A robust two-phase heuristic algorithm for the truck scheduling problem in a resource-constrained crossdock

This paper studies truck scheduling in a resource-constrained crossdock. The problem decides on the sequence of incoming and outgoing trucks at the dock doors of the crossdocking terminal, subject to the availability of crossdock resources including dock doors and material handling systems. The resources are assumed non-preemptive making it necessary to address the feasibility of the problem before its optimality as it might be entrapped in deadlock and no feasible solution is produced. The paper thus aims at developing an algorithmic approach capable of establishing solution feasibility for truck scheduling problem instances of various types and difficulty levels which at the same time can be readily implemented in an industrial setting. The proposed approach is a two-phase heuristic algorithm where in the first phase, a heuristic search is deployed to construct a feasible sequence of trucks for the assignment to dock doors and in the second, a rule-based heuristic is used to assign each sequenced truck to a proper dock door, subject to a limited number of forklifts, such that significant savings in the truck schedule length are achieved. Extensive experiments are conducted to evaluate the efficiency of the algorithm in terms of deadlock avoidance and solution quality. The evaluation is carried out against the solutions generated by the exact mathematical model of the problem and a constructive heuristic developed for a similar truck scheduling problem. Experimental results demonstrate that the proposed algorithm is robust in avoiding deadlock and generates feasible solutions for the instances where the other two approaches cannot. Furthermore, significant improvement in the solution quality is achieved by augmenting the algorithm to a re-starting heuristic.     

Validation of a CFD model of an orbiting culture dish with PIV and analytical solutions

Particle image velocimetry (PIV) and an extended solution of Stokes’ second problem were used to validate a computational fluid dynamics (CFD) model of flow in an orbiting dish. Velocity vector components throughout one complete orbit differed between CFD and PIV by less than 5%. Computational velocity magnitudes averaged over the interior 20% radius, the region where the analytical solution is most applicable, were 0.3% higher than the analytical values, while the experimental values in the same region were 2.4% higher. Velocity profiles in the center of the dish across normalized heights that most influence wall shear stress varied on average by ～–0.00046 for the normalized radial component and by ～0.0038 for the normalized tangential component compared to the analytical solution. These results represent the most comprehensive validation to date for computational models of the orbiting dish system. © 2017 American Institute of Chemical Engineers AIChE J, 63: 4233–4242, 2017     

A comparison of human and near-optimal task management behavior

OBJECTIVE: The primary contribution of this work is the development of an abstract framework to which a variety of multitasking scenarios can be mapped. The metaphor of a juggler spinning plates was introduced to represent an operator performing multiple concurrent tasks. BACKGROUND: This allowed seeking a quantitative model for management of multiple continuous tasks instead of a model for completing multiple discrete tasks, which was considered in previous studies. METHODS: The multitasking performance of 10 participants in five scenarios was measured in a low-fidelity simulator (named Tardast), which was developed based on the concept of the juggler metaphor. This performance was then compared with a normative model, which was a near-optimal solution to a mathematical programming problem found by tabu search heuristics. RESULTS: Tabu outperformed the participants overall, although the best individual performance nearly equaled that of tabu. It was also observed that participants initially tended to manage numerous tasks poorly but that they gradually learned to handle fewer tasks and excel in them. CONCLUSION: This suggests that they initially overreacted to the penalization associated with poor performance in the software. Participants' strategic task management (e.g., what tasks to handle) was more significant in obtaining a good score than their tactical task management (e.g., how often to switch between two tasks). APPLICATION: Potential applications include better design of equipment, procedures, and training of operators of complex systems.     

Sequential testing algorithms for multiple fault diagnosis

We consider the problem of constructing optimal and near-optimal test sequences for multiple fault diagnosis. The computational complexity of solving the optimal multiple-fault isolation problem is super exponential, that is, it is much more difficult than the single-fault isolation problem, which, by itself, is NP-hard. By employing concepts from information theory and AND/OR graph search and by exploiting the single fault testing strategies of Pattipati et al. (1990), we present several test sequencing algorithms for the multiple fault isolation problem. These algorithms provide a trade-off between the degree of suboptimality and computational complexity. Furthermore, we present novel diagnostic strategies that generate a diagnostic directed graph, instead of a traditional diagnostic tree, for multiple fault diagnosis. Using this approach, the storage complexity of the overall diagnostic strategy reduces substantially. The algorithms developed herein have been successfully applied to several real-world systems     

