Age-dependent production planning and maintenance strategies in unreliable manufacturing systems with lost sale

In this paper, we formulate an analytical model for the joint determination of an optimal age-dependent buffer inventory and preventive maintenance policy in a production environment that is subject to random machine breakdowns. Traditional preventive maintenance policies, such as age and periodic replacements, are usually studied based on simplified and non-realistic assumptions, as well as on the expected costs criterion. Finished goods inventories and the age-dependent likelihood of machine breakdowns are usually not considered. As a result, these policies could significantly extend beyond the anticipated financial incomes of the system, and lead to crises. In order to solve this problem, a more realistic analysis model is proposed in this paper to consider the effects of both preventive maintenance policies and machine age on optimal safety stock levels. Hence, a unified framework is developed, allowing production and preventive maintenance to be jointly considered. We use an age-dependent optimization model based on the minimization of an overall cost function, including inventory holdings, lost sales, preventive and corrective maintenance costs. We provide optimality conditions for the manufacturing systems considered, and use numerical methods to obtain an optimal preventive maintenance policy and the relevant age-dependent threshold level production policy. In this work, this policy is called the multiple threshold levels hedging point policy. We include numerical examples and sensitivity analyses to illustrate the importance and the effectiveness of the proposed methodology. Compared with other available optimal production and maintenance policies, the numerical solution obtained shows that the proposed age-dependent optimal production and maintenance policies significantly reduce the overall cost incurred.     

Joint planning of combined heat and power and electric power systems: An efficient model formulation

The growing importance of combined heat and power (CHP) around the world has increased the need to consider its role within electric power systems. In this paper, we show how the problem of joint planning of CHP and electric power systems may be formulated more efficiently than has previously been done, by exploiting the special structure of the optimal solution. Numerical tests indicate that this reformulation typically allows a reduction in the time required for problem solution by a factor of two to five.     

A model and approach to the challenge posed by optimal power systems planning

Currently, there is a need to plan and analyze the electric power transmission system in greater detail and over larger geographic areas. Existing models approach the problem from different perspectives. Each model addresses different aspects of and has different approximations to the optimal planning process. In order to scope out the huge challenge of optimal transmission planning, this paper presents a new modeling approach for inter-regional planning and investment in a competitive environment. This modeling approach incorporates the detailed generator, topology and operational aspects found in production cost planning models into a larger framework that can find optimal sets of transmission expansion projects. The framework proposed here can be used in an auction to award investment contracts or as a part of a more general policy analysis. The solution yields the set of transmission projects that have the highest expected benefits, while also representing generic generation expansions under the same objective. The model is a two-stage, mixed-integer, multi-period, N-1-reliable model with investment, unit commitment, and transmission switching. The combination of combinatorial, stochastic and operational elements means this model may be computationally intractable without judicious modelling aggregations or approximations to reduce its size and complexity. Nevertheless we show via a dual problem that analysing the economics and sensitivity of the solution is computationally more straightforward.     

Financial planning for the preventive maintenance of power distribution systems via fuzzy AHP

One of the most important objectives of electricity distribution companies is to improve the reliability of the distribution networks. To this end, the electricity distribution companies try to optimally use the existing financial resources in the planning of preventive maintenance (PM) programs to reduce the imposed costs on the system due to the failure of network components and to improve the network reliability. In fuzzy analytical hierarchical process (fuzzy AHP) method, the degree of network reliability and the effectiveness of PM budget in the improvement of network reliability are selected as decision criteria in the budget allocation procedure. The areas served by the power distribution network are prioritized relative to each other and are assigned weights based on these priorities. The PM budget is determined based on the obtained weights. The medium voltage distribution network of seven areas in the city of Tehran have been selected for the implementation of the proposed method and the analysis of the obtained results. © 2014 Wiley Periodicals, Inc. Complexity 21: 36–46, 2016     

A Benders decomposition algorithm for a multi-area,multi-stage integrated resource planning in power systems

