Multi-start simulated annealing heuristic for the location routing problem with simultaneous pickup and delivery

The location routing problem with simultaneous pickup and delivery (LRPSPD) is a new variant of the location routing problem (LRP). The objective of LRPSPD is to minimize the total cost of a distribution system including vehicle traveling cost, depot opening cost, and vehicle fixed cost by locating the depots and determining the vehicle routes to simultaneously satisfy the pickup and the delivery demands of each customer. LRPSPD is NP-hard since its special case, LRP, is NP-hard. Thus, this study proposes a multi-start simulated annealing (MSA) algorithm for solving LRPSPD which incorporates multi-start hill climbing strategy into simulated annealing framework. The MSA algorithm is tested on 360 benchmark instances to verify its performance. Results indicate that the multi-start strategy can significantly enhance the performance of traditional single-start simulated annealing algorithm. Our MSA algorithm is very effective in solving LRPSPD compared to existing solution approaches. It obtained 206 best solutions out of the 360 benchmark instances, including 126 new best solutions.     

Multiobjective model predictive control for dynamic pickup and delivery problems

A multiobjective-model-based predictive control approach is proposed to solve a dynamic pickup and delivery problem in the context of a potential dial-a-ride service implementation. A dynamic objective function including two relevant dimensions, user and operator costs, is considered. Because these two components typically have opposing goals, the problem is formulated and solved using multiobjective model predictive control to provide the dispatcher with a more transparent tool for his/her decision-making process. An illustrative experiment is presented to demonstrate the potential benefits in terms of the operator cost and quality of service perceived by the users.     

一类特殊的集送一体化TSP问题及其遗传算法求解

同类商品集送一体化的旅行商问题是经典TSP问题的一种新变体,建立了该问题的数学模型,根据该问题的特征,结合局域搜索优化算法,改进了遗传算法的交叉、变异算子。对算例的求解证明了遗传算法在解决1-PDTSP问题上优越性。     

具有同时集送货需求车辆路径问题的混沌量子进化算法研究

针对量子进化算法中旋转角取值的离散性使其解空间的搜索具有跳跃性,提出了基于混沌理论的精英均值计算旋转角算法,并将其应用于具有同时集送货需求车辆路径问题的求解.在理论上分析了解的强可行和弱可行条件的基础上,使用启发式算子对解进行改进.通过仿真实验与其他算法进行了比较,仿真结果表明所提出算法是求解此类问题的有效方法.     

模糊需求下低碳取送货车辆调度问题与算法

针对物流配送过程中的高碳排放问题,从低碳视角出发,构建考虑模糊需求的低碳取送货车辆调度(LCVRPPD)模型,并提出一种基于2-OPT的差分算法对问题进行求解.该算法中,采用自然数编码方式并设置三种不同的适应度函数;随后,引入2-OPT算法取代差分算法原有的变异机制,并结合二项交叉算子和贪婪选择算子,从而提高改进算法的...     

An ant colony system (ACS) for vehicle routing problem with simultaneous delivery and pickup

In this paper we use an ant colony system (ACS) algorithm to solve the vehicle routing problem with simultaneous delivery and pickup (VRPSDP) which is a combinatorial optimization problem. ACS is an algorithmic approach inspired by the foraging behavior of real ants. Artificial ants are used to construct a solution for the problem by using the pheromone information from previously generated solutions. The proposed ACS algorithm uses a construction rule as well as two multi-route local search schemes. The algorithm can also solve the vehicle routing problem with backhaul and mixed load (VRPBM). An extensive numerical experiment is performed on benchmark problem instances available in literature. It is found that ACS gives good results compared to the existing algorithms.     

A genetic algorithm for the simultaneous delivery and pickup problems with time window

This paper concerns a Simultaneous Delivery and Pickup Problem with Time Windows (SDPPTW). A mixed binary integer programming model was developed for the problem and was validated. Due to its NP nature, a co-evolution genetic algorithm with variants of the cheapest insertion method was proposed to speed up the solution procedure. Since there were no existing benchmarks, this study generated some test problems which revised from the well-known Solomon’s benchmark for Vehicle Routing Problem with Time Windows (VRPTW). From the comparison with the results of Cplex software and the basic genetic algorithm, the proposed algorithm showed that it can provide better solutions within a comparatively shorter period of time.     

