Davide Castellano

Harmony search algorithm for single-machine scheduling problem with planned maintenance

The single-machine jobs-planned maintenance tasks joint scheduling problem is faced.The machine is subjected to failures with a Weibull distributed time-to-failure.The optimum number of planned maintenance tasks is computed.A specific metaheuristic based on the harmony search algorithm is presented. This paper focuses on the single machine scheduling problem, with sequence dependent setup times. Both processing and setup times are deterministic and the objective is to minimize total earliness and tardiness penalties. The novelty of the model can be traced in the fact that the single machine is subjected to breakdowns and that, in order to increase its availability, planned maintenance tasks are also performed. Hence, jobs and maintenance tasks are jointly considered to find the optimal schedule. These features make the problem NP-hard and so, a quasi-optimal solution is searched using a recent metaheuristic, which integrates harmony search and genetic algorithms. In order to validate the proposed metaheuristic, a comprehensive set of scheduling problems was fully investigated. Obtained results, compared with those of exhaustive (for small problems) and standard metaheuristics, confirm both the robustness and the speed of the proposed approach.

Distribution-free approach for stochastic Joint-Replenishment Problem with backorders-lost sales mixtures, and controllable major ordering cost and lead times

In this paper, we study the periodic-review Joint-Replenishment Problem (JRP) with stochastic demands and backorders-lost sales mixtures. We assume that lead times aare made of two major components: a common part to all items and an item-specific portion. We further suppose that the item-specific component of lead times and the major ordering cost are controllable. To reflect the practical circumstance characterized by the lack of complete information about the demand distribution, we adopt the minimax distribution-free approach. That is, we assume that only the mean and the variance of the demand can be evaluated. The objective is to determine the strict cyclic replenishment policy, the length of (the item-specific component of) lead times, and the major ordering cost that minimize the long-run expected total cost. To approach this minimization problem, we present a first optimization algorithm. However, numerical tests highlighted how computationally expensive this algorithm would be for a practical application. Therefore, we then propose two alternative heuristics. Extensive numerical experiments have been carried out to investigate the performance of the developed algorithms. Results have shown that the proposed alternative heuristics are actually efficient and seem therefore promising for a practical application. We study the Joint-Replenishment Problem with stochastic demands.The developed model takes into account mixtures of backorders and lost sales.We assume that major ordering cost and lead times are controllable.We adopt the minimax distribution-free procedure.We propose three alternative algorithms to approach the optimization problem.

An integer linear programming approach to maintenance strategies selection

Purpose – The purpose of this paper is to present a reliability centered maintenance (RCM) embedded integer linear programming approach (suited to the budget monetary resources allocation task) to the maintenance strategies mix selection for an industrial plant equipment. Design/methodology/approach – The developed approach allows to determine the optimal maintenance strategies mix for a set of equipment in a more quantitative way than the classic RCM approach. The proposed model takes into account, for each potential failure determined using the FMECA and for each admissible strategy, the costs and the potential risk priority number (RPN) reduction. Finally, an industrial case concerning an Italian paper-mill plant is reported to demonstrate the effectiveness of the approach presented. Findings – The paper finds that the application of the proposed approach allows to optimally allocate the budget monetary resources, determining which suitable maintenance practice apply to each failure, taking into accoun...

A Novel Game Theory Based Exit Selection Model In Emergency Conditions

In this paper, a new game theory based approach for the evacuees exit selection in emergency conditions is presented. It is founded on the theoretical concepts of multi-stage games with perfect information. In particular, an evacuee is a player and the available actions for a generic player are the accessible emergency exits. The developed mathematical model involves several psychological parameters, in order to make the emergency exit choice also affected by the individual character. Moreover, an evacuation simulation model incorporating this novel approach is shown. The model involves many other parameters and aspects attempting to obtain a satisfactory representation of the actual evacuation process and of the human behavior in emergency conditions. Finally, the effectiveness of the model is demonstrated with a simulative study.

Joint-replenishment problem under stochastic demands with backorders-lost sales mixtures, controllable lead times, and investment to reduce the major ordering cost

In this paper, we study the periodic-review stochastic Joint-replenishment Problem (JRP), with backorders-lost sales mixtures, controllable lead times, and investment to reduce the major ordering cost. The purpose is to determine a strict cyclic replenishment policy, the length of lead times, and the major ordering cost that minimize the total system cost. We first present an effective heuristic algorithm to approach the problem. However, results illustrate how computationally expensive the algorithm would be for a practical application. Hence, we then propose an efficient and more practically applicable solution procedure. In particular, approximating part of the cost function with its second-order Taylor series expansion, we obtain an expression that resembles the deterministic cost structure. Therefore, the problem can be approached exploiting a standard algorithm suitable for the deterministic JRP. Numerical tests compare the performances of the algorithms developed and show that the approximated approach is actually promising for a practical application.

