Solving a bi-objective location routing problem by a NSGA-II combined with clustering approach: application in waste collection problem

Increasing urbanization and rising consumption rates are putting pressure on urban systems to efficiently manage Municipal Solid Waste (MSW). Waste collection, in particular, is one of the most challenging aspects of MSW management. Therefore, developing computer-aided tools to support decision-makers is crucial. In this paper, a Scatter Search algorithm is proposed to address the waste collection problem. The literature is relatively scarce in applying this algorithm, which has proven to be efficient in other routing problems, to real waste management problems. Results from real-world instances of an Argentine city demonstrate that the algorithm is competitive, obtaining, in the case of small instances, the same outcomes as those of an exact solver enhanced by valid inequalities, although requiring more computational time (as expected), and significantly improving the results of the latter for the case of larger instances, now requiring much less computational time. Thus, Scatter Search proves to be a competitive algorithm for addressing waste collection problems.

Capacitated arc routing problem (CARP) is the youngest generation of graph theory that focuses on solving the edge/arc routing for optimality. Since many years, operational research devoted to CARP counterpart, known as vehicle routing problem (VRP), which does not fit to several real cases such like waste collection problem and road maintenance. In this paper, we highlighted several extensions of capacitated arc routing problem (CARP) that represents the real-life problem of vehicle operation in waste collection. By purpose, CARP is designed to find a set of routes for vehicles that satisfies all pre-setting constraints in such that all vehicles must start and end at a depot, service a set of demands on edges (or arcs) exactly once without exceeding the capacity, thus the total fleet cost is minimized. We also addressed the differentiation between CARP and VRP in waste collection. Several issues have been discussed including stochastic demands and time window problems in order to show the complexity and importance of CARP in the related industry. A mathematical model of CARP and its new version is presented by considering several factors such like delivery cost, lateness penalty and delivery time.

A subset of this work was presented at the 17th IFAC Symposium on Control, Optimization and Automation in Mining, Mineral and Metal Processing in 2016, at the fifth workshop on Mining, Mineral and Metal Processing in 2018, and the Ph.D. thesis of the main author.

The aim of a Huff location problem is to locate facilities on a competitive environment to maximize the market share. The customer behavior is formulated based on a Huff gravity model. This paper considers a social impact along with economic and environmental aspects in order to construct new facilities in a competitive supply chain. This problem is a multi-objective mixed integer non-linear programming (MINLP). To deal with the problem, two multi-objective meta-heuristics (i.e., non-dominated sorting genetic algorithm (NSGA-II) and multi-objective particle swarm optimization (MOPSO)) are proposed to cope with this problem. The results verify the superiority of the NSGA-II over MOPSO in terms of some comparison metrics. Additionally, a two-stage algorithm is developed to sort non-dominated solutions generated by the NSGA-II. In the first stage, Pareto-optimal solutions are obtained by the NSGA-II, then in second stage, a ranking method based on TOPSIS is performed to select the best solutions among Pareto-optimal one.

Today, the use of renewable energy is increasing rapidly due to the reduction of pollution and the optimal use of available energy. Microgrids (MGs) are used as systems to control and exploit various resources and loads. Due to the connection of different resources in MGs, control and operation of this system are complex and important. In this paper, the optimal utilization of MGs is presented by considering various parameters including production sources, different loads and energy storage. In this paper for energy management in systems with various MGs, we aim to apply the multi-objective optimization algorithm to obtain the optimal energy management in MG, while simultaneously satisfying pollution and economic objectives. The multi-objective particle swarm optimization (MOPSO) method and non-dominated sorting genetic algorithm (NSGA-II) are used to solve the problem and their results are compared. Various factors are considered in the cost function including operation cost, losses, pollution and other operation characteristics. Using multi-objective optimization functions considering cost functions and various constraints ensures optimal operation of the MG. Simulation results verify various models and effectiveness of the proposed methods. Simulation results show that using NSGA-II for scheduling DG resources gives more optimal results in terms of reducing operation cost and losses such that considering operation cost and pollution cost using NSGA-II reduces operation cost and losses by 10% and 4%, respectively.

