求解0-1背包问题的人工蜂群算法

提出了一种用于求解0-1背包问题的人工蜂群算法,详细阐述了该算法求解背包问题的具体操作过程。算法主要使用了两个思想策略:启发式贪婪算法和人工蜂群算法。通过对其它文献中仿真实例的计算和结果对比,表明该算法对求解0-1背包问题的有效性,这对人工蜂群算法解决其它离散问题会有很大帮助。     

多背包问题的一种求解方法

多背包问题是一个NP-hard的组合优化问题,在预算控制、项目选择、材料切割和货物装载有着广泛的应用背景,在计算方法上,分别有学者提出各种精确算法和近似算法。本文提出启发式规则,将相对优的物品提前接受,将相对劣的物品直接排除,改进了多背包问题的求解速度。文中还分析了相对优和相对劣的选定标准对于计算速度和最优解质量的影响。     

基于贪婪算法的改进自适应遗传算法及其应用

本文将改进的自适应遗传算法和贪婪算法相结合用于0-1背包问题的求解。此算法对交叉率和变异率进行了优化,实现了交叉率和变异率的非线性自适应调整,并对不可行解进行了贪婪修复。实验结果表明,相比传统的自适应遗传算法,新算法收敛速度快,寻优能力强,具有更可靠的稳定性。     

基于遗传算法的多目标集装箱多式联运运输优化模型

基于集装箱多式联运在进行门到门的运输过程中可以选择多种运输方式和路径的组合进行优化运输这个特点，本文将多式联运的运输优化问题转化成为一个最短路径问题，以成本和时间为优化目标建立了选择最优路径的模型，并选择遗传算法作为求解算法对实例问题进行了求解验证。     

多点配送中心选址及求解蚁群算法

将多点配送中心选址问题(multi-distribution center location problem,MDLP)映射为扩展K-TSP过程并设计了改进的蚁群算法。为提高算法的求解性能,在蚂蚁的选择规则里加入了代价引导函数,使用2-opt策略优化可行解并优化了信息素的更新方式。仿真算例及算法对比表明,模型和算法可以有效地表达和求解MDLP。     

基于禁忌搜索算法的货物运输路径和方式选择问题研究

在对货物运输路径和方式选择问题进行简单描述的基础上,提出了将该货物运输问题分解为多个单路径、多种运输方式组合问题进行求解的策略。通过设计解的表示和评价方法,构建了求解该问题的禁忌搜索算法,并进行了实验计算。计算结果表明,用本文设计的算法优化货物运输路径和方式选择问题,不仅可以取得很好的计算结果,而且收敛速度较快,结果也较稳定。     

多式联运路径优化模型与方法研究

考虑到多式联运路径的选择受到运输成本、运输时间、运输质量和服务水平等相关因素的影响,将多式联运的路径优化问题转化成为一个广义最短路径问题,以成本和时间为优化目标建立了适应运量变化情况下的多式联运路径优化数学模型,并选择蚁群算法对实际问题进行了求解验证。     

混合遗传算法求解一类多资源生产调度问题

研究了机器设备、工人人数和机器人三种资源受限制的多资源生产调度的问题,建立了数学模拟。采用遗传算法和模拟退火算法相结合的混合遗传算法对问题进行求解。通过仿真试验验证了该混合遗传算法求解此类多资源生产调度问题是可行的、有效的。     

多式联运路径优化模型与方法研究

考虑到多式联运路径的选择受到运输成本、运输时间、运输质量和服务水平等相关因素的影响,将多式联运的路径优化问题转化成为一个广义最短路径问题.以成本和时间为优化目标建立了适应运量变化情况下的多式联运路径优化数学模型,并选择蚁群算法对实际问题进行了求解验证.     

多点配送中心选址及求解蚁群算法

将多点配送中心选址问题(multi-distribution center location problem,MDLP)映射为扩展K-TSP过程并设计了改进的蚁群算法.为提高算法的求解性能,在蚂蚁的选择规则里加入了代价引导函数,使用2-opt策略优化可行解并优化了信息素的更新方式.仿真算例及算法对比表明,模型和算法可以有效地表达和求解MDLP.     

Knapsack cost sharing

This paper considers cost sharing rules for the continuous knapsack problem. We assume a knapsack with a weight constraint to be filled with items of different weights chosen from a set of items. The cost of the knapsack needs to be shared among the individuals who approve or disapprove of certain items. Cost sharing rules are discussed and—using various axioms—characterization results are provided.     

