Fix-and-relax approaches for controlled tabular adjustment

Controlled tabular adjustment (CTA) is a relatively new protection technique for tabular data protection. CTA formulates a mixed integer linear programming problem, which is challenging for tables of moderate size. Even finding a feasible initial solution may be a challenging task for large instances. On the other hand, end users of tabular data protection techniques give priority to fast executions and are thus satisfied in practice with suboptimal solutions. This work has two goals. First, the fix-and-relax (FR) strategy is applied to obtain good feasible initial solutions to large CTA instances. FR is based on partitioning the set of binary variables into clusters to selectively explore a smaller branch-and-cut tree. Secondly, the FR solution is used as a warm start for a block coordinate descent (BCD) heuristic (approach named FR+BCD); BCD was confirmed to be a good option for large CTA instances in an earlier paper by the second and third co-authors (Comput Oper Res 2011;38:1826–35 [23]). We report extensive computational results on a set of real-world and synthetic CTA instances. FR is shown to be competitive compared to CPLEX branch-and-cut in terms of quickly finding either a feasible solution or a good upper bound. FR+BCD improved the quality of FR solutions for approximately 25% and 50% of the synthetic and real-world instances, respectively. FR or FR+BCD provided similar or better solutions in less CPU time than CPLEX for 73% of the difficult real-world instances.     

API-based two-dimensional dispatching decision-making approach for semiconductor wafer fabrication with operation due date–related objectives

This paper presents an adjacent pairwise interchanges (API)-based two-dimensional dispatching decision-making approach for semiconductor wafer fabrication with operation due date-related objectives. Each time when a machine becomes idle, the proposed dispatcher chooses a target processing job from the competing jobs and assigns it a start time. Giving the operation due date information of each competing job, we formulate this dispatcher as the mean absolute deviation problem to keep the jobs finished around their operation due dates in a proactive way. Dominance properties of this problem are established using proof by APIs. Then, a heuristic comprised of job selection within candidate set, movement of job cluster and local search is designed to solve this problem more efficiently. Numerical experiments validate the efficiency of the proposed heuristic in a single-machine environment as well as in a simulated wafer fab abstracted from practice. In comparison with four most referenced due date-related dispatching rules, the simulation study reveals the benefits brought by the two-dimensional dispatching decision with different due date tightness taken into account.     

Sequencing mixed-model assembly lines operating with a heterogeneous workforce

We study the problem of sequencing mixed-model assembly lines operating with a heterogeneous workforce. The practical motivation for this study comes from the context of managing assembly lines in sheltered work centres for the disabled. We propose a general framework in which task execution times are both worker and model dependent. Within this framework, the problem is defined and mathematical mixed-integer models and heuristic procedures are proposed. These include a set of fast constructive heuristics, two local search procedures based on approximate measures using either a solution upper bound or the solution of a linear program and a GRASP metaheuristic. Computational tests with instances adapted from commonly used literature databases are used to validate the proposed approaches. These tests give insight on the quality of the different techniques, which prove to be very efficient both in terms of computational effort and solution quality when compared to other strategies such as a random sampling or the solution of the MIP models using a commercial solver.     

Key manufacturing capability elements and business performance

The purpose of this research was to extend the recent stream of work in operations strategy on the trade-off and cumulative models of manufacturing capability development. Using results from the 1996 Manufacturing Futures Survey, the paper attempts to test whether pursuing more capabilities (the cumulative model) is reflected in improved return on assets. We consider both industry and country effects. Firms with performance in the upper quartile on a capability are described as 'high' performers and the number of times a firm achieves this level represents the number of capability elements that they have. The results show no evidence of the cumulative model. Quality and delivery capabilities are nevertheless strongly evident. Simply adding more capabilities generally produced no improvement in return on assets. Stepwise regression of ROA on the capabilities produced models with good explanatory power in some countries and industries though not all capabilities loaded. When interactions between capability elements were added to the regression models, results were mixed. In some cases, no interactions loaded, while in others, large increases in adjusted R 2 occurred, particularly in industry models.     

Part family identification using a simple genetic algorithm

Past research in part family identification has focused mainly on the development of efficient procedures for manufacturing-oriented part family formation in which similarities among parts are established primarily on machine or operation requirements. While these part families are essential in cellular manufacturing, they are not well suited for other areas of production, in particular, part design and process planning. A new part family identification technique using a simple genetic algorithm is proposed in this paper to first determine a set of part family differentiating attributes, and second to use these attributes to guide the formation of part families. The technique is implemented in C using a SUN SPARC workstation 1+. Empirical analyses of the technique on both artificially generated data and a real application are performed and discussed.     

