A model to assess risk,equity and efficiency in facility location and transportation of hazardous materials

In recent years there has been increased public and governmental concern regarding hazardous materials management, and a concomitant increase in activities associated with designing and using hazardous material management systems. To be effective these systems must consider the costs and risks associated with the transport of hazardous materials as well as those associated with the facilities that generate, process, or dispose of such materials. To date, most of the modeling literature has considered these two aspects of the problem separately, either addressing the location of facilities with respect to fixed transportation patterns or the routing of materials to or from facilities at fixed locations. These two aspects of the problem are closely interrelated, as facility locations directly influence the routing options available and available routes affect the location of potential facility sites. In this paper we propose a multiobjective model to assist decision makers in the location of facilities that handle hazardous materials, and in the routing of hazardous materials to these facilities. In the model costs are comparised of per unit transportation costs, and fixed and variable costs at facilities. Risks and equity are spatially determined and are addressed on both an aggregate level, through minisum objectives, and at the individual level, through minimax objectives.     

Fuzzy multi-objective chance-constrained programming model for hazardous materials transportation

Hazardous materials transportation is an important and hot issue of public safety. Based on the shortest path model, this paper presents a fuzzy multi-objective programming model that minimizes the transportation risk to life, travel time and fuel consumption. First, we present the risk model, travel time model and fuel consumption model. Furthermore, we formulate a chance-constrained programming model within the framework of credibility theory, in which the lengths of arcs in the transportation network are assumed to be fuzzy variables. A hybrid intelligent algorithm integrating fuzzy simulation and genetic algorithm is designed for finding a satisfactory solution. Finally, some numerical examples are given to demonstrate the efficiency of the proposed model and algorithm.     

A facility location allocation model for reusing carpet materials

Re-using the huge quantities of carpet waste that are yearly generated has become a must. A facility location–allocation model for the collection, preprocessing and redistribution of carpet waste is presented. This model differs from other mathematical models for supporting the design of the logistic structure of reuse networks among others in a completely free choice of the locations for preprocessing and in explicitly taking into account depreciation costs. Two applications of the model, one in Europe and one in the United States of America, are described.     

A genetic algorithm for a bi-objective capacitated arc routing problem

The capacitated arc routing problem (CARP) is a very hard vehicle routing problem for which the objective—in its classical form—is the minimization of the total cost of the routes. In addition, one can seek to minimize also the cost of the longest trip.In this paper, a multi-objective genetic algorithm is presented for this more realistic CARP. Inspired by the second version of the Non-dominated sorted genetic algorithm framework, the procedure is improved by using good constructive heuristics to seed the initial population and by including a local search procedure. The new framework and its different flavour is appraised on three sets of classical CARP instances comprising 81 files.Yet designed for a bi-objective problem, the best versions are competitive with state-of-the-art metaheuristics for the single objective CARP, both in terms of solution quality and computational efficiency: indeed, they retrieve a majority of proven optima and improve two best-known solutions.     

A genetic algorithm for the uncapacitated single allocation planar hub location problem

Given a set of n interacting points in a network, the hub location problem determines location of the hubs (transfer points) and assigns spokes (origin and destination points) to hubs so as to minimize the total transportation cost. In this study, we deal with the uncapacitated single allocation planar hub location problem (PHLP). In this problem, all flow between pairs of spokes goes through hubs, capacities of hubs are infinite, they can be located anywhere on the plane and are fully connected, and each spoke must be assigned to only one hub. We propose a mathematical formulation and a genetic algorithm (PHLGA) to solve PHLP in reasonable time. We test PHLGA on simulated and real life data sets. We compare our results with optimal solution and analyze results for special cases of PHLP for which the solution behavior can be predicted. Moreover, PHLGA results for the AP and CAB data set are compared with other heuristics.     

