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This paper addresses the hybrid flow shop scheduling problem by considering job rejection to minimize the sum of the total tardiness cost of the scheduled jobs and total cost of the rejected jobs as a single-objective problem. A mixed-integer linear programming model is proposed to solve small-sized problems within an acceptable computational time. Also, this paper exhibits two innovative heuristic algorithms, which are presented to discover fast solutions for the problem along with five meta-heuristics are adapted to solve large-sized problems in the model. Another contribution of this paper is to illustrate the different encoding and decoding methods adapted to algorithms, which are capable of obtaining a feasible schedule and furthermore, to guarantee the efficiency of the solutions based on the schedule. The results obtained from the computational study demonstrate the mathematical model and proposed algorithms effectiveness. Additionally, this paper studies the efficacy of job rejection noting the scheduling for a real-world hybrid flow shop in the tile industry production system. As well as, in this paper, the problem is viewed from a bi-objective problem perspective, so that the tardiness costs of the scheduled jobs and rejection costs of rejected jobs as two objectives are minimized simultaneously to obtain the Pareto solutions. We analyze relationship between the results of the single-objective and bi-objective approaches on small and large-sized problems.

Resource scheduling, job sequencing, and assigning them to available resources are the most critical issues in manufacturing systems, such as flexible job-shop systems. In addition, scheduling uncertainties have attracted significant attention in this field. This study investigates the dual-resource constrained flexible job-shop scheduling (DRCFJSS) problem under machine breakdown and operational uncertainty. Stochastic scenario-based methods were utilized to study the uncertain nature of the problem. Because process times have inherent uncertainty, they are considered fuzzy numbers and are controlled by a credibility-based measure. Robust scheduling must be developed to address unexpected disruptions, such as machine breakdowns and operational risks, such as uncertain process times. Accordingly, a novel robust fuzzy stochastic programming (RFSP) model is presented for this problem. In the proposed RFSP model, the objective function is formulated using a hybrid measure (i.e., a combined average-case and worst-case performance of the manufacturing system) under probable machine breakdown scenarios. Because the DRCFJSS problem is NP-hard, two types of meta-heuristic algorithms, evolutionary population-based, genetic algorithm (GA), and vibration damping optimization (VDO) algorithm, are used for large-sized problems. Then, the proposed RFSP model was applied to a case study, and numerical experiments with randomly generated test problems were used. In small-sized problems, the proposed model is solved using the CPLEX solver, GA and VDO algorithms. Also, the computational study confirms the proper quality of the results of the GA and VDO algorithms in medium and large-sized problems.

This paper presents a multi-objective dual-resource constrained flexible job-shop scheduling problem (MODRCFJSP) with the objectives of minimizing the makespan, critical machine workload and total workload of machines simultaneously. Two types of multi-objective evolutionary algorithms including fast elitist non-dominated sorting genetic algorithm (NSGA-II) and non-dominated ranking genetic algorithm (NRGA) are proposed for solving MODRCFJSP. Some efficient mutation and crossover operators are adapted to the special chromosome structure of the problem for producing new solutions in the algorithm’s generations. Besides, we provide controlled elitism based version of NSGA-II and NRGA, namely controlled elitist NSGA-II (CENSGA-II) and controlled elitist NRGA (CENRGA), to optimize MODRCFJSP. To show the performance of the four proposed algorithms, numerical experiments with randomly generated test problems are used. Moreover, different convergence and diversity performance metrics are employed to illustrate the relative performance of the presented algorithms.

Providing appropriate home health care is one of the increasing concerns in the health care organizations. Home Health Care provides various services for disabled or elderly individuals at their homes. Also, deal with the current critical situation of the coronavirus disease (COVID-19) due to the limited capacity of hospitals and the feeling of insecurity in crowded places, home health care is more recommended. This paper addresses a Home Health Care Routing and Scheduling Problem (HHCRSP) with two modes of transportations including public and private modes. Also, multi-depot version of the problem is studied to enhance the service delivery in scattered points. In this study, a mathematical model is presented based on a Mixed Integer Linear P‌rogramming (MILP) whose objective function is minimization of the sum of the travel distance and overtime costs. Furthermore, three meta-heuristic algorithms including Invasive Weed Optimization (IWO), Grasshopper Optimization Algorithm (GOA) and Simulated Annealing (SA) are presented for solving the large-sized problems. Since the performance of meta-heuristic algorithms depends on setting the parameters, Taguchi method is used to statistically set parameters of the developed algorithms. The computational results have shown that the proposed IWO has worked better than the other two proposed algorithms statistically.

