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This paper presents an optimization approach for achieving partial autonomy in residential electricity consumption and production through the integration of photovoltaic systems with a battery storage. It proposes mathematical programming approach for reproducing real consumption and production patterns, offering a calibrated optimization framework for enhanced self-consumption and self-production. The simulation model serves as a rigorous testing ground for critical battery parameters, including capacity, minimum reserve level, and energy losses, ensuring their accuracy and reliability. The optimization model, on the other hand, is used to fine-tune the energy flows in and out of the system, aiming to elevate self-consumption and self-production rates effectively. The proposed approach was empirically validated through a comprehensive analysis and real-world data application conducted across eight households during several months, revealing the model’s capacity to substantially enhance the self-consumption and self-production rates.

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2006.07.010 Byline: Rabia Nessah, Farouk Yalaoui, Chengbin Chu Abstract: In this paper, we consider an identical parallel machine scheduling problem with release dates. The objective is to minimize the total weighted completion time. This problem is known to be strongly NP-hard. We propose some dominance properties and two lower bounds. We also present an efficient heuristic. A branch-and-bound algorithm, in which the heuristic, the lower bounds and the dominance properties are incorporated, is proposed and tested on a large set of randomly generated instances. Author Affiliation: Institut Charles Delaunay - OSI (CNRS FRE 2848), Universite de Technologie de Troyes, 12 rue Marie Curie, BP 2060, 10010 Troyes Cedex, France

To link to full-text access for this article, visit this link: http://dx.doi.org/10.1016/j.cor.2005.10.017 Byline: Rabia Nessah, Chengbin Chu, Farouk Yalaoui Abstract: This paper addresses an identical parallel machine scheduling problem, with sequence-dependent setup times and release dates to minimize total completion time. This problem is known to be strongly NP-hard. We prove a sufficient and necessary condition for local optimality which can also be considered as a priority rule. We then define a dominant subset based on this condition. We present efficient heuristic algorithms using this condition to build a schedule belonging to this subset. We also prove dominance theorem, and develop a lower bound that can be computed in polynomial time. We construct a branch-and-bound algorithm in which the heuristic, the lower bound and the dominance properties are incorporated. Computational experiments suggest that the algorithm can handle test problems with 40 jobs and 2 machines. Author Affiliation: ISTIT-OSI (CNRS FRE 2732), Universite de Technologie de Troyes, 12 rue Marie Curie-BP 2060, 10010 Troyes Cedex, France

In this study, we consider the problem of scheduling a set of independent jobs with sequence-dependent setup times and job splitting, on a set of identical parallel machines such that maximum completion time (makespan) is minimized. For this NP-hard problem, we suggest a heuristic algorithm improving an existing one, using a linear programming modeling with setup times and job splitting considerations. The performance of our new method is tested on over 6000 instances with different size by comparing it with a lower bound. Keywords: Scheduling; Parallel machine: Splitting; Setup times; Linear programming

This paper studies a Multidimensional Assignment Problem (MAP) applied in the medical sector. The case study is taken from a major french medical service provider. This problem consists of assigning medical employees to perform healthcare or administrative tasks at workplaces within a predefined scheduling horizon. The system of constraints, such as ability, compatibility, availability, and accommodation capacity, characterizes the complexity of this study. The objective is to minimize the gap between the assigned workload and the target contractual workload. As far as we know, the Mixed-Integer Linear Programming (MILP) presented in this paper is the first model to handle such an objective. Numerical experiments are conducted on real-world instances to compare the performance of several well-known open-source and commercial solvers. The results serve as preliminary evidence for a feasibility study and allow us to select an appropriate approach for an efficient aid-decision system.

