The Effect of Probability Distribution Functions of Economic Life on Retirement Alternatives

Economic analysis, supported by computer hardware and software, can easily overwhelm a decision maker with data. However, this data can be organized in a readily understandable way using well-designed graphs. Some simple guidelines suggest substantial improvements over available examples from papers and textbooks. Although these graphs and sensitivity analyses are useful in many quantitative problems, the long-range focus of engineering economy and its concomitant uncertainty dramatically increase their value.     

A bicriteria approach to maximize the weighted number of just-in-time jobs and to minimize the total resource consumption cost in a two-machine flow-shop scheduling system

We analyze a two-machine flow-shop scheduling problem in which the job processing times are controllable by the allocation of resources to the job operations and the resources can be used in discrete quantities. We provide a bicriteria analysis of the problem where the first criterion is to maximize the weighted number of just-in-time jobs and the second criterion is to minimize the total resource consumption cost. We prove that although the problem is known to be NP-hard even for constant processing times, a pseudo-polynomial time algorithm for its solution exists. In addition, we show how the pseudo-polynomial time algorithm can be converted into a two-dimensional fully polynomial approximation scheme for finding an approximate Pareto solution.     

A note on the sensitivity analysis of the internal rate of return

In this note we discuss the sensitivity analysis of the internal rates of return (IRR). We show that the use of partial derivatives can be misleading in the identification of key drivers of an investment project's performance. To remedy this shortcoming, we propose the use of an alternative sensitivity measure called the Differential Importance Measure. The analysis shows that, even if the theoretical conditions for using the Net Present Value or the IRR as valuation criteria apply, the sensitivity analysis results for the two indicators may differ.     

Appointment scheduling of outpatient surgical services in a multistage operating room department

This article addresses the appointment scheduling of outpatient surgeries in a multistage operating room (OR) department with stochastic service times serving multiple patient types. We discuss many challenges, such as the limited availability of multiple resources (e.g., staff, operating rooms, surgeons, and recovery beds), and the compatibility of patient and surgeon types. In addition, availability of surgeons is restricted by time window constraints. Three simulation-based optimization methods have been proposed to minimize the patients’ wait time, patients’ completion time, and number of surgery cancellations. The first method is simulation-based tabu search (STS). It combines discrete-event simulation and tabu search to schedule surgery cases. The second and third methods are integer programming enhanced tabu search (IPETS) and binary programming enhanced tabu search (BPETS). IPETS and BPETS improve on STS by incorporating integer programming and binary programming models, respectively. This article includes a case study of an OR department in a major Canadian hospital. We further expand the actual data obtained in the case study to cover a wide range of parameters in sets of test problems, and provide analysis on the efficiency and effectiveness of the proposed methods in comparison with several scheduling rules. Finally, comments on the applications of the proposed methods are provided.     

Constraint-based ACO for a shared resource constrained scheduling problem

We consider a scheduling problem arising in the mining industry. Ore from several mining sites must be transferred to ports to be loaded on ships in a timely manner. In doing so, several constraints must be met which involve transporting the ore and deadlines. These deadlines are two-fold: there is a preferred deadline by which the ships should be loaded and there is a final deadline by which time the ships must be loaded. Corresponding to the two types of deadlines, each task is associated with a soft and hard due time. The objective is to minimize the cumulative tardiness, measured using the soft due times, across all tasks. This problem can be formulated as a resource constrained job scheduling problem where several tasks must be scheduled on multiple machines satisfying precedence and resource constraints and an objective to minimize total weighted tardiness. For this problem we present hybrids of ant colony optimization, Beam search and constraint programming. These algorithms have previously shown to be effective on similar tightly-constrained combinatorial optimization problems. We show that the hybrid involving all three algorithms provides the best solutions, particularly with respect to feasibility. We also investigate alternative estimates for guiding the Beam search component of our algorithms and show that stochastic sampling is the most effective.     

