Service Design Problem Research Articles

A new methodology for the real-time limited-stop bus service design problem

Providing limited-stop bus services can improve the efficiency of bus systems. This paper proposes a new two-stage strategy for providing real-time limited-stop bus services for a corridor and the corresponding model is developed. In the first (tactical planning) stage, given the maximum number of different limited-stop services, an operator determines a set of limited-stop services based on historical bus travel times and passenger arrival rates. In the second (operational) stage, an operator selects one service from the set of limited-stop services obtained in the first stage for each limited-stop vehicle based on (short-term) predictive travel times and passenger arrival rates. Prediction errors are considered in the second stage. An enhanced artificial bee colony algorithm is developed to solve the first-stage model and the Monte Carlo Simulation method is adopted to solve the second-stage model. Numerical results are presented to illustrate the effectiveness and efficiency of the strategy and the effect of prediction errors.

Service Designing Together for Better International Employee Experiences : An example from health and social care services in Finland

The World Health Organization (WHO) estimates that the world needs 4.5 million new nurses by 2030.1 Nurses are a critical touchpoint of health and social care services. Without them, the services could not exist as we know them. The nurse shortage is a vicious service design problem.

The Operational Data Analytics (ODA) for Service Speed Design

We develop the operational data analytics (ODA) framework for the classical service design problem of [Formula: see text] systems. The customer arrival rate is unknown. Instead, some historical data of interarrival times are collected. The data-integration model, specifying the mapping from the arrival data to the service rate, is formulated based on the time-scaling property of the stochastic service process. Validating the data-integration model against the long-run average service reward leads to a uniformly optimal service rate for any given sample size. We further derive the ODA-predicted reward function based on the data-integration model, which gives a consistent estimate of the underlying reward function. Our numerical experiments show that the ODA framework can lead to an efficient design of service rate and service capacity, which is insensitive to model specification. The ODA solution exhibits superior performance compared with the conventional estimation-and-then-optimization solutions in the small sample regime. This paper was accepted by David Simchi-Levi, operations management. Funding: Z. Jiang’s research is supported by the National Natural Science Foundation of China [Grant 71931007]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/mnsc.2023.00655 .

A data-driven discrete simulation-based optimization algorithm for car-sharing service design

This paper formulates a discrete simulation-based optimization (SO) algorithm for a family of large-scale car-sharing service design problems. We focus on the profit-optimal assignment of vehicle fleet across a network of two-way (i.e., round-trip) car-sharing stations. The proposed approach is a metamodel SO approach. A novel metamodel based on a mixed-integer program (MIP) is formulated. The metamodel is embedded within a general-purpose discrete SO algorithm. The proposed algorithm is validated with synthetic toy network experiments. The algorithm is then applied to a high-dimensional Boston case study using reservation data from a major US car-sharing operator. The method is benchmarked versus several algorithms, including stochastic programming. The experiments indicate that the analytical network model information, provided by the MIP to the SO algorithm, is useful both at the first iteration of the algorithm and across subsequent iterations. The solutions derived by the proposed method are benchmarked versus the solution deployed in the field by the car-sharing operator. Via simulation, the proposed solutions improve those deployed with an average improvement of profit of 6% and of vehicle utilization of 3%.The combination of the problem-specific analytical MIP with a general-purpose SO algorithm enables the discrete SO algorithm to: (i) address high-dimensional problems, (ii) become computationally efficient (i.e., it can identify good quality solutions within few simulation observations), (iii) become robust to the quality of the initial points and of the stochasticity of the simulator. More generally, the information provided by the MIP to the SO algorithm enables it to exploit problem-specific structural information. This leads to an algorithm with both asymptotic convergence guarantees as well as good short term performance (i.e., performance given few simulation observations). We view this general idea of combining analytical MIP formulations with general-purpose SO algorithms, or more broadly with general-purpose sampling strategies of high-resolution data, as an innovative and promising area of future research.

