Single machine scheduling with release dates and job delivery to minimize the makespan

AbstractIn single machine scheduling with release dates and job delivery, jobs are processed on a single machine and then delivered by a capacitated vehicle to a single customer. Only one vehicle is employed to deliver these jobs. The vehicle can deliver at most c jobs at a shipment. The delivery completion time of a job is defined as the time at which the delivery batch containing the job is delivered to the customer and the vehicle returns to the machine. The objective is to minimize the makespan, i.e., the maximum delivery completion time of the jobs. When preemption is allowed to all jobs, we give a polynomial-time algorithm for this problem. When preemption is not allowed, we show that this problem is strongly NP-hard for each fixed c≥1. We also provide a 53-approximation algorithm for this problem, and the bound is tight

Reflexive governance of import substitution mechanism in clusters

The article considers reflexive governance of import substitution mechanism in clusters.Specifics of import substitution in clusters have been revealed.It is assumed that effective use of reflexive governance will allow one to optimize the interaction of economic entities in a region on the basis of the allocating a priority economic cluster which will lead to an increase in the social and economic efficiency of the regions of Russia and will help to bridge the gap between the development of regions.Commodity Distribution and Wholesale Market Situation Research Institute for the Study of in the Market.peer-reviewe

Логистическая интеграция в многоуровневых системах управления запасами и дистрибуцией

SUSTAINABLE ECONOMIC DEVELOPMENT: INTERNATIONAL AND NATIONAL ASPECTS (Novopolotsk, 25 - 26 October 2012) V.2. Ivanov DAСекция 4. ПРОБЛЕМЫ И СОВРЕМЕННЫЕ ТЕНДЕНЦИИ ЭВОЛЮЦИИ ЛОГИСТИКИ В КОНТЕКСТЕ УСТОЙЧИВОГО РАЗВИТИ

Collaborative scheduling of machining-assembly in complex multiple parallel production lines environment considering kitting constraints

In multi-stage machining-assembly production, collaborative scheduling for multiple production lines can effectively improve the execution efficiency of production planning and increase the effective output of the production system. In this paper, a production scheduling mathematical model was constructed for the collaborative scheduling problem of machining-assembly multi-production lines with kitting constraints, with the optimization objectives of minimizing assembly completion time and tardiness time. For the scheduling model, the product assembly process is constrained by the machining sequence of the jobs on the machining lines. Only by collaborating on the production scheduling schemes of the machine line and the assembly line as a whole can the output efficiency of the product on the assembly line be improved. An improved hybrid multi-objective optimization algorithm named SMOEA/D is designed to solve this scheduling model. The algorithm uses adaptive parents’ selection and mutation rate strategies and integrates the Tabu search strategy for the search process in the solution space when the solution of the sub-problem has not been improved after specified search generations, to improve the local search ability and search accuracy of MOEA/D algorithm. To verify the performance of the SMOEA/D algorithm in solving machining-assembly collaborative scheduling problems in production systems with different resource configurations and scales, two sets of numerical experiments were designed, corresponding to situations where the number of operations on each production line is equal or unequal. The running results of the proposed algorithm were compared with three other well-known multi-objective algorithms. The comparison results indicate that the SMOEA/D algorithm is effective and superior for solving such problems

Secuenciación de trabajos en sistemas de producción flexibles

Objetivos y método de estudio: Uno de los objetivos de este trabajo es el de estudiar un problema complejo de secuenciación de trabajos en sistemas flexibles, conocido en la literatura como Flexible Job Shop Scheduling Problem (FJSP); y proponer un algoritmo de optimización para resolverlo. El algoritmo se basa en un esquema tipo ALNS (Adaptive Large Neighborhood Search) híbrido, el cual, en ciertas iteraciones, hace llamadas al branch-and-bound de CPLEX para resolver el FJSP, usando un modelo propuesto por Vahid Roshanaei[47] . El segundo de los objetivos es el estudio de un problema de secuenciación de trabajos presente en una empresa cervecera de la localidad. Puesto que las características y restricciones del problema de producción de cerveza difieren con las del clásico FJSP, se desarrolla un algoritmo de optimización de tipo GRASP (Greedy Randomized Adaptive Search Procedure), el cual será el punto de inicio para una futura implementación en la empresa. Contribuciones y conclusiones: El ALNS propuesto para la solución del FJSP probó ser eficiente para un gran número de instancias tomadas de la literatura, alcanzando soluciones óptimas para más de la mitad de las instancias en las que el óptimo ha sido reportado en la literatura. Para mejorar la calidad de las soluciones (no óptimas) generadas por el algoritmo propuesto, se propone variar los parámetros del algoritmo, o bien, añadirle otro tipo de reactividad para que ajuste de manera automática los parámetros. En cuanto al caso estudiado de la compañía cervecera, nos proporcionaron dos instancias reales junto con la solución implementada en la planta, esta se comparó con la solución reportada por el GRASP propuesto y se observó que la solución reportada por GRASP permite un ahorro de hasta el 28 % (6 días) con respecto al tiempo de producción requerido por la solución implementada por la compañía

