A shift sequence based approach for nurse scheduling and a new benchmark dataset

This paper investigates an adaptive constructive method for solving nurse rostering problems. The constraints considered in the problems are categorised into three classes: those that are sequence related, those that are nurse schedule related and those that are roster related. We propose a decomposition approach (to construct solutions) that consists of two stages: (1) to construct high quality sequences for nurses by only considering the sequence constraints, and (2) to iteratively construct schedules for nurses and the overall rosters, based on the sequences built and considering the schedule and roster constraints. In the second stage of the schedule construction, nurses are ordered and selected adaptively according to the quality of the schedules they were assigned to in the last iteration. Greedy local search is carried out during and after the roster construction, in order to improve the (partial) rosters built. We show that the local search heuristic during the roster construction can further improve the constructed solutions for the benchmark problems tested.     

A two-stage solution approach for personalized multi-department multi-day shift scheduling

In this paper, we address a personalized multi-department multi-day shift scheduling problem with a multi-skill heterogeneous workforce where employees can be transferred between departments under some restrictions. The objective is to construct a schedule that minimizes under-coverage, over-coverage, transfer and labor costs. We propose a novel two-stage approach to solve it: the first stage considers an approximate and smaller problem based on data aggregation and produces approximate transfers. The second stage constructs personalized schedules based on the information deduced from the first stage. An exhaustive experimental study is conducted and proves the efficiency of the proposed approach in terms of solution quality and computing times.     

An algorithm for single shift scheduling of hierarchical workforce

This paper presents an optimal algorithm for single shift scheduling of hierarchical workforce in seven-day-a-week industries. There are many categories of workers; their capabilities are arranged hierarchically. The objective is to arrive at the most economical mix of categories of employees satisfying the pattern of demand for employees and desired work characteristics. The demand for employees varies from one category to the other and for each category the demand during weekdays is fixed and is different from that of weekends. The desired work characteristics are: (i) each employee must be given two days off every week including at least a proportion A out of every B weekends off and (ii) each employee can have at most 5 working shifts between any two offdays. The algorithm provides a computational scheme for arriving at the minimum workforce size comprising the most economical mix of categories of employees for this demand context and a new technique for characterizing the problem to achieve feasible assignment of employees to shifts.     

An integer programming approach to short-term production scheduling in a tobacco plant

In this paper, a mathematical model is developed that facilitates daily production scheduling in a tobacco processing plant. The implied objectives are to meet specific horizon production targets (obtained from a master production schedule), to maintain safety stock requirements and to ensure that the demand for labour lies within given limits. The express objective is to minimise the number of machines used in the production process. Additionally, the model incorporates work-in-progress, aspects of the demand for product transportation within the plant and machine capacity (utilisation) reduction effects associated with production sequencing. These aspects are relevant when dealing with time intervals as small as a day but can be averaged out when dealing with monthly time intervals. The developments in this paper represents stage II of the modelling of the tobacco plant, where stage I (already completed) was centred on obtaining a monthly master production schedule for a year ahead and assisting in macro planning activities. This paper also sees the development of a simple user-friendly heuristic which facilitates production sequencing on a daily basis given the master production schedule obtained from Stage I.     

An approach to predictive-reactive scheduling of parallel machines subject to disruptions

In this paper, a new predictive-reactive approach to a parallel machine scheduling problem in the presence of uncertain disruptions is presented. The approach developed is based on generating a predictive schedule that absorbs the effects of possible uncertain disruptions through adding idle times to the job processing times. The uncertain disruption considered is material shortage, described by the number of disruption occurrences and disruption repair period. These parameters are specified imprecisely and modelled using fuzzy sets. If the impact of a disruption is too high to be absorbed by the predictive schedule, a rescheduling action is carried out. This approach has been applied to solving a real-life scheduling problem of a pottery company.     

An approach for solving a class of transportation scheduling problems

An algorithm is developed for solving a class of transportation scheduling problems. It applies for a variety of problems such as: the Combining Truck Trip problem, the Delivery problem, the School Bus problem, the Assignment of Buses to Schedules, and the Travelling Salesman problem. The objective functions of the above problems differ from each other. Yet, by using the “savings method” proposed by Clarke and Wright, and extended by Gaskell, we are able to define each one of the above problems as a series of assignment problems. The cost matrix entries of each one of the assignment problems are a function of the constraints of the particular routing or scheduling problem. The solution to the assignment problem determines an upper bound of the optimal solution to the original problem. By combining the above procedure with a Branch and Bound procedure, it is possible to obtain the optimal solution in a finite number of steps. In some cases the Branch and Bound process can be eliminated due to the nature of the problem and in those cases the algorithm is efficient.     

