Real-life Instances Research Articles

Integrated planning for planting and harvesting sugarcane and energy-cane for the production of sucrose and energy

The main products obtained from sugarcane for the global market are sugar, ethanol and energy. Although the demand for these products is high, in Brazil there is no longer space to expand sugarcane cultivation due to other important food crops and environmental protection areas. Therefore, current research seeks ways to increase sugarcane productivity without expanding the planted area. Consequently, a type of cane with higher concentration of fiber has been developed and cultivated called energy-cane. This cane type has more fiber than sucrose in its composition, which makes it better for producing energy and ethanol and inappropriate for sugar production. To meet the demands for these three products, both energy-cane and sugarcane are needed, making the planning and decision-making more difficult in the energy-sugar sector. In this context, a mixed integer linear optimization model for the integrated planning of the planting and harvesting of sugarcane and energy-cane for a sugar-energy plant is proposed to determine an optimized planting and harvesting schedule, that aims to maximize the sucrose and fiber production in the cane fields. The model takes into account a flexible harvest period to meet the demands of the consumer market and respect the operational constraints of the company, which can cause variations in productivity. To solve real-life instances where exact methods fail, a heuristic based on the relax-and-fix and fix-and-optimize concepts is proposed. Numerical experiences with random instances were generated to test both the model and the heuristic. Feasible solutions with an error close to 1% were obtained, consuming one tenth of the CPU time, on average. Thus, the mathematical model and the heuristic method developed have great potential to support decision-making in sugarcane and energy-cane production planning.

Dynamic railway timetable rescheduling for multiple connected disruptions

Unexpected disruptions occur in the railways on a daily basis, which are typically handled manually by experienced traffic controllers with the support of predefined contingency plans. When several disruptions occur simultaneously, it is rather hard for traffic controllers to make rescheduling decisions, because (1) the predefined contingency plans corresponding to these disruptions may conflict with each other and (2) no predefined contingency plan considering the combined effects of multiple disruptions is available. This paper proposes a Mixed Integer Linear Programming (MILP) model to reschedule the timetable in case of multiple disruptions that occur at different geographic locations but have overlapping periods and are pairwise connected by at least one train line. The dispatching measures of retiming, reordering, cancelling, adding stops and flexible short-turning are formulated in the MILP model that also considers the rolling stock circulations at terminal stations and platform capacity. We develop two approaches for rescheduling the timetable in a dynamic environment: the sequential approach and the combined approach. In the sequential approach, a single-disruption rescheduling model is applied to handle each new disruption with the last solution as reference. In the combined approach, the multiple-disruption rescheduling model is applied every time an extra disruption occurs by considering all ongoing disruptions. A rolling-horizon solution method to the multiple-disruption model has been developed to handle long multiple connected disruptions in a more efficient way. The sequential and combined approaches have been tested on real-life instances on a subnetwork of the Dutch railways with 38 stations and 10 train lines operating half-hourly in each direction. In a few cases, the sequential approach did not find feasible solutions, while the combined approach obtained the solutions for all considered cases. Besides, the combined approach was able to find solutions with less cancelled train services and/or train delays than the sequential approach. For long disruptions, the proposed rolling-horizon method was able to generate high-quality rescheduling solutions in an acceptable time.

Solution approaches for integrated vehicle and crew scheduling with electric buses