A local search algorithm for the optimization of the stochastic economic lot scheduling problem

This paper describes a model for the stochastic economic lot scheduling problem (SELSP) and a Local Search heuristic to find close to optimal solutions to this model. The SELSP considers multiple products, which have to be scheduled on a single facility with limited capacity and significant setup times and costs. The demand is modeled as a stationary compound renewal process. The objective is to find a schedule that minimizes the long-run average costs for setups and inventories while satisfying a given fill rate. We use a cyclic scheduling approach in which the individual cycle time of each product is a multiple of some basic period (fundamental cycle).For the deterministic version of the SELSP, efficient heuristics have been developed which guarantee the feasibility of the solution by adding an additional constraint to the problem. In our case this is not sufficient, because for the calculation of the average inventory levels and fill rates we need to develop a schedule with detailed timing of the lots. We present an efficient heuristic for this scheduling problem, which can also be used to check the feasibility of the solution. Thereby, the most time-consuming step (the calculation of average inventory levels and fill rates) is only performed for a limited set of candidates.The algorithm was tested on deterministic benchmark problems from literature and on a large set of stochastic instances. We report on the performance of the heuristic in both cases and try to identify the main factors influencing the objective.     

A genetic algorithm based heuristic for two machine no-wait flowshop scheduling problems with class setup times that minimizes maximum lateness

Machine scheduling problem has been extensively studied by researchers for many decades in view of its numerous applications on solving practical problems. Due to the complexity of this class of scheduling problems, various approximation solution approaches have been presented in the literature. In this paper, we present a genetic algorithm (GA) based heuristic approach to solve the problem of two machine no-wait flowshop scheduling problems that the setup time on the machines is class dependent, and the objective is to minimize the maximum lateness of the jobs processed. This class of machine scheduling problems has many practical applications in manufacturing industry, such as metal refinery operations, food processing industry and chemical products production processes, in which no interruption between subsequent processes is allowed and the products can be grouped into families. Extensive computation experiments have been conducted to evaluate the effectiveness of the proposed algorithm. Results show the proposed methodology is suitable to be adopted for the development of an efficient scheduling plan for this class of problems in real life application.     

流水工序调度与生产效率的关系模型分析

提出一种基于粒子群算法的流水工序调度任务优化模型。利用流水工序调度任务的特点得到流水工序时间约束条件,利用粒子群算法的原理建立流水工序调度任务优化模型,利用粒子群算法对模型进行求解。仿真实验表明,利用该算法能够得到流水工序调度问题的最优解,提高生产效率。     

A block mining and re-combination enhanced genetic algorithm for the permutation flowshop scheduling problem

The goal of block mining is to obtain a set of genes that contain dependency among gene relationships. Such blocks without overlapping of genes can be further merged to form a new chromosome and the quality of the new chromosome can be greatly improved. Based on this concept, we propose a novel block mining method that is able to locate common structures or to establish new blocks (like a small piece of puzzle) from a set of high fit chromosomes. The identified blocks (puzzles) will also be updated generation by generation through the newly updated high fit chromosomes. We develop a heuristic re-combination procedure to form a new chromosome by re-combining the blocks. We call the new chromosomes generated as artificial chromosomes (ACs) and inject them into the evolutionary process when the convergence slope of the evolutionary process is less than a predefined threshold. This new algorithm retains the regular simple genetic algorithm (SGA) operations of crossover and mutation, and utilizes the ACs generated from elites to speed up the convergence process. Experimental results indicate that the puzzle-based method of chromosome generation is very efficient and effective in solving the traditional permutation flowshop scheduling problem. The new method can be applied to tackle other NP-complete problems such as scheduling and vehicle routing problems.     

