Seyda Topaloglu

A multi-objective programming model for scheduling emergency medicine residents

Scheduling emergency medicine residents (EMRs) is a complex task, which considers a large number of rules (often conflicting) related to various aspects such as limits on the number of consecutive work hours, number of day and night shifts that should be worked by each resident, resident staffing requirements according to seniority levels for the day and night shifts, restrictions on the number of consecutive day and night shifts assigned, vacation periods, weekend off requests, and fair distribution of responsibilities among the residents. Emergency rooms (ERs) are stressful workplaces, and in addition shift work is well-known to be more demanding than regular daytime work. For this reason, preparing schedules that suit the working rules for EMRs is especially important for reducing the negative impact on shift workers physiologically, psychologically, and socially. In this paper, we present a goal programming (GP) model that accommodates both hard and soft constraints for a monthly planning horizon. The hard constraints should be adhered to strictly, whereas the soft constraints can be violated when necessary. The relative importance values of the soft constraints have been computed by the analytical hierarchy process (AHP), which are used as coefficients of the deviations from the soft constraints in the objective function. The model has been tested in the ER of a major local university hospital. The main conclusions of the study are that problems of realistic size can be solved quickly and the generated schedules have very high qualities compared to the manually prepared schedules, which require a lot of effort and time from the chief resident who is responsible for this duty.

A shift scheduling model for employees with different seniority levels and an application in healthcare

This paper addresses the problem of scheduling medical residents that arises in different clinical settings of a hospital. The residents are grouped according to different seniority levels that are specified by the number of years spent in residency training. It is required from the residents to participate in the delivery of patient care services directly by working weekday and weekend day shifts in addition to their regular daytime work. A monthly shift schedule is prepared to determine the shift duties of each resident considering shift coverage requirements, seniority-based workload rules, and resident work preferences. Due to the large number of constraints often conflicting, a multi-objective programming model has been proposed to automate the schedule generation process. The model is implemented on a real case in the pulmonary unit of a local hospital for a 6-month period using sequential and weighted methods. The results indicate that high quality solutions can be obtained within a few seconds compared to the manually prepared schedules expending considerable effort and time. It is also shown that the employed weighting procedure based on seniority levels performs much better compared to the preemptive method in terms of computational burden.

A simulated annealing algorithm for the job shop cell scheduling problem with intercellular moves and reentrant parts

This paper addresses the problem of scheduling parts in job shop cellular manufacturing systems by considering exceptional parts that need to visit machines in different cells and reentrant parts which need to visit some machines more than once in non-consecutive manner. Initially, an integer linear programming (ILP) model is presented for the problem to minimize the makespan, which considers intercellular moves and non-consecutive multiple processing of parts on a machine. Due to the complexity of the model, a simulated annealing (SA) based solution approach is developed to solve the problem. To increase the efficiency of the search algorithm, a neighborhood structure based on the concept of blocks is applied. Subsequently, the efficiency of the ILP model and the performance of the proposed SA are assessed over a set of problem instances taken from the literature. The proposed ILP model was coded in Lingo 8.0 and the solution obtained by the proposed SA was compared to the optimal values. The computational results demonstrate that the proposed ILP model and SA algorithm are effective and efficient for this problem.

An Implicit Goal Programming Model for the Tour Scheduling Problem Considering the Employee Work Preferences

Many organizations face employee scheduling problems under conditions of variable demand for service over the course of an operating day and across a planning horizon. These organizations are concerned with the tour scheduling problem that involves assigning shifts and break times to the work days of employees and allocating days off to individual work schedules. Nowadays, organizations try to adopt various scheduling flexibility alternatives to meet the fluctuating service demand. On the other hand, they have also realized that providing employee productivity and satisfaction is as much important as meeting the service demand. Up to date, tour scheduling solution approaches have neglected considering employee preferences and tried to develop work schedules for employees in a subsequent step.This paper presents a goal programming model that implicitly represents scheduling flexibility and also incorporates information about the preferred working patterns of employees. After solving the proposed model, a work schedule will be generated for each employee without requiring a further step for the assignment of shifts, break times, and work days to employees. The model is capable of handling multiple scheduling objectives, and it can produce optimal solutions in very short computing times.

A hybrid metaheuristic algorithm for heterogeneous vehicle routing problem with simultaneous pickup and delivery

The vehicle routing problem with simultaneous pickup and delivery is studied.The problem is considered with heterogeneous fleet of vehicles.An adaptive local search integrated with tabu search is developed for its solution.Proposed approach performs well on the randomly generated problem instances. The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is a variant of the classical Vehicle Routing Problem (VRP) where the vehicles serve a set of customers demanding pickup and delivery services at the same time. The VRPSPD can arise in many transportation systems involving both distribution and collection operations. Originally, the VRPSPD assumes a homogeneous fleet of vehicles to serve the customers. However, in many practical situations, there are different types of vehicles available to perform the pickup and delivery operations. In this study, the original version of the VRPSPD is extended by assuming the fleet of vehicles to be heterogeneous. The Heterogeneous Vehicle Routing Problem with Simultaneous Pickup and Delivery (HVRPSPD) is considered to be an NP-hard problem because it generalizes the classical VRP. For its solution, we develop a hybrid local search algorithm in which a non-monotone threshold adjusting strategy is integrated with tabu search. The threshold function used in the algorithm has an adaptive nature which makes it self-tuning. Additionally, its implementation is very simple as it requires no parameter tuning except for the tabu list length. The proposed algorithm is applied to a set of randomly generated problem instances. The results indicate that the developed approach can produce efficient and effective solutions.

