Branch and bound based solution algorithms for the budget constrained discrete time/cost trade-off problem

The time/cost trade-off models in project management aim to reduce the project completion time by putting extra resources on activity durations. The budget problem in discrete time/cost trade-off scheduling selects a time/cost mode for each activity so as to minimize the project completion time without exceeding the available budget. There may be alternative modes that solve the budget problem optimally and each solution may have a different total cost value. In this study we consider the budget problem and aim to find the minimum cost solution among the minimum project completion time solutions. We analyse the structure of the problem together with its linear programming relaxation and derive some mechanisms for reducing the problem size. We solve the reduced problem by branch and bound based optimization and heuristic algorithms. We find that our branch and bound algorithm finds optimal solutions for medium-sized problem instances in reasonable times and the heuristic algorithms produce high quality solutions very quickly.     

Optimal control of impulsive switched systems with minimum subsystem durations

This paper presents a new computational approach for solving optimal control problems governed by impulsive switched systems. Such systems consist of multiple subsystems operating in succession, with possible instantaneous state jumps occurring when the system switches from one subsystem to another. The control variables are the subsystem durations and a set of system parameters influencing the state jumps. In contrast with most other papers on the control of impulsive switched systems, we do not require every potential subsystem to be active during the time horizon (it may be optimal to delete certain subsystems, especially when the optimal number of switches is unknown). However, any active subsystem must be active for a minimum non-negligible duration of time. This restriction leads to a disjoint feasible region for the subsystem durations. The problem of choosing the subsystem durations and the system parameters to minimize a given cost function is a non-standard optimal control problem that cannot be solved using conventional techniques. By combining a time-scaling transformation and an exact penalty method, we develop a computational algorithm for solving this problem. We then demonstrate the effectiveness of this algorithm by considering a numerical example on the optimization of shrimp harvesting operations.     

Multi-objective time–cost trade-off in dynamic PERT networks using an interactive approach

We develop a multi-objective model for the time–cost trade-off problem in a dynamic PERT network using an interactive approach. The activity durations are exponentially distributed random variables and the new projects are generated according to a renewal process and share the same facilities. Thus, these projects cannot be analyzed independently. This dynamic PERT network is represented as a network of queues, where the service times represent the durations of the corresponding activities and the arrival stream to each node follows a renewal process. At the first stage, we transform the dynamic PERT network into a proper stochastic network and then compute the project completion time distribution by constructing a continuous-time Markov chain. At the second stage, the time–cost trade-off problem is formulated as a multi-objective optimal control problem that involves four conflicting objective functions. Then, the STEM method is used to solve a discrete-time approximation of the original problem. Finally, the proposed methodology is extended to the generalized Erlang activity durations.     

动态最小费用路在L1模下的逆问题讨论

考察动态最小费用路在L_1模下的逆问题,其中在弧费用的定义中,将弧(i,j)上的运行时间d_(ij)(t)分成最小可能运行时间d_(ij)~*和超出的运行时间(excess time)e_(ij)(t)两部分,弧(i,j)上费用即为两者赋权之和.在逆问题的讨论中考虑先将动态网络中的问题通过时间扩张网络G~T转化为静态问题,然后再利用解线性规划的逆问题的方法来解该动态最短路问题的逆问题.     

Resource Constrained Scheduling within Fixed Project Durations

This paper presents a practical approach for resource constrained scheduling within a given project duration. The method ensures a stable project plan when activity durations are reduced to achieve this. It handles all four categories of resource (unconstrained, renewable, non-renewable and doubly-constrained resources), and aims to shorten the project duration at minimum cost. Soft constraints and resource availabilities which vary over time are allowed in the method. Computational experience is reported.     

The biobjective undirected two-commodity minimum cost flow problem

We address the two-commodity minimum cost flow problem considering two objectives. We show that the biobjective undirected two-commodity minimum cost flow problem can be split into two standard biobjective minimum cost flow problems using the change of variables approach. This technique allows us to develop a method that finds all the efficient extreme points in the objective space for the two-commodity problem solving two biobjective minimum cost flow problems. In other words, we generalize the Hu's theorem for the biobjective undirected two-commodity minimum cost flow problem. In addition, we develop a parametric network simplex method to solve the biobjective problem.     