SUPPLY CHAIN SCHEDULING FOR MULTI-MACHINES AND MULTI-CUSTOMERS

Manufacturing today is no longer a single point of production activity but a chain of activities from the acquisition of raw materials to the delivery of products to customers. This chain is called supply chain. In this chain of activities, a generic pattern is: processing of goods (by manufacturers) and delivery of goods (to customers). This thesis concerns the scheduling operation for this generic supply chain. Two performance measures considered for evaluation of a particular schedule are: time and cost. Time refers to a span of the time that the manufacturer receives the request of goods from the customer to the time that the delivery tool (e.g. vehicle) is back to the manufacturer. Cost refers to the delivery cost only (as the production cost is considered as fi xed). A good schedule is thus with short time and low cost; yet the two may be in conflict. This thesis studies the algorithm for the supply chain scheduling problem to achieve a balanced short time and low cost. Three situations of the supply chain scheduling problem are considered in this thesis: (1) a single machine and multiple customers, (2) multiple machines and a single customer and (3) multiple machines and multiple customers. For each situation, di fferent vehicles characteristics and delivery patterns are considered. Properties of each problem are explored and algorithms are developed, analysed and tested (via simulation). Further, the robustness of the scheduling algorithms under uncertainty and the resilience of the scheduling algorithms under disruptions are also studied. At last a case study, about medical resources supply in an emergency situation, is conducted to illustrate how the developed algorithms can be applied to solve the practical problem. There are both technical merits and broader impacts with this thesis study. First, the problems studied are all new problems with the particular new attributes such as on-line, multiple-customers and multiple-machines, individual customer oriented, and limited capacity of delivery tools. Second, the notion of robustness and resilience to evaluate a scheduling algorithm are to the best of the author's knowledge new and may be open to a new avenue for the evaluation of any scheduling algorithm. In the domain of manufacturing and service provision in general, this thesis has provided an e ffective and effi cient tool for managing the operation of production and delivery in a situation where the demand is released without any prior knowledge (i.e., on-line demand). This situation appears in many manufacturing and service applications

Green Logistics Oriented Framework for the Integrated Scheduling of Production and Distribution Networks - A Case of the Batch Process Industry

Nowadays, most consumable goods are produced and transported in batches. Within the globalized environment, the flow of these batches is raising dramatically to satisfy the recurrent demands of the increasing population. Planning the flow of these batches from suppliers to customers, through dynamic logistics systems, has a high degree of uncertainties on supply chain related decisions. In order to respond effectively and efficiently to these uncertainties, the supply chain network has to be redesigned, considering the economic and environmental requirements. To handle these requirements sustainably, green logistics is a promising approach. However, there is a lack of green logistics models which integrate both the production and distribution decisions within the batch process industries. This research develops a green logistics oriented framework in the case of the batch process industry. The framework integrates the tactical and operational levels of planning and scheduling to generate the optimum production and distribution decisions. A two-stage stochastic programming model is formulated to design and manage batch supply chain. This is a mixed-integer linear program of the two-stage stochastic production-distribution model with economic-environmental objectives. The first stage is concerned with optimum schedules of the production and distribution of the required batches. The second stage subsequently generates the optimum delivering velocities for the optimal distribution routes which are resulted from the first stage. Carbon emissions under uncertainties are incorporated as a function of random delivery velocities at different distribution routes within the network of the supply chain. To examine the applicability of the developed framework, the model is verified and validated through four theoretical scenarios as well as two real world case studies of multi-national batch process industries. The results of the analysis provide some insights results into supply chain costs and emissions. Based on the results, savings of about 43 percent of the total related economic and environmental costs were achieved compared to the actual situation at the case study companies. Cost savings mean long-term profitability, which is essential to sustain a worldwide competitive advantage. Furthermore, the stochastic and expected value solutions are compared in several scenarios. The stochastic solutions are consistently better with respect to costs and emissions. Calculations indicate that up to 13 percent of total cost savings are achieved when a stochastic approach is used to solve the problem as opposed to an expected value approach. The proposed framework supports academic green logistics models and real world supply chain decision making in batch process industry. Building such a framework provides a practical tool which links being green and being economically successful

Advances and Novel Approaches in Discrete Optimization

Discrete optimization is an important area of Applied Mathematics with a broad spectrum of applications in many fields. This book results from a Special Issue in the journal Mathematics entitled ‘Advances and Novel Approaches in Discrete Optimization’. It contains 17 articles covering a broad spectrum of subjects which have been selected from 43 submitted papers after a thorough refereeing process. Among other topics, it includes seven articles dealing with scheduling problems, e.g., online scheduling, batching, dual and inverse scheduling problems, or uncertain scheduling problems. Other subjects are graphs and applications, evacuation planning, the max-cut problem, capacitated lot-sizing, and packing algorithms