A hybrid constraint programming approach to a wood procurement problem with bucking decisions

In this article, we present a wood procurement problem that arises in Eastern Canada. We solve a multi-period wood supply planning problem, while taking into account bucking decisions. Furthermore, we present a new form of flexibility which allows the harvesting capacity to change from one time period to another. We study the impact of such flexibility upon the harvesting cost. We assess the performance of the problem by comparing it with a variant where the harvesting capacity is fixed during sites’ harvesting. To address this problem, we develop a hybrid approach based on both constraint and mathematical programming. In the first phase, we propose a constraint programming model dealing with forest sites harvesting and bucking problems. The result of this model is used as part of an initial solution for the whole problem formulated as a mixed integer model. We test the two versions of the problem on a set of different demand instances and we compare their results.     

Supply chain pricing and coordination with markdown strategy in the presence of conspicuous consumers

This paper studies the problem of supply chain pricing and coordination with markdown policy in the presence of conspicuous consumption. A two‐period pricing model is proposed to investigate the effect of conspicuous consumption on pricing decisions with markdown pricing policy for a supply chain that consists of a manufacturer and retailer. The optimal pricing strategies for the integrated supply chain and decentralized supply chain are derived respectively. Our results show that an anticipated conspicuous consumption can benefit the supply chain even though the conspicuous consumption aggravates the double marginalization effect. The conspicuous consumption can serve as a level to segment the market and can promote the supply chain revenue. In addition, our results indicate that the supply chain can benefit from rationing. We also introduce a revenue‐sharing contract and prove that it can be applied to coordinate the supply chain with conspicuous consumption. An interval of the revenue‐sharing rate for coordinating with the supply chain can be coordinated without any additional transfer payment being provided.     

Agent-based negotiation and decision making for dynamic supply chain formation

Modern businesses are facing the challenge of effectively coordinating their supply chains from upstream to downstream services. It is a complex problem to search, schedule, and coordinate a set of services from a large number of service resources under various constraints and uncertainties. Existing approaches to this problem have relied on complete information regarding service requirements and resources, without adequately addressing the dynamics and uncertainties of the environments. The real-world situations are complicated as a result of ambiguity in the requirements of the services, the uncertainty of solutions from service providers, and the interdependencies among the services to be composed. This paper investigates the complexity of supply chain formation and proposes an agent-mediated coordination approach. Each agent works as a broker for each service type, dedicated to selecting solutions for each service as well as interacting with other agents in refining the decision making to achieve compatibility among the solutions. The coordination among agents concerns decision making at strategic, tactical, and operational level. At the strategic level, agents communicate and negotiate for supply chain formation; at the tactical level, argumentation is used by agents to communicate and understand the preferences and constraints of each other; at the operational level, different strategies are used for selecting the preferences. Based on this approach, a prototype has been implemented with simulated experiments highlighting the effectiveness of the approach.     

Supervisory control of a multi-echelon supply chain: A modular Petri net approach for inter-organizational control

Petri nets (PNs) are frequently utilized to model system dynamics due to their ability to handle concurrencies and sequential dependence. In this paper, a portion of the supply chain operations reference (SCOR) model has been extracted and modeled using PNs for the purpose of exerting supervisory control upon a multi-echelon supply chain (SC). The activities of source, make and deliver, inherent in the SCOR model form the basis of the representation of the PN model for each echelon considered in the SC model. Two control nets are utilized: one above the base model of each echelon to exert local constraints and an enterprise level supply chain manager (SCM). The local constraints are at the tactical and operational levels while the SCM enforces additional constraints consisting of long term planning goals at the strategic level. Place invariants are used to create the supervisors. Performance measures of the total SC are formulated to determine the effectiveness of any partnership. An efficient method for finding the current state of the system is developed which is used to determine the performance measures of each echelon. This paper presents a modular approach to the overall structure and PN modeling for a SC system. It is intended to extend the use of supervisory control from a shop-floor level to an inter-organizational facility and enterprise level.     

Supplier selection and purchase problem for multi-echelon defective supply chain system with stochastic demand

In the real production process, some members in the supply chain system sometimes cannot effectively complete their production task because of defects involving the production or purchasing of components. A supply chain system that has defects in at least one echelon is called a multi-echelon defective supply chain (MDSC) system. Most supply chain systems are MDSC systems. Determining parts or components supply quota from different suppliers with limited suppliers, factories and distribution centers capacities in the supply chain system are becoming an important issue for businesses. In this study, we propose a new heuristic (H2) which is an extension of H1 heuristic that was previously presented. The MDSC system was formed with the mixed integer linear programming by LINDO software for calculation of the lower bound. The heuristics and MDSC system were modeled by using ProModel software. The heuristics were applied to a case from the Turkish furniture industry. The heuristics were compared with each other by considering different coefficients of variation, service levels, and deviation from lower bound. Simulation experiments showed that the proposed H2 heuristic outperformed the H1 heuristic.     