The use of electric buses is expected to rise due to its environmental benefits. However, electric vehicles are less flexible than conventional diesel buses due to their limited driving range and longer recharging times. Therefore, scheduling electric vehicles adds further operational difficulties. Additionally, various labor regulations challenge public transport companies to find a cost-efficient crew schedule. Vehicle and crew scheduling problems essentially define the cost of operations. In practice, these two problems are often solved sequentially. In this paper, we introduce the integrated electric vehicle and crew scheduling problem (E-VCSP). Given a set of timetabled trips and recharging stations, the E-VCSP is concerned with finding vehicle and crew schedules that cover the timetabled trips and satisfy operational constraints, such as limited driving range of electric vehicles and labor regulations for the crew while minimizing total operational cost. An adaptive large neighborhood search that utilizes branch-and-price heuristics is proposed to tackle the E-VCSP. The proposed method is tested on real-life instances from public transport companies in Denmark and Sweden that contain up to 1109 timetabled trips. The heuristic approach provides evidence of improving efficiency of transport systems when the electric vehicle and crew scheduling aspects are considered simultaneously. By comparing to the traditional sequential approach, the heuristic finds improvements in the range of 1.17–4.37% on average. A sensitivity analysis of the electric bus technology is carried out to indicate its implications for the crew schedule and the total operational cost. The analysis shows that the operational cost decreases with increasing driving range (120–250 km) of electric vehicles.

Transporting COVID-19 testing specimens by routing unmanned aerial vehicles with range and payload constraints: the case of Istanbul

ABSTRACT This paper aims to emphasize the impacts of the COVID-19 pandemic at the healthcare logistics and supply chain networks and to transport polymerase chain reaction testing samples between hospitals and laboratories by using unmanned aerial vehicles (UAVs). To achieve the mission, range and payload capacitated UAV routing problem is defined and a mixed-integer linear programming model (MILP) is developed. The MILP model aims to decide the requisite quantity of the UAVs and to minimize the total transportation distance while satisfying the predetermined constraints. As a real-life instance, the healthcare network of Istanbul is selected and the network includes 219 hospitals and 23-unit basic testing laboratories for the COVID-19 pandemic. The results indicated that the proposed optimization model can find the needed number of UAVs with the minimum tour distances in reasonable run times and achieves to transport 25,000 testing specimens between hospitals and laboratories via UAVs.

Semi-cyclic rostering of ranked surgeons — A real-life case with stability and flexibility measures

We consider the rostering problem for surgeons in residency at the Clinic of Surgery at St. Olav’s Hospital, Trondheim University Hospital, in Trondheim, Norway. Each surgeon in residency has a rank depending on experience. An exact number of surgeons of each rank must work emergency shifts in a cyclic structure. Each surgeon is affiliated to a section, which has a minimum staffing level. Section shifts can be planned in an acyclic structure, thus establishing a semi-cyclic structure in the full roster. The addition of more typical rostering constraints establishes the novel Semi-Cyclic Ranked Physician Rostering Problem. In manually created rosters, the staffing at sections varies greatly, leading to frequent understaffing. With the addition of absence among staff when rosters are executed, this is problematic for the Clinic of Surgery. We present a two-step matheuristic based on mixed integer linear programming to solve the problem for five real-life instances. Comparing our results with a manually created roster demonstrates superior results in terms of staff availability at sections, greatly improving roster resilience to absence. We also introduce shadow shifts designed to increase the flexibility of rosters to cover for absence at emergency night shifts.

ATM cash replenishment under varying population coverage requirements

Inspired by an automated teller machine (ATM) cash replenishment problem involving population coverage requirements (PCRs) in the Netherlands, we propose the vehicle tour problem with minimum coverage requirements. In this problem, a set of minimum-cost routes is constructed subject to constraints on the duration of each route and the population coverage of the replenished ATMs. A compact formulation incorporating a family of valid inequalities and an efficient tour-splitting metaheuristic are proposed and tested on 77 instances derived from real-life data involving up to 98 ATMs and 237,604 citizens and on 144 newly generated synthetic instances. Our results for the real-life instances indicate significant cost differences in replenishing ATMs for seven major Dutch cities when the PCRs vary. Additionally, we illustrate the impact of different PCRs on the ATM replenishment costs for seven major cities in the Netherlands by presenting an aggregated cost evaluation of 11 PCRs involving 1,003,519 citizens, 338 ATMs, and 19 cash distribution vehicles.