This paper addresses a multi-product integrated lot-sizing and pricing problem for an entity which produces and sells different products under a finite production capacity constraint. The demands for each product is assumed to be linear and takes into account the complementarity or substitution characteristics of the products. The objective is to find prices and the production planning (productions quantities, inventory level, setups configuration) for each product during several time periods to maximize the profit. The problem is formulated as a mixed integer nonlinear model with the consideration of different constraints related to production capacity and setup costs. Some new theoretical properties regarding the convexity of the problem and its relaxation are established. Finally, numerical examples are presented to illustrate the obtained results.

Emergency Departments are highly stochastic environments caused by the unscheduled patients’ arrivals and the diverse nature of their health problems. Health services research, statistical modeling and operational research all attempt to give tools and strategies to manage crowding through crowding scores, forecasting and optimization models. This paper proposes a novel approach that evaluates crowding using a new saturation indicator. It also sets an original data-driven queuing network to model the system and help forecasting crowding and then optimizes it. This study is illustrated using data from the Emergency Department of Troyes hospital.

In order to limit climate change and reduce greenhouse gas emissions, renewable energies are increasingly being used to replace fossil energy resources such as gas, coal, and oil. To accelerate this energy transition, energy communities are encouraging factories to share and use renewable energy sources in a collective self-consumption framework. This article presents a configuration of photovoltaic self-consumption in an energy community that combines collective and individual self-consumption. A bi-objective mathematical model has been proposed for this configuration to minimize energy costs and CO2emissions. To achieve a trade-off between these two goals, two methods are used to solve this problem by converting the bi-objective problem into a single objective: the weighted sum method (WSM) and an algorithm based on the ϵ-constraint method. The performance of these two methods is evaluated using several numerical experiments. The results show that WSM gives the minimum average value (2.8026%) for the summation of the gap between the obtained objectives for each goal and their optimal values when they are considered separately. Moreover, this method yields the smallest gap value (1.3071%) compared to the ϵ-constraint method.

This paper addresses, using an exact method, the identical parallel machine scheduling problem to minimize total tardiness. In this problem, a set of jobs has to be scheduled, without preemption, on identical parallel machines. Each machine is able to treat only one job at a time. In order to prune the search tree, dominance properties are proved and lower and upper bounding schemes are proposed. A branch and bound (BAB) algorithm is developed taking into account these theoretical properties, lower and upper bounds. This BAB algorithm has been tested on a large set of randomly generated medium-sized problems. Computational results demonstrate the effectiveness of our approach over existing methods in the literature. Keywords: Parallel machine scheduling; Total tardiness; Dominance properties; Lower bound; Branch and bound

This paper addresses a dedicated lot-sizing problem from off-the-road tire industry. This case study considers a multi-item capacitated lot sizing problem with assignment on unrelated parallel machines. Several specific constraints imposed by the industrial context are considered such as the limitation of number of mold setups, the number of campaign ending per weeks and the demand class prioritization. A novel mixed integer linear program is introduced to solve this problem taking into account several primary and secondary optimization criteria. In order to analyze the sensitivity of the objective function over the different criteria weights, Taguchi method is carried out based on real data issued from the industrial case.

In this paper, we consider a dedicated lot sizing and scheduling problem inspired from a real-world application in tire industry. This problem consists in scheduling several products on parallel machines with eligibility constraints within a finite planning horizon. We consider several specific constraints such as the number of simultaneous scheduled products, the number of setup per period and minimum quantities to produce. A mathematical model that determines a production schedule minimizing backlogging, low and high inventory surplus is proposed and tested on real data with up to 210 products, 70 machines and 7 periods. The obtained results show the effectiveness of the proposed model which improve significantly the industrial solution.

We consider a single-machine bi-objective scheduling problem with rejection. In this problem, it is possible to reject some jobs. Four algorithms are provided to solve this scheduling problem. The two objectives are the total weighted completion time and the total rejection cost. The aim is to determine the set of efficient solutions. Four heuristics are described; they are implicit enumeration algorithms forming a branching tree, each one having two versions according to the root of the tree corresponding either to acceptance or rejection of all the jobs. The algorithms are first illustrated by a didactic example. Then they are compared on a large set of instances of various dimension and their respective performances are analysed.

