Performance analysis of the cultural and creative industry: a network‐based approach

The cultural and creative industries (CCIs) in Taiwan have gradually contributed to the national economy under the impetus of government policies. We employ a two‐stage data envelopment analysis model with an additive efficiency decomposition approach to measure the profitability and marketability of 22 Taiwanese cultural and creative companies. Furthermore, we employ the network‐based ranking approach to identify benchmark inputs/outputs, and the strengths and weakness of each company. Our empirical results show that the profitability of the cultural and creative companies is better than their marketability. Companies in the industries of publishing, creative life, popular music, and cultural content averagely perform better than those in the other three types of CCIs in terms of profitability. Companies in the creative life industry are on average more efficient than those in the other five types of CCIs in terms of marketability. The profitability/marketability matrix of cultural and creative companies is also presented.     

Two‐agent scheduling on a single sequential and compatible batching machine

We study two‐agent scheduling on a single sequential and compatible batching machine in which jobs in each batch are processed sequentially and compatibility means that jobs of distinct agents can be processed in a common batch. A fixed setup time is required before each batch is started. Each agent seeks to optimize some scheduling criterion that depends on the completion times of its own jobs only. We consider several scheduling problems arising from different combinations of some regular scheduling criteria, including the maximum cost (embracing lateness and makespan as its special cases), the total completion time, and the (weighted) number of tardy jobs. Our goal is to find an optimal schedule that minimizes the objective value of one agent, subject to an upper bound on the objective value of the other agent. For each problem under consideration, we provide either a polynomial‐time or a pseudo‐polynomial‐time algorithm to solve it. We also devise a fully polynomial‐time approximation scheme when both agents’ scheduling criteria are the weighted number of tardy jobs.     

Optimally scheduling interfering and non‐interfering cranes

This article treats the problem of scheduling multiple cranes processing jobs along a line, where cranes are divided into different groups and only cranes in the same group can interfere with each other. Such crane scheduling problems occur, for example, at indented berths or in container yards where double rail‐mounted gantry cranes stack containers such that cranes of the same size can interfere with each other but small cranes can pass underneath larger ones. We propose a novel algorithm based on Benders decomposition to solve this problem to optimality. In a computational study, it is shown that this algorithm solves small and medium‐sized instances and even many large instances within a few seconds or minutes. Moreover, it improves several best known solutions from the literature with regard to the simpler problem version with only one crane group. We also look into whether investment in more complicated crane configurations with multiple crane groups is actually worthwhile.     

基于多种群遗传算法的远程作战体系加油路径优化

为解决加油机在远程作战体系中的高效使用与经济性问题,推导了远程作战体系中加油机和歼击机的油耗方程模型,建立以耗油量最少为目标的空中加油路径优化模型。该模型基于一定的作战任务构想,利用多种群遗传算法进行了求解,从求解结果来看,该算法可以快速搜索到能够在所有满足作战任务要求的任务路径中寻找到最小消耗的位置坐标,以获得最大的作战效能,从而达到优化远程作战体系中的作战和训练中加油机使用问题。     

A single‐period inventory placement problem for a serial supply chain

This article addresses the inventory placement problem in a serial supply chain facing a stochastic demand for a single planning period. All customer demand is served from stage 1, where the product is stored in its final form. If the demand exceeds the supply at stage 1, then stage 1 is resupplied from stocks held at the upstream stages 2 through N, where the product may be stored in finished form or as raw materials or subassemblies. All stocking decisions are made before the demand occurs. The demand is nonnegative and continuous with a known probability distribution, and the purchasing, holding, shipping, processing, and shortage costs are proportional. There are no fixed costs. All unsatisfied demand is lost. The objective is to select the stock quantities that should be placed different stages so as to maximize the expected profit. Under reasonable cost assumptions, this leads to a convex constrained optimization problem. We characterize the properties of the optimal solution and propose an effective algorithm for its computation. For the case of normal demands, the calculations can be done on a spreadsheet. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48:506–517, 2001     

Bayesian inference based on partial monitoring of components with applications to preventive system maintenance