Connection problems in mountains and monotonic allocation schemes

Directed minimum cost spanning tree problems of a special kind are studied, namely those which show up in considering the problem of connecting units (houses) in mountains with a purifier. For such problems an easy method is described to obtain a minimum cost spanning tree. The related cost sharing problem is tackled by considering the corresponding cooperative cost game with the units as players and also the related connection games, for each unit one. The cores of the connection games have a simple structure and each core element can be extended to a population monotonic allocation scheme (pmas) and also to a bi-monotonic allocation scheme. These pmas-es for the connection games result in pmas-es for the cost game.     

Medium-term hydro-thermal coordination via hydro and thermal subsystem decomposition

Summary This paper addresses the medium-term hydro-thermal coordination problem in an electric energy system. That is, the problem of finding the energy production of every power plant (hydro or thermal) in every subperiod of a given planning period, so that the customer load is supplied at minimum cost. The planning horizon is typically one to two months and the first week of this planning period is modeled in detail. The solution method proposed decomposes the problem in two subproblems corresponding to the hydro and thermal subsystems. These two subproblems are coordinated using a coordinating function for every subperiod. The coordinating function of a given subperiod expresses total production cost in that subperiod as a function of the total hydro production in that subperiod. The decomposition proposed makes it possible to use specialized algorithms to solve the hydro and thermal subproblems. This results in a very efficient computational procedure. From an experimental point of view the coordinating mechanism is robust. A case study is provided. It considers 61 thermal plants, a hydro system including 8 cascaded hydro plants and a 48 subperiods planning period.     

A Heuristic Method for a Job-Scheduling Problem

A batch of jobs is to be processed on a certain production line. Each job can be done in several states of the line but at varying production costs and there is also a cost of changing states. The batch is to be scheduled at minimum total cost. A method of breaking down the problem and using a branching process to generate sequences of states is given. It takes advantage of special features of the cost structure.     

Global optimization of a quadratic functional with quadratic equality constraints

In this paper, we investigate a constrained optimization problem with a quadratic cost functional and two quadratic equality constraints. While it is obvious that, for a nonempty constraint set, there exists a global minimum cost, a method to determine if a given local solution yields the global minimum cost has not been established. We develop a necessary and sufficient condition that will guarantee that solutions of the optimization problem yield the global minimum cost. This constrained optimization problem occurs naturally in the computation of the phase margin for multivariable control systems. Our results guarantee that numerical routines can be developed that will converge to the global solution for the phase margin.     

Multiple objective minimum cost flow problems: A review

In this paper, theory and algorithms for solving the multiple objective minimum cost flow problem are reviewed. For both the continuous and integer case exact and approximation algorithms are presented. In addition, a section on compromise solutions summarizes corresponding results. The reference list consists of all papers known to the authors which deal with the multiple objective minimum cost flow problem.     

Transportation Problems with Inadmissible Routes

Transportation problems with inadmissible routes (routes that cannot be used) need not have a feasible solution, even if the total supply exceeds the total demand. A method for adjusting the demands equitably to obtain a feasible solution is presented. The algorithm used generates a feasible minimum cost solution in roughly twice the amount of time that it would take to solve a transportation problem of the same size. It is shown how the solution time is reduced by aggregating the adjustment of demands and decomposing the cost minimization.     

Resource allocation on condition of random resource demand of network activities

A general network model is considered. Each activity may require several storable and productive resources. Fixed amounts of storable resources are delivered at fixed times. The capacities of the productive resources are assumed to be constant. A general resource allocation problem is derived in order to optimize the cost for the execution of the network activities, In the main, the case is considered where the resource demands of the activities are random variables. For this case, the means of resource demands are estimated and a method is presented to obtain a vector of start times and durations of the activities so that a cost function is minimized and the probability that, at any time, there appears no lack of resources is maximized.     

Some inverse optimization problems under the Hamming distance

Given a feasible solution to a particular combinatorial optimization problem defined on a graph and a cost vector defined on the arcs of the graph, the corresponding inverse problem is to disturb the cost vector such that the feasible solution becomes optimal. The aim is to optimize the difference between the initial cost vector and the disturbed one. This difference can be measured in several ways. We consider the Hamming distance measuring in how many components two vectors are different, where weights are associated to the components. General algorithms for the bottleneck or minimax criterion are described and (after modification) applied to the inverse minimum spanning tree problem, the inverse shortest path tree problem and the linear assignment problem.     

