The Job-Shop Scheduling Problem (JSSP) stands as one of the most renowned challenges in scheduling. It is characterized as a disjunctive problem, wherein a solution is fully depicted through an oriented disjunctive graph, with earliest starting times computed using a longest path algorithm. The complexity of solving this problem arises in part from the requirement that disjunctive graphs representing solutions must be acyclic. Consequently, enumerating these graphs is feasible for small-scale instances only. A significant advancement in this field, credited to (Bierwith, 1995), is the introduction of the 'vector by repetition' (commonly known as Bierwith's vector). Notably, this vector possesses the property that it can be mapped to an acyclic disjunctive graph, thereby enabling the mapping of a vector to a solution. This property has facilitated the development of highly efficient resolution schemes, as it allows the enumeration of solutions only i.e. acyclic disjunctive graphs. Our objective is to demonstrate how Bierwith's vector can be integrated into a Quantum Approximate Optimization Algorithm (QAOA) to tackle the job-shop problem using a novel quantum approach.

Grover Search is currently one of the main quantum algorithms leading to hybrid quantum-classical methods that reduce the worst-case time complexity for some combinatorial optimization problems. Specifically, the combination of Quantum Minimum Finding (obtained from Grover Search) with dynamic programming has proved particularly efficient in improving the complexity of NP-hard problems currently solved by classical dynamic programming. For these problems, the classical dynamic programming complexity in O-& lowast;(c(n)), where O-& lowast; denotes that polynomial factors are ignored, can be reduced by a hybrid algorithm to O-& lowast;(c(quant)(n)), with c(quant)<c. In this paper, we provide a bounded-error hybrid algorithm that achieves such an improvement for a broad class of NP-hard single-machine scheduling problems for which we give a generic description. Moreover, we extend this algorithm to tackle the 3-machine flowshop problem. Our algorithm reduces the exponential-part complexity compared to the best-known classical algorithm, sometimes at the cost of an additional pseudo-polynomial factor.

The problem of Fleet Conversion aims to reduce the carbon emissions and cost of operating a fleet of vehicles for a given set of tours. It can be modelled as a column generation scheme with the Maximum Weighted Independent Set (MWIS) problem as the slave. Quantum variational algorithms have gained significant interest in the past several years. Recently, a method to represent Quadratic Unconstrained Binary Optimization (QUBO) problems using logarithmically fewer qubits was proposed. Here we use this method to solve the MWIS Slaves and demonstrate how quantum and classical solvers can be used together to approach an industrial-sized use-case (up to 64 tours).

Quantum Approximate Optimization Algorithm (QAOA) is one of the most short-term promising quantum-classical algorithm to solve unconstrained combinatorial optimization problems. It alternates between the execution of a parametrized quantum circuit and a classical optimization. There are numerous levers for enhancing QAOA performances, such as the choice of quantum circuit meta-parameters or the choice of the classical optimizer. In this paper, we stress on the importance of the input problem formulation by illustrating it with the resolution of an industrial railway timetabling problem. Specifically, we present a generic method to reformulate any polynomial problem into a Polynomial Unconstrained Binary Optimization (PUBO) problem, with a specific formulation imposing penalty terms to take binary values when the constraints are linear. We also provide a generic reformulation into a Quadratic Unconstrained Binary Optimization (QUBO) problem. We then conduct a numerical comparison between the PUBO with binary penalty terms and the QUBO formulations proposed on a railway timetabling problem solved with QAOA. Our results illustrate that the PUBO reformulation outperforms the QUBO one for the problem at hand.

NP-hard problems are not believed to be exactly solvable through general polynomial time algorithms. Hybrid quantum-classical algorithms to address such combinatorial problems have been of great interest in the past few years. Such algorithms are heuristic in nature and aim to obtain an approximate solution. Significant improvements in computational time and/or the ability to treat large problems are some of the principal promises of quantum computing in this regard. The hardware, however, is still in its infancy and the current Noisy Intermediate Scale Quantum (NISQ) computers are not able to optimize industrially relevant problems. Moreover, the storage of qubits and introduction of entanglement require extreme physical conditions. An issue with quantum optimization algorithms such as QAOA is that they scale linearly with problem size. In this paper, we build upon a proprietary methodology which scales logarithmically with problem size - opening an avenue for treating optimization problems of unprecedented scale on gate-based quantum computers. In order to test the performance of the algorithm, we first find a way to apply it to a handful of NP-hard problems: Maximum Cut, Minimum Partition, Maximum Clique, Maximum Weighted Independent Set. Subsequently, these algorithms are tested on a quantum simulator with graph sizes of over a hundred nodes and on a real quantum computer up to graph sizes of 256. To our knowledge, these constitute the largest realistic combinatorial optimization problems ever run on a NISQ device, overcoming previous problem sizes by almost tenfold.

