Constraint Programming Research Articles

Network configuration distributed production scheduling problem: A constraint programming approach

Nowadays, many massive factories are forced to distribute their products in several manufacturing units. This issue has caused the emergence of a novel category of problems called distributed production scheduling, which is vital in today's growing world. In this paper, the distributed production scheduling problem by considering network configuration with two echelons is addressed. The first and second echelon factories have different job configurations and have a hybrid flow shop and a flexible job shop environment, respectively. For this problem, A bi-objective mixed integer linear programming (MILP) model is presented to minimize the maximum completion time of jobs and transportation costs between the selected factories in two echelons, respectively. Consequently, the epsilon constraint method is used to deal with this bi-objective model. In addition, since distributed scheduling problems are classified as NP-Hard problems, it is very challenging to solve them for large-sized instances. For this reason, a constraint programming model (CP) is also proposed. To evaluate the performance of the proposed MILP model and CP model, a total of 180 numerical instances are randomly generated in small, medium, and large sizes. The obtained results demonstrate the significant ability of the constraint programming approach in solving complex distributed scheduling problems even for large-sized instances with 30 jobs, 10 stages/operations for each job, 6 machines for each stage/operation, and 4 factories at each echelon in a reasonable time and proof that the CP model can outperform the MILP model in this problem.

Automation of Triangle Ruler-and-Compass Constructions Using Constraint Solvers

Automation of Triangle Ruler-and-Compass Constructions Using Constraint Solvers

Automated Completion of Statements and Proofs in Synthetic Geometry: an Approach based on Constraint Solving

Automated Completion of Statements and Proofs in Synthetic Geometry: an Approach based on Constraint Solving

Interval constraint programming for globally solving catalog-based categorical optimization

Interval constraint programming for globally solving catalog-based categorical optimization

Heuristic for Railway Crew Scheduling With Connectivity of Schedules

This paper addresses the crew scheduling for long-distance passenger train services. A heuristic with bin packing features is developed to generate repeatable crew schedules that satisfy the operational and crew allocation rules. By ensuring the connectivity of crew duties that can be repeated over periodic train schedules, a better estimate of the crew requirement in a region is also obtained. Further, the heuristic ensures a fair division of the total workload and creates long duty cycles, which also makes the process of cyclic rostering easier. The paper also presents an exact approach for crew scheduling using a combination of constraint programming and set covering formulations. The exact approach is not computationally viable for practical scale problem instances, but the heuristic generates good quality solutions (often very close to optimal) even on large data sets. We illustrate the approach on data from the Mumbai Division in Indian Railways and the computational results show that there is potential to reduce the total number of crew duties in the region by around 12%. The heuristic approach provides an efficient way to generate improved crew schedules every time there is a change in the train timetable.

Robust Scheduling of Multi-Skilled Workforce Allocation: Job Rotation Approach

This paper addresses scheduling challenges in software development organizations, specifically focusing on a novel version of the software project scheduling problem (SPSP). This enhanced model incorporates the dynamics of learning and forgetting phenomena, crucial in maintaining employee competencies, particularly when unexpected events such as absenteeism or shifts in project priorities occur. The paper introduces a new declarative reference model for SPSP, aimed at proactively managing the assignment of versatile programmers to tasks within an portfolio of IT projects, while considering the effects of forgetting. Implemented within a constraints programming environment, this model facilitates decision making in project management for software companies. It serves to find feasible solutions and identify conditions necessary to meet specified expectations. The effectiveness of the proposed SPSP model is demonstrated through numerical examples.

MILP/MIQCP-Based Fully Automatic Method of Searching for Differential-Linear Distinguishers for SIMON-Like Ciphers

Differential-linear (DL) cryptanalysis is an important cryptanalytic method in cryptography and has received extensive attention from the cryptography community since its proposal by Langford and Hellman in 1994. At CT-RSA 2023, Bellini et al. introduced continuous difference propagations of XOR, rotation, and modulo-addition operations and proposed a fully automatic method using Mixed-Integer Linear Programing (MILP) and Mixed-Integer Quadratic Constraint Programing (MIQCP) techniques to search for DL distinguishers of Addition-Rotation-XOR (ARX) ciphers. In this paper, we propose continuous difference propagation of AND operation and construct an MILP/MIQCP-based fully automatic model of searching for DL distinguishers of SIMON-like ciphers. We apply the fully automatic model to all versions of SIMON and SIMECK. As a result, for SIMON, we find 13 and 14-round DL distinguishers of SIMON32, 15, 16, and 17-round DL distinguishers of SIMON48, 20-round DL distinguishers of SIMON64, 25 and 26-round DL distinguishers of SIMON96, 31 and 32-round DL distinguishers of SIMON128. For SIMECK, we find 14-round DL distinguishers of SIMECK32, 17 and 18-round DL distinguishers of SIMECK48, 22, 23, 24, and 25-round DL distinguishers of SIMECK64. As far as we know, our results are currently the best.