带时间窗同时送取货选址路径问题及其烟花算法求解

随着电子商务的飞速发展以及互联网的普及,退换货更加便捷,因此客户对商品的需求呈现出时效性、多品种、小批量、退换货等特点。针对有容量的同时送取货选址路径问题（LRPSPD）,同时考虑客户多样性需求的特点,建立了带时间窗的同时送取货选址路径问题（LRPSPDTW）的数学模型。使用改进烟花算法（IFWA）对模型进行求解,对烟花爆炸和变异进行相应的邻域操作,并用一些基准LRPSPD算例来评估烟花算法的性能。通过大量的数值实验验证了所提模型和算法的正确性和有效性。实验结果表明,相较于分支切割算法（B&C）,IFWA得到的结果与标准解的平均误差缩小了0.33个百分点。所提算法缩短了寻求最优解的时间,为解决选址路径相关问题提供了一种新的解决思路。     

A particle swarm optimization for the vehicle routing problem with simultaneous pickup and delivery

This paper proposes a formulation of the vehicle routing problem with simultaneous pickup and delivery (VRPSPD) and a particle swarm optimization (PSO) algorithm for solving it. The formulation is a generalization of three existing VRPSPD formulations. The main PSO algorithm is developed based on GLNPSO, a PSO algorithm with multiple social structures. A random key-based solution representation and decoding method is proposed for implementing PSO for VRPSPD. The solution representation for VRPSPD with n customers and m   vehicles is a (n+2m)$(n+2m)$-dimensional particle. The decoding method starts by transforming the particle to a priority list of customers to enter the route and a priority matrix of vehicles to serve each customer. The vehicle routes are constructed based on the customer priority list and vehicle priority matrix. The proposed algorithm is tested using three benchmark data sets available from the literature. The computational result shows that the proposed method is competitive with other published results for solving VRPSPD. Some new best known solutions of the benchmark problem are also found by the proposed method.     

A hybrid genetic algorithm for the traveling salesman problem with pickup and delivery

In this paper, a hybrid genetic algorithm （GA） is proposed for the traveling salesman problem （TSP） with pickup and delivery （TSPPD）. In our algorithm, a novel pheromone-based crossover operator is advanced that utilizes both local and global information to construct offspring. In addition, a local search procedure is integrated into the GA to accelerate convergence. The proposed GA has been tested on benchmark instances, and the computational results show that it gives better convergence than existing heuristics.     

A hybrid discrete particle swarm optimization for vehicle routing problem with simultaneous pickup and delivery

Vehicle routing problem (VRP) is an important and well-known combinatorial optimization problem encountered in many transport logistics and distribution systems. The VRP has several variants depending on tasks performed and on some restrictions, such as time windows, multiple vehicles, backhauls, simultaneous delivery and pick-up, etc. In this paper, we consider vehicle routing problem with simultaneous pickup and delivery (VRPSPD). The VRPSPD deals with optimally integrating goods distribution and collection when there are no precedence restrictions on the order in which the operations must be performed. Since the VRPSPD is an NP-hard problem, we present a heuristic solution approach based on particle swarm optimization (PSO) in which a local search is performed by variable neighborhood descent algorithm (VND). Moreover, it implements an annealing-like strategy to preserve the swarm diversity. The effectiveness of the proposed PSO is investigated by an experiment conducted on benchmark problem instances available in the literature. The computational results indicate that the proposed algorithm competes with the heuristic approaches in the literature and improves several best known solutions.     

基于剩余装载能力的蚁群算法求解同时送取货车辆路径问题

建立了带车辆最大行程约束的同时送取货车辆路径问题的混合整数规划模型; 采用了基于排序的蚂蚁系统和最大最小蚂蚁系统的信息素更新策略; 设计了基于车辆剩余装载能力的启发信息策略, 可在满足车辆负载的限制下, 提高车辆的负载利用率; 并在改进阶段使用了节点交换的局部搜索策略,以提高算法收敛速度. 仿真结果表明本文算法能够在可接受的计算时间内得到满意解.     