Home care vehicle routing problem with chargeable overtime and strict and soft preference matching

A new scheduling problem arising in the home care context is addressed, whose novelty with respect to the literature lies in the way overtime is paid. In this problem, some clients are willing to pay a higher fee to cover the additional overtime cost, if such overtime is incurred because a caregiver works extra time with the client to preserve continuity of care. These overtime hours charged to clients unburden the company, which no longer has to balance between cost and continuity of care in a traditional way. The problem is also studied in a context that includes preferences expressed by both clients and caregivers. Strict preferences must be respected with a high priority, while soft preferences increase the satisfaction and should be preferably respected. We formalize the problem as a Mixed Integer Linear Problem and also propose a cluster-based decomposition to solve real-life instances. The problem is inspired by the real case study of a provider operating in the USA. Numerical results validate the model and confirm the capability of the decomposition approach to deal with real-life instances.

Constraint-based Scheduling for Paint Shops in the Automotive Supply Industry

Factories in the automotive supply industry paint a large number of items requested by car manufacturing companies on a daily basis. As these factories face numerous constraints and optimization objectives, finding a good schedule becomes a challenging task in practice, and full-time employees are expected to manually create feasible production plans. In this study, we propose novel constraint programming models for a real-life paint shop scheduling problem. We evaluate and compare our models experimentally by performing a series of benchmark experiments using real-life instances in the industry. We also show that the decision variant of the paint shop scheduling problem is NP-complete.

A greedy-tabu approach to the patient bed assignment problem in the Hospital Universitario San Ignacio

Patient Bed Assignment (PBA) consists of assigning patients to hospital beds according to specific requirements such as patient diagnosis, equipment requirements, age and gender policies, among others. We worked in conjunction with the Hospital Universitario San Ignacio (HUSI) with the goal of designing an application to support decision-making during the bed assignment process. We introduced a mathematical model for the PBA. We used Analytic Hierarchy Process (AHP) to determine the weights attributed to each part of the objective function. Due to the long execution time required, we used a Greedy Algorithm and Tabu Search (TS) to optimize the match between the patient’s requirements and the characteristics of the assigned bed. To test the algorithms, we created 15 test instances of various sizes. The results showed that the gap between the value of the objective function resulting from using the Greedy/TS in comparison with the optimal solution is on average 6.2%. Also, the TS takes 84% less time than the MILP for medium and large instances. We collected data from real life instances and compared the actual method with the designed metaheuristic. On average, the value of the objective function resulting from using the proposed Greedy/Tabu algorithm is 8.6% higher.

Full cover charging station location problem with routing

In this paper, a new full cover modeling framework is developed to design refueling station infrastructure, where the focus is on locating fast-charging stations for battery electric vehicles to enable long-distance transportation. A mathematical model is introduced to determine the optimal locations of these charging stations so that every origin-destination trip on a given transportation network is covered with respect to vehicle range. This full cover model allows deviations from the shortest paths and also determines an optimal route for each trip that requires the minimum total en route recharging. Two variants of this model are proposed: one that minimizes the total cost of locating charging stations and total en route recharging, and another that determines the locations of a predetermined number of stations to minimize the total en route recharging. Computational experiments performed on benchmark data sets validate that the proposed full cover models perform better than the maximum or set cover problem settings in the literature in terms of routing-related measures, such as total trip distance and maximum deviation from the shortest paths. A Benders decomposition algorithm is developed to optimally solve real-life instances of the problem. The Benders subproblem is identified as a many-to-many shortest path problem with an additional constraint that restricts the nodes that can be used to open facilities that are determined by the master problem. A new algorithmic methodology is developed to construct the dual solution for this subproblem and to generate non-dominated optimality cuts and strong valid inequalities for feasibility cuts. This novel algorithm accelerates the performance of the Benders algorithm up to 900 times over the tested large-size instances.