The aim of this paper is to propose an integrated model for resource planning in power systems by taking into account both supply and demand sides options simultaneously. At supply-side, investment in generation capacity and transmission lines is considered. Demand side management (DSM) technologies are also incorporated to correct the shape of the load duration curve in terms of peak clipping and load shifting programmes. A mixed integer non-linear programming model is developed to find the optimal location and timing of electricity generation/transmission as well as DSM options. To solve the resulting complex model, nonlinearity caused by transmission loss terms are first eliminated using the piecewise linearization technique. Then, a Benders decomposition (BD) algorithm is developed to solve the linearized model. The performance of the proposed BD algorithm is validated via applying it to the 6-bus Garver test system and a modified 21-bus IEEE reliability test system.     

Modification planning in technical systems

Translated from Vychislitel'nye Kompleksy i Modelirovanie Slozhnykh Sistem, pp. 55–70, Moscow State University, 1989.     

Misrepresentation-proof strategies in poly-agent systems

False representation prevention is an important issue in the management of poly-agent systems when a system manager needs to collect information from agents. This paper aims at proposing strategies to prevent false representation effectively and efficiently.We suppose that the manager can know the truth by inspecting but can not inspect all reports due to the high cost of inspection, and the manager will announce a penalty rule and a probability of doing inspection for preventing false representation. After formulating three penalty rules, we obtain the minimal inspections required for inducing true information under each penalty rule.     

Capacity planning under different inspection strategies

This paper addresses the issue of location of inspection stations along a serial production line. As a function of inspection site, the capacity required to test and repair the parts would vary. The production-inspection model developed provides management with information on such capacity planning issues. It integrates the issues of inspection location, inspection capacity, and production capacity. We consider a two-stage production line and evaluate cases when there are inspection sites after each production stage and when inspection is carried out after the final stage. In the latter case, we find the conditions when the safety stock required to meet the demand is higher. This has managerial implications because the inventory level is not only higher, it is held after the final stage, i.e. has more value added onto it. However, if there are inspection stations after each stage, the capacity required to test and repair the parts is higher. The intent of this work is to provide insights into designing production-inventory systems for the serial model that we consider. We also discuss the generalization of the scope of the two-stage model to an N stage production line. In an extension to the problem, we consider the situation when not all the defectives can be repaired. We extend the basic model by considering the effect of partial scraping of the defectives.     

Multiobjective vendor allocation in multiechelon inventory systems: a spreadsheet model

The vendor selection problem (VSP) is a critical element of the numerous managerial decisions in the consideration of both outsourcing and integrated supply chain management. Many papers in the literature have dealt with VSPs from a multicriteria perspective, but few have looked into the implications of such decisions in a multiechelon supply chain with the explicit consideration of multiple time-phased demands. A new integrated supply chain model is proposed for a multiechelon supply chain. This model takes into account the usual cost objective and other important criteria in a multiechelon supply chain ranging from the most upstream suppliers' quality to end customers' satisfaction level through a large-scale multiobjective linear programme (MOLP). Furthermore, various Pareto optimal solutions can be graphically presented to facilitate decision making and negotiations with existing and potential suppliers.     

Multilevel Stackelberg strategies in linear-quadratic systems

Open-loop multilevel Stackelberg strategies in deterministic, sequential decision-making problems for continuous linear systems and quadratic criteria are developed. Characterization of the Stackelberg controls via the solution of a higher-order square-matrix-Riccati differential equation is established; also, the basic structural properties of the coefficient matrices of this differential equation are established, and the basic structural properties of its solution are inferred.This work was supported in part by the Energy Research and Development Administration, Contract No. ERDA E(49-18)-2088.on leave from the Mihailo Pupin Institute, Belgrade, Yugoslavia.     