Consider a binary, monotone system of n components. The assessment of the parameter vector, θ, of the joint distribution of the lifetimes of the components and hence of the reliability of the system is often difficult due to scarcity of data. It is therefore important to make use of all information in an efficient way. For instance, prior knowledge is often of importance and can indeed conveniently be incorporated by the Bayesian approach. It may also be important to continuously extract information from a system currently in operation. This may be useful both for decisions concerning the system in operation as well as for decisions improving the components or changing the design of similar new systems. As in Meilijson [12], life‐monitoring of some components and conditional life‐monitoring of some others is considered. In addition to data arising from this monitoring scheme, so‐called autopsy data are observed, if not censored. The probabilistic structure underlying this kind of data is described, and basic likelihood formulae are arrived at. A thorough discussion of an important aspect of this probabilistic structure, the inspection strategy, is given. Based on a version of this strategy a procedure for preventive system maintenance is developed and a detailed application to a network system presented. All the way a Bayesian approach to estimation of θ is applied. For the special case where components are conditionally independent given θ with exponentially distributed lifetimes it is shown that the weighted sum of products of generalized gamma distributions, as introduced in Gåsemyr and Natvig [7], is the conjugate prior for θ. © 2001 John Wiley & Sons, Inc. Naval Research Logistics 48: 551–577, 2001.     

Franchising cooperation through chance cross‐constrained games

This paper explores the role of franchising arrangements in the context of game theory. We assume a single franchisor and a single franchisee channel and address the impact of fixed lump‐sum fees, royalties, wholesale price, and retail price on the franchise contracts. We start with the chance cross‐constrained noncooperative situation where the franchisor, as the leader, first specifies his/her strategies. The franchisee, as the follower, then decides on his/her decision. We then relax the assumption of franchisee's inability to influence the franchisor's decisions and discuss cooperative situation between the franchisor and the franchisee. Nash's bargaining model is utilized to select the best Pareto‐efficient payment scheme for the franchisor and the franchisee to achieve their cooperation. © 2000 John Wiley & Sons, Inc. Naval Research Logistics 47: 669–685, 2000     

Timely exposure of a secret project: Which activities to monitor?

A defender wants to detect as quickly as possible whether some attacker is secretly conducting a project that could harm the defender. Security services, for example, need to expose a terrorist plot in time to prevent it. The attacker, in turn, schedules his activities so as to remain undiscovered as long as possible. One pressing question for the defender is: which of the project's activities to focus intelligence efforts on? We model the situation as a zero‐sum game, establish that a late‐start schedule defines a dominant attacker strategy, and describe a dynamic program that yields a Nash equilibrium for the zero‐sum game. Through an innovative use of cooperative game theory, we measure the harm reduction thanks to each activity's intelligence effort, obtain insight into what makes intelligence effort more effective, and show how to identify opportunities for further harm reduction. We use a detailed example of a nuclear weapons development project to demonstrate how a careful trade‐off between time and ease of detection can reduce the harm significantly.     

An exact branch‐and‐price algorithm for multitasking scheduling on unrelated parallel machines

We consider the multitasking scheduling problem on unrelated parallel machines to minimize the total weighted completion time. In this problem, each machine processes a set of jobs, while the processing of a selected job on a machine may be interrupted by other available jobs scheduled on the same machine but unfinished. To solve this problem, we propose an exact branch‐and‐price algorithm, where the master problem at each search node is solved by a novel column generation scheme, called in‐out column generation, to maintain the stability of the dual variables. We use a greedy heuristic to obtain a set of initial columns to start the in‐out column generation, and a hybrid strategy combining a genetic algorithm and an exact dynamic programming algorithm to solve the pricing subproblems approximately and exactly, respectively. Using randomly generated data, we conduct numerical studies to evaluate the performance of the proposed solution approach. We also examine the effects of multitasking on the scheduling outcomes, with which the decision maker can justify making investments to adopt or avoid multitasking.     

Shortest path interdiction problem with arc improvement recourse: A multiobjective approach

We consider the shortest path interdiction problem involving two agents, a leader and a follower, playing a Stackelberg game. The leader seeks to maximize the follower's minimum costs by interdicting certain arcs, thus increasing the travel time of those arcs. The follower may improve the network after the interdiction by lowering the costs of some arcs, subject to a cardinality budget restriction on arc improvements. The leader and the follower are both aware of all problem data, with the exception that the leader is unaware of the follower's improvement budget. The effectiveness of an interdiction action is given by the length of a shortest path after arc costs are adjusted by both the interdiction and improvement. We propose a multiobjective optimization model for this problem, with each objective corresponding to a different possible improvement budget value. We provide mathematical optimization techniques to generate a complete set of strategies that are Pareto‐optimal. Additionally, for the special case of series‐parallel graphs, we provide a dynamic‐programming algorithm for generating all Pareto‐optimal solutions.