A Multi-Start Granular Skewed Variable Neighborhood Tabu Search for the Roaming Salesman Problem

This paper presents a novel hybrid metaheuristic algorithm for the Roaming Salesman Problem (RSP), called Multi-Start Granular Skewed Variable Neighborhood Tabu Search (MS-GSVNTS). The objective in RSP is to design daily tours for a traveling campaigner who collects rewards from activities in cities during a fixed planning horizon. RSP exhibits a number of exclusive features: It is selective which implies that not every node needs a visit. The rewards of cities are time-dependent. Daily tours can be either an open or a closed tour which implies the absence of a fixed depot. Instead, there is a campaign base that is to be attended frequently. Multiple visits are allowed for certain cities. The proposed method MS-GSVNTS is tested on 45 real-life instances from Turkey which are built with actual travel distances and times and on 10 large scale instances. Computational results suggest that MS-GSVNTS is superior to the existing solution methods developed for RSP. It produces 50 best known solutions including 18 ties and 32 new ones. The performance of MS-GSVNTS can be attributed to its multi-start feature, rich neighborhood structures, skewed moves, and granular neighborhoods.

Mathematical modeling and heuristic approaches for a multi-stage car sequencing problem

This paper addresses the scheduling problem in three successive processes in car production including the paint shop, painted body storage (PBS) and the assembly shop. The paint shop, which aims to reduce the costs for color changes, is connected to the final assembly shop, which aims to level the usage of the options. A linear PBS is set in between to change the sequence from the upstream shop to the downstream shop. The joint sequencing problem is formulated as a mixed integer linear programming (MILP) model and some properties including the lower bounds are presented. A structural architecture is designed to solve the large-scale real-life instances, and several meta-heuristic algorithms are embedded in the architecture to enhance the ability of finding good solutions. Numerical experiments validate the quality of the proposed algorithms.

The airport shuttle bus scheduling problem

This paper introduces the airport shuttle bus scheduling problem (ASBSP) as a new practical scheduling variant. In this problem, a number of identical vehicles that have a specific number of available seats provides transfer service between the airport and the city centre. After making a transfer in one direction, the vehicle can either make a new transfer in the opposite direction depending on the availability and the schedule of the passengers or make an empty return to make a new transfer in the same direction. The vehicles can wait in either location until their next transfer. The passengers have certain time windows for the transfer in relation to their flight times and operational rules to satisfy customer satisfaction. This is a profit-seeking service where transfer requests can also be rejected. The ASBSP aims to prepare a daily schedule for the available vehicles and to assign passengers to these vehicles with the objective of maximising the total profit. This paper presents two alternative mixed integer programming formulations and proposes two valid inequalities to get better bounds. Furthermore, it develops a hybrid metaheuristic that integrates multi-start, simulated annealing and large neighbourhood search for its solution. Extensive computational experiments on real-life benchmark instances have been made to test the performances of the formulations and the hybrid metaheuristic. Furthermore, the impacts of several problem parameters including the number of vehicles, vehicle capacity, transfer fee, transportation time and allowable passenger waiting times on the problem complexity and results have been investigated.

