Two-machine flow shop scheduling of polyurethane foam production

This paper studies a two-machine flow shop scheduling problem with a supporting precedence relation. The model originates from a real production context of a chemical factory that produces foam-rubber products. We extend the traditional two-machine flow shop by dividing the operations into two categories: supporting tasks and regular jobs. In the model, several different compositions of foam rubber can be mixed at the foam blooming stage, and products are processed at the manufacturing stage. Each job (product) on the second machine cannot start until its supporting tasks (parts) on the first machine are all finished and the second machine is not occupied. The objective is to find a schedule that minimizes the total job completion time. The studied problem is strongly NP-hard. In this paper, we propose a branch-and-bound algorithm incorporating a lower bound and two dominance rules. We also design a simple heuristic and an iterated local search (ILS) algorithm to derive approximate solutions. The performances of the proposed algorithms are examined through computational experiments.     

Heuristics for operational fixed job scheduling problems with working and spread time constraints

Operational fixed job scheduling problems select a set of jobs having fixed ready and processing times and schedule the selected jobs on parallel machines so as to maximize the total weight. In this study, we consider working time and spread time constrained versions of the operational fixed job scheduling problems. The working time constraints limit the total processing load on each machine. The spread time constraints limit the time between the start of the first job and the finish of the last job on each machine. For the working time constrained problem, we present a filtered beam search algorithm that evaluates the promising nodes of the branch and bound tree. For the spread time constrained problem we propose a two phase algorithm that defines the promising sets for the first jobs and finds a solution for each promising set. The results of our computational tests reveal that our heuristic algorithms perform very well in terms of both solution quality and time.     

An efficient hybrid genetic algorithm for scheduling projects with resource constraints and multiple execution modes

Multi-mode Resource Constrained Project Scheduling Problem (MRCPSP) aims at finding the start times and execution modes for the activities of a project that optimize a given objective function while verifying a set of precedence and resource constraints. In this paper, we focus on this problem and develop a hybrid Genetic Algorithm (MM-HGA) to solve it. Its main contributions are the mode assignment procedure, the fitness function and the use of a very efficient improving method. Its performance is demonstrated by extensive computational results obtained on a set of standard instances and against the best currently available algorithms.     

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.     

Ant colony optimization for the single model U-type assembly line balancing problem

An assembly line is a production line in which units move continuously through a sequence of stations. The assembly line balancing problem is defined as the allocation of tasks to an ordered sequence of stations subject to precedence constraints with the objective of optimizing a performance measure. In this paper, we propose ant colony algorithms to solve the single-model U-type assembly line balancing problem. We conduct an extensive experimental study in which the performance of the proposed algorithm is compared against best known algorithms reported in the literature. The results indicate that the proposed algorithms display very competitive performance against them.     

Minimizing mean absolute deviation of completion time about a common due window subject to maximum tardiness for a single machine

This study deals with the problem of scheduling jobs on a single machine to minimize the mean absolute deviation of the job completion time about a large common due window subject to the maximum tardiness constraint. Using the well-known three-field notation, the problem is identified as MAD/large DueWindow/T_max. The common due window is set to be large enough to allow idle time prior to the beginning of a schedule to investigate the effect of the T_max constraint. Penalties arise if a job is completed outside the due window. A branch and bound algorithm and a heuristic are proposed. Many properties of the solutions and precedence relationships are identified. Our computational results reveal that the branch and bound algorithm is capable of solving problems of up to 50 jobs and the heuristic algorithm yields approximate solutions that are very close to the exact solution.     

