrial concentration district in the area, studied the methodology to search industrial concentreation district with the use of the analytic function of influence scope and aggregate district etc. in GIS and then searched indusrial concentraion district in 00 industrial area by applying this method. We should no longer arrange new industry in such industrial concentration district in the future and have to rationally control the industrial structure by realizing the industrial relocation

Enhancing Metaheuristic Optimization: A Novel Nature-Inspired Hybrid Approach Incorporating Selected Pseudorandom Number Generators

In this paper, a hybrid nature-inspired metaheuristic algorithm based on the Genetic Algorithm and the African Buffalo Optimization is proposed. The hybrid approach adaptively switches between the Genetic Algorithm and the African Buffalo Optimization during the optimization process, leveraging their respective strengths to improve performance. To improve randomness, the hybrid approach uses two high-quality pseudorandom number generators—the 64-bit and 32-bit versions of the SIMD-Oriented Fast Mersenne Twister. The effectiveness of the hybrid algorithm is evaluated on the NP-hard Container Relocation Problem, focusing on a test set of restricted Container Relocation Problems with higher complexity. The results show that the hybrid algorithm outperforms the individual Genetic Algorithm and the African Buffalo Optimization, which use standard pseudorandom number generators. The adaptive switch method allows the algorithm to adapt to different optimization problems and mitigate problems such as premature convergence and local optima. Moreover, the importance of pseudorandom number generator selection in metaheuristic algorithms is highlighted, as it directly affects the optimization results. The use of powerful pseudorandom number generators reduces the probability of premature convergence and local optima, leading to better optimization results. Overall, the research demonstrates the potential of hybrid metaheuristic approaches for solving complex optimization problems, which makes them relevant for scientific research and practical applications.

A new modeling approach for the unrestricted block relocation problem

We consider the block relocation problem (BRP), a combinatorial optimization problem that may arise in storage systems where items are organized in stacks. The objective is to retrieve all items in a predefined order with a minimal number of relocations. It can be distinguished between a restricted and an unrestricted version of the BRP. While in the restricted BRP (R-BRP) only relocations of items located above the item to be retrieved next are permitted, in the unrestricted BRP (U-BRP) all possible relocations are allowed. Existing exact methods concerning the BRP are frequently search-based methods which appear to be very effective. Nevertheless, recent literature concerning the R-BRP has shown that model-based methods can be competitive and therefore should also be taken into consideration. In this paper, we propose a new model-based approach for the U-BRP. It eliminates the fact that the number of variables is increasing with the number of necessary relocations; a disadvantage most mathematical models for the U-BRP have in common.

Development of Machine Learning Based State-of-Charge Prediction Model for Plug-in Electric Vehicle's Relocation in Sharing System

This paper presents a Plug-in electric vehicle (PEV) relocation strategy in a sharing system considering machine learning based state-of-charge (SOC) prediction model. The proposed work involves the machine learning approaches like the Adaboost algorithm and XGBoost algorithm for PEVs SOC prediction in relocation problems for the first time. Also, the low-capacity batteries of PEVs are considered as it is affordable by the common people with less charging time. It is also believed that the large number of low capacities PEVs based taxi owners in metropolitan cities will be attracted towards relocation-based sharing system considering machine learning based SOC prediction model. The customer request for sharing PEVs will be reflected in the management system at the beginning. Assuming all the PEVs with a certain level of SOC during pre-relocation are connected to the management system. The customer request for PEVs service will be assigned to the nearest PEVs depending on the predicted value of SOC by machine learning approaches. The machine learning-based predicted value of PEVs SOC will help the management system to identify the nearest PEVs for the customer sharing system. The present work steps are listed here. Firstly, the PEVs with higher predicted SOC value by machine learning have been shared with the customer from the nearest location considering descending order of their SOC. Further, the rest of the PEVs will be assigned to the nearest charging station by the PEV sharing management system. Secondly, the customer request is assigned from the station to relocate PEVs with higher SOC values in descending order. Different case studies are assumed including one practical scenario of Mumbai, India. The optimization problem of multi-depot vehicle routing problem (MDVRP) with the minimization of relocation cost has been solved by IBM CPLEX optimizer in this work. Finally, the proposed work efficacy has been demonstrated with some validated results including less relocation costs for all the case studies under consideration with lower capacity based PEVs.