Dual decomposition of a single-machine scheduling problem

We design a fast ascent direction algorithm for the Lagrangian dual problem of the single-machine scheduling problem of minimizing total weighted completion time subject to precedence constraints. We show that designing such an algorithm is relatively simple if a scheduling problem is formulated in terms of the job completion times rather than as an 0–1 linear program. Also, we show that upon termination of such an ascent direction algorithm we get a dual decomposition of the original problem, which can be exploited to develop approximative and enumerative approaches for it. Computational results exhibit that in our application the ascent direction leads to good Lagrangian lower and upper bounds.     

An enhanced decomposition algorithm for multistage stochastic hydroelectric scheduling

Handling uncertainty in natural inflow is an important part of a hydroelectric scheduling model. In a stochastic programming formulation, natural inflow may be modeled as a random vector with known distribution, but the size of the resulting mathematical program can be formidable. Decomposition-based algorithms take advantage of special structure and provide an attractive approach to such problems. We develop an enhanced Benders decomposition algorithm for solving multistage stochastic linear programs. The enhancements include warm start basis selection, preliminary cut generation, the multicut procedure, and decision tree traversing strategies. Computational results are presented for a collection of stochastic hydroelectric scheduling problems.     

An evolutionary and constructive approach to a crew scheduling problem in underground passenger transport

Operations management of subway systems is associated with combinatorial optimization problems (i.e. crew and train scheduling and rostering) which belong to the np-hard class of problems. Therefore, they are generally solved heuristically in real situations. This paper considers the problem of duty generation, i.e. identifying an optimal trips set that the conductors should complete in one workday. With regard to operational and labor conditions, the problem is to use the lowest possible number of conductors and minimize total idle time between trips. The problem is modeled and solved using a constructive hybrid approach, which has the advantage of visualizing a solution construction similar to the manual approach typically used. Our approach takes advantage of the benefits offered by evolutionary methods, which store a candidate solutions population in each stage, thus controlling the combinatorial explosion of possible solutions. The results thus obtained for problems similar to those that are solved manually in the Santiago Metro System were compared with two alternative approaches, based on tabu search and a greedy method. The hybrid method produced solutions with the minimum number of duties in six of the ten problems solved. However, the tabu search method provided better results in terms of idle time than either the hybrid method or the greedy method.     

An extremal metric approach to some problems on extremal decomposition

The paper presents a simple extremal metric approach to problems on extremal decomposition in families of systems of nonoverlapping domains of different types. The approach is based on using general properties of trajectories of the associated quadratic differentials for simpler extremal decomposition problems. Bibliography: 8 titles. Dedicated to the 100th anniversary of G. M. Goluzin’s birthday __________ Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 337, 2006, pp. 191–211.     

An approach for decomposition of finite rotations

Based on Lie groups theory, this work considers the problem of decomposition of a given rotation into three successive finite rotations with prescribed in advance axes. (© 2012 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

An approach for integrated scheduling and lot-sizing

In this paper a non time discrete approach is developed for an integrated planning procedure, applied to a multi-item capacitated production system with dynamic demand. The objective is to minimize the total costs, which consist of holding and setup costs for one period. The model does not allow backlog. Furthermore, a production rate of zero or full capacity is the only possibility. The result is a schedule, lot-sizes and the sequences for all lots. The approach is based on a specific property of the setup cost function, which allows for replacement of the integer formulation for the number of setup activities in the model. In a situation where the requirements for the multi-item continuous rate economic order quantity, the so-called economic production lot (EPL) formula, are fulfilled, both the EPL as well as the presented model results are identical for the instances dealt with. Moreover, with the new model problems with an arbitrary demand can be solved.     

An evolutionary approach to rehabilitation patient scheduling: A case study

Focusing on real settings, this study aimed to develop an evolutionary approach based on genetic algorithm for solving the problem of rehabilitation patient scheduling to increase service quality by reducing patient waiting time and improve operation efficiency by increasing the therapy equipment utilization. Indeed, due to partial precedence constraints of rehabilitation therapies, the problem can be structured as a hybrid shop scheduling problem that has received little attention to date. In addition, a mixed integer programming model was also constructed as a benchmark to validate the solution quality with small problems. Based on empirical data from a Medical Center in Taiwan, several experiments were conducted to estimate the validity of the proposed algorithm. The results showed that the proposed algorithm can reduce patient waiting time and enhance resource utilization and thus demonstrated the practicality of the proposed algorithm. Indeed, a decision support system embedded with the developed algorithm has been implemented in this medical center.     