A population-based metaheuristic for the pickup and delivery problem with time windows and LIFO loading

In this paper, we solve the pickup and delivery problem with time windows and last-in-first-out (LIFO) loading. LIFO loading minimizes handling while unloading items from the vehicle: the items are loaded according to a linear stack structure, and an item can only be delivered if it is the one on top of the stack. Three exact branch-price-and-cut algorithms are available for this problem. These can solve instances with up to 75 requests within one hour. We propose a population-based metaheuristic capable of handling larger instances much faster. First, a set of initial solutions is generated with a greedy randomized adaptive search procedure. For each of these solutions, local search is applied in order to first decrease the total number of vehicles and then the total traveled distance. Two different strategies are used to create offspring. The first selects vehicle routes from the solution pool. The second selects two parents to create an offspring with a crossover operator. For both strategies, local search is then performed on the child solution. Finally, the offspring is added to the population and the best survivors are kept. The population is managed so as to maintain good quality solutions with respect to total cost and population diversity. Computational results on medium to large instances confirm the effectiveness of the proposed metaheuristic.     

Two solution representations for solving multi-depot vehicle routing problem with multiple pickup and delivery requests via PSO

Two solution representations for solving the generalized multi-depot vehicle routing problem with multiple pickup and delivery requests (GVRP-MDMPDR) is presented in this paper. The representations are used in conjunction with GLNPSO, a variant of PSO with multiple social learning terms. The computational experiments are carried out using benchmark test instances for pickup and delivery problem with time windows (PDPTW) and the generalized vehicle routing problem for multi-depot with multiple pickup and delivery requests (GVRP-MDMPDR). The preliminary results illustrate that the proposed method is capable of providing good solutions for most of the test problems.     

Development of timed Colour Petri net simulation models for air cargo terminal operations

This paper presents the development and the application of simulation models for air cargo terminal operations. As air cargo volume is fast increasing in recent years, air cargo terminals which handle cargos for carriers face challenges to streamline their operations. Air cargo terminals employ diverse storage media and are also equipped with various material handling equipments. Due to the complex and stochastic nature of terminal operations, which makes formulation of analytical model difficult, in this study, simulation models are developed to analyze the air cargo terminal operations. We first employ timed CPN (Colour Petri nets) to model the terminal operations, and then the corresponding simulation model will be developed. In order to validate this simulation model, we run the model based on actual cargo retrieval schedules obtained from an air cargo terminal. The results show that this model provides a good estimate of the terminal performance. The model is then employed to analyze the performance of the airline assignment policy for the objective of minimizing the cargo processing times, and the results show that the proposed policy significantly improves the operational performance of air cargo terminals. Second, the simulation model is employed to analyze the performance of an AS/RS (Automated Storage and Retrieval System), and the performance of the proposed storage policy is compared with that of the current policy. The simulation experiment also validates the optimal parameter value from the analytic model. The developed models simulate air cargo terminal operations effectively and efficiently, and will aid future studies in the design and control of terminal operations.     

Integrated product and process designenvironment tool for manufacturing T/R modules

We present a decision-making assistant tool for an integrated product and process design environment for manufacturing applications. Specifically, we target microwave modules that use electro-mechanical components and require optimal solutions to reduce cost, improve quality, and gain leverage in time to market the product. This tool will assist the product and process designer to improve their productivity and enable them to cooperate and coordinate their designs through a common design interface. We consider a multiobjective optimization model that determines components and processes for a given conceptual design for microwave modules. This model outputs a set of solutions that the Pareto optimal concerning cost, quality, and other metrics. In addition, we identify system integration issues for manufacturing applications, and propose an architecture that will serve as a building block to our continuing research in virtual manufacturing applications.     