An extension of the stochastic Joint-Replenishment Problem under the class of cyclic policies

This paper presents an extension of the Joint-Replenishment Problem under the class of cyclic policies. The developed model includes stochastic demands, backorders-lost sales mixtures, and controllable lead times. With the objective of minimizing total system cost, we propose two heuristics. Numerical experiments investigate the algorithms performance and the model sensitivity.

A study on the importance of selection rules within unbalanced MTO POLCA-controlled production systems

The present paper is aimed at investigating, through simulation, the behaviour of the POLCA method when a make-to-order (MTO) production system is highly unbalanced, in terms of both routings and times. In particular, the study is addressed to verify: 1) the effective capability of the POLCA method to improve the uncontrolled system; 2) the impact of how orders are processed at each workstation, to show that POLCA performance can be further improved in such circumstances by adopting an appropriate selection rule. Germs and Riezebos (2010) proved that POLCA is very effective in reducing the total throughput time (TTT) with respect to the corresponding unconstrained production systems. Owing to this, it appears evident that POLCA represents a valuable and effective make-to-order (MTO) production control method. However, they only considered balanced systems, hence some observations must be carefully addressed. In fact, often, in real world applications, the systems to be controlled are designed to process units with very different routings, each with significantly different probability to occur.

Diffusion theory applied to tool-life stochastic modeling under a progressive wear process

In this paper, a novel approach to the derivation of the tool-life distribution, when the tool useful life ends after a progressive wear process, is presented. It is based on the diffusion theory and exploits the Fokker–Planck equation. The Fokker–Planck coefficients are derived on the basis of the injury theory assumptions. That is, tool-wear occurs by detachment of small particles from the tool working surfaces, which are assumed to be identical and time-independent. In addition, they are supposed to be small enough to consider the detachment process as continuous. The tool useful life ends when a specified total volume of material is thus removed. Tool-life distributions are derived in two situations: (i) both Fokker–Planck coefficients are time-dependent only and (ii) the diffusion coefficient is neglected and the drift is wear-dependent. Theoretical results are finally compared to experimental data concerning flank wear land in continuous turning of a C40 carbon steel bar adopting a P10 type sintered carbide insert. The adherence to the experimental data of the tool-life distributions derived exploiting the Fokker–Planck equation is satisfactory. Moreover, the tool-life distribution obtained, when the diffusion coefficient is neglected and the drift is wear-dependent, is able to well-represent the wear behavior at intermediate and later times.

Computer-Aided Activity Planning (CAAP) in Large-Scale Projects With an Application in the Yachting Industry

Abstract The present paper provides the schema for an innovative and modular computer-based approach to the planning of activities in large-scale projects. Such projects are characterized by tens of thousands of tasks, which are consequently burdensome and difficult to plan manually. This is true to the point that in many shipyards only a low level of detail is used and poor planning is generally performed. The proposed approach is called computer-aided activity planning (CAAP), and an application in the yachting industry is shown to demonstrate its effectiveness. In particular, the so-called outfitting planning problem is faced. The CAAP system, taking into account the available shipyard resources and the knowledge on the building rules is able to automatically define, sequence, and schedule the activities of the whole outfitting process acting as a “planning configurator”. Moreover, it allows the industry-specific knowledge to be stored, maintained and shared within the (extended) organization. Owing to these “building blocks”, plans can be defined accurately and in a shorter time starting from pre-defined templates, with particular impact on lead times whenever variations to complex projects are needed. Finally, to verify the actual capabilities of the approach, the CAAP was implemented within a prototypical software called NautiCAAP.

A periodic review policy with quality improvement, setup cost reduction, backorder price discount, and controllable lead time

Abstract This paper explores a periodic review inventory model under stochastic demand. The setup (or ordering) cost and the lead time are controllable. The model considers an imperfect production process, whose quality can be improved by means of an investment. A backorder price discount to motivate customers to wait for backorders is included. The demand in the protection interval is first assumed Gaussian; then, the distribution-free approach is adopted. The objective is to determine the review period, the setup cost, the quality level, the backorder price discount, and the length of lead time that minimize the long-run expected total cost per time unit. A solution method for each case is presented. Numerical experiments show that substantial savings can be achieved if the quality level, the setup cost and the lead time are controlled, and if a backorder price discount is applied. A sensitivity analysis is finally carried out.