Analisis kesediaan membayar dan kesediaan menerima tata kelola bencana banjir pada daerah aliran Sungai Wosi

Wosi is a sub-district located in Manokwari Regency and is a part of the Wosi River Alisan Area (DAS) which has vulnerability to forest cover due to the conversion of settlements and trade centers. Based on this, it is necessary to mitigate the flood disaster structure or carry out flood risk management. One of the government's efforts or policies in flood risk management in Wosi Village is to restore the function of the Wosi watershed through relocation. The Willingness to Pay (WTP) and Willingness to Accept (WTA) approaches related to relocation problems can estimate the WTP and WTA values so that they can optimize the form of policies in reducing flood risk. The results of the analysis show that first, Willingness to Pay flood disaster management through relocation is significantly influenced by education level, income, number of dependents, length of stay and area of residence. Willing to accept a compensation fund of approximately Rp. 32,000,000 to Rp. 506.666,666 and Total WTA is around 1,096,000,000. WTP funds that are willing to be paid are Rp. 82,655,533 (Average WTP) and Rp. 2,479,666,000 (Total value of WTP). The amount of Willingness to Accept is smaller than Willingness to Pay, proving that the paired comparison method can reduce the WTA-WTP gap. In addition, the community's understanding of flood risk reduction in the Wosi watershed is still not optimal, so that the efforts of all parties, especially the local government, in carrying out various policies, plans, and programs that can contribute to risk reduction.

Modeling of the relocation of high-tech enterprises for the release of innovative products

A multivariate task related to the modeling of high-tech enterprises relocation under new challenges and threats is described and solved. The relevance of the study is related to the complex solution of the complex task of moving a high-tech enterprise to a new location for the production of competitive products. The purpose of the publication is to present the created set of models that allow: to justify the choice of the location (locations) of the enterprise; to form a set of suppliers of components, considering long logistics chains; and to research the relocation of a high-tech enterprise in a special period related to martial law. The existing problems of the relocation of high-tech enterprises are analyzed: the change in the political and economic conditions of global and local production; existing long logistics supply chains of components that have vulnerabilities and are triggered when threats appear; the problem of the location of distributed production in a large area; economic losses due to complex distributed logistics of supply of components; many manufacturers of components that provide the main production process; the problem of relocation (evacuation) of enterprises in a special period, in the conditions of martial law. The model for choosing a new location for the enterprise is proposed, considering contradictory indicators: the cost (rent) of land plots for the location of the enterprise; territory preparation for the location of the enterprise; logistics costs for the enterprise moving; expenses for training (retraining) of workers; relocation project risks; etc. Taking into account the combinatorial nature of the task under consideration and the complexity of the location of the distributed enterprise (not in one, but in several locations), a model of rational placement of production was created. A method of choosing a set of suppliers of components for high-tech enterprises is developed; this method considers the length of logistics chains, the time spent on delivery, the quality of components produced by suppliers, and supply risks. A multi-criteria optimization model for choosing suppliers is created, considering some contradictory indicators. The model of relocation (evacuation) of a high-tech enterprise in a special period, in the conditions of wartime threats and risks of moving technological equipment, is proposed. A simulation model is developed to study the logistics of enterprise relocation in the form of an agent-based representation; this model simulates the events associated with the sequence of relocation actions: dismantling of technological equipment, transportation of equipment, and installation of enterprise subsystems. The emergence of threats and the consequences of their actions, which are associated with a violation of the logistics of moving the enterprise, are simulated. An illustrated example of the study of enterprise relocation in the conditions of the emergence of threats and the cessation of technological equipment movement, which leads to the search for new routes with minimal transportation risks, is given. The scientific novelty of the study is associated with the development of a complex of original optimization models, an agent-based simulation model, which allows for a scientifically justified forming requirements for the relocation of a high-tech enterprise to a new location, for ensuring the reduction of long logistics chains for the supply of components, for minimization logistics costs, for the formation of relatively dangerous logistics channels and supply routes, for minimization of risks in the conditions of new challenges and threats of a political and economic nature. The results of the study should be used for planning the relocation project of a high-tech enterprise, for the formation of measures and actions related to the relocation of a high-tech enterprise, for the creation of new safe logistics supply chains, and for the evacuation of the enterprise in a special period.