This paper presents an extension to the classical capacitated lot-sizing problem. Demand for the products is price-dependent and modelized as an isoelastic function. The capacity is not considered as a parameter anymore, it becomes a decision variable for the company as the production, inventory and pricing decisions. This variable capacity leads to two variants of the problem: one with a time-dependent capacity and the other one a uniform capacity over the periods. We propose heuristic methods, which are based on Lagrangian multipliers, to solve variations of the problem. The methods provide high-quality results.

The majority of flow shop scheduling problems considers machines as the only resource. However, in most real-life manufacturing environments, jobs for their processing on machines may require additional non-renewable resources. Considering such resources, the scheduling problem is more realistic and much harder to solve. In this paper, we investigate the permutation flow shop scheduling problem subject to non-renewable resources constraints. The objective is to find a schedule that minimises the maximum completion time. An integer linear programming model is developed. Because of the computation time constraint, we propose an approximate resolution method based on genetic algorithm. To obtain better and more robust solutions, the Taguchi method is performed for tuning the parameters and operators of the algorithm. Furthermore, a local search is proposed to enhance the searching ability. Finally, computational experiments are conducted to evaluate the performance of both mathematical model and algorithm on different configurations of non-renewable resources availability.

While many children walk or cycle to and from school, buses and minibuses are major modes of travel for children. A typical journey of these pupils involves walking to and from the bus stop, waiting at bus stops, and boarding. The school bus routing problem, which consists of a set of students spread in a region who must be brought to and from their schools every school day, is considered in this paper. This paper describes the application of an efficient optimization software, Opta Scholar, to solve the school bus routing and scheduling problem. The application of this software showed major improvements in terms of the number of buses used and travelling costs. Furthermore, the embedded algorithm outperforms existing benchmark results on relaxed forms of the problem.

We discuss the two-machine job shop scheduling problem with availability constraints on one machine for maximum completion time (makespan) minimization. We consider the problem when unavailability periods are planned in advance and operations are non-preemptive. Firstly, some propositions considering Jackson’s rule under availability constraints are introduced. Then, we provide polynomial-time algorithm based on Jackson’s rule and an iterated local search tabu based algorithm exploiting the optimality of Jackson’s rule between each two consecutive availability periods to solve the problem. Finally, we present some experimental results.

The resource constraint project scheduling problem (RCPSP) has attracted growing attention since the last decades. Precedence constraints are considered as well as resources with limited capacities. During the project, the same resource can be required by several in-process jobs and it is compulsory to ensure that the consumptions do not exceed the limited capacities. In this paper, several criteria are involved, namely makespan, total job tardiness, and workload balancing level. Our problem is firstly solved by the non-dominated sorting genetic algorithm-II (NSGAII) as well as the recently proposed NSGAIII. Giving emphasis to the selection procedure, we apply both the traditional Pareto dominance and the less documented Lorenz dominance into the niching mechanism of NSGAIII. Hence, we adopt and modify L-NSGAII to our problem and propose L-NSGAIII by integrating the notion of Lorenz dominance. Our methods are tested by 1350 randomly generated instances, considering problems with 30–150 jobs and different configurations of resources and due dates. Hypervolume and C-metric are considered to evaluate the results. The Lorenz dominance leads the population more toward the ideal point. As experiments show, it allows improving the original NSGA approach.

The cumulative scheduling problem has been traditionally represented using a discrete time formulation, incorporating the variable resource consumption of each job as decision variables. In our research, we aim to provide a more accessible approach to formulating a specific configuration of this problem. We establish an optimality condition for resource allocation, which enables us to eliminate these decision variables and develop a simpler, more concise, and efficient continuous-time formulation.