Optimizing bi-objective imperfect preventive maintenance model for series-parallel system using established hybrid genetic algorithm

This study establishes a bi-objective imperfect preventive maintenance (BOIPM) model of a series-parallel system. The improvement factor method is used to evaluate the extent to which repairing components can restore the system reliability. The total maintenance cost and mean system reliability are optimized simultaneously through determining the most appropriate maintenance alternative. A bi-objective hybrid genetic algorithm (BOHGA) is established to optimize the BOIPM model. The BOHGA utilizes a Pareto-based technique to determine and retain the superior chromosomes as the GA chromosome evolutions are performed. Additionally, a unit-cost cumulative reliability expectation measure (UCCREM) is developed to evaluate the extent to which maintaining each individual component benefits the total maintenance cost and system reliability over the operational lifetime. This UCCREM is then incorporated into the genetic algorithm to construct a superior initial chromosome population and thereby enhance its solution efficiency. In order to obtain diverse bi-objective solutions as the Pareto-efficient frontier is approached, the closeness metric and diversity metric are employed to evaluate the superiority of the non-dominated solutions. Accordingly, decision makers can easily determine the most appropriate maintenance alternative. Three simulated cases verify the efficacy and practicality of this approach for determining an imperfect preventive maintenance strategy.     

Applying a sectioned genetic algorithm to word segmentation

This article presents a novel approach for morphological analysis based on the concept of genetic algorithms (GAs). Morphological analysis is of critical importance in data mining and information retrieval systems because it leads to a more homogeneous representation of words. The system presented here makes minimal use of language specific information and is therefore more general than the rule-based techniques that have been proposed in literature. A number of heuristics are created and tested as evaluation functions; both general-purpose ones as well as heuristics specifically designed for the task, and decisions are made on the optimum models for the genetic operators suitable for the specific implementation. Finally the system addresses the problem of simultaneous processing of a great number of words without excessively increasing the execution time or deteriorating the segmentation quality of the final results. This is accomplished by the division of the individuals into sections, following the application of a group of masks, and the operation of the GA on these smaller sections instead of on the entire individual.     

基于改进遗传算法的省级医疗中心选址研究

针对公共设施选址问题中因多目标约束条件造成的复杂空间搜索问题,提出了一种基于遗传算法的P-中值模型,以设施点与供应点间的分配关系作为基因序列,将出行时间消耗、建设投入成本、容量限制条件等因素构成目标函数,用于设施供需分配过程中的优化求解。同时从初始种群构成方式和变异率两方面对遗传算法进行改进以提高求解准确性。实验将该模型运用于河南省省级医疗中心选址,并结合多种评价指标得出多样化的医疗中心布局方案,验证了模型的有效性和可行性。     

Uncertain location–allocation decisions for a bi-objective two-stage supply chain network design problem with environmental impacts

In the cases that the historical data of an uncertain event is not available, belief degree-based uncertainty theory is a useful tool to reflect such uncertainty. This study focuses on uncertain bi-objective supply chain network design problem with cost and environmental impacts under uncertainty. As such network may be designed for the first time in a geographical region, this problem is modelled by the concepts of belief degree-based uncertainty theory. This article is almost the first study on belief degree-based uncertain supply chain network design problem with environmental impacts. Two approaches such as expected value model and chance-constrained model are applied to convert the proposed uncertain problem to its crisp form. The obtained crisp forms are solved by some multi-objective optimization approaches of the literature such as TH, Niroomand, MMNV. A deep computational study with several test problems are performed to study the performance of the crisp models and the solution approaches. According to the results, the obtained crisp formulations are highly sensitive to the changes in the value of the cost parameters. On the other hand, Niroomand and MMNV solution approaches perform better than other solution approaches from the solution quality point of view.     

A new integrated mathematical model for a bi-objective multi-depot location-routing problem solved by a multi-objective scatter search algorithm

The location of manufacturing facilities is one of the most important strategic decisions considered in the design of logistic systems. Another important strategic decision is the structure and management of the fleets. Most often, even if two types of problem (i.e., location of facilities and vehicle routing) have occurred in a given scenario, they have been studied and solved separately. This paper presents a new integrated mathematical model for a bi-objective multi-depot location-routing problem where the total demand served is to be maximized and the total cost, consisting of start-up of the facility, fixed and variable depots and variable delivery cost, is to be minimized. Since this type of the problem is NP-hard, a new multi-objective scatter search (MOSS) algorithm is proposed to obtain the Pareto frontier for the given problem. To validate the performance of the proposed MOSS algorithm in terms of the solution quality and diversity level, various test problems are carried out and the efficiency of this algorithm based on some comparison metrics is compared with the elite tabu search (ETS). The computational results show that the proposed MOSS outperforms the ETS, especially in large-sized problems.     