When the Former CEO Stays on Board: The Role of the Predecessor’s Board Retention for Product Innovation in Family Firms

Understanding product innovation in family firms is an important research endeavor given the economic predominance of those firms, their idiosyncrasies, and the importance of constant renewal for those firms to achieve transgenerational survival. Recently, family firm research has highlighted the role of next-generation chief executive officers (CEOs; i.e., successors) who are often seen as drivers for innovating a family firm’s products. However, prior research has typically neglected that predecessors, who are often portrayed as less willing to introduce product innovation, frequently remain involved postsuccession through occupying board positions and thus still substantially influence the decision-making processes and outcomes of family firms, such as product innovation. As a result, our understanding of the role of predecessors and their postsuccession involvement in family firms’ product innovation remains unclear. Building on stakeholder salience theory and on insights from the literature on innovation and succession in family firms, we develop hypotheses about how and under which conditions the predecessor’s board retention affects product innovation in family firms after succession. Building on more than 200 family firm CEO succession cases in small- and medium-sized, privately owned family firms, our results reveal that the predecessor’s board retention negatively affects product innovation. This negative effect is strengthened with increasing involvement of the predecessor in the successor selection process, and it is offset in the case of family succession. Our findings contribute to the emerging stream of research on family firm succession and product innovation and provide important implications for practice.     

Multikriterielles Entscheidungsmodell für Anpassungsmaßnahmen am Beispiel der L/H-Gas-Umstellung in Deutschland

The German gas supply system is separated into two networks: one for high-calorific gas (H-gas) and one for low-calorific gas (L-gas). In particular, the L-gas volume is declining significantly, which means that there will be problems in security of supply in near future. This paper highlights the most important aspects that have to be considered when adjusting the German gas network to the future decline of L-gas production. First, the article discusses different possible adjustment options: (1) adjustment of market areas from low-calorific gas supply to high-calorific gas supply, (2) blending facilities (conversion of high calorific gas using nitrogen or air) and (3) the combination of both options. Subsequently, the authors outline the necessary planning process that must precede any adjustment option. The so-called network differentiation is followed by the network modeling: Using the net present value method and taking into account certain constraints, the authors define a simple optimization problem to determine the most economical investment and adjustment times for each option. Based on first model considerations it can be concluded that the implementation of adjustment options should be delayed as long as possible without compromising the security of supply. Nevertheless, an early start of the planning process is mandatory to ensure that adjustment options can be established in due time before an L-gas supply deficit occurs. The question, which adjustment option should be chosen, cannot be answered universally. It depends on the conditions and structure of the network as well as the structure of the final consumers.     

MODELLING REGULATORY DISTORTIONS WITH REAL OPTIONS

The introduction of uncertainty can make a significant difference in the valuation of a project. This manifests itself, inter alia, in the regulatory constraints that can affect the valuations of the firm's investment which, in turn has an adverse impact on consumers' welfare. In particular, the inability to exercise any or all of the delay, abandon, start/stop, and time-to-build options has an economic and social cost. With this view in mind, we specify and estimate a model where regulatory constraints impact on the firm's cash flow and on investment valuation with real options methods.

This paper uses real options analysis to address issues of regulation that have not been previously quantified. We show that regulatory constraints on cash flow have an impact on investment valuations in the telecommunications industry. Specifically, a model is developed to estimate the cost of regulation for broadband services. We show that the cash flow constraints and the inability to delay and abandon has a significant cost. Because some costs are not recognized in a static view of the world, this failure to recognize the operation and implications of non-flexibility by regulators (which can be modeled by real options methods) will lead to a reduction in company valuations which in turn will lead to a reduction in economics welfare.     

The use of an interim CEO during succession episodes and firm performance

Our study investigates an unexplored succession process—interim CEO successions. We define an interim CEO succession as a case where the title of chief executive officer is vacated by the incumbent and the board of directors has not announced a permanent successor, but instead designates a particular individual as ‘interim CEO,’ or ‘acting CEO,’ or ‘CEO until a permanent successor is named.’ Theory predicts that interim CEO successions will lead to the type of disruption that can harm firm performance, even after a permanent successor is appointed. Our data show that interim CEO succession processes are widely employed by publicly‐traded U.S. firms, and that they are associated with lower performance during the period in which the interim serves. However, whether the interim CEO also simultaneously serves as chairman moderates the impact of this type of succession on firm performance, as well as on long‐term firm survival. Copyright © 2009 John Wiley & Sons, Ltd.     