Integrating train service route design with passenger flow allocation for an urban rail transit line

Train service design problem considers many operating strategies, i.e., multiple service routes, multiple train compositions, and express/local modes. Incorporating multiple operating strategies, this study first formulates an integer linear programming model for integrating train service route design with the passenger flow allocation problem. Consistency between the two components is enforced by a set of linking constraints that consider the relationship between the number of transit trips assigned to a route and the capacity of a single train. To solve the proposed model on real-life instances, we develop an approach that utilizes the Alternating Direction Method of Multipliers (ADMM). This dualizes the linking constraints and decomposes the problem into two subproblems: a train service route design subproblem and a passenger flow allocation subproblem. The latter can be subdivided into a set of passenger group-specific subproblems and is solved by a label correcting algorithm. Through Lagrangian multipliers, the interplay between the train service route design and passenger flow allocation subproblems is explored. To address the nonlinearities that arise in ADMM, we describe a new linearization technique for the quadratic penalty terms in the two subproblems by exploiting the rolling update mechanism of ADMM. The proposed approach is tested on synthetic and real-life instances from an urban rail company in China. The numerical results show that the proposed ADMM approach provides objective values that are on average 7.63% better than the conventional sequential approach. We also demonstrate that ADMM provides smaller optimality gaps in general when compared to a Lagrangian relaxation approach.

Integrated optimization of bus bridging service design and passenger assignment in response to urban rail transit disruptions

As the urban rail transit (URT) system plays an increasingly important role in supporting large cities’ mobility around the world, service disruptions have become more prevalent, potentially resulting in severe economic losses and passenger safety issues. It is imperative to investigate effective response strategies to mitigate the effects of such disruptions. In response to URT service disruptions, this paper systematically investigates the bus bridging service design (BBSD) problem, which concerns the integration of bus bridging route design, frequency determination, and passenger assignment in the integrated URT and bus network. The problem is formulated as a path-based integer linear programming (ILP) model with the goal of simultaneously minimizing operator-oriented and passenger-oriented costs. A column generation-based approach is proposed to solve this model efficiently, allowing nonintuitive bus routes to be freely generated on the network dynamically. Our method has been tested with two different case studies based on real data from the Hong Kong Mass Transit Railway (MTR). Experiments demonstrate that our proposed approach can assist public transit (PT) operators in developing efficient emergency response plans for various potential disruption situations in advance. Even in the face of unexpected disruptions that necessitate a quick response, our approach can generate high-quality solutions in a matter of minutes.

Service design from the perspective of “six thinking hats”: A comparison of storytelling strategies of experts and novices

It has been broadly proved that storytelling, which is regarded as a natural creative human activity, serves as an effective thinking tool for improving creativity in service design. In practice, however, the lack of support for storytelling affects the perceived usefulness and ease of storytelling for novice designers. This results in a low willingness to use storytelling, poor usage, and low design innovation among novice designers. Research has identified significant differences between experts and novices in the design process, and summarizing these differences in thinking strategies can effectively guide novices in design activities. In previous research, we have successfully used the six thinking hats for novices to learn expert strategies and enhance their creativity. The purpose of this study is to compare differences between experts and novices in storytelling strategies and design behaviors in process, in order to understand how to support novices in storytelling and enhance creativity. The study uses verbal protocol analysis to explore the differences between three teams of novices and three teams of experts. We use the De Bono's "six thinking hats" thinking tool to decode designers' implicit and latent design thinking and use Burke's "dramatistic pentad" theory to extract the design teams' storytelling thinking. The results show significant differences between experts and novices in storytelling strategy and construction process, where experts tend to have more efficient storytelling strategies and design behaviors than novices. The expert strategies we found can guide design educators to support students when solving service design problems and provide important theoretical support and methodological guidance for instructing novices in expert storytelling strategies.

Integrated optimization of customized bus routes and timetables with consideration of holding control

Customized bus (CB) is an innovative urban transport system that plays a significant role in meeting personalized travel needs of passengers. This paper deals with the CB service design problem, which is the problem of driving a fleet of capacitated vehicles to pick up and deliver passengers, with consideration of holding control that keeps vehicles at stops for a period of time. A mixed-integer linear programming model is proposed to jointly optimize a set of vehicle routes and timetables with passenger assignment scheme. Through decomposing the model into a bilevel one, we combine genetic algorithm (GA) and large neighborhood search (LNS) with embedding branch-and-bound (BB) method, and develop a GA-LNS-BB hybrid algorithm. Furthermore, an order clustering based divide and conquer (OC-D&C) approach is provided for the ultra-large-scale problems, in which the order clustering problem is formulated as solving an integer linear programming. Finally, we conduct several numerical experiments on the Sioux Falls network and a real-world city-scale network to evaluate the performance of the proposed model and solution methods. The results indicate that: (1) holding control could enhance the flexibility of CB service, and significantly increase the CB carrier’s profit; (2) the GA-LNS-BB hybrid algorithm and the OC-D&C approach could provide high-quality solutions in short time for large-scale/ultra-large-scale problems compared to other standard algorithms.