A genetic algorithm approach for location-inventory-routing problem with perishable products

In this paper, we address a location-inventory-routing model for perishable products. The model determines the number and location of required warehouses, the inventory level at each retailer, and the routes traveled by each vehicle. The proposed model adds location decisions to a recently published inventory routing problem in order to make it more practical, thus supporting the prevalent claim that integration of strategic, tactical and operational level decisions produces better results for supply chains. Given that the model developed here is NP-hard, with no algorithm capable of finding its solution in polynomial time, we develop a Genetic Algorithm approach to solve the problem efficiently. This approach achieves high quality near-optimal solutions in reasonable time. Furthermore, the unique structure of the problem requires developing a new chromosome representation, as well as local search heuristics. Finally, an analysis is carried out to verify the effectiveness of the algorithm.     

A heuristic to solve the synchronized log-truck scheduling problem

We present a synchronized routing and scheduling problem that arises in the forest industry, as a variation of the log-truck scheduling problem. It combines routing and scheduling of trucks with specific constraints related to the Canadian forestry context. This problem includes aspects such as pick-up and delivery, multiple products, inventory stock, multiple supply points and multiple demand points. We developed a decomposition approach to solve the weekly problem in two phases. In the first phase we use a MIP solver to solve a tactical model that determines the destinations of full truckloads from forest areas to woodmills. In the second phase, we make use of two different methods to route and schedule the daily transportation of logs: the first one consists in using a constraint-based local search approach while the second one is a hybrid approach involving a constraint programming based model and a constraint-based local search model. These approaches have been implemented using COMET2.0. The method, was tested on two industrial cases from forest companies in Canada.     

Joint optimization of pricing and planning decisions in divergent supply chain

In divergent supply chains, such as in the oil industry, processing raw materials results in an outflow of multiple products. Final products are stored at international depots, from where they are ready to be shipped to the markets. Even if one company controls the entire chain, when production and sales organizations are decoupled, a relevant problem is to determine the internal prices of products at depots for achieving coordination. We propose an optimization model involving pricing and production decisions, and several constraints commonly used in divergent chains. In our approach, the producer incorporates the sellers’ behavior by expressing demand as a function of the internal price. As a result, our model serves as a coordination mechanism in trying to get an overall coordinated integrated solution in a decoupled reality. Numerical examples in single and multiple periods problems show the advantages of our approach over cost‐based methods.     

Two-echelon, multi-commodity supply chain network design with mode selection, lead-times and inventory costs

Designing distribution networks - as one of the most important strategic issues in supply chain management - has become the focus of research attention in recent years. This paper deals with a two-echelon supply chain network design problem in deterministic, single-period, multi-commodity contexts. The problem involves both strategic and tactical levels of supply chain planning including locating and sizing manufacturing plants and distribution warehouses, assigning the retailers' demands to the warehouses, and the warehouses to the plants, as well as selecting transportation modes.We have formulated the problem as a mixed integer programming model, which integrates the above mentioned decisions and intends to minimize total costs of the network including transportation, lead-times, and inventory holding costs for products, as well as opening and operating costs for facilities. Moreover, we have developed an efficient Lagrangian based heuristic solution algorithm for solving the real-sized problems in reasonable computational time.     

An interactive possibilistic programming approach for a multi-objective closed-loop supply chain network under uncertainty

In this article, we first propose a closed-loop supply chain network design that integrates network design decisions in both forward and reverse supply chain networks into a unified structure as well as incorporates the tactical decisions with strategic ones (e.g., facility location and supplier selection) at each period. To do so, various conflicting objectives and constraints are simultaneously taken into account in the presence of some uncertain parameters, such as cost coefficients and customer demands. Then, we propose a novel interactive possibilistic approach based on the well-known STEP method to solve the multi-objective mixed-integer linear programming model. To validate the presented model and solution method, a numerical test is accomplished through the application of the proposed possibilistic-STEM algorithm. The computational results demonstrate suitability of the presented model and solution method.     