The presented paper considers the pricing and lot-sizing decisions for a manufacturer who produces and sells a single product in different selling channels i.e physical stock, website, mobile, etc. The objective is to find the production plan and prices of each channel to maximize the total profit defined from difference between the revenues and the productions, holding and setups costs. The consumers’ demand in each channel is represented by attraction demand models which include the multinomial logit (MNL), multiplicative competitive interaction (MCI) and linear demand models. The addressed problem is formulated as a non-convex mixed-integer nonlinear program (MINLP). Based on properties of attraction functions, an efficient reformulation which transforms the initial non-convex problem into a convex one is presented. Therefore, an optimization approach based on the outer approximation algorithm is presented to solve the problem. Numerical tests based on large benchmark of real inspired instances show the efficiency of the proposed approach to solve the addressed problem compared to the initial non-convex model.

In present manufacturing environment, the reentrant scheduling problem is one of the most important issues in the planning and operation of production systems. It has a large scope such as capacity distribution and inventory control. On the other hand, the markets are very competitive; it is a critical requirement of operational management to have effective management of resources (consumable and renewable) so as to achieve optimal production plan. This study considers a reentrant parallel machines scheduling problem with consumable resources. Each job consumes several components and must be processed more than once in a stage composed of identical parallel machines. The resources availability, jobs assignment and sequencing at each cycle and are considered and optimized simultaneously. On the basis of this representation, a MILP model is developed. Thus, that MILP model can be used for the problem in order to find the exact solution. Since, this problem is clearly NP-hard, and optimal solutions for large instances are highly intractable, a genetic algorithm is developed to obtain near-optimal solution. Then, an improvement phase based on different local search procedures are performed and examined to generate better solutions. The system performances are assessed in terms of measures such as the solution quality and the execution time. The effectiveness of the proposed metaheuristic is examined based on comparative study. The simulation results demonstrate that the presented algorithm is able to find an optimal solution for small-sized problems and can effectively find a near optimal solution for large-sized problems to minimize the makespan of the considered problem.

This paper proposes a new optimization approach to find the best redundancy allocation of dependent repairable series k—out—of—n : Gsystem using ant colony optimization. A model for the failure redundant dependency between the system components is formulated. It is considered in the system performance evaluation. An optimization model is then derived to be solved using the software LINGO and the proposed ACO algorithm. The objective consists of minimizing the system cost while satisfying a required level of system availability. The best system configuration in terms of number of components and number of repair teams in each subsystem is to be determined. The components are characterized by their failure rate, repair rate, unit acquisition cost and unit repair cost. In order to validate the proposed approach, an illustrative example is applied. A comparison between LINGO and ACO results is facilitated. The results show that the redundant dependency may positively affect the design configuration and the system cost. Furthermore, the proposed ACO outperforms LINGO for large scale systems in term of solution quality.

We treat a two-machine job shop scheduling problem with availability constraint on one machine to minimize the makespan. We consider the deterministic case where the unavailability period, corresponding to preventive maintenance tasks, is known in advance and fixed. We assume that jobs are non-preemptive. First, two mixed-integer programming (MIP) models are first presented. Some propositions concerning the optimality of Jackson’s algorithm when availability constraint exists is provided. Then a branch and bound (B&B) method is developed to solve the problem. The obtained results prove the efficiency of the proposed B&B.

This article proposes a mixed integer model and a genetic algorithm to address a two cranes scheduling problem with non-interference constraints. The problem is about trucks’ loading scheduling with two cranes. Cranes must load trucks with a predefined amount of coils without preemption and the coils retrieval must be scheduled as well. The equipments are subject to non-interference constraints because they share the same rail inside the shed. The mathematical model and the heuristic are computationally tested among different instances.

Parallel redundancy is a fundamental concept widely used to maximize system reliability/availability. However, any enhancement in the system reliability requires resources which consequently increases the costs. Hence, it is important to optimize the system configuration at the design phase. It is demonstrated that modeling the dependency between system components can be an effective option to improve system reliability in an economic way. But, the resolution of the optimization problem by analytic methods may be difficult and expensive in terms of computational time especially for large scale and complex systems. This paper proposes a rapid and effective approach to optimize the cost of a reparable system with dependent components under the availability constraint. The optimization model is solved using the software tool LINGO and the genetic algorithm (GA). A comparison between LINGO and the GA results is established. The results show the superiority of the GA in term of speed with a small difference in the exactitude of the obtained solutions.