Maintenance practices in Swedish industries: Survey results

The purpose of this paper is to investigate the maintenance practices that are used in Swedish industry. The study was performed by conducting a cross-sectional survey within Swedish firms that have at least 100 employees. The main results achieved from the study show that the role of maintenance is not highly recognised. There is a need to spend and invest more in maintenance, which is considered by the majority of Swedish industries as a necessary expense. Although about 13% of the maintenance department time is spent on planning maintenance tasks, nevertheless, about one third of the time is spent on unplanned tasks. There is a need for more adoption of maintenance concepts such as total productive maintenance (TPM) and reliability-centred maintenance (RCM). The lack or ineffectiveness of planning and scheduling can significantly restrict the maintenance department in achieving its objectives and can thus prevent the company from maximising business profits and offering competitive advantages.     

A tabu search approach for cell scheduling problem with makespan criterion

Scheduling problem in a cellular manufacturing system is treated as the group scheduling problem, assuming that intercellular moves can be eliminated by duplicating machines. However, in a typical CMS, duplicating bottleneck machines may be costly and infeasible. This fact limits the applicability of group scheduling. Scheduling problem in the presence of bottleneck machines is termed as cell scheduling. A mixed-integer linear programming model is proposed for the attempted cell scheduling problem and a nested application of tabu search approach is investigated in this paper to solve the problem heuristically. The effectiveness of the proposed nested tabu search (NTS) algorithm is evaluated on 16 problems selected from the literature. Comparison of the results of NTS with SVS-algorithm reveals the effectiveness and efficiency of the proposed algorithm.     

Using Conjoint Analysis to Help Design Product Platforms

This article illustrates how one can combine different conjoint analysis studies, each containing a core of common attributes, to help design product platforms that serve as the foundation for multiple derivative products. The illustration is based on actual, but disguised, data from a small company that makes electronic test equipment.
This article demonstrates that decisions that consider products individually are likely to be suboptimal and can be significantly different than those based on product platforms. Suboptimality can occur either when preferences for product features differ across markets or when a technology is more important to the overall company than it is to an individual product. Additionally, we show the importance of considering both fixed and variable costs when performing this type of analysis as sales, contribution, and profit-maximizing products are quite different. Finally, sensitivity analyses show that these results are robust with respect to assumptions about price sensitivity, fixed costs, and timing of entry. © 1999 Elsevier Science Inc.     

Two-stage flowshop earliness and tardiness machine scheduling involving a common due window

This paper studies a non-preemptive two-stage flowshop scheduling problem to minimize the earliness and tardiness under the environment of a common due window. The window size and the window location are considered to be given parameters. The just-in-time problem exists naturally and has many practical applications. The problem is shown to be NP-complete in the strong sense. We develop a branch and bound algorithm and a heuristic to solve the problem. We conduct the computational experiments to test the performances of the algorithms. A strong lower bound is derived for the branch and bound algorithm that can efficiently solve 15 jobs problem for about 5 minutes. The heuristic is shown to be efficient and effective, which can solve the problem of 150 jobs for about 20 seconds and provide near-optimal solution. We justify that the heuristic is an excellent solution approach for large problem instances. We also show that four special cases are either polynomial solvable or NP-complete in the ordinary sense.     

Production flow analysis—Cases from manufacturing and service industry

Production flow analysis (PFA) is a well-established methodology used for transforming traditional functional layout into product-oriented layout. The method uses part routings to find natural clusters of workstations forming production cells able to complete parts and components swiftly with simplified material flow. Once implemented, the scheduling system is based on period batch control aiming to establish fixed planning, production and delivery cycles for the whole production unit. PFA is traditionally applied to job-shops with functional layouts, and after reorganization within groups lead times reduce, quality improves and motivation among personnel improves. Several papers have documented this, yet no research has studied its application to service operations management. This paper aims to show that PFA can well be applied not only to job-shop and assembly operations, but also to back-office and service processes with real cases. The cases clearly show that PFA reduces non-value adding operations, introduces flow by evening out bottlenecks and diminishes process variability, all of which contribute to efficient operations management.     

