Service scheduling in garden maintenance

Neoturf is a Portuguese company working in the area of project, building and garden’s maintenance. Neoturf would like to have a procedure for scheduling and routing efficiently the clients from garden maintenance services. The company has two teams available during the whole year and an additional team during summer to handle all the maintenance jobs. Each team consists of two or three employees with a vehicle fully equipped with the tools that allow to carry out every kind of maintenance service. In the beginning of each year, the number and frequency of maintenance interventions to conduct during the year, on each client, are accorded. Each client is assigned to the same team and, usually, time windows are established so that visits to the client should occur only within these periods. As the Neoturf costumers’ are geographically spread over a wide region, the total distance on visiting clients is a factor that has a heavy weight on the costs of the company. Neoturf is concerned with reducing these costs, while satisfying the agreements with the clients

A reclaimer scheduling problem arising in coal stockyard management

We study a number of variants of an abstract scheduling problem inspired by the scheduling of reclaimers in the stockyard of a coal export terminal. We analyze the complexity of each of the variants, providing complexity proofs for some and polynomial algorithms for others. For one, especially interesting variant, we also develop a constant factor approximation algorithm.Comment: 26 page

Layered graph approaches for combinatorial optimization problems

Extending the concept of time-space networks, layered graphs associate information about one or multiple resource state values with nodes and arcs. While integer programming formulations based on them allow to model complex problems comparably easy, their large size makes them hard to solve for non-trivial instances. We detail and classify layered graph modeling techniques that have been used in the (recent) scientific literature and review methods to successfully solve the resulting large-scale, extended formulations. Modeling guidelines and important observations concerning the solution of layered graph formulations by decomposition methods are given together with several future research directions

Design and Control of Warehouse Order Picking: a literature review

Order picking has long been identified as the most labour-intensive and costly activity for almost every warehouse; the cost of order picking is estimated to be as much as 55% of the total warehouse operating expense. Any underperformance in order picking can lead to unsatisfactory service and high operational cost for its warehouse, and consequently for the whole supply chain. In order to operate efficiently, the orderpicking process needs to be robustly designed and optimally controlled. This paper gives a literature overview on typical decision problems in design and control of manual order-picking processes. We focus on optimal (internal) layout design, storage assignment methods, routing methods, order batching and zoning. The research in this area has grown rapidly recently. Still, combinations of the above areas have hardly been explored. Order-picking system developments in practice lead to promising new research directions.Order picking;Logistics;Warehouse Management

A novel flexible model for lot sizing and scheduling with non-triangular, period overlapping and carryover setups in different machine configurations

© 2017, Springer Science+Business Media New York. This paper develops and tests an efficient mixed integer programming model for capacitated lot sizing and scheduling with non-triangular and sequence-dependent setup times and costs incorporating all necessary features of setup carryover and overlapping on different machine configurations. The model’s formulation is based on the asymmetric travelling salesman problem and allows multiple lots of a product within a period. The model conserves the setup state when no product is being processed over successive periods, allows starting a setup in a period and ending it in the next period, permits ending a setup in a period and starting production in the next period(s), and enforces a minimum lot size over multiple periods. This new comprehensive model thus relaxes all limitations of physical separation between the periods. The model is first developed for a single machine and then extended to other machine configurations, including parallel machines and flexible flow lines. Computational tests demonstrate the flexibility and comprehensiveness of the proposed models

A branch-and-cut algorithm for the generalized traveling salesman problem with time windows

International audienceThe generalized traveling salesman problem with time windows (GTSPTW) is defined on a directed graph where the vertex set is partitioned into clusters. One cluster contains only the depot. Each vertex is associated with a time interval, the time window, during which the visit must take place if the vertex is visited. The objective is to find a minimum cost tour starting and ending at the depot such that each cluster is visited exactly once and time constraints are respected, i.e., for each cluster, one vertex is visited during its time window. In this paper, two integer linear programming formulations for GTSPTW are provided as well as several problem-specific valid inequalities. A branch-and-cut algorithm is developed in which the inequalities are separated dynamically. To reduce the computation times, an initial upper bound is provided by a simple and fast heuristic. We propose different sets of instances characterized by their time window structures. Experimental results show that our algorithm is effective and instances including up to 30 clusters can be solved to optimality within one hour