Emergency department (ED) has become the patient’s main point of entrance in modern hospitals causing it frequent overcrowding. Thus hospital managers are increasingly giving attention to the ED in order to provide better quality service for patients. One of the key elements for a good management strategy is demand forecasting. In this case, forecasting patients flow, which will help decision makers to optimize human(doctors, nurses...) and material (beds, boxs...) resources allocation. The main interest of this research is forecasting daily attendance at an emergency department. The study was conducted on the Emergency Department of the Hospital of Troyes city, France, in which we propose a new practical ED patients classification that consolidate the CCMU and GEMSA into one single category and an innovative time-series based forecasting models to predict long and short term daily attendance. The models we developed for this case study shows very good performances (up to 92, 29% for the annual Total flow forecast) and robustness to epidemic periods.

Most of traditional scheduling problems deal with machine as the only resource, however, other resources such as raw materials is often disregards. Considering the second resource makes scheduling problems more realistic and practical to implement in manufacturing industries. Due to the applicability of flow shop environment in different manufacturing, scheduling of these types of shops are extensively studied by several authors. However, introducing an additional resource in this environment is not well studied. The present work deals with makespan minimization in flow shop scheduling problems where no renewable resources constraints are considered. The paper illustrates the importance of Johnson (1954) algorithm for the two machine flow shop under resources constraints. A mathematical model is also presented. Then a well-know heuristic is adapted to propose fast solution for the m machines flow shop problem subject to resource constraint.

This paper presents an open shop scheduling problem based on a real mechanical workshop made of mmachines that process njobs. It deals with different resource constraints related to the tools allocation and the multi-skills staff assignment. Resource skills and their availability are required to execute one process task. In this work, we expose a multi-objective problem where the idea is to minimize three objectives simultaneously. The first one considers the minimization of the total flow time of jobs in the production system, then the workload balancing concerning both, humans and machines is addressed. We propose and compare two multi-objective methods: NSGA-II and NSGA-III. Computational experiments are designed according to the literature, we expose the small and large sized instances to present the general performance of these algorithms using the hyper-volume metric to compare them.

This study examines large sample size of instances and tries to extract useful knowledge about the domain of parallel machine scheduling problem (PMSP) and solution space explored. The aim of this study is to provide statistical interpretations and classify the differences between the instances. The interrelationship between specified predetermined inputs and the output is examined through artificial neural networks (ANNs) along with regression analysis since they can easily explore which inputs are related to the output and develop regression model. The results of both analyses reveal significancies of the relationships and predicted importance of the predetermined inputs on the output. Furthermore, we examined the behaviours or patterns of the instances, after realizing the easiness and hardiness of the instances accentuating the differences. In order to link the predetermined inputs of instances with the performances of the set of tested methods, the differences between instances are evaluated in terms of variability. Then, we grouped instances into three clusters, specifying as exact, equal and difficult zones, for information retrieval about their complexities via hierarchical clustering method.

We consider a chain reentrant shop problem with m machines, in which a no-wait constraint is imposed. Each job goes through m machines in a fixed order and return back to the first machine for its last operation. We seek to minimize the overall finish time (makespan). We first proof some new NP-hard problems in the case of two machines. For the resolution of the general problem we propose a genetic algorithm with numerical experiments.