Two-machine flowshop scheduling with a truncated learning function to minimize the makespan

Scheduling with learning effects has continued to attract the attention of scheduling researchers. However, the majority of the research on this topic has been focused on the single-machine setting. Moreover, under the commonly adopted learning model in scheduling, the actual processing time of a job drops to zero precipitously as the number of jobs increases, which is at odds with reality. To address these issues, we study a two-machine flowshop scheduling problem with a truncated learning function in which the actual processing time of a job is a function of the job's position in a schedule and the learning truncation parameter. The objective is to minimize the makespan. We propose a branch-and-bound and three crossover-based genetic algorithms (GAs) to find the optimal and approximate solutions, respectively, for the problem. We perform extensive computational experiments to evaluate the performance of all the proposed algorithms under different experimental conditions. The results show that the GAs perform quite well in terms of both efficiency and solution quality.     

Managing inventory with two suppliers under yield uncertainty and risk aversion

In this paper, we consider a single-product single-period inventory model in which the retailer can source from two suppliers. The primary supplier is cheaper but unreliable in the sense that it generates supply yield uncertainty, whereas the secondary supplier is perfectly reliable but more expensive. The reliable supplier's capacity is fixed and the retailer cannot order more than the quantity reserved in advance. We study the problem in the context of a risk-averse retailer who has to determine the optimal order quantity from the primary supplier and the optimal reserved quantity from the secondary supplier. We develop the model in the perspective of a low risk averse retailer and quantify the risk via an exponential utility function. We show by numerical experiments how the resulting dual sourcing strategies differ from those obtained in the risk-neutral analysis. We also examine the sensitivity of some model-parameters on the optimal decisions.     

Logistics scheduling to minimize inventory and transport costs

We study a logistics scheduling problem where a manufacturer receives raw materials from a supplier, manufactures products in a factory, and delivers the finished products to a customer. The supplier, factory and customer are located at three different sites. The objective is to minimize the sum of work-in-process inventory cost and transport cost, which includes both supply and delivery costs. For the special case of the problem where all the jobs have identical processing times, we show that the inventory cost function can be unified into a common expression for various batching schemes. Based on this characteristic and other optimal properties, we develop an O(n) algorithm to solve this case. For the general problem, we examine several special cases, identify their optimal properties, and develop polynomial-time algorithms to solve them optimally.     

A REAL OPTIONS DESIGN FOR PRODUCT OUTSOURCING

Product outsourcing is recognized as a way to gain flexibility for competitive advantage. We formulate the outsourcing problem using real options. We develop a financial model to assess the option value of product outsourcing. Specifically, we consider a three state-variable problem and use Monte Carlo simulation to estimate the value of the option. This valuation gives decision makers a way to choose the appropriate outsourcing strategy based on an integrated view of the market dynamics. A case example from the apparel manufacturing industry is used to demonstrate the application of real options to value outsourcing flexibility. We show that the inability of classical net present value methods to address dynamics in the market condition leads to an undervaluing of the outsourcing strategy. Numerical results and sensitivity analysis show how the real options approach can be used to give a better view of the long-term value of outsourcing.     

A practical approach to sensitivity analysis in linear programming under degeneracy for management decision making

Linear programming (LP) is a widely used tool in management decision making. Theoretically, sensitivity analysis of LP problems provides useful information for the decision maker. In practice, however, most LP software provides misleading sensitivity information if the optimal solution is degenerate. The paper shows how sensitivity analysis of LP problems can be done correctly when the optimal solution is degenerate. A production planning example is presented to illustrate the incorrect sensitivity analysis results automatically provided by most LP solvers. The general characteristics of the misleading results and the possible effects of this incorrect information on management decisions are also discussed.     

The relationship between supply chain factors and adoption of e-Collaboration tools: An empirical examination

We consider minimum-cost scheduling of different vehicle types on a predetermined set of one-way trips. Trips have predetermined ready times, deadlines and associated demands. All trips must be performed. The total time of operations on any vehicle is limited. We develop a mixed integer model to find the optimal number of vehicles at a minimum cost. Based on the hard nature of the problem, we propose six heuristics. Computational results reveal that heuristics return exceptionally good solutions for problem instances with up to 100 jobs in very small computation times, and are likely to perform well for larger instances.