순서의존적 작업준비가 있는 생산계획 문제에 대한 정수 최적화 및 근사 동적 계획법 기반 해법

학위논문(박사) -- 서울대학교대학원 : 공과대학 산업공학과, 2022. 8. 이경식.Lot-sizing and scheduling problem, an integration of the two important decision making problems in the production planning phase of a supply chain, determines both the production amounts and sequences of multiple items within a given planning horizon to meet the time-varying demand with minimum cost. Along with the growing importance of coordinated decision making in the supply chain, this integrated problem has attracted increasing attention from both industrial and academic communities. However, despite vibrant research over the recent decades, the problem is still hard to be solved due to its inherent theoretical complexity as well as the evolving complexity of the real-world industrial environments and the corresponding manufacturing processes. Furthermore, when the setup activity occurs in a sequence-dependent manner, it is known that the problem becomes considerably more difficult. This dissertation aims to propose integer optimization and approximate dynamic programming approaches for solving the lot-sizing and scheduling problem with sequence-dependent setups. Firstly, to enhance the knowledge of the structure of the problem which is strongly NP-hard, we consider a single-period substructure of the problem. By analyzing the polyhedron defined by the substructure, we derive new families of facet-defining inequalities which are separable in polynomial time via solving maximum flow problems. Through the computational experiments, these inequalities are demonstrated to provide much tighter lower bounds than the existing ones. Then, using these results, we provide new integer optimization models which can incorporate various extensions of the lot-sizing and scheduling problem such as setup crossover and carryover naturally. The proposed models provide tighter linear programming relaxation bounds than standard models. This leads to the development of an efficient linear programming-based heuristic algorithm which provides a primal feasible solution quickly. Finally, we devise an approximate dynamic programming algorithm. The proposed algorithm incorporates the value function approximation approach which makes use of both the tight lower bound obtained from the linear programming relaxation and the upper bound acquired from the linear programming-based heuristic algorithm. The results of computational experiments indicate that the proposed algorithm has advantages over the existing approaches.공급망의 생산 계획 단계에서의 주요한 두 가지 단기 의사결정 문제인 Lot-sizing 문제와 Scheduling 문제가 통합된 문제인 Lot-sizing and scheduling problem (LSP)은 계획대상기간 동안 주어진 복수의 제품에 대한 수요를 최소의 비용으로 만족시키기 위한 단위 기간 별 최적의 생산량 및 생산 순서를 결정한다. 공급망 내의 다양한 요소에 대한 통합적 의사 결정의 중요성이 커짐에 따라 LSP에 대한 관심 역시 산업계와 학계 모두에서 지속적으로 증가하였다. 그러나 최근 수십 년에 걸친 활발한 연구에도 불구하고, 문제 자체가 내포하는 이론적 복잡성 및 실제 산업 환경과 제조 공정의 고도화/복잡화 등으로 인해 LSP를 해결하는 것은 여전히 어려운 문제로 남아있다. 특히 순서의존적 작업준비가 있는 경우 문제가 더욱 어려워진다는 것이 잘 알려져 있다. 본 논문에서는 순서의존적 작업준비가 있는 LSP를 해결하기 위한 정수 최적화 및 근사 동적 계획법 기반의 해법을 제안한다. 먼저, 이론적으로 강성 NP-hard에 속한다는 사실이 잘 알려진 LSP의 근본 구조에 대한 이해를 높이기 위하여 단일 기간만을 고려하는 부분구조에 대해 다룬다. 