The impact of using a naïve approach in the limited-stop bus service design problem

The proven benefits of limited-stop services have captured the attention of researchers, especially during the last decade. However, to solve the limited-stop service design problem many existing works directly impose a capacity constraint to a total social cost objective function. This “naïve approach” implicitly assumes that passengers behave altruistically, basing their decisions on what is best for the whole system. Although this issue has been identified in earlier works, the magnitude of the error induced by this simplification has not been studied yet. The objective of this work is to measure this error and to understand how it misrepresents passenger flows and bus occupation rates. To measure this error gap, we optimize a set of test scenarios by applying a naïve approach, and then take the resulting design and obtain a benchmark passenger assignment using a simple behavioral model. We propose two main indicators to compare both passenger assignment: the total passenger deviation, and the total capacity deficit. This comparison reveals that the assignment of the naïve approach may indeed be unrealistic, and raises concerns that a network design based on the naïve approach might have severe problems when implemented. Thus, the work highlights the importance of taking the results of the naïve approach with caution and verify them with a passenger assignment model before their implementation.

Cooperatively coevolutionary optimization design of limited-stop services and operating frequencies for transit networks

The objective of the limited-stop service design and frequency setting problem (LSDFSP) is to realize the operation of a transit route with a set of elaborate service patterns and corresponding frequencies that can minimize the total social cost of the users and operators. In practice, these different patterns and frequencies involve a trade-off between the user and operator requirements, and this aspect cannot be fully clarified by a single-objective optimization problem. Therefore, in this study, the LSDFSP is considered at the network level and formulated as a multi-objective optimization problem with competing objectives of minimizing the user and operator costs. The cooperative coevolutionary multi-objective evolutionary algorithm is redesigned to collaboratively optimize the service patterns and frequencies. A prioritization method is proposed to separately incorporate different types of unsatisfied demand as critical indicators, prompting the algorithm to dig deeper into valuable genes and evolve more feasible solutions. The proposed algorithm is tested on a small network and a real intricate network. It is noted that higher frequencies increased the fleet size and decreased the users’ waiting time and in-vehicle time. Furthermore, skipping more stations reduced the fleet size and users’ in-vehicle time, while increasing the users’ waiting time and number of transfers. The computational results indicated that the proposed algorithm can suitably incorporate the trade-offs and generate an accurate set of Pareto-optimal solutions.

Urban rail service design for collaborative passenger and freight transport

This paper develops an operational strategy in which urban rail transit is used for freight transport. An environment-friendly urban freight transportation alternative is analyzed by employing optimization techniques to support the collaborative transportation of passengers and freight. Practical cases are investigated to test the technical feasibility of this transportation scheme. The paper formulates the train service design problem on a single urban rail line with passenger and freight. Passenger trains have a prescribed timetable which is allowed to be slightly modified to facilitate the freight service. Freight can be transported by inserting dedicated freight trains or utilizing the extra space inside the passenger train carriages. Station platforms are able to load and unload both goods and passengers. An optimization model for combined train service design is proposed to maximize profit resulting from the balance of revenues and costs brought by the freight service. The efficient schedules of trains and freight allocation plans are to be determined. This problem is formulated as a mixed-integer linear programming model. An iterative scheduling approach which includes a pre-processing method and two heuristic iterative algorithms is designed to solve the model. Two numerical examples are introduced to demonstrate the efficiency of the proposed model and the iterative scheduling approach.