Cost allocation in the Chinese postman problem

This paper considers a cost allocation problem that arises from a delivery problem associated with the Chinese postman problem (CPP). A delivery problem is described by a connected undirected graph in which each edge belongs to a different player, a cost function on the edges of this graph and a fixed vertex which is referred to as the post office. Assume that the post office is providing some service to the players. The nature of this service, which can be thought of as mail delivery, requires that a server will travel along the edges of the graph and returns to the post office. The cost allocation problem is concerned with the cost of providing the service to all players. A specific cost allocation rule is introduced and characterized. Further, the class of delivery problems gives rise to a new class of cooperative combinatorial optimization games called delivery games. It is shown that the outcome of the allocation rule with respect to a bridge-connected Euler graph is a core element of the corresponding delivery game.     

Analysis of some greedy algorithms for the single-sink fixed-charge transportation problem

The single-sink fixed-charge transportation problem (SSFCTP) consists of finding a minimum cost flow from a number of nodes to a single sink. Beside a cost proportional to the amount shipped, the flow cost encompass a fixed charge. The SSFCTP is an important subproblem of the well-known fixed-charge transportation problem. Nevertheless, just a few methods for solving this problem have been proposed in the literature. In this paper, some greedy heuristic solutions methods for the SSFCTP are investigated. It is shown that two greedy approaches for the SSFCTP known from the literature can be arbitrarily bad, whereas an approximation algorithm proposed in the literature for the binary min-knapsack problem has a guaranteed worst case bound if adapted accordingly to the case of the SSFCTP.     

An efficient memetic algorithm for the graph partitioning problem

We consider the component testing problem of a device that is designed to perform a mission consisting of a random sequence of phases with random durations. Testing is done at the component level to attain desired levels of mission reliability at minimum cost. The components fail exponentially where the failure rate depends on the phase of the mission. The reliability structure of the device involves a series connection of nonidentical components with different failure characteristics. The optimal component testing problem is formulated as a semi-infinite linear program. We present an algorithmic procedure to compute optimal test times based on the column generation technique, and illustrate it with numerical examples.     

Fuzzy mixture two warehouse inventory model involving fuzzy random variable lead time demand and fuzzy total demand

This paper considers a two-warehouse fuzzy-stochastic mixture inventory model involving variable lead time with backorders fully backlogged. The model is considered for two cases—without and with budget constraint. Here, lead-time demand is considered as a fuzzy random variable and the total cost is obtained in the fuzzy sense. The total demand is again represented by a triangular fuzzy number and the fuzzy total cost is derived. By using the centroid method of defuzzification, the total cost is estimated. For the case with fuzzy-stochastic budget constraint, surprise function is used to convert the constrained problem to a corresponding unconstrained problem in pessimistic sense. The crisp optimization problem is solved using Generalized Reduced Gradient method. The optimal solutions for order quantity and lead time are found in both cases for the models with fuzzy-stochastic/stochastic lead time and the corresponding minimum value of the total cost in all cases are obtained. Numerical examples are provided to illustrate the models and results in both cases are compared.     

The minimum cost shortest-path tree game

A minimum cost shortest-path tree is a tree that connects the source with every node of the network by a shortest path such that the sum of the cost (as a proxy for length) of all arcs is minimum. In this paper, we adapt the algorithm of Hansen and Zheng (Discrete Appl. Math. 65:275?C284, 1996) to the case of acyclic directed graphs to find a minimum cost shortest-path tree in order to be applied to the cost allocation problem associated with a cooperative minimum cost shortest-path tree game. In addition, we analyze a non-cooperative game based on the connection problem that arises in the above situation. We prove that the cost allocation given by an ??à la?? Bird rule provides a core solution in the former game and that the strategies that induce those payoffs in the latter game are Nash equilibrium.     

有时间窗的车辆路径问题的局部下降搜索算法

介绍了一个求解有时间窗的车辆路径问题(vehicle routing problem with time windows,VRPTW)的启发式算法——基于λ-交换的局部下降搜索算法(Local search descent method based on λ-interchange).VRPTW是指合理安排车辆行驶路线,为一组预先设定有时间限制的客户运送货物,在不违反时间要求和车辆容量限制的条件下使得成本最小.它是一个典型的NP-难题,可以通过启发式算法获得近优解来解决.通过两个实验验证,显示了局部下降搜索算法的优良性能,取得了很好的效果,可以作为进一步研究复杂算法的基础.