Optimal recruitment strategies in a multi-level manpower planning model

Markov manpower planning models have extensively been analysed in the past in order to find an optimal personnel strategy for which the stocks of the manpower system evolve towards desirable ones. So far, those models do not take into account interactions among different organizational decision levels. In this paper, a multi-level manpower planning model is presented that considers, besides the desirable stock vector at overall level, proposals for the departmental stocks from lower organizational levels. Attainability of the stock vectors at departmental level is examined under control by recruitment and interdepartmental transitions. A multi-level optimization algorithm is presented to determine an optimal recruitment strategy resulting in attainable and acceptable stocks that are a compromise between the proposal from the top and the proposals from the departments.     

Expert systems for planning and scheduling manufacturing systems

In this paper, research and applications of expert systems in production planning and scheduling are reviewed. Components of expert systems are briefly discussed. Relationship between expert system and operations research approaches are presented. Integration of operations research and expert system techniques is explored.     

Minimax production planning in failure-prone manufacturing systems

In this paper, we consider a minimax production planning model of a flexible manufacturing system with machines that are subject to random breakdown and repair. The objective is to choose the rate of production that minimizes the related minimax cost of production and inventory/shortage. The value function is shown to be the unique viscosity solution to the associated Hamilton-Jacobi-Isaacs equation. Under certain conditions, it is shown that the value function is continuously differentiable. A verification theorem is given to provide a sufficient condition for optimal control. Finally, two examples are solved explicitly.This research was supported by the Natural Sciences and Engineering Research Council of Canada under Grants OGP0036444 and A4169.     

Parallel computing applied to the stochastic dynamic programming for long term operation planning of hydrothermal power systems

In this paper, parallel processing techniques are employed to improve the performance of the stochastic dynamic programming applied to the long term operation planning of electrical power system. The hydroelectric plants are grouped into energy equivalent reservoirs and the expected cost functions are modeled by a piecewise linear approximation, by means of the Convex Hull algorithm. In order to validate the proposed methodology, data from the Brazilian electrical power system is utilized.     

Priority dispatching strategies for EMS systems

Emergency medical service (EMS) systems provide urgent medical care and transport. In this study we implement dispatching policies for EMS systems that incorporate the severity of the call in order to increase the survival probability of patients. A simulation model is developed to evaluate the performance of EMS systems. Performance is measured in terms of patients’ survival probability, since survival probability more directly mirrors patient outcomes. Different response strategies are evaluated utilizing several examples to study the nature of the optimal dispatching policy. The results show that dispatching the closest vehicle is not always optimal and dispatching vehicles considering priority of the call leads to an increase in the average survival probability of patients. A heuristic algorithm, that is easy to implement, is developed to dispatch ambulances for large-scale EMS systems. Computational examples show that the dispatching algorithm is valuable in increasing the patients’ survival probability.     

Interaction between intelligent agent strategies for real-time transportation planning

In this paper we study the real-time scheduling of time-sensitive full truckload pickup-and-delivery jobs. The problem involves the allocation of jobs to a fixed set of vehicles which might belong to different collaborating transportation agencies. A recently proposed solution methodology for this problem is the use of a multi-agent system where shipper agents offer jobs through sequential auctions and vehicle agents bid on these jobs. In this paper we consider such a system where both the vehicle agents and the shipper agents are using profit maximizing look-ahead strategies. Our main contribution is that we study the interrelation of these strategies and their impact on the system-wide logistical costs. From our simulation results, we conclude that the system-wide logistical costs (i) are always reduced by using the look-ahead strategies instead of a myopic strategy (10–20%) and (ii) the joint effect of two look-ahead strategies is larger than the effect of an individual strategy. To provide an indication of the savings that might be realized under centralized decision making, we benchmark our results against an integer programming approach.     

Optimal replacement strategies for repairable systems

This paper considers repair-replacement models introduced by Lam Yeh [6] and [7], and Stadje and Zuckerman [9]. Without imposing reliability theory conditions on the repair and operating distributions, the optimal replacement problem is first solved in a finite horizon setting and then extensions are given to the infinite horizon case.