An alternative framework to Lagrangian relaxation approach for job shop scheduling

A new Lagrangian relaxation (LR) approach is developed for job shop scheduling problems. In the approach, operation precedence constraints rather than machine capacity constraints are relaxed. The relaxed problem is decomposed into single or parallel machine scheduling subproblems. These subproblems, which are NP-complete in general, are approximately solved by using fast heuristic algorithms. The dual problem is solved by using a recently developed “surrogate subgradient method” that allows approximate optimization of the subproblems. Since the algorithms for subproblems do not depend on the time horizon of the scheduling problems and are very fast, our new LR approach is efficient, particularly for large problems with long time horizons. For these problems, the machine decomposition-based LR approach requires much less memory and computation time as compared to a part decomposition-based approach as demonstrated by numerical testing.     

A new decomposition approach for the single machine total tardiness scheduling problem

This paper deals with the single machine total tardiness problem. From Emmons’ basic dominance conditions a new partition theorem is derived which generalises Lawler’s decomposition rule and leads to a new double decomposition procedure. This procedure is embedded into a branch and bound method which applies a new lower bound based on due dates reassignment. The branch and bound method is tested on problems with size up to 150 jobs.     

A Lagrangian decomposition approach for the pump scheduling problem in water networks

Dynamic pricing has become a common form of electricity tariff, where the price of electricity varies in real time based on the realized electricity supply and demand. Hence, optimizing industrial operations to benefit from periods with low electricity prices is vital to maximizing the benefits of dynamic pricing. In the case of water networks, energy consumed by pumping is a substantial cost for water utilities, and optimizing pump schedules to accommodate for the changing price of energy while ensuring a continuous supply of water is essential. In this paper, a Mixed-Integer Non-linear Programming (MINLP) formulation of the optimal pump scheduling problem is presented. Due to the non-linearities, the typical size of water networks, and the discretization of the planning horizon, the problem is not solvable within reasonable time using standard optimization software. We present a Lagrangian decomposition approach that exploits the structure of the problem leading to smaller problems that are solved independently. The Lagrangian decomposition is coupled with a simulation-based, improved limited discrepancy search algorithm that is capable of finding high quality feasible solutions. The proposed approach finds solutions with guaranteed upper and lower bounds. These solutions are compared to those found by a mixed-integer linear programming approach, which uses a piecewise-linearization of the non-linear constraints to find a global optimal solution of the relaxation. Numerical testing is conducted on two real water networks and the results illustrate the significant costs savings due to optimizing pump schedules.     

An integrated approach for scheduling health care activities in a hospital

To effectively utilise hospital beds, operating rooms (OR) and other treatment spaces, it is necessary to precisely plan patient admissions and treatments in advance. As patient treatment and recovery times are unequal and uncertain, this is not easy. In response, a sophisticated flexible job-shop scheduling (FJSS) model is introduced, whereby patients, beds, hospital wards and health care activities are respectively treated as jobs, single machines, parallel machines and operations. Our approach is novel because an entire hospital is describable and schedulable in one integrated approach. The scheduling model can be used to recompute timings after deviations, delays, postponements and cancellations. It also includes advanced conditions such as activity and machine setup times, transfer times between activities, blocking limitations and no wait conditions, timing and occupancy restrictions, buffering for robustness, fixed activities and sequences, release times and strict deadlines. To solve the FJSS problem, constructive algorithms and hybrid meta-heuristics have been developed. Our numerical testing shows that the proposed solution techniques are capable of solving problems of real world size. This outcome further highlights the value of the scheduling model and its potential for integration into actual hospital information systems.     

An impulse control approach for optimal maintenance scheduling of a gas turbine

Today gas turbine outages are determined by one global life counter and deterministic life times. In the future the gas turbine will be equipped with multiple life counters for outage determination. Therefore we present an impulse control approach which takes the probabilistic nature of the damage mechanism into account to calculate an optimized maintenance schedule. (© 2016 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim)     

An integrated staff-sizing approach considering feasibility of scheduling decision

This paper presents an integrated staff-sizing system for analyzing and determining workforce management policies with consideration of staff flexibility in service organizations, which addresses and captures the integrated requirements between long-term manpower planning and short-term staff scheduling in the service sector. Multiple Objective Linear Programming (MOLP) is applied to optimize several diversified goals. Solution methods to the MOLP models for the staff planning and staff scheduling are developed respectively, then a solution approach is proposed to iteratively revise the unacceptable staff-sizing plan or scheduling plan. Finally, an example of nurse sizing is analyzed and computational studies are carried out to investigate managerial insights.