Noisy intermediate-scale quantum computers (NISQ computers) are now readily available, motivating many researchers to experiment with Variational Quantum Algorithms (VQAs). Among them, the Quantum Approximate Optimization Algorithm (QAOA) is one of the most popular one studied by the combinatorial optimization community. In this tutorial, we provide a mathematical description of the class of Variational Quantum Algorithms, assuming no previous knowledge of quantum physics from the readers. We introduce precisely the key aspects of these hybrid algorithms on the quantum side (parametrized quantum circuit) and the classical side (guiding function, optimizer). We devote a particular attention to QAOA, detailing the quantum circuits involved in that algorithm, as well as the properties satisfied by its possible guiding functions. Finally, we discuss the recent literature on QAOA, highlighting several research trends.

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Grover search is currently one of the main approaches to obtain quantum speed-ups for combinatorial optimization problems. The combination of Quantum Minimum Finding (obtained from Grover search) with dynamic programming has proved particularly efficient to improve the worst-case complexity of several NP-hard optimization problems. Specifically, for these problems, the classical dynamic programming complexity (ignoring the polynomial factors) in O* ( c n ) can be reduced by a bounded-error hybrid quantum-classical algorithm to O* ( c n quant ) for c quant < c. In this paper, we extend the resulting hybrid dynamic programming algorithm to three examples of single-machine scheduling problems: minimizing the total weighted completion time with deadlines, minimizing the total weighted completion time with precedence constraints, and minimizing the total weighted tardiness. The extension relies on the inclusion of a pseudo-polynomial term in the state space of the dynamic programming as well as an additive term in the recurrence.

Precision farming in viticulture raises challenging combinatorial issues such as the Differential Harvest Problem. This problem might appear at first as similar to a simple Capacitated Vehicle Routing Problem but it exhibits problem-specific constraints that make this problem much harder to solve and which are discussed in this article. Our objective was to develop efficient exact methods using column generation and VRPSolver™ based models. In order to investigate possibilities of hybridization, two other models were designed: a constraint programming model and a local search model using LocalSolver™ . In order to reach good scalability, new valid inequalities and a hybrid solving scheme are proposed. Extensive experiments were performed both on simulated and real data sets. The results are discussed from operational cost point of view and also performance.

This paper concerns quantum heuristics that are able to extend the domain of quantum computing defining a promising way in the large number of well-known classical algorithms. Quantum approximate heuristics take advantage of alternation between one Hamiltonian defining the problem to solve and one mixing Hamiltonian. The adiabatic theorem initially defines in quantum physic allow to compute a solution for the Schrödinger equation, but the foundation of this methods requires strong skill in physics and mathematics. Our main objectives in this paper are at first to provide an algorithm-based presentation (as close as possible of the classical computer science community in operational research practice) of the adiabatic optimization and secondly to give a comprehensive resolution of the well-known SAT problem. This presentation gives opportunities to provide a concise but explicit analysis of the adiabatic capability to define a new efficient operational research trend. Our experiments encompass numerical evaluations on both simulator and on real quantum computer provided by IBM. Numerical evaluations using the QLM library meet the Qiskit evaluations. This contribution is at the crossroad of physic and computer science in the sense it proves the capabilities of quantum concepts to define a new and promising research trends in optimization.

We propose an Indirect Quantum Approximate Optimization Algorithm (referred to as IQAOA) where the Quantum Alternating Operator Ansatz takes into consideration a general parameterized family of unitary operators to efficiently model the Hamiltonian describing the set of string vectors. This algorithm creates an efficient alternative to QAOA, where: 1) a Quantum parametrized circuit executed on a quantum machine models the set of string vectors; 2) a Classical meta-optimization loop executed on a classical machine; 3) an estimation of the average cost of each string vector computing, using a well know algorithm coming from the OR community that is problem dependent. The indirect encoding defined by dimensional string vector is mapped into a solution by an efficient coding/decoding mechanism. The main advantage is to obtain a quantum circuit with a strongly limited number of gates that could be executed on the noisy current quantum machines. The numerical experiments achieved with IQAOA permits to solve 8-customer instances TSP using the IBM simulator which are to the best of our knowledge the largest TSP ever solved using a QAOA based approach.