Heuristics for tactical time slot management: a periodic vehicle routing problem view

In this study, we consider a tactical problem where a time slot schedule for delivery service over a given planning horizon must be selected in each zone of a geographical area. A heuristic search evaluates each schedule selection by constructing a corresponding tactical routing plan of minimum cost based on demand and service time estimates. At the end, the schedule selection leading to the best tactical routing plan is selected. The latter can then be used as a blueprint when addressing the operational problem (i.e., when real customer orders are received and operational routes are constructed). We propose two heuristics to address the tactical problem. The first heuristic is a three‐phase approach: a periodic vehicle routing problem (PVRP) is first solved, followed by a repair phase and a final improvement phase where a vehicle routing problem (VRP) with time windows is solved for each period of the planning horizon. The second heuristic tackles the problem as a whole by directly solving a PVRP with time windows. Computational results compare the two heuristics under various settings, based on instances derived from benchmark instances for the VRP with time windows.     

The impact of consumer behavior on preannounced pricing for a dual‐channel supply chain

This study discusses a dual‐channel supply chain in which a manufacturer sells a regular‐priced product through dual channels in the normal sales period and only sells overstocked products through the direct channel in the discounted sales period in the presence of strategic consumers. The manufacturer acts as a Stackelberg leader to adopt a preannounced pricing policy. This study first proposes demand functions for a two‐period dual‐channel model by incorporating consumer utility functions. Based on the demand functions, optimal pricing strategies for both manufacturer and retailer are established. The results show that the manufacturer prefers to raise prices in both periods for consumers with a short delivery lead time. However, counterintuitively, the selling prices set by the manufacturer do not decrease as the degree of consumer patience increases. Finally, there is a Pareto zone under a certain condition where both the manufacturer and the retailer in the two‐period dual‐channel model outperform their counterparts in terms of profit in the single‐period dual‐channel model.     

A framework for measuring the performance of service supply chain management

Despite the increasing attention to the service supply chain management by both practitioners and academics, the performance measurement of service supply chains still remains unexplored. Most service firms realize that, in order to evolve an efficient and effective service supply chain, service supply chain management needs to be assessed for its performance. A literature review was conducted on performance measurement issues of service supply chains. This paper develops a framework of service supply chain performance measurement. Based on the strategic, tactical and operational level performance in a service supply chain, measures and metrics are discussed. The emphasis is on performance measures dealing with service supply chain processes such as demand management, customer relationship management, supplier relationship management, capacity and resource management, service performance, information and technology management and service supply chain finance. And to prioritize service supply chain performance measurement indicators to improve service supply chain performance, a methodology based on the extent fuzzy analytic hierarchy process is stressed. The developed framework of service supply chain performance measurement is applied to the hotel supply chain. The results of this study are useful both to practitioners in the service supply chain and to researchers carrying out further studies in the field.     

基于HCPN的BTO供应链的动态构建与仿真

采用层次着色Petri网（HCPN）对企业从接到用户订单、编制供应链计划,直到选定上游供应商的BTO供应链网络的动态构建过程进行建模和仿真分析,其结果可以评价企业的供应链战略联盟响应用户个性化需求的能力。为企业管理者动态构建BTO供应链网络和评估供应链战略联盟响应用户个性化需求的能力提供了一种有效的实用方法,从而有助于改进整个供应链的绩效。     

Aggregation of orders in distribution centers using data mining

This paper considers the problem of constructing order batches for distribution centers using a data mining technique. With the advent of supply chain management, distribution centers fulfill a strategic role of achieving the logistics objectives of shorter cycle times, lower inventories, lower costs and better customer service. Many companies consider both their cost effectiveness and market proficiency to depend primarily on efficient logistics management. Warehouse management system (WMS) presently is considered a key to strengthening company logistics. Order picking is routine in distribution centers. Before picking a large set of orders, effectively grouping orders into batches can accelerate product movement within the storage zone. The order batching procedure has to be implemented in WMS and may be run online many times daily. The literature has proposed numerous batching heuristics for minimizing travel distance or travel time. This paper presents a clustering procedure for an order batching problem in a distribution center with a parallel-aisle layout. A data mining technique of association rule mining is adopted to develop the order clustering approach. Performance comparisons between the developed approach and existing heuristics are given for various problems.     