Train service design in an urban rail transit line incorporating multiple service routes and multiple train compositions

This paper focuses on the train service design problem within a given period in an urban rail transit line, where multiple either full-length or short-turn service routes can be operated, and each service route can utilize one of several different train compositions. The problem lies on determining the turn-back stations, train composition and frequency of each service route operated on the line. Considering the interests of operators and passengers, we decompose the problem as two subproblems namely train service configuration and passenger assignment. The first subproblem is formulated as an integer linear programming model with the objective of minimizing operators’ cost. Given a train service scheme, the second subproblem is modelled as a capacitated continuous multi-commodity flow model to minimize passengers’ waiting time cost and transfer cost. The optimal strategy is extended to determine the behaviour of passengers and capture the extra waiting time of passengers under capacity constraint. The two sub-models are weighted and integrated into a mixed integer nonlinear programming model, which is further transformed into a mixed integer linear programming model using a novel linearization method. By exploiting the special characteristics of the model, a tailored and easy to implement local search algorithm is developed to solve large-scale instances. Starting from the operator-optimum solution which can be easily obtained, the algorithm solves the two sub-models iteratively to search better solutions within a precalculated search range which is smaller than the complete feasible domain. Finally, different sizes of instances constructed from two urban rail transit lines are utilized to demonstrate the performance and practicability of the proposed approaches.

Integrated Optimization for Commuting Customized Bus Stop Planning, Routing Design, and Timetable Development With Passenger Spatial-Temporal Accessibility

The customized bus (CB) is an innovative type of transit service that can provide a personalized efficient transit services for passengers and environmental friendliness and congestion alleviation in metropolitan areas. This work develops an integrated optimization method for CB stop deployment, route design, and timetable development optimization problems while meeting travel demands as much as possible to obtain system-optimal CB service plans. Through the perspective of space-time network, the CB service design problem (CBSDP) is formulated as an integrated optimization model with the objectives of maximizing passenger accessibility and minimizing operating cost. An inconvenience index of passengers is introduced in the problem to measure the service quality, and the total number of stops for all involved CB routes is set as one of the objectives to optimize the total cost of the CB system. A heuristic approach is applied to generate efficient solutions for the CBSDP. Two types of instance, namely, a numerical experiment and a real-world instance, are implemented to demonstrate the performance of the proposed method. We also conduct a series of sensitive analyses to explore the influences of various parameters on the CB system for capturing the interaction among stops, routes, and timetables. Final results show that the CB plans obtained by the proposed method can provide efficient services by balancing passenger convenience and operating cost.

Customized bus service design for uncertain commuting travel demand

This study proposes an interesting customized bus service design problem by considering travel demand uncertainty. Given a fleet of heterogeneous vehicles, a mixed integer linear programming (MILP) model is put forward for the complex decision making on bus routing, timetabling and bus deployment, with the objective of generating a set of profitable bus services to cater for diverse commuting-trip requests. To capture the risk-averse level of the bus operator in uncertain travel demand environment, a random variable describing the likelihood that the offered bus services are rejected by potential passengers and two associated control parameters are embedded in the MILP model, facilitating an adjustable robust optimization framework. A branch-and-price method is implemented to solve the model exactly. A column-generation-based heuristic method is proposed to solve large-scale problems. The effectiveness of both the exact and heuristic methods is assessed in numerical experiments.

The Impact of Scalability on Advisory and Service Delivery Efforts of Nonprofits

Motivated by several nonprofit organizations (NPOs) that support and serve distressed individuals, we study the impact of scalability on the service design of resource-constrained NPOs who need to balance advisory and service delivery efforts to maximize their social impact. We model the NPO's service-design problem that captures this essential trade-off: while increasing advisory efforts increases the likelihood of clients receiving the appropriate services (reduces mismatches), it limits the resources available for different service delivery efforts. We derive optimal advisory and service delivery efforts as a function of the state of the NPO (resources and services) and clients’ characteristics (mix and diversity of needs). We show that an NPO should invest more in its advisory effort as compared to service delivery efforts when service offerings are not scalable or when the NPO is not severely resource-constrained. We present an illustration of our analysis in a practical context, and suggest ways that NPOs can implement our findings in designing their services.

Deep Diving into Service Design Problems: Visualizing the Iceberg Model of Design Problems through a Literature Review on the Relation and Role of Service Design with Wicked Problems

This article examines and discusses the connection and the role of service design in the wicked problem field. Many management theorists divide problems into three categories, simple, complex and wicked. The latter is the most difficult and requires specific methods and tools. Systematic literature review was performed to see what the relation and role of service design are in wicked problems. A summarized list of the findings was made to uncover it. In conclusion, the paper presents an ‘Iceberg Model of Design Problems’ so that service designers can use proper tools designed for each category and so as not to treat wicked problems too simplistically.