Capital Investment and Amortization Policies in the U.S.S.R.

This article deals with the two major problems in depreciation: how to determine actual wear and tear and, thus, life of equipment; and how to treat depreciation and its associated expenses in the accounting records. With regard to the first, the author recognizes that a certain amount of arbitrary action is probably inevitable. Industry in the USSR is evidently bound by its own equivalent of “Bulletin F”, which sets “amortization norms” or depreciation allowances for each class of machinery; and it is found that these often do not correspond to actual operating experience. The machine may be scrapped either before its allocated life span, or survive fully depreciated. This fact is, of course, quite familiar, and the author has no suggestions to offer beyond pointing out the problem. He also questions, for technical reasons, the straight line method which is now generally used. Yet it is almost impossible to justify, on functional grounds, any regulated alternative method, such as declining balance or sum-of-the-years-digits.     

An LP-based tabu search for batch scheduling in a cutting process with finite buffers

This paper addresses a cutting stock problem under typical resource constraints that arise when working centres with nesting capabilities are associated with automatic feeders/stackers. The critical resource is the number of buffers available to host the batches built up by the centre. To cope with it, pattern and batch sequencing problems must be addressed simultaneously. A tabu-search algorithm exploring batch output sequences is proposed. The algorithm never opens more stacks than buffers, respects batch compatibility/precedence constraints, and keeps the maximum order spread under control. To demonstrate its effectiveness and efficiency, a computational study was set up, solving 920 test problems derived from the literature. The study enabled a proper tuning of the method and offered encouraging results: in 228 cases an optimum was found; in nearly all, the gap from the optimum was below 1%. Computation times range from fractions of seconds to a couple of minutes in the worst cases. Compared to existing methods, the algorithm provides on average the same solution quality, with the advantage of solving a problem which is more general and hence closer to application. The paper includes a discussion on the method extensions required to deal with asynchronous stacking and heterogeneous batches.     

Robust scheduling for multi-objective flexible job-shop problems with random machine breakdowns

This study addresses robust scheduling for a flexible job-shop scheduling problem with random machine breakdowns. Two objectives – makespan and robustness – are simultaneously considered. Robustness is indicated by the expected value of the relative difference between the deterministic and actual makespan. Utilizing the available information about machine breakdowns, two surrogate measures for robustness are developed. Specifically, the first suggested surrogate measure considers the probability of machine breakdowns, while the second surrogate measure considers the location of float times and machine breakdowns. To address this problem, a multi-objective evolutionary algorithm is presented in this paper. The experimental results indicate that, compared with several other existing surrogate measures, the first suggested surrogate measure performs better for small cases, while the second surrogate measure performs better for both small and relatively large cases.     

Optimal Farm-to-Market Transportation and Storage of Certain Agriculture Commodities: Bogota,Colombia ∗

Decision rules for the make-buy decision most often assume the form of single-item, incremental cost rules which, for application, require decisions to be made for each item in isolation from all others. While it has been recognized that such decision rules may be non-optimal in situations where equipment capacity limits exist, the literature has not considered other cases where non-optimality arises. The purpose of this paper is to use linear programming as a framework for evaluating the optimality of single-item, incremental cost rules. The major conclusion of this paper is that the presence of any type of binding constraint in the planning problem, regardless of its origin, renders a single-item decision rule potentially non-optimal. A set of optimal screening decision rules is developed to be used in conjunction with a linear programming model for make-buy decisions in cases where such binding constraints exist.     

Scheduling with processing set restrictions: PTAS results for several variants

We consider multiprocessor scheduling to minimize makespan. Each job has a given processing time and in addition, a subset of machines associated with it, also called its processing set. Each job has to be assigned to one machine in its set, thus contributing to the load of this machine. We study two variants of this problem on identical machines, the case of nested processing sets, and the case of tree-hierarchical processing sets. In addition, we consider uniformly related machines with a special case of inclusive processing sets, which has a clear motivation. We design polynomial time approximation schemes for these three variants. The first case resolves one of the open problems stated in the survey by Leung and Li (2008).     