The utilization of renewable biomass as feedstock for biofuel production has been gaining prestige to mitigate carbon emissions. This paper aims to render an optimization model for designing a resilient and reliable biomass-to-biofuel supply chain network in which multi-modal terminals, biorefineries, and their connection link could be faced with disruption. For this purpose, risk-averse optimization, transitional probabilities, and spatial statistics models are employed. What is more, to overcome the repercussions of uncertain demand and the availability of raw materials, risk pooling and M/M/1 queuing system are considered. Finally, since the proposed model is an NP-hard problem, two meta-heuristic algorithms called improved ray optimization and colliding bodies optimization are employed to solve the proposed model. Computational results reveal that by increasing 20% of the conversion rate of biomass to biofuel, which leads to increase biomass supply, biofuel production increases by 20.88%, and the SC cost declines by 9.32% due to the better capacity distribution of bio-refineries. Also, the proposed model increases by 20.87% in the total SC costs under random disruptions than the risk-neutral model, although the failure costs of SC decline by 80.45%.

Among various distribution networks, the idea behind the cross-docking for cost reduction is to decrease/eliminate inventory to the extent possible. In classical cross-dock, it is assumed that there is one truck for each supplier and customer. Yet, one truck for each supplier and customer can be very costly and consequently ineffective. Each truck likely can serve more than one supplier/customer in its pickup/delivery process. Therefore, to more actualize the cross-dock problem, it can be extended with the truck routing problem, i . e ., the truck scheduling in the cross-docking system and truck routing in the pickup/delivery process. Hence, this paper considers the integrated truck routing and scheduling problem. First, the problem is formulated as a mixed integer linear programming model. Using this model, we solve small-sized instances to optimality. Moreover, two metaheuristics, a reactive tabu search with path relinking and a generational genetic algorithm with a local search and restart phase, are proposed to solve large instances. The parameters of the proposed algorithms are tuned. Finally, the performance of the proposed algorithms is evaluated.

This paper considers the problem of multi factory parallel machine problems.We analyzed the available model and algorithm and showed that they suffer from serious shortcomings.We propose three intelligent mathematical models for makespan and total completion time objectives.We propose effective metaheuristics based on artificial bee colony algorithm.The proposed models and algorithm significantly outperform the available model and algorithm. This paper considers the problem of multi factory parallel machine problems. This problem is already studied in the literature and there is a mathematical model and a genetic algorithm for the problem. We analyzed the model and algorithm, and showed that they suffer from serious shortcomings. Then, we propose three mathematical models for makespan and total completion time objectives. The proposed models are compared with the available model in both size and computational complexities. The available model is significantly outperformed. Moreover, we propose three effective metaheuristics based on artificial bee colony algorithms. The proposed metaheuristics are compared against the four available algorithms on both small and large instances. The proposed metaheuristics perform much more effectively.

The cross-docking is a warehousing strategy where trucks arrive from suppliers to unload, regroup, and reload their items onto trucks going to the customers. There is a common assumption that the receiving (shipping) dock has only one single door, but companies usually duplicate doors in parallel to expedite operations. This paper studies the problem of scheduling trucks in the cross-dock with multiple inbound and outbound doors with makespan minimization. The problem is first formulated by a new mixed integer programming model. Using the model, small instances are solved to optimality. The paper then proposes a hunting search metaheuristic inspired from group behavior of animals when searching and hunting for food. A set of experimental instances are carried out to evaluate the algorithm. The algorithm is carefully evaluated against the adaptation of two existing algorithms. The results show that the algorithm provides sound performance comparing with other algorithms.

Rad se bavi problemom planiranja predavanja gdje postoji niz kolegija, predavača i učionica. Kolegiji se dodjeljuju i planiraju tako da se maksimalno zadovolje preferencije. Razvijamo matematički model problema u obliku linearnog programa cijelih brojeva. Manji se problem može optimalno riješiti primjenom komercijalnog softvera. Zatim razvijamo tri različite metaheuristike na temelju umjetnih imunih, genetičkih i algoritama simuliranog kaljenje. Te tri metode rješenja opremljene su novim postupcima kao što su operatori kretanja i križanja. Parametri predložene metaheuristike najprije se usklađuju, a zatim procjenjuju optimalnim rješenjima koje je model pronašao. Nadalje se procjenjuju usporedbom njihovih performansi. Eksperimenti pokazuju da je umjetni imuni algoritam uspješniji od drugih algoritama., This paper deals with the problem of course scheduling where we have a set of courses, lecturers and classrooms. Courses are assigned and scheduled in such a way that the total preference is maximized. We develop the mathematical model of the problem in form of a linear integer program. The small sized problem can be solved to optimality using commercial software. We then develop three different metaheuristics based on artificial immune, genetic and simulated annealing algorithms. These three solution methods are equipped with novel procedures such as move and crossing operators. The parameters of the proposed metaheuristics are first tuned, and then they are evaluated with optimal solutions found by the model. They are, furthermore, evaluated by comparing their performance. The experiments demonstrate that the artificial immune algorithm performs better than the other algorithms.