Elektrikli Araçla Periyodik Teknisyen Rotalama ve İstasyon Yeri Seçim Problemi

In this study, Electric Multiperiodic Technician Routing and Charging Station Location Problem is defined. A series of preventive and corrective maintenance-repair and spare parts supply services are offered to customers located in different geographical regions of the problem. In addition, the customers can request these tasks in different time windows throughout the planning horizon. Teams of technicians with different competencies are formed and allocated to these tasks that are provided in the customer locations. Considering that the workforce scheduling and routing problem is in the NP-hard class, thus the defined problem is also in the same class. Unlike the literature, in this study, it is taken into account that technicians reach customers with electric vehicles instead of using conventional internal combustion engine vehicles. In this way, it will be possible to reduce the use of fossil fuels and the environmental impact of these fuels due to transportation. In addition, due to the operating costs of electric vehicles are lower than those of conventional vehicles, electric vehicles emerge as an economical option for businesses. In addition to their advantages, the limited range of electric vehicles makes them dependent on using charging stations. For this reason, routing plans need to be made efficiently in order for these vehicles to compete with conventional vehicles. In the proposed problem, while creating a daily schedule and route for the teams throughout the planning horizon, tracking the state of charge of the vehicles and determining the location of the charging station are also considered. In addition, the legal rest periods of the technicians are also taken into account. The problem is modelled with the mixed integer programming formulation. Furthermore, the data set generated from the real-life instances. In order to solve the problem variable neighbourhood search heuristic is used. Computational comparisons are conducted to compare the performance of the heuristic. The results indicate that it can find the optimum solutions. Moreover, the heuristic is able to produces better results than the CPLEX solver in a reasonable time.

Matching and pricing in ride-sharing: Optimality, stability, and financial sustainability

In ride-sharing, a sustainable transportation mode, drivers share idle seats with riders. This paper addresses two key subproblems to find a ride-sharing solution: matching drivers and riders to achieve the system-wide optimality and allocating the resulting travel-cost saving among them by considering their self interests or the stability of the solution. Solutions to either subproblem significantly affect the efficiency of overall dynamic ride-sharing systems measured in total travel-cost saving and individual benefits of people involved. We evaluate the worst-case performance of the best stable solutions associated with four ridematching-defined pricing methods existing in the literature and widely used in practice. We then analyze a Vickrey-Clarke-Groves (VCG)-based pricing mechanism that lets the system achieve optimal system efficiency, prove its stability, and identify its drawbacks such as budget unbalance. We formulate the matching subproblem to maximize the total travel-cost saving and propose a simple and consistent pricing method to guarantee that the resulting ride-sharing solution not only is stable and system-wide optimal but also makes the system deficit-free and thus financially sustainable. Furthermore, such a ride-sharing solution can be obtained in polynomial time. In the numerical experiments, we evaluate our method against existing ones through a real-life instance and randomly generated instances in a dynamic environment.

Consultant assignment and routing problem with priority matching

The consultant assignment and routing problem is to simultaneously assign consultant supplies to client demands and determine the best traveling routes for consultants. Constraints to be considered include skill requirement, capacity limitation, fixed demand, and a maximum number of travel legs. This paper further takes into consideration priority matching, which restricts that clients can only be assigned to consultants with appropriate priority levels. In order to solve this problem, we present a decomposition algorithm named RMIP and a MIP-based neighborhood search algorithm. In addition, we extend an existing MILP formulation and compare our algorithms with it. The effectiveness and efficiency of the proposed algorithms are evaluated on a hundred synthetic instances and twelve real-life instances. Computational results show that the modified MILP formulation is only suitable for solving small-scale instances and a part of the medium-scale instances. For large-scale and real-life instances, the proposed two algorithms are significantly superior to the MILP formulation in solution quality and computational time.

Benevolent Conformity: The Influence of Perceived Motives on Judgments of Conformity.

Although people often disapprove of conformity, they also dislike when others deviate from group norms. What explains this ambivalence? We hypothesized that judgments of conformity would be affected by whether people view it as motivated by self-interested or benevolent motives. Four studies (N = 808), using both hypothetical and real-life instances of conformity, support this prediction. We find that people judge those who conform to gain social approval (self-interested conformity) as weak-willed, but those who conform out of concern for their group (benevolent conformity) as competent and possessing strong character. In addition, we predict and find that people view self-interested conformity as "fake" but benevolent conformity as revealing one's true self. Finally, we show that differences in perceived intentions explain how people sustain positive self-regard while succumbing to group pressures and why people judge their own conformity more favorably than others' conformity. We discuss implications for encouraging and discouraging conformity.

