On the location selection problem using analytic hierarchy process and multi-choice goal programming

Location selection is a crucial decision in cost/benefit analysis of restaurants, coffee shops and others. However, it is difficult to be solved because there are many conflicting multiple goals in the problem of location selection. In order to solve the problem, this study integrates analytic hierarchy process (AHP) and multi-choice goal programming (MCGP) as a decision aid to obtain an appropriate house from many alternative locations that better suit the preferences of renters under their needs. This study obtains weights from AHP and implements it upon each goal using MCGP for the location selection problem. According to the function of multi-aspiration provided by MCGP, decision makers can set multi-aspiration for each location goal to rank the candidate locations. Compared to the unaided selection processes, the integrated approach of AHP and MCGP is a better scientific and efficient method than traditional methods in finding a suitable location for buying or renting a house for business, especially under multiple qualitative and quantitative criteria within a shorter evaluation time. In addition, a real case is provided to demonstrate the usefulness of the proposed method. The results show that the proposed method is able to provide better quality decision than normal manual methods.     

A goal programming model for the coal blending problem

This paper presents a coal blending management system incorporating a goal programming (GP) model. The main aim of this paper is to develop a goal programming (GP) model with an objective to provide a decision support system for coal stockyard managers. This model determines appropriate quantities of coal from different stockpiles for a consistent feeding of blended coal while meeting environmental and boiler performance requirements. Finally, the model is implemented as an operational decision making support system so that a novice operator will have no difficulty is using it. A real-world case is presented to illustrate the application of the model.     

Cross-border logistics with fleet management: A goal programming approach

In this paper, we introduce the cross-border logistics problem with fleet management. A major phenomenon of implementation of open-door policy in China is the move of Hong Kong-based manufacturers’ production lines to China, crossing the border to take advantages of lower production costs, lower wages and lower rental costs. The finished products are then transshipped to Hong Kong, an efficient logistics hub well-equipped with reliable transportation facility, for exporting. We present a preemptive goal programming model for multi-objective cross-border logistics problem, in which three objectives are optimized hierarchically. We also describe a framework for incorporating decision-makers’ opinions for determination of goal priorities and target values. A set of Hong Kong data have been used to test the effectiveness and efficiency of the proposed model. Results demonstrate the decision-makers can find the flexibility and robustness of the proposed model by adjusting the goal priorities with respect to the importance of each objective.     

A set partitioning based goal programming model for the team formation problem

In this paper, we offer a multi-objective set-partitioning formulation for team formation problems using goal programming. Instead of selecting team members to teams, we select suitable teams from a set of teams. This set is generated using a heuristic algorithm that uses the social network of potential team members. We then utilize the proposed multi-objective formulation to select the desired number of teams from this set that meets the skill requirements. Therefore, we ensure that selected teams include individuals with the required skills and effective communication with each other. Two real datasets are used to test the model. The results obtained with the proposed solution are compared with two well-known approaches: weighted and lexicographic goal programming. Results reveal that weighted and lexicographic goal programming approaches generate almost identical solutions for the datasets tested. Our approach, on the other hand, mostly picks teams with lower communication costs. Even in some cases, better solutions are obtained with the proposed approach. Findings show that the developed solution approach is a promising approach to handle team formation problems.     

A new humanitarian relief logistic network for multi-objective optimization under stochastic programming

Millions of affected people and thousands of victims are consequences of earthquakes, every year. Therefore, it is necessary to prepare a proper preparedness and response planning. The objectives of this paper are i) minimizing the expected value of the total costs of relief supply chain, ii) minimizing the maximum number of unsatisfied demands for relief staff and iii) minimizing the total probability of unsuccessful evacuation in routes. In this paper, a scenario based stochastic multi-objective location-allocation-routing model is proposed for a real humanitarian relief logistics problem which focused on both pre- and post-disaster situations in presence of uncertainty. To cope with demand uncertainty, a simulation approach is used. The proposed model integrates these two phases simultaneously. Then, both strategic and operational decisions (pre-disaster and post-disaster), fairness in the evacuation, and relief item distribution including commodities and relief workers, victim evacuation including injured people, corpses and homeless people are also considered simultaneously in this paper. The presented model is solved utilizing the Epsilon-constraint method for small- and medium-scale problems and using three metaheuristic algorithms for the large-scale problem (case study). Empirical results illustrate that the model can be used to locate the shelters and relief distribution centers, determine appropriate routes and allocate resources in uncertain and real-life disaster situations.