Land constraints and agricultural intensification in Ethiopia: A village-level analysis of high-potential areas

Highland Ethiopia is one of the most densely populated regions of Africa and has long been associated with both Malthusian disasters and Boserupian agricultural intensification. This paper explores the race between these two countervailing forces, with the goal of inform two important policy questions. First, how do rural Ethiopians adapt to land constraints? And second, do land constraints significantly influence welfare outcomes in rural Ethiopia? To answer these questions we use a recent household survey of high-potential areas. We first show that farm sizes are generally very small in the Ethiopian highlands and declining over time, with young rural households facing particularly severe land constraints. We then ask whether smaller and declining farm sizes are inducing agricultural intensification, and if so, how. We find strong evidence in favor of the Boserupian hypothesis that land-constrained villages typically use significantly more purchased input costs per hectare and more family labor, and achieve higher maize and teff yields and higher gross income per hectare. However, although these higher inputs raise gross revenue, we find no substantial impact of greater land constraints on net farm income per hectare once family labor costs are accounted for. Moreover, farm sizes are strongly positively correlated with net farm income, suggesting that land constraints are an important cause of rural poverty. We conclude with some broad policy implications of our results.     

A new approach to job shop scheduling problems with due date constraints considering operation subcontracts

We consider a kind of job shop scheduling problems with due-date constraints, where temporal relaxation of machine capacity constraint is possible through subcontracts. In practice, this kind of problem is frequently found in manufacturing industries where outsourcing of manufacturing operation is possible through subcontract. We present a heuristic algorithm that addresses the problem by solving a series of smaller subproblems to optimality. For the sake of efficiency, the algorithm repeatedly executes in two steps—(1) improving the sequence of operations and (2) picking out the operations to be subcontracted—on bottleneck machines. Experiments are conducted for example problems, and the result of the experiment confirms the viability of the suggested algorithm.     

Industrial Relations in the Privatized Mining Industry

This article examines the impact of privatization on industrial relations in the UK coal industry. It focuses on aspects of continuity and change in the transition to privatization, assessing how far the processes of trade union exclusion and decollectivization, begun by British Coal, have been extended or modified in the first year of private operation. The paper argues that the segmentation of trade union interests has been exacerbated in a variety of ways, not least by the fragmentation of the industry under different forms of ownership and control. It concludes that, while the likelihood of industrial action in the smaller companies is remote, RJB Mining is more vulnerable to sanctions than was its predecessor. However, it remains debatable to what extent future cost-cutting and closures might precipitate such action, given the extent of decollectivization that has occurred.     

Machine and labor flexibility in manufacturing networks

Many industrial companies today operate under strict employment legislation and work agreements. Since flexible capacity has been widely recognized as an important hedge against uncertain demand, there exist strong efforts to introduce more flexible workforce models. In this paper, we investigate the benefits of such labor flexibility and its interplay with machine flexibility from a network capacity investment perspective.We consider a firm that has to invest simultaneously in labor and machine capacity under uncertain demand and a given network configuration while anticipating the deployment of labor flexibility after demand has been realized. Instruments of labor flexibility range from temporary employment to personnel transfers between plants. The underlying decision problem is formulated as a two-stage stochastic program with recourse.Based on numerical studies and the analysis of a stylized model, we demonstrate the impact of labor flexibility on the optimal levels of machine and labor capacity. We compare the benefits obtained by personnel transfers with those of temporary workers and find that temporary employment always decreases the number of permanent workers, while personnel transfers may even allow for a larger workforce. Our results further indicate that personnel transfers are more effective in larger manufacturing networks although these benefits are decreasing when most plants in the network are capable of producing more than one product (machine flexibility). Finally, we present evidence for the efficiency of a combined usage of personnel transfers and temporary workers.