This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, ...
详细信息
This study considers a problem of coordinating production, transportation and sales in a multi-echelon supply chain network. A simulation model is built to generate the random customer demands at different locations, which are affected by a marketing strategy. Customer demands need to be satisfied by the supply chain through production, transportation and distribution. The optimization problem for coordination of production, transportation and distribution is first formulated as a linear programming with demands as input parameters in the constraint. Our objective is to maximize the expectation of the optimal profit of the supply chain given random demands by selecting an optimal marketing strategy. A simulation optimization technique is proposed to control the generation of random demands and solve the linear programming for efficiently learning the optimal marketing strategy. Numerical results show that our method can significantly improve the expected profit of the supply chain and reduce the computational burden of solving linear programming for achieving a given level of probability of correct selection of the optimal marketing strategy. Furthermore, we extend the optimization problem to a mixed integer programming and also demonstrate the computational efficiency of our proposed method.
The inventory routing problem (IRP) poses a significant optimization challenge across various industries. This paper focuses on the uncapacitated IRP, by introducing fast constructive heuristics integrating insights f...
详细信息
The inventory routing problem (IRP) poses a significant optimization challenge across various industries. This paper focuses on the uncapacitated IRP, by introducing fast constructive heuristics integrating insights from approximation algorithms, particularly rounding techniques in linear programming (LP). The proposed heuristics efficiently deliver effective solutions, providing advantages over methods such as branch-and-cut and metaheuristics. Methodologically, we emphasize scalability, subjecting our algorithms to rigorous stress tests with larger instances. Computational experiments, utilizing 420 instances, demonstrate the effectiveness and scalability of our heuristics, notably those tailored to specific problem variants, achieving an average gap of 2.2%. Our work underscores the effectiveness of leveraging approximation algorithms for the uncapacitated IRP, with future directions aimed at enhancing heuristics for broader real-world applicability, including the capacitated version of the IRP.
This paper provides sufficient conditions for the optimal value function of a given linear semi-infinite programming (LSIP) problem to depend linearly on the size of the perturbations, when these perturbations involve...
详细信息
This paper provides sufficient conditions for the optimal value function of a given linear semi-infinite programming (LSIP) problem to depend linearly on the size of the perturbations, when these perturbations involve either the cost coefficients or the right-hand side function or both, and they are sufficiently small. Two kinds of partitions are considered. The first concerns the effective domain of the optimal value as a function of the cost coefficients and consists of maximal regions on which this value function is linear. The second class of partitions considered in this paper concerns the index set of the constraints through a suitable extension of the concept of optimal partition from ordinary to LSIP. These partitions provide convex sets, in particular, segments, on which the optimal value is a linear function of the size of the perturbations, for the three types of perturbations considered in this paper.
This paper proposes a dynamic model-based optimisation method for cigarette production planning to meet market demand and optimise inventory management. The model contains three objectives: Minimising the difference b...
详细信息
This paper proposes a dynamic model-based optimisation method for cigarette production planning to meet market demand and optimise inventory management. The model contains three objectives: Minimising the difference between annual demand and production output, balancing monthly production and demand, and minimising the average age of inventory. The model sets annual demand constraints, production capacity constraints, and considers seasonal adjustment and contingency inventory. The model is solved by a combination of linear programming and genetic algorithms. The results show that the optimised production plan can effectively reduce total costs, avoiding the risks of under-supply and over-stocking. The analysis shows that the planned monthly production quantity closely matches the actual allocation quantity, the inventory management is effective, and hence the ability to cope with peak demand is enhanced.
Estimating the cardinality of the output of a query is a fundamental problem in database query processing. In this article, we overview a recently published contribution that casts the cardinality estimation problem a...
详细信息
Estimating the cardinality of the output of a query is a fundamental problem in database query processing. In this article, we overview a recently published contribution that casts the cardinality estimation problem as linear optimization and computes guaranteed upper bounds on the cardinality of the output for any full conjunctive query. The objective of the linear program is to maximize the joint entropy of the query variables and its constraints are the Shannon information inequalities and new information inequalities involving p-norms of the degree sequences of the join attributes. The bounds based on arbitrary norms can be asymptotically lower than those based on the 1 and infinity norms, which capture the cardinalities and respectively the max-degrees of the input relations. They come with a matching query evaluation algorithm, are computable in exponential time in the query size, and are provably tight when each degree sequence is on one join attribute.
This paper explores the potential of quantum computing for solving the choice-based deterministic linear program (CDLP) for network revenue management. The CDLP is a model that incorporates customer choice between par...
详细信息
This paper explores the potential of quantum computing for solving the choice-based deterministic linear program (CDLP) for network revenue management. The CDLP is a model that incorporates customer choice between parallel itineraries serving the same origin-destination market and has been shown to offer significant revenue benefits compared to traditional methods. However, the CDLP is computationally intractable for realistic network sizes due to the exponential growth of the offer set space. We propose a novel quantum algorithm that uses quantum column generation and quantum search to achieve a quadratic speed-up over the classical counterparts. We demonstrate the feasibility and performance of our quantum algorithm on a toy network example and discuss the implications and challenges for future applications. Our work stands as the first of its kind to apply quantum computing to the domain of airline revenue management.
Vehicle numbers have been on the increase with the current growth in the economy and the rise of the middle income social status, thus allocation of parking space becomes a problem which needs to be addressed over par...
详细信息
Vehicle numbers have been on the increase with the current growth in the economy and the rise of the middle income social status, thus allocation of parking space becomes a problem which needs to be addressed over parked lots. This study addresses the optimization of parking lot design and obtaining optimum parking spots for vehicles. Types of parking designs are in two main categories: parallel parking and angular parking. Techniques in mathematical linear programming are utilized in an effort to increase and maximize the capacity of vehicles parked in the lot. The study investigates different angles of inclination to determine the angle that best optimizes the available space. Five angles, mathematical special angles, are evaluated to determine the combination that provides the optimal number of spots. Mixed integer linear programing (MILP) and integer linear programing are used for optimization formulation. LINDO and LINGO API software are used for analysis. The results show that the parking lot using linear programing can accommodate 41 parking spots at an angle of 75 degrees.
This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal soli...
详细信息
This paper proposes a genetic-algorithms-based approach as an all-purpose problem-solving method for operation programming problems under uncertainty. The proposed method was applied for management of a municipal solid waste treatment system. Compared to the traditional interactive binary analysis, this approach has fewer limitations and is able to reduce the complexity in solving the inexact linear programming problems and inexact quadratic programming problems. The implementation of this approach was performed using the Genetic Algorithm Solver of MATLAB (trademark of MathWorks). The paper explains the genetic-algorithms-based method and presents details on the computation procedures for each type of inexact operation programming problems. A comparison of the results generated by the proposed method based on genetic algorithms with those produced by the traditional interactive binary analysis method is also presented.
Proposes an iterative procedure for fuzzy programming problems with linear fractional objectives. Application of multiobjective programming; Maximization of aggregate membership function; Sequence of linear inequality...
详细信息
Proposes an iterative procedure for fuzzy programming problems with linear fractional objectives. Application of multiobjective programming; Maximization of aggregate membership function; Sequence of linear inequality problems; Efficacy of the proposed method; Practicality of the fuzzified model of multiobjective fractional programming.
暂无评论