Analysis of the problem on street vendor relocation in Menara Kudus Tourist Area

This paper examines the problems in relocating street vendors in the religious tourism area of Menara Kudus. The relocation of street vendors does improve the visual quality and environment of the region, but it becomes a problem for street vendors, especially in the decline of consumers, revenue and the return of street vendors to the original location. Using qualitative methodology, this study examines the problem of relocation more deeply through depth interview analysis with street vendors. The results showed that there are problems in each parameter, such as the visibility of relocation sites whose image is less potent when compared to parks. Accessibility that this type of mode of transportation cannot traverse. The environment where the relocation is different from the flexibility of the previous environment. Limited land expansion that requires temporary arrangements related to land use. Competition with street vendors in the red zone (on street) can be in direct contact with consumers compared to the relocation of street vendors.

Toward Balancing the Efficiency and Effectiveness in k-Facility Relocation Problem

Facility Relocation (FR), which is an effort to reallocate the placement of facilities to adapt to the changes of urban planning, has remarkable impact on many areas. Existing solutions fail to guarantee the result quality on relocating k > 1 facilities. As k -FR problem is NP-complete and is not submodular or non-decreasing, traditional greedy algorithm cannot be directly applied. We propose to transform k -FR into another facility placement problem, which is submodular and non-decreasing. We prove that the optimal solutions of both problems are equivalent. Accordingly, we present the first approximate solution toward the k -FR, FR2FP. Our extensive comparison over both FR2FP and the state-of-the-art solution shows that FR2FP, although it provides approximation guarantee, cannot necessarily given superior results. The comparison motivates us to present an advanced approximate solution, FR2FP-ex. Moreover, based on Lagrangian relaxation, we develop an algorithm that can adjust the approximation ratio. Extensive experiments verified that, FR2FP-ex demonstrates the best result quality, and it is very close to the optimal solution. In addition, we also unveil the scenarios when the state-of-the-art would fail. We further generalize the k -FR problem, considering the budget for relocation and the cost of each facility. We also present corresponding approximate solutions toward the new problem and prove the approximation ratio.

A decomposition-based multiobjective evolutionary algorithm using Simulated Annealing for the ambulance dispatching and relocation problem during COVID-19

The outbreak of the COVID-19 epidemic has had a significant impact in increasing the number of emergency calls, which causes significant problems to emergency medical services centers (EMS) in many countries around the world, such as Saudi Arabia, which attracts a huge number of pilgrims during pilgrimage seasons. Among these issues, we address real-time ambulance dispatching and relocation problems (real-time ADRP). This paper proposes an improved MOEA/D algorithm using Simulated Annealing (G-MOEA/D-SA) to handle the real-time ADRP issue. The simulated annealing (SA) seeks to obtain optimal routes for ambulances to cover all emergency COVID-19 calls through the implementation of convergence indicator based dominance relation (CDR). To prevent the loss of good solutions once they are found in the G-MOEA/D-SA algorithm, we employ an external archive population to store the non-dominated solutions using the epsilon dominance relationship. Several experiments are conducted on real data collected from Saudi Arabia during the Covid-19 pandemic to compare our algorithm with three relevant state-of-art algorithms including MOEA/D, MOEA/D-M2M and NSGA-II. Statistical analysis of the comparative results obtained using ANOVA and Wilcoxon test demonstrate the merits and the outperformance of our G-MOEA/D-SA algorithm.