The Resource Constraint Project Scheduling Problem (RCPSP) is one of the most challenging and interesting problems within the scheduling topic. It brings more and more attention to the researchers since the recent decades. In the RCPSP, we consider a project scheduling problem with a set of jobs submitted to precedence relationships as well as a set of resources with limited capacities. During each period, it is compulsory to guarantee enough resources since one resource could be required by several jobs in process. In this paper, we tackle a bi-objective RCPSP with minimization of the makespan and the total job tardiness. In order to find optimal Pareto fronts, we have introduced two bi-objective mathematical formulations and solved our problem with the two phase method (TPM). Our method is tested with instances randomly generated considering 10, 15, 20 jobs and 4 types of resources. In this paper, we report the experimental results within 1800s and more tests will be launched to explore the limits of the exact method.

This article discusses a variant of flow shop scheduling problem based on a real case issued from an electro-electronic material industry. The environment under study consists of an assembly line formed by two semi-lines and a final synchronization operation. Semi-lines have parallel operations and provide semi-products for the synchronization operation which completes the manufacturing activity by the assembly of semi-products. The aim of this study is to achieve the schedule of jobs in order to minimize the makespan. This article presents the achieved results by a mixed integer linear programming model, variants of the NEH heuristic and an adaptation of Johnson’s algorithm dedicated to the proposed special case. The proposed methods are tested among different instances inspired from Taillard (1993)’s benchmark. Based on the obtained results, we discuss the performance of the different methods.

An open shop scheduling problem based on a mechanical workshop is described here. The main objective is to find the sequence of jobs which minimizes the total flow time. For that reason, we first formulate the problem as a mixed integer linear programming model which considers different resource constraints related to the personnel assignment. Resource skills and their availability are required to process tasks. A mathematical model is described and solved optimally. Besides that, a fuzzy ant colony optimization method is proposed due to the difficulty to fix the different parameters of an ACO and improve the quality of the solution. Finally, some computational experiments are defined using the references of the literature to get efficiency of ant colony optimisation. A first kind of tests are related to the small-sized instances allowing to evaluate the general performance of the model and the algorithm while a second one involves the large-size instances showing a further evaluation of the algorithm.

This article deals with a job scheduling on two cranes subject to non-interference constraints. It is based on a real case at a distribution center of steel coils, where two cranes sharing the same rail must load a sequence of trucks, which has a defined demand of coils. The problem is mathematically modeled as a parallel machines with ordering variables and with time index variables. The proposed formulations are computationally tested among different instances. Based on the results, we discuss which formulation might work best for these problems.

This paper addresses the workload balancing problem in identical parallel machines context. The problem consists of assigning ndifferent jobs to midentical parallel machines in order to minimize the workload imbalance among the different machines. This problem is formulated as a linear mixed integer program to minimize the difference between the greatest and smallest workload assigned to each machine. Based on some numerical examples reported in the literature, we establish that the classical formulation which consists of minimizing the greatest machine completion time does not provide the optimal workload repartition. That is why we consider a new mathematical formulation based on the minimization of the difference between the workload of the bottleneck machine and the workload of the fastest machine. The proposed programming method is also used to provide optimal solutions in reasonable computational times for different test problems presented in the literature by Raghavendra and Murthy 10to test their genetic algorithm.

Multiobjective optimisation methods for an assembly line design problem are presented in this paper. The studied problem consists of two parts. Among a set of candidate machines, the first part of the problem aims to assign a single machine to each workstation. The goal of the second part is to size the intermediate buffers. Two objectives are taken in consideration for our design problem: the minimisation of the cost of the line and the maximisation of the throughput rate. Different new multiobjective methods are developed to solve the problem. First, a multiobjective ant colony optimisation algorithm is proposed. Then, in order to get better results, the first algorithm is coupled with a guided local search. The third method proposed for the first time to solve our problem, called L-ant, is based on a multiobjective ant colony algorithm but using the Lorenz dominance. The fourth algorithm is another new method based on genetic algorithms and the Lorenz dominance and called Lorenz-archive. In order to compare the different methods to each others and to assess their efficiency, different measuring criteria are applied on the best fronts with the non-dominated solutions.