     

Supplier selection model using Taguchi loss function,analytical hierarchy process and multi-choice goal programming

In recent years, determining the best supplier has become a key strategic consideration in the competitive market. Since the decision commonly involves evaluating different criteria or attributes, supplier selection process is a multiple criteria decision-making (MCDM) problem. This study integrates the Taguchi loss function, analytical hierarchy process (AHP) and multi-choice goal programming (MCGP) model for solving the supplier selection problem. The advantage of this proposed method is that it allows decision makers to set multiple aspiration levels for the decision criteria. A numerical example of application is also presented.     

A heuristic-based branch and bound algorithm for unconstrained quadratic zero-one programming

In this paper we describe a branch and bound algorithm for solving the unconstrained quadratic 0–1 programming problem. The salient features of it are the use of quadratic programming heuristics in the transformation of subproblems and exploiting some classes of facets of the polytope related to the quadratic problem in deriving upper bounds on the objective function. We develop facet selection procedures that form a basis of the bound computation algorithm. We present computational experience on four series of randomly generated problems and 14 real instances of a quadratic problem arising in design automation. We remark that the same ideas can also be applied to some other combinatorial optimization problems.     

Uncertain goal programming models for bicriteria solid transportation problem

This paper investigates an uncertain bicriteria solid transportation problem. The supplies, demands, conveyance capacities, transportation cost and transportation time are regarded as uncertain variables. According to two types of methods to rank the uncertain variables, expected value goal programming model and chance-constrained goal programming model for the bicriteria solid transportation problem are constructed. It is proved that the expected value goal programming model and chance-constrained goal programming model can be respectively transformed into the corresponding deterministic equivalents by taking advantage of some properties of uncertainty theory. Based on these equivalence relations, the optimal transportation plans of the uncertain goal programming models can be obtained. Several numerical experiments are presented to illustrate the applications of the models.     

A global algorithm for minimax solutions to a stochastic programming problem

A minimax solution to a stochastic program occurs when the objective function is maximized subject to the random parameters jointly taking on their most adverse or pessimistic values. Minimax solutions have been proposed for decision making in agricultural planning, and to provide a lower bound to the values of the objective function of the “wait and see” stochastic program. In this paper a non-convex minimax problem and the occurrence of local optima are discussed. A global algorithm is presented for the minimax problem of a stochastic program in which some of the right hand side parameters are stochastic. It is also shown how minimax solutions may be obtained where stochastic parameters occur solely in the objective function, and in the objective function and right hand sides simultaneously.     

基于二阶段随机规划的回收物流网络优化设计研究

针对含有连续分布随机参数的回收物流网络优化设计问题,结合抽样理论,建立了由样本数量决定求解效率的二阶段随机规划模型.指出适量小样本对应的模型最优值必然是实际最优值的下界,提出了基于大样本分析的物流网络稳健性评价方法以及实际最优值上界的确定方法.给出模型求解的混合遗传算法,并总结了物流网络的优化设计步骤.通过具体算例说明了模型及其算法在设计决策中的应用.     

A goal programming approach for fuzzy multiobjective fractional programming problems

Fuzzy multiple objective fractional programming (FMOFP) is an important technique for solving many real-world problems involving the nature of vagueness, imprecision and/or random. Following the idea of binary behaviour of fuzzy programming (Chang 2007 Chang, C-T. 2007. Binary Behavior of Fuzzy Programming with Piecewise Membership Functions. IEEE Transactions on Fuzzy Systems, 15: 342–349.  [Google Scholar]), there may exist a situation where a decision-maker would like to make a decision on FMOFP involving the achievement of fuzzy goals, in which some of them may meet the behaviour of fuzzy programming (i.e. level achieved) or the behaviour of binary programming (i.e. completely not achieved). This is turned into a fuzzy multiple objective mixed binary fractional programming (FMOMBFP) problem. However, to the best of our knowledge, this problem is not well formulated by mathematical programming. Therefore, this article proposes a linearisation strategy to formulate the FMOMBFP problem in which extra binary variable is not required. In addition, achieving the highest membership value of each fuzzy goal defined for the fractional objective function, the proposed method can alleviate the computational difficulties when solving the FMOMBFP problem. To demonstrate the usefulness of the proposed method, a real-world case is also included.     