단일 기간 부분구조에 의해 정의되는 다면체에 대한 이론적 분석을 통해 새로운 유효 부등식 군을 도출하고 해당 유효 부등식들이 극대면(facet)을 정의할 조건에 대해 밝힌다. 또한, 도출된 유효 부등식들이 다항시간 내에 분리 가능함을 보이고, 최대흐름문제를 활용한 다항시간 분리 알고리듬을 고안한다. 실험 결과를 통해 제안한 유효 부등식들이 모형의 선형계획 하한강도를 높이는 데 큰 영향을 줌을 확인한다. 또한 해당 부등식들이 모두 추가된 모형과 이론적으로 동일한 하한을 제공하는 확장 수리모형(extended formulation)을 도출한다. 이를 활용하여, 실제 산업에서 발생하는 LSP에서 종종 고려하는 주요한 추가 요소들을 다룰 수 있는 새로운 수리 모형을 제안하며 해당 모형이 기존의 모형에 비해 더욱 강한 선형계획 하한을 제공함을 보인다. 이 모형을 바탕으로 빠른 시간 내에 가능해를 찾을 수 있는 선형계획 기반 휴리스틱 알고리듬을 개발한다. 마지막으로 해당 문제에 대한 근사 동적 계획법 알고리듬을 제안한다. 제안하는 알고리듬은 가치함수 근사 기법을 활용하며 특정 상태의 가치를 근사하기 위해 해당 상태에서의 근사함수의 상한 및 하한을 활용한다. 이 때, 좋은 상한 및 하한값을 구하기 위해 제안된 모형의 선형계획 완화문제와 선형계획 기반 휴리스틱 알고리듬을 사용한다. 실험 결과를 통해 제안한 알고리듬이 기존의 방법들과 비교하여 우수한 성능을 보임을 확인한다.Abstract i Contents iii List of Tables vii List of Figures ix Chapter 1 Introduction 1 1.1 Backgrounds 1 1.2 Integrated Lot-sizing and Scheduling Problem 6 1.3 Literature Review 9 1.3.1 Optimization Models for LSP 9 1.3.2 Recent Works on LSP 14 1.4 Research Objectives and Contributions 16 1.5 Outline of the Dissertation 19 Chapter 2 Polyhedral Study on Single-period Substructure of Lot-sizing and Scheduling Problem with Sequence-dependent Setups 21 2.1 Introduction 22 2.2 Literature Review 27 2.3 Single-period Substructure 30 2.3.1 Assumptions 31 2.3.2 Basic Polyhedral Properties 32 2.4 New Valid Inequalities 37 2.4.1 S-STAR Inequality 37 2.4.2 Separation of S-STAR Inequality 42 2.4.3 U-STAR Inequality 47 2.4.4 Separation of U-STAR Inequality 49 2.4.5 General Representation of the Inequalities 52 2.5 Extended Formulations 55 2.5.1 Single-commodity Flow Formulations 55 2.5.2 Multi-commodity Flow Formulations 58 2.5.3 Time-ow Formulations 59 2.6 Computational Experiments 63 2.6.1 Experiment Settings 63 2.6.2 Experiment Results on Single-period Instances 65 2.6.3 Experiment Results on Multi-period Instances 69 2.7 Summary 73 Chapter 3 New Optimization Models for Lot-sizing and Scheduling Problem with Sequence-dependent Setups, Crossover, and Carryover 75 3.1 Introduction 76 3.2 Literature Review 78 3.3 Integer Optimization Models 80 3.3.1 Standard Model (ST) 82 3.3.2 Time-ow Model (TF) 84 3.3.3 Comparison of (ST) and (TF) 89 3.3.4 Facility Location Reformulation 101 3.4 LP-based Naive Fixing Heuristic Algorithm 104 3.5 Computational Experiments 108 3.5.1 Test Instances 108 3.5.2 LP Bound 109 3.5.3 Computational Performance with MIP Solver 111 3.5.4 Performance of LPNF Algorithm 113 3.6 Summary 115 Chapter 4 Approximate Dynamic Programming Algorithm for Lot-sizing and Scheduling Problem with Sequence-dependent Setups 117 4.1 Introduction 118 4.1.1 Markov Decision Process 118 4.1.2 Approximate Dynamic Programming Algorithms 121 4.2 Markov Decision Process Reformulation 124 4.3 Approximate Dynamic Programming Algorithm 127 4.4 Computational Experiments 131 4.4.1 Comparison with (TF-FL) Model 131 4.4.2 Comparison with Big Bucket Model 134 4.5 Summary 138 Chapter 5 Conclusion 139 5.1 Summary and Contributions 139 5.2 Future Research Directions 141 Bibliography 145 Appendix A Pattern-based Formulation in Chapter 2 159 Appendix B Detailed Test Results in Chapter 2 163 Appendix C Detailed Test Results in Chapter 3 169 국문초록 173박