This article examines the multi-objective scheduling of a reentrant hybrid flow shop. This type of shop is composed of several stages made of several identical parallel machines. When a task has to be processed on a stage, it is assigned to the machine with the smallest workload. This problem shows a reentrant structure: each task must be processed several times at each stage. In this paper, this problem is solved by minimizing two objectives: the makespan (maximum completion time of the jobs) and the total tardiness of the tasks. A new method is improved with different local searches: Adjacent and Non Adjacent Pairwise Interchange, Extract and Backward-Shifted Reinsertion, and Extract and Forward-Shifted Reinsertion. Every local search is tuned with statistical method (design of experiment) and the best one is worked out. This method is compared with the best one in several instances. The results involve three different measures.

In this paper, the authors consider a single machine scheduling problem with rejection. In traditional research, it is assumed all jobs must be processed. However, in the real-world situation, certain jobs can be rejected. In this study, the jobs can be either accepted and scheduled or be rejected at the cost of a penalty. Two objective functions are considered simultaneously: (1) minimization of the sum of weighted completion times for the accepted jobs, and (2) minimization of the sum of penalties for the rejected jobs. The authors apply two-phase method (TPM), which is a general technique to solve bi-objective combinatorial optimization problems, to find all supported and non-supported solutions for small-sized problems. The authors present a mathematical model for implementing both phases. On the other hand, three different bi-objective simulated annealing algorithms have also been developed to find a good estimation of Pareto-optimal solutions for large-sized problems. Finally the authors discuss the results obtained from each of these algorithms.

ABSTRACTArtificial intelligence techniques are important tools for modelling and optimizing the solid-state fermentation (SSF) factors. The performance of fermentation processes is affected by numerous factors, including temperature, moisture content, agitation, inoculum level, carbon and nitrogen sources, etc. In this paper, the identification of non-linear relationship between fermentation factors and targeted objectives is performed, first, using the learning capabilities of a neural network (NN). Then, this approach is coupled with various artificial intelligence techniques to optimize the fermentation process, such as Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The effectiveness of different approaches is compared with the classical statistical techniques, such as Response Surface Methodology (RSM), that are increasingly being used. This paper presents the first attempt to adapt these approaches on the solid state fermentation process. The obtained results prove the effectiveness of the proposed approach. Particularly, we show that this approach leads to a significant improvement on the fermentation process performance.

The considered problem is the scheduling of a set of jobs on identical parallel machines in order to minimize the total tardiness without any preemption or splitting. Each job has its own processing time, release date and due date. All the machines are considered identical (with same speed) and available during all the scheduling period. This problem is NP-hard. An exact resolution, an Ant Colony Algorithm (ACO), a Tabu Search (TS) method, a set of Heuristics based on priority rules and an adapted Biskup Hermann Gupta (BHG) method are proposed to solve the problem. The obtained results are discussed and analyzed.

Reliability optimization is an important step in industrial systems design. In order to develop a reliable system, designers may introduce different redundant technologies with the same functionality in parallel. In this paper, each technology is assumed to be composed of series components. The obtained configuration belongs to the series-parallel systems. The presented tool is for the design or the improvement of such systems, in order to minimize the system cost with a reliability constraint. The aim is to find the reliability to allocate to each component in order to minimize the total cost, such that the global system reliability verifies a minimal level constraint. This problem is known to be NP-hard. In this paper, a metaheuristic approach, based on the Ant Colony Optimization technics (ACO), is used in order to improve an existing approach. The experimental results, based on randomly generated instances, outperform the one of previous method dedicated to this problem.

In this paper, we are studying the buffer sizing problem in production lines. It is one of the most studied subjects in the literature with one criterion but shows a lack of studies in its multiobjective form. Therefore, we present two multiobjective optimization techniques to solve the problem and which are: the second version of the Strength Pareto Evolutionary Algorithm (SPEA2) and a multiobjective ant colony optimization algorithm. Three measures are used in order to compare the performances of the two developed algorithms. The algorithms are then applied for buffers sizing in a packaging line. A simulation model with the ARENA software is used for the performances evaluations. The simulation model is coupled with the optimization algorithms applying then the simulation based optimization technique. The numerical results of the algorithm based on ant colony are very relevant and show an advantage compared to the SPEA2.