Vehicle routing problems (VRPs) have usually been studied with a single objective function defined by the distances associated with the routing of vehicles. The central problem is to design a set of routes to meet the demands of customers at minimum cost. However, in real life, it is necessary to take into account other objective functions, such as social functions, which consider, for example, the drivers' workload balance. This has led to growth in both the formulation of multiobjective models and exact and approximate solution techniques. In this article, to verify the quality of the results, first, a mathematical model is proposed that takes into account both economic and work balance objectives simultaneously and is solved using an exact method based on the decomposition approach. This method is used to compare the accuracy of the proposed approximate method in test cases of medium mathematical complexity. Second, an approximate method based on the Iterated Local Search (ILS) metaheuristic and Decomposition (ILS/D) is proposed to solve the biobjective Capacitated VRP (bi-CVRP) using test cases of medium and high mathematical complexity. Finally, the nondominated sorting genetic algorithm (NSGA-II) approximate method is implemented to compare both medium- and high-complexity test cases with a benchmark. The obtained results show that ILS/D is a promising technique for solving VRPs with a multiobjective approach.

SUMMARYThe future of designing optical networks focuses on the wavelength division multiplexing technology. This technology divides the huge bandwidth of an optical fiber into different wavelengths, providing different available channels per link of optical fiber. However, when it is required to establish a set of demands, a problem comes up. This problem is known as routing and wavelength assignment problem. In this work, we have tackled the static routing and wavelength assignment problem by using multiobjective evolutionary computing. The algorithm applied is the differential evolution but modified with the Pareto tournaments concept for being adapted to the multiobjective context. By using OpenMP, an application programming interface that supports multiplatform shared memory multiprocessing programming, we have demonstrated that this algorithm is highly suitable to be parallelized. We have performed several experiments in multicore systems with two, four, and eight cores, obtaining 97.57% of mean efficiency. To ensure that our heuristic obtains relevant results, we have compared it with a parallel version of the standard fast nondominated sorting genetic algorithm. Finally, in order to show the goodness and effectiveness of the differential evolution with Pareto tournaments algorithm when dealing with this problem, we present diverse multiobjective comparisons with the nondominated sorting genetic algorithm and other approaches published in the literature. Copyright © 2013 John Wiley & Sons, Ltd.

Study on Medical Waste Collection and Routing Problem

Great quantities of energy are consumed by heat pump drying systems (HPDSs), such as tobacco drying. It can be reduced by increasing evaporating temperature using waste heat. In this paper, based on the case of two main industries in southwest China (monocrystalline silicon industry and tobacco industry), a novel integrated HPDS especially for the tobacco drying using waste heat from single crystal silicon furnaces is proposed. It is then analyzed from an energetic and economical perspective, investigating the effects of heat exchanger areas on system performance. To simultaneously minimize system CO2 emissions and total annual cost (TAC), a multiobjective optimization using nondominated sorting genetic algorithm is conducted based on the support vector regression model, which has been rarely reported for integrated HPDS improved by waste heat. The final optimum solution on the Pareto optimal front is selected using different decision‐making methods (LINMAP, TOPSIS, and fuzzy decision method), and are compared with the performance of the initial system. The results show that the TOPSIS method is most suitable in three decision‐making methods with a great reduction in (−12.7%) and nearly unchanged in TAC (+0.5%) compared with the initial solution. And the decreases in and TAC of the optimized system is as large as 50% and 1/3, compared with traditional vapor compression heat pump dryer without heat recovery of the monocrystal silicon furnace.

In this paper, we consider the waste collection problem from customers’ location with assumptions that are closer to real life applications of the problem. The fleet of vehicles is heterogeneous and vehicles have separated compartments, namely they have different capacity for each type of waste. Also, the vehicles have different traveling time limitation and different variable and fixed cost according to their types. As well as, the multi-depot vehicle routing problem and the mixed close-open vehicle routing problem are combined together. The objective of the problem is minimizing the cost of servicing to customers with respect to customers’ demands and available constraints. A new mathematical MIP model is proposed and to deal with this problem, three meta-heuristic algorithms are investigated and the results are compared with the results of CPLEX solver. The results of experiments show that the proposed Meta-heuristic algorithms are able to produce satisfied solutions with regard to the MIP solver CPLEX.