Utility of wealth with many indivisibilities

We introduce a class of utility of wealth functions, called knapsack utility functions, which are appropriate for agents who must choose an optimal collection of indivisible goods subject to a spending constraint. We investigate the concavity/convexity and regularity properties of these functions. We find that convexity–and thus a demand for gambling–is the norm, but that the incentive to gamble is more pronounced at low wealth levels. We consider an intertemporal version of the problem in which the agent faces a credit constraint. We find that the agent’s utility of wealth function closely resembles a knapsack utility function when the agent’s saving rate is low.     

Service load balancing,task scheduling and transportation optimisation in cloud manufacturing by applying queuing system

Recently, there is a great deal of attention in Cloud Manufacturing (CMfg) as a new service-oriented manufacturing paradigm. To integrate the activities and services through a CMfg, both Service Load balancing and Transportation Optimisation (SLTO) are two major issues to ease the success of CMfg. Based on this motivation, this study presents a new queuing network for parallel scheduling of multiple processes and orders from customers to be supplied. Another main contribution of this paper is a new heuristic algorithm based on the process time of the tasks of the orders (LBPT) to solve the proposed problem. To formulate it, a novel multi-objective mathematical model as a Mixed Integer Linear Programming (MILP) is developed. Accordingly, this study employs the multi-choice multi-objective goal programming with a utility function to model the introduced SLTO problem. To better solve the problem, a Particle Swarm Optimisation (PSO) algorithm is developed to tackle this optimisation problem. Finally, a comparative study with different analyses through four scenarios demonstrates that there are some improvements on the sum of process and transportation costs by 6.1%, the sum of process and transportation times by 10.6%, and the service load disparity by 48.6% relative to the benchmark scenario.     

Investment planning for irrigation development projects

This paper considers the problem of choosing among the technologies available for irrigation by tubewells to obtain an investment plan which maximizes the net agricultural benefits from a proposed project in a developing country. Cost and benefit relationships are derived and incorporated into a mathematical model which is solved using a modification of the dynamic programming procedure for solving the knapsack problem. The optimal schedule is seen to favor small capacity wells, drilled by indigenous methods, with supplementary water distribution systems.     

Optimal requirement allocation among quantity-discount quoting suppliers

We consider a problem motivated by a central purchasing organization for a major office products distributor. This purchasing organization must source a quantity of a particular resale item from a set of capacitated suppliers. In our case each supplier offers an incremental quantity discount purchase price structure. The purchaser’s objective is to obtain a quantity of a required item at minimum cost. The resulting problem is one of allocating order quantities among an approved supply base and involves minimizing the sum of separable piecewise linear concave cost functions. We develop a branch and bound algorithm that arrives at an optimal solution by generating linear knapsack subproblems with feasible solutions to the original problem. This research was partially supported by a 2007 Summer Research Grant awarded to Asoo J. Vakharia.     

基于背包问题的散货航次方案优选方法研究

针对散货航运市场的航次方案优选问题,使得公司能够在自身的能力约束下获得最大收益,最终目标是快速地在潜在的商业机会中找出最优的航次方案组合。通过调研定义了合理的航次收益、成本定量预估方法,在此基础上结合背包问题构建了航次方案优选模型,并进行改进,从而快速地求解航次方案优选问题,提高企业的核心竞争力。     

A MCGP decision aid for homebuyers to make the best choice

This study adopts a new approach, the multi-choice goal programming (MCGP), to evaluate houses in order to help homebuyers to find better house based on the residential preferences. According to the function of MCGP, homebuyers can set multiple housing goals with multiple aspiration levels. This increases the flexibility to find a suitable house. Compared with other classical methods such as checklist and analytic hierarchy process, MCGP is more efficient, especially while considering a lot of housing criteria and house alternatives. In order to demonstrate the usefulness of MCGP decision aid for housing selection, a real case study is then provided. Furthermore, ten volunteers are invited to participate in the empirical experiment. The results also validate the effectiveness and efficiency of MCGP decision aid.     

具有模糊加工时间的偏柔性作业车间调度问题研究

研究了以最小化制造跨度为目标的,具有模糊加工时间的偏柔性作业车间调度问题。针对该问题,采用三角模糊数来表征时间参数;并采用预处理算法,将偏柔性作业车间调度问题转化为更容易处理的完全柔性作业车间调度问题,给出了基于粒子群优化的调度模型;最后通过实例验证了模型的有效性。     

一类VRP问题的改进PSO算法求解

VRP问题是物流领域的热点研究问题。在对一类典型的VRP问题建立了数学模型,提出了一种改进粒子群优化算法以求解该模型。算法针对问题设计了顺序编码方案,并引入了局部搜索以提高算法的局部搜索能力。仿真结果表明了所提离散粒子群优化算法求解此类VRP问题的有效性。