Multi-criteria Optimization of Real-time DAGs on Heterogeneous Platforms under P-EDF

This article tackles the problem of optimal placement of complex real-time embedded applications on heterogeneous platforms. Applications are composed of directed acyclic graphs of tasks, with each directed-acyclic-graph (DAG) having a minimum inter-arrival period for its activation requests and an end-to-end deadline within which all of the computations need to terminate since each activation. The platforms of interest are heterogeneous power-aware multi-core platforms with Dynamic Voltage and Frequency Scaling (DVFS) capabilities, including big.LITTLE Arm architectures and platforms with GPU or FPGA hardware accelerators with Dynamic Partial Reconfiguration capabilities. Tasks can be deployed on CPUs using partitioned EDF-based scheduling. Additionally, some of the tasks may have an alternate implementation available for one of the accelerators on the target platform, which are assumed to serve requests in non-preemptive FIFO order. The system can be optimized by minimizing power consumption, respecting precise timing constraints, maximizing the applications’ slack, respecting given power consumption constraints, or even a combination of these, in a multi-objective formulation. We propose an off-line optimization of the mentioned problem based on mixed-integer quadratic constraint programming (MIQCP). The optimization provides the DVFS configuration of all the CPUs (or accelerators) capable of frequency switching and the placement to be followed by each task in the DAGs, including the software-vs.-hardware implementation choice for tasks that can be hardware accelerated. For relatively big problems, we developed heuristic solvers capable of providing suboptimal solutions in a significantly reduced time compared to the MIQCP strategy, thus widening the applicability of the proposed framework. We validate the approach by running a set of randomly generated DAGs on Linux under SCHED_DEADLINE, deployed onto two real boards, one with Arm big.LITTLE architecture, the other with FPGA acceleration, verifying that the experimental runs meet the theoretical expectations in terms of timing and power optimization goals.

Water–Food Nexus System Management under Uncertainty through an Inexact Fuzzy Chance Constraint Programming Method

This study discusses the planning of a regional-scale water–food nexus (WFN) system using an inexact fuzzy chance constraint programming (IFCCP) method. The IFCCP approach can handle uncertainties expressed as interval and fuzzy parameters, as well as the preferences of decision makers. An inexact fuzzy chance constraint programming-based water–food nexus (IFCCP-WFN) model has been developed for the City of Jinan with the consideration of various restrictions related to water and land availability, as well as food and vegetable demands. Solutions for the planting areas for different crops in different periods have been generated under the different preferences of decision makers. The water resource availability would be the priority factor affecting the WFN system under demanding conditions, in which wheat cultivation would be dominated by this factor under fuzzy confidence levels of 0.2 and 0.5, and the planting area of corn would be determined by this factor under high fuzzy confidence levels (e.g., 0.8). In addition, the reliability of irrigation would decrease with increasing fuzzy confidence levels under demanding conditions, limiting the planting areas for crops and leading to a decreasing trend of the system benefit. Adequate water resources would be available for irrigation under optimistic conditions, implying no significant contributions to the planting schemes. Nevertheless, increasing food loss rates would result in more planting areas to satisfy food requirements and thus a greater system benefit under advantageous conditions. Compared with the developed IFCCP-WFN model, the interval-linear-programming-based water–food nexus (ILP-WFN) model can merely reflect the lower and upper bounds of uncertain parameters and neglects the inherent distributional information within the fuzzy parameters. Thus, the ILP-WFN model is unable to reveal the inherent impacts of the fuzzy parameters on the resulting planting strategies.

Integrated Inbound and Outbound Scheduling for Coal Port: Constraint Programming and Adaptive Local Search

The effective production scheduling of dry bulk ports is a challenging task that demands meticulous planning, task allocation based on customer requirements, as well as strategic route and timing scheduling. Dry bulk ports dedicated to handling commodities like coal and iron ore frequently engage in blending operations as a strategic imperative to gain market competitiveness. The process of blending coal and ore entails the timely arrival of the requisite raw materials at predetermined locations. Simultaneously, it necessitates the coordination of the sequencing of goods entering and departing the port to align with the operational demands associated with material stockpiles. This paper describes and analyzes an operational scheduling problem encountered by one of the largest coal blending sea ports in China. Specifically, a rich constraint programming model is presented to define operation sequences integrating daily inbound and outbound services provided by the port, minimizing the overall operation time. In order to enhance the practicality of the method, a CP-based adaptive simulated annealing local search algorithm has been designed and developed for the optimization problem. The empirical validation of the proposed method is conducted using both real production data and generated experimental data adhering to specific rules. The results conclusively demonstrate the efficacy and feasibility of the proposed method. This also substantiates its practicality and effectiveness in real-world applications, facilitating efficient production and energy-saving operations for the coal port.