多配送中心下生鲜农产品同步取送选址-路径优化

多配送中心下生鲜农产品配送工作中配送中心选址和车辆取送是两项最为重要的工作，故本文研究带同步取送的生鲜农产品选址?路径问题。首先，建立考虑车辆容量、货物作业时间、取送作业时间窗等约束条件的非线性规划模型，模型以各配送区域内产生的运输成本、惩罚费用、货损费用总和最小为目标函数。然后，根据模型特点设计融合中心评估指数和改进遗传算法的启发式算法，算法先利用中心评估指数确定配送中心和车辆的配送区域，将区域划分的信息传递给改进遗传算法进行各区域内的路径优化。最后，通过对比取送分离和同步取送两种配送方式验证本文提出的配送模式及模型是合理有效的，可为企业的生鲜农产品配送提供决策依据。     

A tabu search heuristic for the single vehicle pickup and delivery problem with time windows

The single vehicle pickup and delivery problem with time windows is a generalization of the traveling salesman problem. In such a problem, a number of transportation requests have to be satisfied by one vehicle, each request having constraints to respect: a pickup at its origin and a delivery at its destination, and a time window at each location. The capacity of the vehicle has to be respected. The aim is to minimize the total distance traveled by the vehicle. A number of exact and approximate solution methods exists in the literature, but to the authors knowledge none of them make use of metaheuristics, still promising with other vehicle routing problems. In this paper we present tabu search and probabilistic tabu search. Results obtained on classical traveling salesman problems and a class of randomly generated instances indicate that our approach often produces optimal solutions in a relatively short execution time.     

A mixed integer programming model for National Ambient Air Quality Standards (NAAQS) attainment strategy analysis

The United States federal government establishes National Ambient Air Quality Standards (NAAQS) for six pollutants, including ozone. States with areas designated in nonattainment of the standards are required to develop State Implementation Plans (SIPs) to demonstrate how pollution levels will be reduced to meet the standards. Historically, most states have developed SIPs independently. However, for ozone and other air pollutants, some states have agreed to cooperate to address regional pollution problems. These types of cooperative efforts have the potential to improve pollution control efficiency. We present a mathematical programming model that can help identify potential minimum-cost emissions control strategies that employ regional strategies. We present a series of national-level applications using information from a set of air quality simulations along with spatially and technologically detailed emissions control information. The model quickly evaluates alternative attainment planning scenarios, tests regional strategies, and identifies monitors that potentially have significant influence on attainment strategies.     

Unsupervised feature selection using swarm intelligence and consensus clustering for automatic fault detection and diagnosis in Heating Ventilation and Air Conditioning systems

Various sensory and control signals in a Heating Ventilation and Air Conditioning (HVAC) system are closely interrelated which give rise to severe redundancies between original signals. These redundancies may cripple the generalization capability of an automatic fault detection and diagnosis (AFDD) algorithm. This paper proposes an unsupervised feature selection approach and its application to AFDD in a HVAC system. Using Ensemble Rapid Centroid Estimation (ERCE), the important features are automatically selected from original measurements based on the relative entropy between the low- and high-frequency features. The materials used is the experimental HVAC fault data from the ASHRAE-1312-RP datasets containing a total of 49 days of various types of faults and corresponding severity. The features selected using ERCE (Median normalized mutual information (NMI) = 0.019) achieved the least redundancies compared to those selected using manual selection (Median NMI = 0.0199) Complete Linkage (Median NMI = 0.1305), Evidence Accumulation K-means (Median NMI = 0.04) and Weighted Evidence Accumulation K-means (Median NMI = 0.048). The effectiveness of the feature selection method is further investigated using two well-established time-sequence classification algorithms: (a) Nonlinear Auto-Regressive Neural Network with eXogenous inputs and distributed time delays (NARX-TDNN); and (b) Hidden Markov Models (HMM); where weighted average sensitivity and specificity of: (a) higher than 99% and 96% for NARX-TDNN; and (b) higher than 98% and 86% for HMM is observed. The proposed feature selection algorithm could potentially be applied to other model-based systems to improve the fault detection performance.