Modern cities produce larger amounts of waste causing profound negative impacts on our natural environment. For this reason, processes related to waste collection, transportation, transformation and final disposal have increased their importance and major adequacy is desirable. Several Waste Management (WM) problems have to be solved in order to obtain efficient related services. Traditional Vehicle Routing Problem and Waste Collection Problem solution approaches presented in the literature are not suitable to face problems related to specific topology of the city, districts, street dimension and shape of the clusters. In this paper we propose a pre-process cluster solution in order to take into account different information such as street names, street dimension and civic number in order to create cluster more acceptable by the final users. In order to show the effectiveness of the proposed methodology, we compare hierarchical and spectral clustering-based algorithms. Finally, we apply our proposal in a real case study based on a data set extracted from a city in the north-east of Italy.

A novel multi-objective meta-heuristic model for solving cross-docking scheduling problems

Distribution is an important logistical problem in tape commodities because it has a short expiration time that requires speed and accuracy in shipping. This research collaborates implementation two methods. These are North West corner (NWC) and stepping stone. These methods make distribution scheduling model from many sources to destinations with minimum cost. This case has many steps. The first step is initial feasible solution with NWC. NWC is used this case because simple and fastest method for solving problem. NWC does not observe the cost of each shipping line in procedure. The second step revised with try and error the plan using stepping stone for optimization. In this research, collaboration these methods can reduce total cost of distribution equal to Rp.266.050. In fact, total distribution cost is Rp.317.500. After calculation, we can save cost Rp.51.450. Determination of the first source and destination in the North-west corner cell is greatly affect. If the first source and the destination have a small cost so the result is better. Stepping stone find optimization in second iteration because all of closed lanes have positive value. A positive value in repair index means that when given an allocation it will causing an increase in transportation costs. The allocation of product does not need to be changed because it will increase the cost of transportation so that the stepping stone work is completed. Percentage of differences is a formula to derive efficiency comparison between initial solution and optimal solution is 0.45%.

An iterative approach is developed for solving general goal programming problems. This approach is then used to solve several nonlinear examples, including an integer solution problem. A dual for linear goal programming problems is developed and sensitivity analysis is discussed. Several new advances in modeling with goal programming which are made possible with this iterative approach are presented, namely least squares attainability of goals and priority dependent constraints.

This study aims to provide tactical and operational decisions in multidepot recycling logistics networks with consideration of resource sharing (RS) and time window assignment (TWA) strategies. The RS strategy contributes to efficient resource allocation and utilization among recycling centers (RCs). The TWA strategy involves assigning time windows to customers to enhance the operational efficiency of logistics networks. A biobjective mathematical model is established to minimize the total operating cost and number of vehicles for solving the multidepot recycling vehicle routing problem with RS and TWA (MRVRPRSTWA). A hybrid heuristic algorithm including 3D k-means clustering algorithm and nondominated sorting genetic algorithm- (NSGA-) II (NSGA-II) is designed. The 3D k-means clustering algorithm groups customers into clusters on the basis of their spatial and temporal distances to reduce the computational complexity in optimizing the multidepot logistics networks. In comparison with NSGA algorithm, the NSGA-II algorithm incorporates an elitist strategy, which can improve the computational speed and robustness. In this study, the performance of the NSGA-II algorithm is compared with the other two algorithms. Results show that the proposed algorithm is superior in solving MRVRPRSTWA. The proposed model and algorithm are applied to an empirical case study in Chongqing City, China, to test their applicability in real logistics operations. Four different scenarios regarding whether the RS and TWA strategies are included or not are developed to test the efficacy of the proposed methods. The results indicate that the RS and TWA strategies can optimize the recycling services and resource allocation and utilization and enhance the operational efficiency, thus promoting the sustainable development of the logistics industry.