基于收益-风险双目标规划的随机能力扩张模型

能力扩张问题是指在不同计划期,根据不断增长的市场需求,调整生产能力,使产能与需求合适匹配以寻求企业盈利的最佳表现.能力扩张涉及长期的产能投资,是生产领域的战略决策问题.当面对不确定需求时,这一决策任务变得十分复杂,需要从回报与风险两个角度评估能力扩张方案的性能.本文基于Scenario树描述多阶段的随机需求,从而建立随机环境下能力扩张问题的Scenario决策模型.其中考虑两个决策目标:最大化利润和最小化期望下方风险,因而形成了一个双目标规划问题.为求解该问题,基于二进制粒子群优化技术,提出了双目标优化的粒子群算法.算例表明该算法可以得到近似Pareto前沿,且能揭示利润与风险的同向变化关系.

Stochastic capacity expansion model based on bi-objective programming of return and risk

Capacity expansion is the problem that an enterprise adjusts its production capacity to meet the continuously increasing market demand, and appropriately matches the production capacity and the demand to pursue the optimal performance of the profit. Capacity expansion concerns the investment of the production capacity during a long term, such that it is a strategic problem in production. When confronted with uncertain demand, the decision becomes very complicated. The decision maker need evaluate the performance of the capacity expansion scheme simultaneously considering return and risk. This paper describes the stochastic demand information based on scenario tree and formulates scenario-based programming model of capacity expansion under stochastic environments. The objective of the model is two-fold: maximizing the expected profit and minimizing the expected downside risk, which forms a bi-objective programming problem. To solve the model, a particle swarm optimizer of bi-objective optimization is proposed, which is developed based on the binary particle swarm optimization. The numerical study shows that the proposed algorithm can obtain an approximate Pareto frontier, as well as reveal the positive correlation between the profit and the decision risk.