Resource-constrained repetitive project scheduling with soft logic

PurposeIn repetitive projects, repetition offers more possibilities for activity scheduling at the sub-activity level. However, existing resource-constrained repetitive scheduling problem (RCRSP) models assume that there is only one sequence in performing the sub-activities of each activity, resulting in an inefficient resource allocation. This paper proposes a novel repetitive scheduling model for solving RCRSP with soft logic.Design/methodology/approachIn this paper, a constraint programming model is developed to solve the RCRSP using soft logic, aiming at the possible relationship between parallel execution, orderly execution or partial parallel and partial orderly execution of different sub activities of the same activity in repetitive projects. The proposed model integrated crew assignment strategies and allowed continuous or fragmented execution.FindingsWhen solving RCRSP, it is necessary to take soft logic into account. If managers only consider the fixed logic between sub-activities, they are likely to develop a delayed schedule. The practicality and effectiveness of the model were verified by a housing project based on eight different scenarios. The results showed that the constraint programming model outperformed its equivalent mathematical model in terms of solving speed and solution quality.Originality/valueAvailable studies assume a fixed logic between sub-activities of the same activity in repetitive projects. However, there is no fixed construction sequence between sub-activities for some projects, e.g. hotel renovation projects. Therefore, this paper considers the soft logic relationship between sub-activities and investigates how to make the objective optimal without violating the resource availability constraint.

A practical approach to college timetable scheduling

This paper formulates the college timetable scheduling (CTTS) as a constraint satisfaction problem (CSP) in a manner that is easy to implement. Timetable scheduling is a process that is revised in every academic session and requires a lot of constraint checking, so it is inefficient to check the same type of constraints repeatedly to create a proper schedule manually. CSP is a natural choice for automating this process. The prototype presented in this paper is aimed to offer such assistance in scheduling classes for different courses running in an academic session keeping given different constraint checking like the limit check of the maximum number of lectures that can be assigned to a faculty, limit check of the maximum number of lectures that can be assigned to a course, detecting conflicts between courses scheduled in the same time slots, preventing overlapping assignments for faculty members in the same time slots. Here, first, the different aspects of the timetable scheduling problem are addressed, and then a technique is devised to help map the problem as a CSP.

New Differential‐Based Distinguishers for Ascon via Constraint Programming

As the winner of the NIST lightweight cryptography project, Ascon has undergone extensive self‐evaluation and third‐party cryptanalysis. In this paper, we use constraint programming (CP) as a tool to analyze the Ascon permutation and propose several differential‐based distinguishers. We first propose a search methodology for finding truncated differentials for Ascon with CP, the core of which is modeling with the undisturbed bits of the S‐box. By using this method, we find the five‐ and six‐round truncated differentials with a probability of 2−44 and 2−162, respectively. Considering the application of permutation in the context, we also provide the five‐ and six‐round truncated differential distinguishers under the weak‐key setting. Then, inspired by our five‐round truncated differentials, we propose a six‐round boomerang characteristic, and based on this, we obtain the five‐ and six‐round sandwich distinguishers with a complexity of 270 and 2134, respectively. Using the CP tool again and specifying that the “3‐3” differential pattern is satisfied in the middle rounds, we propose a six‐round differential characteristic with a probability of 2−280, which increases the probability by 225 compared to the best known six‐round differential characteristic.

A constraint programming approach for multi-objective tourist trip design problem with mandatory visits: A case study for İzmir Turkey

The Orienteering Problem (OP) is an optimization problem that finds the locations and routes that will return the highest profit/benefit, starting from the initial location of the traveler/vehicle, visiting these locations, and ending with the starting location of the tour within a given time or distance limit. There is no obligation to visit all locations in the problem structure. OP has many real-life applications, such as staff routing and disaster relief routing. In this study, OP with Time Windows (OPTW), an extension of OP, is discussed with hotel selection and mandatory visits. Although the main objective of OPTW is profit maximization, it is also essential to minimize the total travel time to complete the tour efficiently. For this reason, we consider the OPTW as a multi-objective problem. In the problem considered here, it is assumed that the profit/benefit, travel time between locations, service period, and time interval that each location can be visited are determined to be known. Within the scope of the study, first, a Mixed Integer Programming (MIP) model is prepared for the problem. Since the proposed mathematical model does not provide solutions in a reasonable time for large networks, the problem is solved by a Constraint Programming (CP) approach. Attractive tourist points of interest for Izmir, one of Turkey's major tourist cities, are determined, and the proposed method is applied to the real-life problem. The problem is modeled as Multi-Objective OPTW with MIP and CP and solved. Also, sensitivity analysis is performed by considering two different scenarios.