A scheduling problem in blocking hybrid flow shop robotic cells with multiple robots

Abstract This paper addresses the robotic scheduling problem in blocking hybrid flow shop cells that consider multiple part types, unrelated parallel machines, multiple robots and machine eligibility constraints. Initially, a mixed integer linear programming (MILP) model is proposed to minimize the makespan for this problem. Due to the complexity of the model, a simulated annealing (SA) based solution approach is developed for its solution. To increase the efficiency of the SA algorithm, a new neighborhood structure based on block properties is applied. The performance of the proposed SA is assessed over a set of randomly generated instances. The computational results demonstrate that the SA algorithm is effective with the employed neighborhood structure. Additionally, this study shows that the appropriate number of robots depends on the sequence of processing operations to be performed at each stage.

An adaptive local search algorithm for vehicle routing problem with simultaneous and mixed pickups and deliveries

Vehicle routing problem with simultaneous and mixed pickups and deliveries have been addressed.An adaptive local search algorithm is proposed to solve the problems.The proposed approach generates high-quality solutions to the benchmark instances in reasonable computation time. The Vehicle Routing Problem with Simultaneous Pickup and Delivery (VRPSPD) is an extension to the classical Vehicle Routing Problem (VRP), where customers may both receive and send goods simultaneously. The Vehicle Routing Problem with Mixed Pickup and Delivery (VRPMPD) differs from the VRPSPD in that the customers may have either pickup or delivery demand. However, the solution approaches proposed for the VRPSPD can be directly applied to the VRPMPD. In this study, an adaptive local search solution approach is developed for both the VRPSPD and the VRPMPD, which hybridizes a Simulated Annealing inspired algorithm with Variable Neighborhood Descent. The algorithm uses an adaptive threshold function that makes the algorithm self-tuning. The proposed approach is tested on well-known VRPSPD and VRPMPD benchmark instances derived from the literature. The computational results indicate that the proposed algorithm is effective in solving the problems in reasonable computation time.

Assembly line balancing with positional constraints, task assignment restrictions and station paralleling: A case in an electronics company

In this paper, we present a real-life Assembly Line Balancing Problem for an electronics manufacturing company. The main characteristics of the problem are as follows: (i) a set of operations are related to the front part of the workpiece and others are related to the back part of the workpiece, which in turn makes all tasks dependent on the position of the workpiece, (ii) some of the tasks must be executed on the same station and no other tasks should be assigned to this station due to technological restrictions, (iii) parallel stations are allowed to increase the line efficiency at the required production rate and to overcome the problem of assigning tasks with operation times that exceed the cycle time. Initially, the problem is formulated as a 0-1 integer programming model and solved using CPLEX solver. Then, the effect of alternative work schedules such as multiple shifts and overtime on the expected labor cost of the line is analyzed. Considering alternative work schedules while balancing the line for corresponding cycle times allows us to select an efficient assembly line for the company, resulting in a lower labor cost and a more balanced line with respect to the operation times and the activity of the workers at each station. Lastly, a computational study is conducted to evaluate the performance of the proposed model. It is found that the model is capable of producing high quality solutions in reasonable solution times.

Rule-based modeling and constraint programming based solution of the assembly line balancing problem

Highlights? Instead of using precedence graphs, a rule-based assembly model is proposed to represent all possible assembly sequences of a product. ? The proposed rule base simultaneously chooses the assembly plan and balances the assembly line. ? A rule-based model is more effective in balancing the assembly line while addressing alternative assembly plans. ? It is easier to map a rule-based model to a constraint programming model than to an integer programming model. ? Constraint programming is more successful compared to solution quality and time than integer programming. The assembly line balancing problem employs traditional precedence graphs to model precedence relations among assembly tasks. Yet they cannot address alternative ways of assembling a product. That is, they only model conjunctions, not disjunctions. Moreover, some additional constraints need also to be considered, but these constraints cannot be modeled effectively through precedence graphs, e.g., constraints indicating certain tasks cannot be assigned into the same station. To address these issues, this paper proposes to model assembly constraints through the well known If-then rules, and to solve the rule-based model through constraint programming (CP), as CP naturally models logical assertions. The paper also shows how to map a rule-based model to a CP or an integer programming (IP) model. Finally, a computational experiment is carried out to analyze the performances of CP and IP models with respect to modeling capability, solution quality and time. The results reveal that CP is more effective and efficient than IP.

Scheduling multiple parts in hybrid flow shop robotic cells served by a single robot

This paper addresses the robotic scheduling problem in blocking hybrid flow shop cells that consider multiple part-types, different speed parallel machines at each stage, machine eligibility constraints and a single transportation robot to move the parts between stages. Initially, a mixed integer linear programming (MILP) model is proposed to minimise the makespan. Due to the complexity of the model, a simulated annealing (SA)-based solution approach is developed to solve the problem. This approach uses both simple insertion method and a new neighbourhood structure based on block properties while generating neighbour solutions, which yields two different SA algorithms respectively. The performance of proposed SA approach is assessed over a set of randomly generated instances. The computational results demonstrate that the SA algorithm is effective with the employed neighbourhood structure for this problem.