This article presents the scheduling of a reentrant maintenance line with parallel machine stages. In this study the system is composed of machines with their upstream buffer and are modeled by queuing system. The criteria are the maximization of the utilization rate of the bottleneck and the minimization of the mean cycle time of the products. We present the results obtained by a multi-objective ant colony algorithm with local search (MOACS-LS), which are compared with the results obtained by one of the most competitive genetic algorithm called Non-dominated Sorting Genetic Algorithm version 2 (NSGA2). This two metaheuristics are coupled with a discrete event simulation module. Our results are compared with an industrial solution.

To study the impact of COVID-19 pandemic lockdown on avoided emergency department visits and consequent hospitalizations.

This article deals with line configuration problems for mass production. Such a production line is made of several workstations. Each station can contain one or several pieces of equipment and employees. Multi-objective optimization algorithms are proposed for two closed problems: P1, choosing equipment for each station and P2, both line balancing and equipment selection. Incompatibilities of pieces of equipment as well as precedence constraints among operations are taken into account. A multi-objectives MULTISTART algorithm and adapted NSGA-II are developed to solve these two problems. The obtained results are promising.

In this paper we consider a transfer line balancing and equipment selection problem. The considered lines consist of several workstations in series. Each workstation can contain one or more multi-spindle heads. All tasks of each spindle head are executed in parallel. We take into account the constraints related to the impossibility of combining some spindles heads at the same workstation and the precedence constraints among the tasks. Our objective is to carry out the assignment of tasks to spindle heads and spindle heads to workstations minimizing the total equipment cost. For this problem, we propose a B&B algorithm. For large scale problems, we developed a heuristic based on a branching (exploration) strategy and Genetic Algorithm. The suggested exact and approximate algorithms give some interesting results.

A heuristic-based optimization algorithm is proposed in this paper for on-line scheduling and assignment of preventive maintenance jobs to processors, to minimize under availability constraints, on a given time-window, the total cost of the maintenance operations of a distributed system. This algorithm minimizes the cost of discharge of preventive maintenance tasks or jobs, while assigning the tasks along with balancing the processors load. It is shown that the problem is NP-hard. To solve it, the concept of job emergency is introduced and the priority rule for total flow time (PRTF) criterion is used in an adapted heuristic job-scheduling model. In addition, the algorithm considers the constraints of precedence among consecutive standby jobs and their emergency. It is depicted the specific properties of the proposed heuristic allowing jobs scheduling in the right order. Computational results illustrate the efficiency of the approach implemented on different system configurations.

Abstract: The traditional production planning model based upon the famous linear programming formulation has been well documented. However, the integration of preventive maintenance planning in the same model is a recent problem. This paper proposes an extended linear programming model as a hybrid approach for computing the optimum production plan with minimum total cost. The dual objective problem of production planning and maintenance is treated into a mixed integer linear program. This program is not only considering cases of multi-lines, multi-periods and multi-items but also taking into account the deterioration of the lines. This deterioration is represented in the model as a reduction of production lines capacities in function of the time evolution. Maintenance operations are supposed to provide lines in an operational state as good as new, i.e. with a maximum capacity. Through the study of the models limits, it is shown that the proposed approach can deal with a broader range of problems than that of Aghezzaf and Najid (2008) [3]. An optimal relaxation technique based on the polyhedral theory is developed to improve the computational time and expand the limits of the proposed model. Also, a “Fix and Relax heuristic” is developed for complex problems. Their computation time and their difference are computed referring to the same lower bound and the same considerations as those presented by Aghezzaf and Najid. It is proved through more than 880 several simulations for each model with different capacities and different setup costs, that this approach can solve large size problems with moderate computational time and gap. [Copyright &y& Elsevier]