Analytical Framework for the Management of Risk in Supply Chains

In this paper, we develop a framework to classify supply chain risk-management problems and approaches for the solution of these problems. We argue that risk-management problems need to be handled at three levels: 1) strategic, 2) operational, and 3) tactical. In addition, risk within the supply chain might manifest itself in the form of deviations, disruptions, and disasters. To handle unforeseen events in the supply chain, there are two obvious approaches: 1) to design chains with built-in risk tolerance and 2) to contain the damage once the undesirable event has occurred. Both of these approaches require a clear understanding of undesirable events that may take place in the supply chain and the associated consequences and impacts from these events. Having described these approaches, we then focus our efforts on mapping out the propagation of events in the supply chain due to supplier nonperformance, and employ our insight to develop two mathematical programming-based preventive models for strategic level deviation and disruption management. The first model, a simple integer quadratic optimization model, adapted from the Markowitz model, determines optimal partner selection with the objective of minimizing both the operational cost and the variability of total operational cost. The second model, a simple mixed integer programming optimization model, adapted from the credit risk minimization model, determines optimal partner selection such that the supply shortfall is minimized even in the face of supplier disruptions. Hence, both of these models offer possible approaches to robust supply chain design     

A dual-channel network design model in a green supply chain considering pricing and transportation mode choice

A green supply chain with a well-designed network can strongly influence the performance of supply chain and environment. The designed network should lead the supply chain to efficient and effective management to meet the efficient profit, sustainable effects on environment and customer needs. The proposed mathematical model in this paper identifies locations of productions and shipment quantity by exploiting the trade-off between costs, and emissions for a dual channel supply chain network. Due to considering different prices and customers zones for channels, determining the prices and strategic decision variables to meet the maximum profit for the proposed green supply chain is contemplated. In this paper, the transportation mode as a tactical decision has been considered that can affect the cost and emissions. Lead time and lost sales are considered in the modeling to reach more reality. The developed mathematical model is a mixed integer non-linear programming which is solved by GAMS. Due to NP-hard nature of the proposed model and long run time for large-size problems by GAMS, artificial immune system algorithm based on CLONALG, genetic and memetic algorithms are applied. Taguchi technique is used for parameter tuning of all meta-heuristic algorithms. Results demonstrate the strength of CLONALG rather than the other methods.     

Impact of the integration of tactical supply chain planning determinants on performance

In fulfilling customers’ orders, one of the goals of tactical supply chain planning is to satisfy the customers in terms of delivery efficiency, delivery quantity accuracy and on-time delivery. These performance objectives can be impacted by the way firms plan each of the three phases of the supply chain: procurement, production and distribution. Though the link between each of these phases and supply chain performance has been studied in extant literature, very few authors have considered all three phases at the same time. By adopting an integrated approach, this paper therefore aims to study the manner in which, taken together in one model, the planning determinants of the different phases impact on supply chain performance. It is important for managers to understand, from a holistic and integrated perspective, how a given combination of the planning determinants of the supply chain functions impacts positively or negatively on the performance of the supply chain. To carry out this study, this paper starts by proposing an integrated framework that is based on the SCOR model and the customer order decoupling point (CODP), followed by a five-step methodology for tactical supply chain planning. Then, using an analytical model and simulations, and based on a numerical example, it shows how the proposed methodology can be applied in a given decision-making situation. Our results enabled to identify the worst and the best combinations of planning determinants.     

Optimal pricing decisions for an omni‐channel supply chain with retail service

Motivated by the emergence of dominant physical stores utilizing a buy‐online‐and‐pickup‐in‐store (BOPS) channel, we examine the interaction of such a stronger retailer and a manufacturer's strategies on the pricing and service value. The dominant retailer provides an added service value to end consumers by the traditional channel and BOPS option, which has a positive influence on the market demand. The manufacturer makes the channel selection decision between a dual channel and an omni‐channel. We formulate a retailer Stackelberg game and characterize the equilibrium pricing and service solutions under centralized and decentralized cases. Our results indicate that the service value of a physical store strongly influences the players' pricing strategies. The sensitivity analysis reveals that the total revenue of the supply chain increases with the market scale expansion coefficient of the BOPS option. Furthermore, the result suggests that offering the BOPS channel is always an effective initiative to boost supply chain performance, especially in the centralized structure.     

Aggressive pricing to exploit market niches in supply chains

E-markets' growing popularity has given business-to-business trading an unprecedented boost. In supply chains connecting enterprises that trade services and goods, an organization's success depends on its ability to maintain stable and profitable relationships with other supply chain participants. We evaluate how predictive scheduling strategies and aggressive pricing schemes can help suppliers exploit market niches in B2B supply chains. Robust, opportunistic scheduling strategies can significantly improve suppliers' competitiveness by identifying market opportunities and strategically positioning and pricing available resources to exploit them.