In this paper, the resource constrained project scheduling problem with preemption is studied in which fixed setup time is needed to resume the preempted activities. The project entails activities with finish-to-start precedence relations, which need a set of renewable resources to be done. A mathematical model is presented for the problem and a hybrid of Tabu Search (TS) and Simulated Annealing (SA) with tuned parameters is developed to solve it. In order to evaluate the performance of the proposed TS/SA a set of 100 test problems is applied. Comprehensive statistical analysis shows that the proposed algorithm efficiently solves the problem. Furthermore, the benefits of preemption with setup times and its justifiability is demonstrated numerically.

Scheduling is a major issue faced every day in manufacturing systems as well as in the service industry, so it is essential to develop effective and efficient advanced manufacturing and scheduling technologies and approaches. Also, it can be said that bi-criteria scheduling problems are classified in two general categories respecting the approach used to solve the problem. In one category, the aim is to determine a schedule that minimizes a convex combination of two objectives and in the other category is to find a good approximation of the set of efficient solutions. The aim of this paper is to determine a schedule for hybrid flowshop problem that minimizes a convex combination of the makespan and total tardiness. For the optimization problem, a meta-heuristic procedure is proposed based on the simulated annealing/local search (SA/LS) along with some basic improvement procedures. The performance of the proposed algorithm, SA/LS, is compared with a genetic algorithm which had been presented in the literature for hybrid flowshop with the objective of minimizing a convex combination of the makespan and the number of tardy jobs. Several computational tests are used to evaluate the effectiveness and efficiency of the proposed algorithm against the other algorithm provided in the literature. From the results obtained, it can be seen that the proposed algorithm in comparison with the other algorithm is more effective and efficient.

The aim of this paper is to study multi-objective flexible job shop scheduling problem (MOFJSP). Flexible job shop scheduling problem is a modified version of job shop scheduling problem (JSP) in which an operation is allowed to be processed by any machine from a given set of capable machines. The objectives that are considered in this study are makespan, critical machine work load, and total work load of machines. In the literature of the MOFJSP, since this problem is known as an NP-hard problem, most of the studies have developed metaheuristic algorithms to solve it. Most of them have integrated their objective functions and used an integrated single-objective metaheuristic algorithm though. In this study, two new version of multi-objective evolutionary algorithms including non-dominated sorting genetic algorithm and non-dominated ranking genetic algorithm are adapted for MOFJSP. These algorithms use new multi-objective Pareto-based modules instead of multi-criteria concepts to guide their process. Another contribution of this paper is introducing of famous metrics of the multi-objective evaluation to literature of the MOFJSP. A new measure is also proposed. Finally, through using numerous test problems, calculating a number of measures, performing different statistical tests, and plotting different types of figures, it is shown that proposed algorithms are at least as good as literature’s algorithm.

In problem of portfolio selection, financial Decision Makers (DMs) explain objectives and investment purposes in the frame of multi-objective mathematic problems which are more consistent with decision making realities. At present, various methods have introduced to optimize such problems. One of the optimization methods is the Compromise Programming (CP) method. Considering increasing importance of investment in financial portfolios, we propose a new method, called Nadir Compromising Programming (NCP) by expanding a CP-based method for optimization of multi-objective problems. In order to illustrate NCP performance and operational capability, we implement a case study by selecting a portfolio with 35 stock indices of Iran stock market. Results of comparing the CP method and proposed method under the same conditions indicate that NCP method results are more consistent with DM purposes.

This article addresses the flexible job-shop scheduling problem (FJSP) to minimize makespan. The FJSP is strongly NP-hard and consists of two sub-problems. The first one is to assign each operation to a machine out of a set of capable machines, and the second one deals with sequencing the assigned operations on all machines. To solve this problem, an artificial immune algorithm (AIA) based on integrated approach is proposed. This algorithm uses several strategies for generating the initial population and selecting the individuals for reproduction. Different mutation operators are also utilized for reproducing new individuals. To show the effectiveness of the proposed method, numerical experiments by using benchmark problems are conducted. Consequently, the computational results validate the quality of the proposed approach.

Following an idea of Benedict H. Gross [2] who presented an elliptic curve test for Mresenne numbers Mk= 2k− 1 we propose a similar test.