Locating electric vehicle charging stations under uncertain battery energy status and power consumption

Fostering the adoption of electric vehicles (EVs) by private drivers requires the development of a wide network of fast charging stations in which drivers traveling long-distance trips will be able to easily recharge their battery within a few minutes. However, due to their high installation costs, the number of stations that can actually be deployed with the available investment budget is strongly limited. It is thus necessary to carefully choose their location so that the charging demand satisfaction is maximized. This leads to the formulation of a facility location problem known as the flow refueling location problem (FRLP). In this paper, we study the FRLP and seek to take into account uncertainties on the vehicle driving range in the problem modeling. We propose to relax several modeling assumptions previously used in the literature to handle this problem. First, we allow the power consumption on a road segment to depend on the crossing direction. Second, we take into account uncertainties related to the energy available in the battery after recharging at a station as well as uncertainties related to the power consumption on each portion of the road network. Finally, we consider statistical dependencies between the stochastic power consumption on different arcs of the network.We focus on the chance-constrained flow refueling location model, which seeks to maximize the number of drivers for whom the probability of running out of fuel when carrying out their trip is below a certain threshold. To solve the resulting stochastic optimization problem, we propose to use a solution approach based on a partial sample approximation of the stochastic parameters and compare its performance with the one of a previously published approach based on Bonferroni’s inequality. We carry out numerical experiments on a set of medium-size randomly generated and real life instances. Our results show that the proposed partial sample approximation approach outperforms the Bonferroni approach in terms of solution quality and gives station locations which provide a significantly improved demand coverage in practice.

A heuristic approach to the task planning problem in a home care business

In this paper, we study a task scheduling problem in a home care business. The company has a set of supervisors in charge of scheduling the caregivers' weekly plans. This can be a time-consuming task due to the large number of services they work with, as well as the need to consider user preferences, services required time windows and travel times between users' homes. Apart from that, it is also important to have a continuity of care, i.e., that users generally prefer not to have their caregiver changed. This problem involves both route planning and employee task planning, which are usually very challenging. We first propose to model it using integer linear programming methodology. Since the real instances that the company needs to solve are very large, we design a heuristic algorithm, based on the simulated annealing philosophy, that allows the company to obtain the caregivers' weekly schedules. Lastly, we check the algorithm's good performance, by comparing the solutions it proposes with those provided by the integer linear programming methodology, in small size problems, and we present a case study to confirm that the algorithm correctly solves real-life instances.

A decision support framework for home health care transportation with simultaneous multi-vehicle routing and staff scheduling synchronization

Due to the ageing population and the prevalence of chronic diseases, Home Health Care (HHC) practices are significantly increasing in developed countries to provide coordinated health related services to patients at their homes. Accordingly, the scope of HHC services is also expanding from typical nursing and postoperative care at home to cover all types of needs of elderly patients (e.g., personal care, drug delivery and meal services). This paper aims to address the pressing demand for HHC services and develop a novel and effective mathematical model and solution methodology for supporting health care service delivery decisions. Our decision support framework captures the real needs of HHC services, including the challenges of creating simultaneous schedules and route plans for a set of HHC staff and Home Delivery Vehicles (HDVs) under the requirements of synchronization between HHC staff and HDVs visits, multiple visits to patients, multiple routes of HDVs and pickup/delivery visits related precedence for HDVs. A Mixed Integer Linear Programming (MILP) model is developed to characterize the optimization problem. Considering the computational complexity of the problem, a Hybrid Genetic Algorithm (HGA) is proposed to suggest HHC planning decisions. The model formulation and proposed HGA are examined on real-life instances for demonstrating its practicality and randomly generated test instances for assessing the scalability of the proposed approach. The results show the effectiveness and efficiency of our solution methodology. Experimental results indicate that the proposed algorithm provided a good performance even with an increasing number of required synchronized services, whereas the heuristic tactics facilitate the HGA to produce better-quality solutions in a significantly shorter time. Our framework is expected to contribute to an important aspect of shared healthcare mobility.