A clustering approach for vehicle routing problems with hard time windows

Dissertação para obtenção do Grau de Mestre em Logica ComputicionalThe Vehicle Routing Problem (VRP) is a well known combinatorial optimization problem and many studies have been dedicated to it over the years since solving the VRP optimally or near-optimally for very large size problems has many practical applications (e.g. in various logistics systems). Vehicle Routing Problem with hard TimeWindows (VRPTW) is probably the most studied variant of the VRP problem and the presence of time windows requires complex techniques to handle it. In fact, finding a feasible solution to the VRPTWwhen the number of vehicles is fixed is an NP-complete problem. However, VRPTW is well studied and many different approaches to solve it have been developed over the years. Due to the inherent complexity of the underlying problem VRPTW is NP-Hard. Therefore, optimally solving problems with no more than one hundred requests is considered intractably hard. For this reason the literature is full with inexact methods that use metaheuristics, local search and hybrid approaches which are capable of producing high quality solutions within practical time limits. In this work we are interested in applying clustering techniques to VRPTWproblem. The idea of clustering has been successfully applied to the basic VRP problem. However very little work has yet been done in using clustering in the VRPTW variant. We present a novel approach based on clustering, that any VRPTW solver can adapt, by running a preprocessing stage before attempting to solve the problem. Our proposed method, tested with a state of the art solver (Indigo), enables the solver to find solutions much faster (up to an order of magnitude speed-up). In general this comes with at slightly reduced solution quality, but in somes types of problems, Indigo is able to obtain better solutions than those obtained with no clustering

Essays on Shipment Consolidation Scheduling and Decision Making in the Context of Flexible Demand

This dissertation contains three essays related to shipment consolidation scheduling and decision making in the presence of flexible demand. The first essay is presented in Section 1. This essay introduces a new mathematical model for shipment consolidation scheduling for a two-echelon supply chain. The problem addresses shipment coordination and consolidation decisions that are made by a manufacturer who provides inventory replenishments to multiple downstream distribution centers. Unlike previous studies, the consolidation activities in this problem are not restricted to specific policies such as aggregation of shipments at regular times or consolidating when a predetermined quantity has accumulated. Rather, we consider the construction of a detailed shipment consolidation schedule over a planning horizon. We develop a mixed-integer quadratic optimization model to identify the shipment consolidation schedule that minimizes total cost. A genetic algorithm is developed to handle large problem instances. The other two essays explore the concept of flexible demand. In Section 2, we introduce a new variant of the vehicle routing problem (VRP): the vehicle routing problem with flexible repeat visits (VRP-FRV). This problem considers a set of customers at certain locations with certain maximum inter-visit time requirements. However, they are flexible in their visit times. The VRP-FRV has several real-world applications. One scenario is that of caretakers who provide service to elderly people at home. Each caretaker is assigned a number of elderly people to visit one or more times per day. Elderly people differ in their requirements and the minimum frequency at which they need to be visited every day. The VRP-FRV can also be imagined as a police patrol routing problem where the customers are various locations in the city that require frequent observations. Such locations could include known high-crime areas, high-profile residences, and/or safe houses. We develop a math model to minimize the total number of vehicles needed to cover the customer demands and determine the optimal customer visit schedules and vehicle routes. A heuristic method is developed to handle large problem instances. In the third study, presented in Section 3, we consider a single-item cyclic coordinated order fulfillment problem with batch supplies and flexible demands. The system in this study consists of multiple suppliers who each deliver a single item to a central node from which multiple demanders are then replenished. Importantly, demand is flexible and is a control action that the decision maker applies to optimize the system. The objective is to minimize total system cost subject to several operational constraints. The decisions include the timing and sizes of batches delivered by the suppliers to the central node and the timing and amounts by which demanders are replenished. We develop an integer programing model, provide several theoretical insights related to the model, and solve the math model for different problem sizes