Constructive heuristics for the Mixed Capacity Arc Routing Problem under Time Restrictions with Intermediate Facilities

The Mixed Capacity Arc Routing Problem under Time Restrictions with Intermediate Facilities (MCARPTIF) is an extension of the Arc Routing Problem under Capacity and Length Restrictions with Intermediate Facilities (CLARPIF) with application in municipal waste collection. This paper evaluates four constructive heuristics capable of computing feasible solutions for the MCARPTIF with a primary objective to either minimise total cost or to minimise the fleet size. The heuristics were adapted from Path-Scanning and Improved-Merge for the Mixed Capacitated Arc Routing Problem, and compared against two Route-First-Cluster-Second heuristics for the MCARPTIF. The objective was to identify the best performing heuristic for application purposes. In practice, the CARP is often solved for real-time or near real-time decision support. Computational time required by the heuristics was thus also evaluated. Identifying the best heuristic proved difficult due to a lack of realistic MCARPTIF benchmark sets, with the two CLARPIF sets predominantly solved in the literature not resembling actual waste collection instances. Route-First-Cluster-Second heuristics, linked with a new vehicle reduction heuristic performed the worst on the two CLARPIF sets, yet performed the best on new waste collection sets taken from the literature and introduced in this paper. Improved-Merge performed the best on two existing CLARPIF sets and on a realistic set with Intermediate-Facilities incident with the vehicle depot, but struggled on all other sets and in minimising fleet size. Path-Scanning was the most robust heuristic, performing reasonably well on all benchmark sets and both objectives. Results further show that due to the high computational time of one of the Route-First-Cluster-Second heuristics, which was only exposed on realistically sized sets, the slightly worse version is the best alternative when real-time support is required for waste collection applications.     

Creating Order: The Role of Heuristics in Website Selection

Heuristics, or mental shortcuts, can be efficient tools for decision making; however, they can also lead to biases. This study examines how undergraduate students might use heuristics when evaluating websites retrieved from a Google search. Student participants were asked to review a Google search for “genetically modified food” and select the websites they would likely use for an academic project. The Google search was presented in either the original or reversed order. After indicating initial preferences, students reviewed screen shots of six preselected websites from the Google search and rated each on various content attributes, after which they were asked whether they would use each for their research. The order in which participants saw the results affected their initial choices. Participants were more likely to choose the results near the top of the list. Once students evaluated websites on content attributes, the order effects diminished, but were still detectable. When asked to evaluate the websites, students demonstrated an understanding of what to look for and rated them effectively based on their content.     

Mathematical programming-based approaches for multi-facility glass container production planning

This paper introduces a mathematical model (together with a relaxed version) and solution approaches for the multi-facility glass container production planning (MF-GCPP) problem. The glass container industry covers the production of glass packaging (bottle and jars), where a glass paste is continuously distributed to a set of parallel molding machines that shape the finished products. Each facility has a set of furnaces where the glass paste is produced in order to meet the demand. Furthermore, final product transfers between facilities are allowed to face demand. The objectives include meeting demand, minimizing inventory investment and transportation costs, as well as maximizing the utilization of the production facilities. A novel mixed integer programming formulation is introduced for MF-GCPP and solution approaches applying heuristics and meta-heuristics based on mathematical programming are developed. A multi-population genetic algorithm defines for each individual the partitions of the search space to be optimized by the MIP solver. A variant of the fix-and-optimize improvement heuristic is also introduced. The computational tests are carried on instances generated from real-world data provided by a glass container company. The results show that the proposed methods return competitive results for smaller instances, comparing to an exact solver method. In larger instances, the proposed methods are able to return high quality solutions.     

Optimal joint replenishment policy for multiple non-instantaneous deteriorating items

In this paper, we deal with the problem of determining the optimal economic operating policy when a number of non-instantaneous deteriorating items are jointly replenished. We establish a multi-item joint replenishment model for non-instantaneous deteriorating items under constant demand rate allowing full backlogging. This problem is challenging, in particular, the cost function is a piecewise function with exponential parts, which makes the problem more complicated. To solve this problem, an approximation method is used to simplify the objective function and a bound-based heuristic algorithm is developed to solve the model. Numerical examples illustrate the effectiveness of the proposed method and the quality of the approximation. Experimental results on a real-life case study show that the proposed model can achieve substantial cost savings compared to the individual replenishment policy for non-instantaneous deteriorating items. Furthermore, sensitivity analysis of key parameters is carried out and the implications are discussed in detail.     

Evaluating the quality of project planning: a model and field results

Faulty planning will result in project failure, whereas high-quality project planning increases the project's chances of success. The paper reports on the successful development and implementation of a model aimed at evaluating the quality of project planning. The model is based on both the abilities required of the project manager and the organizational support required for a proper project management infrastructure. The model was validated and applied by 282 project managers in nine organizations, where strong and weak planning processes were identified and analysed.