Test sequencing algorithms with unreliable tests

In this paper, we consider imperfect test sequencing problems under a single fault assumption. This is a partially observed Markov decision problem (POMDP), a sequential multistage decision problem wherein a failure source must be identified using the results of imperfect tests at each stage. The optimal solution for this problem can be obtained by applying a continuous-state dynamic programming (DP) recursion. However, the DP recursion is computationally very expensive owing to the continuous nature of the state vector comprising the probabilities of faults. In order to alleviate this computational explosion, we present an efficient implementation of the DP recursion. We also consider various problems with special structure (parallel systems) and derive closed form solutions/index-rules without having to resort to DP. Finally, we present various top-down graph search algorithms for problems with no special structure, including multistep DP, multistep information heuristics, and certainty equivalence algorithms     

Purification and characterisation of thermo-alkaline pectinase enzyme from Hylocereus polyrhizus

The thermo-alkaline pectinase enzyme from Hylocereus polyrhizus was purified 232.3-fold with a 73.3 % recovery through ammonium sulphate precipitation, gel filtration, and ion exchange chromatography. Ion exchange chromatography combined with sodium dodecyl sulphate gel electrophoresis (SDS-PAGE) revealed that the enzyme was monomeric with a molecular weight of 34.2 kDa. The pectinase exhibited broad specificity towards polygalacturonic acid, arabinan, oat spelt xylan, and pNP-α-glucopyranoside. The optimum pH and temperature were 8.0 and 75 °C, respectively. This enzyme was stable over a wide pH range (3.0–11.0) and at relatively high temperature (85 °C for 1 h). The K_m and V_max values of pectinase towards polygalacturonic acid were 2.7 mg/ml and 34.30 U/mg proteins, respectively. In addition, the enzyme activity was inhibited by Ni²⁺, Al³⁺, and Fe²⁺ and was increased in the presence of Ca²⁺ and Mg²⁺ by 120 and 112 %, respectively. The purified pectinase demonstrated robust stability in response to surfactants and oxidising agents. EDTA, which is a powerful chelating agent, did not exert any significant effect on the enzyme stability. Thus, enzymes with these unique properties may be widely used in different types of industries and biotechnological applications.     

Integrated System for Remediation of Contaminated Soils

A pilot-scale study was conducted to evaluate an integrated system for the remediation of soils contaminated primarily with pentachlorophenol (PCP), a wood preserver. The integrated soil remediation system consisted of three unit processes: (1) Soil solvent washing; (2) solvent recovery; and (3) biotreatment of the contaminant residual. Pilot-scale countercurrent solvent washing was carried out using a 95% ethanol solution—a solvent that in an earlier bench-scale study was found to be effective in removing PCP and hydrocarbons (HCs) from soils. Three-stage countercurrent solvent washing of a field-contaminated soil was performed using batches of 7.5 kg of soil and 30 L of solvent (1 kg:4 L soil-to-solvent contact ratio). The washed soil was rinsed with water in a single stage after three countercurrent wash stages. Pilot-scale, three-stage countercurrent solvent washing with 95% ethanol reduced the PCP and HC contamination on the soil by 98 and 95%, respectively. The spent solvent and the spent rinse water were combined as the spent wash fluid for further treatment. A pilot-scale distillation unit was used to recover the ethanol from the spent wash fluid. The HC constituents of the spent wash fluid were removed by pH adjustment prior to feeding the spent wash fluid to a distillation unit. Greater than 96% of the ethanol in the spent wash fluid was recovered in the distillate stream, whereas PCP was captured in the bottoms stream. The bottoms stream was treated sequentially in anaerobic and aerobic granular-activated carbon fluidized-bed reactors. Complete mineralization of PCP was achieved using this treatment train.     

Spatial experience based route finding using ontologies

Spatial experiences in route finding, such as the ability of finding low‐traffic routes, exert a significant influence on travel time in big cities; therefore, the spatial experiences of seasoned individuals such as taxi drivers in route finding can be useful for improving route‐finding algorithms and preventing using routes having considerable traffic. In this regard, a spatial experience‐based route‐finding algorithm is introduced through ontology in this paper. To this end, different methods of modeling experiences are investigated. Then, a modeling method is chosen for modeling the experiences of drivers for route finding depending on the advantages of ontology, and an ontology based on the taxi drivers’ experiences is proposed. This ontology is employed to create an ontology‐based route‐finding algorithm. The results are compared with those of Google maps in terms of route length and travel time at peak traffic time. According to the results, although the route lengths of route‐finding method based on the ontology of drivers’ experiences in three cases (from nine cases) are greater than that based on Google maps, the travel times are shorter in most cases, and in some routes, the difference in travel time reaches only 10 minutes.