Using cooperative coevolution in large-scale black-box constraint satisfaction problems

Solving constrained large-scale global optimization problems poses a challenging task. In these problems with constraints, when the number of variables is measured in the thousands, when the constraints are presented in the form of a black box, and neither the size nor the configuration of the feasible region is known, it is very difficult to find at least one feasible solution. In general, such a problem of finding a feasible region is known as a constraint satisfaction problem. In this paper, we have extended a well-known benchmark set based on constrained optimization problems up to 1000 variables. We have evaluated the CC-SHADE performance, to tackle constraints in large-scale search space. CC-SHADE merges the power of cooperative coevolution and self-adaptive differential evolution. Our extensive experimental evaluations on a range of benchmark problems demonstrate the strong dependence of the performance of CC-SHADE on the number of individuals and the subcomponent number. The numerical results emphasize the importance of using a cooperative coevolution framework for evolutionary-based approaches compared to conventional methods. All numerical experiments are proven by the Wilcoxon test.

Modeling the Integrated Flexible Job-Shop and Operator Scheduling in Flexible Manufacturing Systems

This paper addresses the integrated flexible job shop and operators scheduling problem, introducing shift-based constraints on operators. We investigate how the advanced modeling and solution techniques, specifically, Mixed-Integer Programming (MIP) and Constraint Programming (CP) perform on this intricate scheduling problem. We test the effectiveness of both the MIP and CP models on an illustrative example as well as on a set of larger instances and draw conclusions, which indicate the need to integrate these two models into some approximation scheme to tackle large-scale instances for this complex problem.

Solving blocking flowshop scheduling problem with makespan criterion using q-learning-based iterated greedy algorithms

This study proposes Q-learning-based iterated greedy (IGQ) algorithms to solve the blocking flowshop scheduling problem with the makespan criterion. Q learning is a model-free machine intelligence technique, which is adapted into the traditional iterated greedy (IG) algorithm to determine its parameters, mainly, the destruction size and temperature scale factor, adaptively during the search process. Besides IGQ algorithms, two different mathematical modeling techniques. One of these techniques is the constraint programming (CP) model, which is known to work well with scheduling problems. The other technique is the mixed integer linear programming (MILP) model, which provides the mathematical definition of the problem. The introduction of these mathematical models supports the validation of IGQ algorithms and provides a comparison between different exact solution methodologies. To measure and compare the performance of IGQ algorithms and mathematical models, extensive computational experiments have been performed on both small and large VRF benchmarks available in the literature. Computational results and statistical analyses indicate that IGQ algorithms generate substantially better results when compared to non-learning IG algorithms.

Ecodesign approach for complex systems – Electric Vehicle case study

The reduction of environmental impacts from the early stages of product and system design is a major challenge. Thus, traditional ecodesign methods are not appropriate because they reason by fixing the functional definition of products/systems and address the complexity of these new systems in a simplistic way, which leads to a marginal reduction of impacts. Moreover, these methods do not consider the attractiveness for users, leading to an optimization oriented towards functional reduction. In this paper, a comprehensive ecodesign approach is presented suggesting a paradigm shift, which involves a hybrid ecodesign approach based on constraint modeling of systems based on the constraint satisfaction problem formalism (CSP) with more traditional environmental assessment methods to decide which design choices to make. This approach was applied to generate and characterize product and life cycle architectures of a serial hybrid vehicle powertrain focusing on the use and end-of-life phase.

Capability Reconciliation for a CSP Approach to Virtual Device Composition

The dynamic composition of systems of networked appliances, or virtual devices, enables users to generate complex, strong, specific systems. Leading mobile ad hoc network (MANET)-based composition schemes currently make use of service discovery mechanisms that depend on periodic service advertising by controlled broadcast. The result is the unnecessary depletion of node resources such as battery and processing power. The dynamic and ad hoc nature of the discovery and composition of services is also not addressed by current schemes; this inevitably leads to capability differences that need to be reconciled when the input of one service is not compatible with the output of another. Our approach addresses the distributed constraint satisfaction problem in virtual device composition in MANETs; our simulation shows its effectiveness and efficiency.

基于边际概率分布重新进行单变量选取的置信传播算法求解约束满足问题

基于边际概率分布重新进行单变量选取的置信传播算法求解约束满足问题