This paper focuses on optimizing the collective self-consumption rate in energy communities by scheduling members’ loads. The community remains connected to the public grid and comprises pro-sumers, traditional consumers, and distributed storage units. Prosumers can exchange their energy with the public grid or other members. The proposed strategy aims at implementing a Demand Side Management program taking advantage of controllable loads’ characteristics. A MILP formulation of the problem allows, on the one hand, to give the optimal planning for electrical devices’ operations. On the other hand, it provides optimal solutions for managing the storage units, peer-to-peer exchanges, and interactions with the public grid to minimize the energy flows from the public grid over time. However, this MILP only allows for solving small problem instances. Thus, we develop a column generation-based heuristic for large problem instances. Our numerical experiments based on real data collected in the south of France show that joining an energy community saves money on energy bills and reduces the total energy drawn from the primary grid by at least 15%.

This paper deals with the Generalised Workforce Scheduling and Routing Problem (GWSRP) where 9 temporal constraints ensuring visit dependencies are all together taken into account and where customer and worker's quality of service are taken into consideration. A Constraint-Programming based Decomposition Method (CPDM) is proposed, firstly based on a relaxation of coordination constraints and a column generation, and secondly with an iterative insertion of coordination constraint by constraint programming solver. Numerical experiments are achieved on huge instances derived from WSRP benchmark instances with up to 177 customers, 59 vehicles and coordination constraints. The CPDM is able to find nearly optimal solution for medium-size instances and find high-quality solution for huge-size instances whereas CPLEX solver applied to a mixed integer linear model is not able to give a solution in this case.

This paper concerns quantum heuristics based on Mixer Hamiltonians that allow to restrict investigation on a specific subspace. Mixer Hamiltonian based approaches can be included in QAOA algorithm and we can state that Mixer Hamiltonians are mapping functions from the set of qubit-strings to the set of solutions. Mixer Hamiltonian offers an approach very similar to indirect representations commonly used in routing or in scheduling community for decades. After the initial publication of Cheng et al. in 1996 (Cheng et al., 1996), numerous propositions in OR lies on 1-to-n mapping functions, including the split algorithm that transform one TSP solution into a VRP solution. The objective is at first to give a compact and readable presentation of these Mixer Hamiltonians considering the functional analogies that exist between the OR community practices and the quantum field. Our experiments encompass numerical evaluations of circuit using the Qiskit library of IBM meeting the theoretical considerations.

Pickups and deliveries problems (PDPs) are extensively studied in the literature. Numerous extensions appear in the last decades since they are motivated by real-life applications. The transportation and routing problems are now focusing on more systemic problems by including real and challenging considerations. Our motivation is to extend classical modelling by addressing simultaneously two interrelated problems.The current paper aims to develop an efficient constraint programming approach, integrated in a global resolution scheme based on a multi-start local search heuristic, to solve the 1SVRPDP-SP (One-Commodity Split Vehicle Routing Problem with Delivery and Pickup including Service Time and Precedence Relation). This problem investigates a Vehicle Routing Problem, with a unique depot, a homogeneous fleet of vehicles and a single commodity. Each customer requires a certain number of commodities during a given time (service time) and can be visited several times. The resolution difficulty of the problem relies on the proper definition of the starting and finishing time of all customer services that are strongly interrelated with all the incoming delivery transportation operations and all the outcoming pickup transportation operations.This resolution scheme takes advantage of a decomposition approach based on several constraint programming models allowing to efficiently investigate the solution search space from their sub-problems. Numerical experiments are conducted on instances composed of six customers with two vehicles (possibly leading to 60 operations to schedule) and compared to previous published MIP model. Instances based on 60 operations characterized a strongly difficult problems since the two problems are interrelated and quality solutions required a proper coordination of both transportation operations and customer services. The results prove that models based on constraint programming and methods introduced in this paper are competitive to solve the 1SVRPDP-SP.

Rules on driving, working and breaks scheduling are crucial to enhance road safety and to respect drivers’ work conditions. Thus, adapting the vehicle routes that are commonly defined in routing optimization problems to integrate these rules leads us to the Truck Drive Scheduling Problem (TDSP) which purpose is to guarantee their compliance according to the European Union regulations. In this respect, we present the first Constraint Programming (CP) based approach for the TDSP which integrates rules regarding the driving, service, working time and breaks duration considering a one-week horizon time. Driving time concerns only the transportation time from one customer to another, service time as transportation and processing duration and working time encompass all activities within a day of work. Breaks between activities and night breaks must meet the law requirements. Therefore, the main idea of our CP-based algorithm is to schedule the arrival, starting, finishing and departure dates and define the breaks duration before and/or after the activities considering the EC regulation 561/2006 so that the completion time is minimized. Tests were performed in a set of 29 instances and results show that the majority is solved into optimality in less than one second and others present at most a 1% gap, which validates our algorithm effectiveness.