An exact algorithm for the single liner service design problem with speed optimisation

ABSTRACT This paper models a single liner service design and operations problem. The model selects the ports to be included, their sequence, the sailing speed of vessels, the number of vessels and the amounts of cargo to transport by the service. The objective is to maximise profit. First, a relaxation with a mixed-integer nonlinear programming (MINLP) formulation is proposed. We show how to obtain the optimal speed value. Once this value is obtained, the mathematical programming formulation becomes a mixed-integer linear program (MILP). Then, a two-step exact algorithm is presented to solve the problem. Using real data, the optimal solution was found in less than 1 min for small-size problems and in few hours for relatively large-size problems. More tests were carried out on randomly generated data sets with up to 25 ports. The results of these tests are rather promising, and they enabled us to identify the performance limits of the algorithm.

Semi-liner Shipping Service Design

Semi-liner shipping transports various types of cargo, such as containers, break-bulk cargo, and heavy-lift project cargo, between different ports. Similar to liner shipping, semi-liner shipping publishes shipping routes for customers’ reference. However, it does not strictly follow the published route and usually makes some adjustments for each ship voyage by adding some port calls to transport more cargo considering the excess ship capacity. This study first proposes the semi-liner shipping service design (SLSSD) problem that aims to maximize the shipping profit by determining a shipping route subject to the potential adjustments. The proposed SLSSD problem is subsequently formulated as a two-stage stochastic mixed integer programming model with integer recourse variables. The first stage determines the visit sequence of a set of compulsory ports under shipping demand uncertainty. The second stage decides whether to add or remove some ports in the route in view of the realized shipping demand for each ship voyage. To effectively solve the model, two decomposition methods are developed, namely, the stage decomposition method and the scenario decomposition method, that decompose the problem by stage and demand scenario, respectively. In addition, two novel acceleration techniques are also provided to expedite the scenario decomposition method. Numerical experiments reveal satisfactory efficiency of these two methods to solve the semi-liner shipping service design problem, especially the scenario decomposition method, which is generally better than the stage decomposition method and can be thousands of times faster than the classic branch-and-cut algorithm.

Parallel shuttle bus service design for planned mass rapid transit shutdown: The Singapore experience

19 MRT stations in Singapore experienced a two full-day shutdown as planned in order to upgrade the infrastructure of aging mass rapid transit (MRT) lines. A large bus fleet, running entirely parallel to MRT lines, was deployed to ferry passengers between affected stations, which however results in prolonged congestion and queue of buses near terminals. Rail transit systems in many metropolitan areas are threatened because of aging infrastructure. This study, motivated by the Singapore case, proposes a novel parallel shuttle bus service design (PSBSD) problem that addresses route design, terminal selection, berth allocation, and bus deployment to minimize inconvenience caused to passengers, which is seldom addressed in previous studies. We further develop a mixed-integer nonlinear programming (MINLP) model for the PSBSD problem. In order to solve the non-convex MINLP model optimally, we put forward an effective decomposition method, which comprises (i) the generation of candidate shuttle bus routes through a brute-force search for semi-route and a terminal selection procedure and (ii) the deployment of buses and assignment of terminal berths for selected shuttle bus routes by using an optimization-based approach. Numerical experiments were conducted using data sets from the MRT closure incident in Singapore. The results demonstrate that the decomposition method is capable of finding optimal solutions. Useful insights are also obtained from testing the models under different scenarios.

Machine diagnostic service centre design under imperfect diagnosis with uncertain error cost consideration

As modern machines are always highly customised, it is important to diagnose the specific maintenance requirement of each machine before performing regular maintenance tasks. However, the diagnosis cannot always be accurate. In this study, we focus on a diagnostic service design problem considering imperfect diagnosis with uncertain error cost, which increases in the inaccuracy of the diagnosis. The service system is modelled as a multiple server queue, with servers performing a sequential diagnosis and customers deciding whether or not to use the service. We consider the case where the expert skill level is exogenous, uncertain and endogenous, respectively. The results suggests that (1) when the expert skill level is exogenous, the congestion of the system increases in expert skill level. In addition, the error costs for the two major stakeholders-the service centre and the customer-may affect the optimal service time and number of experts in different ways; (2) when expert skill level becomes more uncertain, the service centre should improve the optimal service time further and will see a further erosion of the profit; and (3) different from the exogenous expert skill level, high skill level experts always accompany with long service time when expert skill level is endogenous.