Applying a genetic algorithm to the optimization of composite patches

This paper describes the application of genetic algorithms (GAs) to the optimization of a composite patch bonded on a metallic structure. The objective is to reduce the stress level in a given area under some constraints such as a maximum surface of the patch and some forbidden zones which cannot be covered by the patch. GAs are presently used for optimizing ply orientations of the stacking sequence as well as for the location and the shape of the patch which is modelled with a spline function. The design variables are ply orientations and coordinates of interpolation points which define a closed plane spline curve. Stress field calculations in the structure are carried out using the Ansys finite element package. The structure considered in this study is an aluminium plate with a circular hole around which the stress level must be reduced by bonding the composite patch. Shapes and ply stacking sequences resulting from the optimization procedure enable a local reinforcement in the neighbourhood of the patch and also a long-distance effect which consists in a deviation of the stress flow adjoining the area over which the stress level must be reduced.     

A genetic algorithm for solving the fixed-charge transportation model: Two-stage problem

Transportation of goods in a supply chain from plants to customers through distribution centers (DCs) is modeled as a two-stage distribution problem in the literature. In this paper we propose genetic algorithms to solve a two-stage transportation problem with two different scenarios. The first scenario considers the per-unit transportation cost and the fixed cost associated with a route, coupled with unlimited capacity at every DC. The second scenario considers the opening cost of a distribution center, per-unit transportation cost from a given plant to a given DC and the per-unit transportation cost from the DC to a customer. Subsequently, an attempt is made to represent the two-stage fixed-charge transportation problem (Scenario-1) as a single-stage fixed-charge transportation problem and solve the resulting problem using our genetic algorithm. Many benchmark problem instances are solved using the proposed genetic algorithms and performances of these algorithms are compared with the respective best existing algorithms for the two scenarios. The results from computational experiments show that the proposed algorithms yield better solutions than the respective best existing algorithms for the two scenarios under consideration.     

Effective selection and allocation of material handling equipment for stochastic production material demand problems using genetic algorithm

This paper addresses the stochastic production demand problem in a manufacturing company. The objective of this research is to minimize the waiting time of production workstations and reduce stochastic production material problems through coordinating pickup and delivery orders in a warehouse. RFID technology is adopted to visualize the actual status of operations in production and warehouse environments. A mathematical model is developed to address this problem and a meta-heuristic algorithm using genetic algorithm (GA) is also developed to improve performance. Computational experiments are undertaken to examine the performance of the algorithm when dealing with congestion in cases of heavy and normal demand for production material. The overall result shows that the algorithm efficiently minimizes the total makespan of the production shop floor.     

Modified savings heuristics and genetic algorithm for bi-objective vehicle routing problem with forced backhauls

The cost of distribution and logistics accounts for a sizable part of the total operating cost of a company. However, the cost associated with operating vehicles and crews for delivery purposes form an important component of total distribution costs. Small percentage saving in these expenses could result in a large amount of savings over a number of years. Increase in the number of automated teller machines (ATMs) in the bank industry enforced the researchers to concentrate much on the optimization of distribution logistics problem. The process of replenishing money in the ATMs is considered as a scope with bi-objectives such as minimizing total routing cost and minimizing the span of travel tour. Some of the pick-up routes of the problem are forced and it is termed as forced backhauls. This problem is termed as bi-objective vehicle routing problems with forced backhauls (BVFB). We developed three heuristics to solve BVFB. Two heuristics are modified savings heuristics and the third heuristic is based on adapted genetic algorithm (GA). Standard data sets of VRPB of real life cases for BVFB and randomly generated datasets for BVFB are solved using all the three heuristics. The results are compared and found that all the three heuristics are competitive in solving BVFB. GA yields better solution compared to the other two heuristics.     