A two-stage stochastic programming model for the parallel machine scheduling problem with machine capacity

This paper proposes a two-stage stochastic programming model for the parallel machine scheduling problem where the objective is to determine the machines' capacities that maximize the expected net profit of on-time jobs when the due dates are uncertain. The stochastic model decomposes the problem into two stages: The first (FS) determines the optimal capacities of the machines whereas the second (SS) computes an estimate of the expected profit of the on-time jobs for given machines' capacities. For a given sample of due dates, SS reduces to the deterministic parallel weighted number of on-time jobs problem which can be solved using the efficient branch and bound of M’Hallah and Bulfin [16]. FS is tackled using a sample average approximation (SAA) sampling approach which iteratively solves the problem for a number of random samples of due dates. SAA converges to the optimum in the expected sense as the sample size increases. In this implementation, SAA applies a ranking and selection procedure to obtain a good estimate of the expected profit with a reduced number of random samples. Extensive computational experiments show the efficacy of the stochastic model.     

多选择背包问题的人工蜂群算法*

多选择背包问题是组合优化中的NP难题之一,采用一种新的智能优化算法——人工蜂群算法进行求解。该算法通过雇佣蜂、跟随蜂和侦察蜂的局部寻优来实现全局最优。基于算法实现的核心思想,用MATLAB编程实现,对参考文献的算例进行仿真测试。与其他算法进行了比较,获得了满意的结果。这说明了算法在解决该问题上的可行性与有效性,拓展了人工蜂群算法的应用领域。     

动态规划算法在基于子区间消除的随机点定位问题中的应用

针对原有基于判决方程的子区间消除算法中所存在的判决结果与决策表不相符,以及当子区间划分规模增大时,运行时间呈平方次增长的问题,本文提出了一种全新的基于动态规划的子区间消除算法。新算法充分利用动态规划在多阶段决策问题中的卓越性能,将子区间的消除问题划分为合理性判断和新区间生成两部分,这两个部分均可以利用动态规划中子问题分割的思想来解决。文中证明了通过解决这些子问题可以构造得到原问题的最优解,分析了算法的时间复杂度和空间复杂度。为了检验新算法的性能,本文从理论和实验两种维度,进行了新旧两种算法的对比。实验结果表明,该方法大大降低了算法的时间复杂度,有效克服了子区间规模增大所导致的问题,提高了算法的灵活性和运行速度。     

The contract formation problem in preventive pavement maintenance: A fixed-charge goal programming model

This paper examines the problem of clustering highway sections for purposes of forming preventive maintenance contracts. In order to achieve the desired low unit costs which contractors can provide, a sufficient number of sections must be grouped together. The preventive maintenance clustering problem is formulated as a fixed charge goal programming model. An algorithm for solving this model is presented.     

A stochastic programming model for the thermal optimal day-ahead bid problem with physical futures contracts

The reorganization of the electricity industry in Spain completed a new step with the start-up of the Derivatives Market. One main characteristic of MIBEL's Derivatives Market is the existence of physical futures contracts; they imply the obligation to physically settle the energy. The market regulation establishes the mechanism for including those physical futures in the day-ahead bidding of the generation companies. The goal of this work is to optimize coordination between physical futures contracts and the day-ahead bidding which follow this regulation. We propose a stochastic quadratic mixed-integer programming model which maximizes the expected profits, taking into account futures contracts settlement. The model gives the simultaneous optimization for the Day-Ahead Market bidding strategy and power planning production (unit commitment) for the thermal units of a price-taker generation company. The uncertainty of the Day-Ahead Market price is included in the stochastic model through a set of scenarios. Implementation details and some first computational experiences for small real cases are presented.