求解TTP问题新优化模型的混合遗传算法

流窜犯问题（Traveling Thief Problem,TTP）是旅行商问题和背包问题的一个组合问题,同时具有两个问题的计算复杂度。在现有TTP问题中考虑了小偷提前不知道物品具体位置的情况,给出了新的具有概率分布信息的优化模型;利用有效价值指标,给出了物品的选取方法;基于一个TSP的遗传算法框架和新设计的局部搜索策略,提出了求解该模型的混合遗传算法。数值仿真结果表明,提出的算法是可行有效的。     

A genetic algorithm using priority-based encoding with new operators for fixed charge transportation problems

In this paper, we propose a genetic algorithm using priority-based encoding (pb-GA) for linear and nonlinear fixed charge transportation problems (fcTP) in which new operators for more exploration are proposed. We modify a priority-based decoding procedure proposed by Gen et al. [1] to adapt with the fcTP structure. After comparing well-known representation methods for a transportation problem, we explain our proposed pb-GA. We compare the performance of the pb-GA with the recently used spanning tree-based genetic algorithm (st-GA) using numerous examples of linear and nonlinear fcTPs. Finally, computational results show that the proposed pb-GA gives better results than the st-GA both in terms of the solution quality and computation time, especially for medium- and large-sized problems. Numerical experiments show that the proposed pb-GA better absorbs the characteristics of the nonlinear fcTPs.     

A new stochastic approach for a reliable p-hub covering location problem

A hub location problem (HLP) is a fertile research field, in the aspect of interdisciplinary studies, such as transportation, operation research, network design, telecommunication and economics. The location of hub facilities and allocation of non-hub nodes to hubs configure the backbone of HLPs. This study presents a new mathematical model for a reliable HLP by a new stochastic approach to minimize the total transportation cost and obtain maximum flows that the network can carry, when its link capacities are subject to stochastic degradations, as in a form of daily traffic, earthquake, flood, etc. We consider the road capacity reliability as a probability that ensures the maximum network capacity is greater than or equal to the total incoming flow to the network by considering the road capacity as random variable. As a result, this paper assumes that link capacities satisfy in a Truncated Erlang $(\mathit{TErl})$ distribution function. Due to complexity of the HLP, a meta-heuristic algorithm, namely differential evolution (DE) algorithm, is applied on the problem in order to achieve near-optimal solutions. Furthermore, the performance of the proposed algorithm (i.e., DE) is evaluated by the performance of the genetic algorithm (GA) applied on the given problem. Some computational experiments are presented to illustrate the effectiveness of the presented model and proposed algorithm. Finally the conclusion is provided.     

A new hybrid PSO algorithm based on a stochastic Markov chain model

Based on the recent research concerning the PageRank Algorithm used in the famous search engine Google [1], a new Inverse-PageRank-Particle Swarm Optimizer (I-PR-PSO) is presented in order to improve the performances of classic PSO. The resulted algorithm uses a stochastic Markov chain model to define an intelligent topological structure of the swarm’s population, in which the better particles have an important influence on the others. In the presented experiments, calculations on some benchmark functions classically used to test optimization methods are performed, and the results are compared to different versions of the standard PSO, that is using different topological structures of the population. The experimental results show that I-PR-PSO can converge quicker on the tested functions, and can find better results in the solution domain than its tested peers.     

Design of a genetic algorithm for bi-objective unrelated parallel machines scheduling with sequence-dependent setup times and precedence constraints

This paper presents a novel, two-level mixed-integer programming model of scheduling N jobs on M parallel machines that minimizes bi-objectives, namely the number of tardy jobs and the total completion time of all the jobs. The proposed model considers unrelated parallel machines. The jobs have non-identical due dates and ready times, and there are some precedence relations between them. Furthermore, sequence-dependent setup times, which are included in the proposed model, may be different for each machine depending on their characteristics. Obtaining an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches or optimization tools is extremely difficult. This paper proposes an efficient genetic algorithm (GA) to solve the bi-objective parallel machine scheduling problem. The performance of the presented model and the proposed GA is verified by a number of numerical experiments. The related results show the effectiveness of the proposed model and GA for small and large-sized problems.